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Ecology and Management of Flower Thrips in Southern Highbush Blueberries in Florida

Permanent Link: http://ufdc.ufl.edu/UFE0042089/00001

Material Information

Title: Ecology and Management of Flower Thrips in Southern Highbush Blueberries in Florida
Physical Description: 1 online resource (157 p.)
Language: english
Creator: Rhodes, Elena
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 2010

Subjects

Subjects / Keywords: bispinosa, blueberries, corymbosum, frankliniella, thrips, vaccinium
Entomology and Nematology -- Dissertations, Academic -- UF
Genre: Entomology and Nematology thesis, Ph.D.
bibliography   ( marcgt )
theses   ( marcgt )
government publication (state, provincial, terriorial, dependent)   ( marcgt )
born-digital   ( sobekcm )
Electronic Thesis or Dissertation

Notes

Abstract: In Florida, southern highbush (SHB) blueberries are grown for a highly profitable early season fresh market. Flower thrips are the key pest of these blueberries. Frankliniella bispinosa (Morgan) is the most common species found. They injure blueberry flowers by feeding and ovipositing in all developing tissues. These injuries can lead to scarring of developing fruit. The overall goal of this dissertation was to improve monitoring and management of flower thrips in southern highbush blueberries in Florida. To this end, five specific objectives were set up. Objective 1 was to find alternate hosts of F. bispinosa and to determine if F. bispinosa moves into blueberry plantings from these hosts. Preliminary plant surveys conducted in the spring of 2007 and from November 2007 until March 2008 revealed several reproductive hosts of F. bispinosa, including: Carolina geranium (Geranium carolinianum L.), white clover (Trifolium repens L.), and wild radish (Raphanus raphanistum L.). Thrips population development was monitored in a blueberry planting and neighboring white clover field on a farm in Windsor, FL during early spring 2009 and 2010. The flower thrips population in the white clover and blueberries developed at the same time with the highest numbers of thrips recorded from the center of the blueberry field in both years. Objective 2 sought to determine the relationship between thrips and yield in different SHB blueberry varieties and determine an action threshold. It involved experiments during early spring 2007 and 2008 on three farms, two in Hernando Co., FL and the third at the Plant Science Research and Education Unit (PSREU) in Citra, FL. On the Hernando Co. farms, two treatment thresholds (100 and 200 thrips per trap) and an untreated control and four varieties (Emerald, Jewel, Millennia, and Windsor) were compared. At the Citra PSREU, the varieties Emerald, Jewel, Millennia, and Star were compared in 2007 and all but Star were compared in 2008. Thrips numbers exceeded the threshold on only one farm in 2007 and although there were no differences in thrips numbers among treatments, the threshold of 100 thrips per trap appeared to result in a significantly lower proportion of injured and malformed fruit compared with the control. Emerald consistently had more thrips per trap and per flower than the other varieties on all three farms. However, this did not always lead to an increase in fruit injury. The third objective was to model thrips spatial distribution with geostatistical techniques and to use these models to determine optimum trap spacing. The study was conducted in early spring 2008 and 2009 on a farm in Inverness, FL. A grid of 100 traps spaced at 15.24-m intervals in 2008 and 7.61-m intervals in 2009 was set up with an additional 30 traps interspersed randomly throughout the sample area. Inverse distance weighting and kriging produced maps with similar accuracy. The semivariogram analysis showed that traps should be spaced at least 28.8 m apart to insure spatial independence. Objective 4 sought to determine if hot spots of high thrips density were correlated with flower density. The percent of open flower data were recorded from all rows in the Inverness 2009 study each week when traps were collected. Linear regression analysis revealed a positive relationship between percent of open flowers and thrips per trap on three of the five sampling dates. Objective 5 was to examine the efficacy of several reduced-risk compounds, which were compared with malathion, SpinTorregistered trademark, and an untreated control. During the course of the trials, one of these compounds, spinetoram, was registered in Florida blueberries as DelegateTM. Rynaxypyr also reduced thrips numbers, while thrips numbers in the QRD-452 high dose treatment were higher than in the control.
General Note: In the series University of Florida Digital Collections.
General Note: Includes vita.
Bibliography: Includes bibliographical references.
Source of Description: Description based on online resource; title from PDF title page.
Source of Description: This bibliographic record is available under the Creative Commons CC0 public domain dedication. The University of Florida Libraries, as creator of this bibliographic record, has waived all rights to it worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law.
Statement of Responsibility: by Elena Rhodes.
Thesis: Thesis (Ph.D.)--University of Florida, 2010.
Local: Adviser: Liburd, Oscar E.
Electronic Access: RESTRICTED TO UF STUDENTS, STAFF, FACULTY, AND ON-CAMPUS USE UNTIL 2011-08-31

Record Information

Source Institution: UFRGP
Rights Management: Applicable rights reserved.
Classification: lcc - LD1780 2010
System ID: UFE0042089:00001

Permanent Link: http://ufdc.ufl.edu/UFE0042089/00001

Material Information

Title: Ecology and Management of Flower Thrips in Southern Highbush Blueberries in Florida
Physical Description: 1 online resource (157 p.)
Language: english
Creator: Rhodes, Elena
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 2010

Subjects

Subjects / Keywords: bispinosa, blueberries, corymbosum, frankliniella, thrips, vaccinium
Entomology and Nematology -- Dissertations, Academic -- UF
Genre: Entomology and Nematology thesis, Ph.D.
bibliography   ( marcgt )
theses   ( marcgt )
government publication (state, provincial, terriorial, dependent)   ( marcgt )
born-digital   ( sobekcm )
Electronic Thesis or Dissertation

Notes

Abstract: In Florida, southern highbush (SHB) blueberries are grown for a highly profitable early season fresh market. Flower thrips are the key pest of these blueberries. Frankliniella bispinosa (Morgan) is the most common species found. They injure blueberry flowers by feeding and ovipositing in all developing tissues. These injuries can lead to scarring of developing fruit. The overall goal of this dissertation was to improve monitoring and management of flower thrips in southern highbush blueberries in Florida. To this end, five specific objectives were set up. Objective 1 was to find alternate hosts of F. bispinosa and to determine if F. bispinosa moves into blueberry plantings from these hosts. Preliminary plant surveys conducted in the spring of 2007 and from November 2007 until March 2008 revealed several reproductive hosts of F. bispinosa, including: Carolina geranium (Geranium carolinianum L.), white clover (Trifolium repens L.), and wild radish (Raphanus raphanistum L.). Thrips population development was monitored in a blueberry planting and neighboring white clover field on a farm in Windsor, FL during early spring 2009 and 2010. The flower thrips population in the white clover and blueberries developed at the same time with the highest numbers of thrips recorded from the center of the blueberry field in both years. Objective 2 sought to determine the relationship between thrips and yield in different SHB blueberry varieties and determine an action threshold. It involved experiments during early spring 2007 and 2008 on three farms, two in Hernando Co., FL and the third at the Plant Science Research and Education Unit (PSREU) in Citra, FL. On the Hernando Co. farms, two treatment thresholds (100 and 200 thrips per trap) and an untreated control and four varieties (Emerald, Jewel, Millennia, and Windsor) were compared. At the Citra PSREU, the varieties Emerald, Jewel, Millennia, and Star were compared in 2007 and all but Star were compared in 2008. Thrips numbers exceeded the threshold on only one farm in 2007 and although there were no differences in thrips numbers among treatments, the threshold of 100 thrips per trap appeared to result in a significantly lower proportion of injured and malformed fruit compared with the control. Emerald consistently had more thrips per trap and per flower than the other varieties on all three farms. However, this did not always lead to an increase in fruit injury. The third objective was to model thrips spatial distribution with geostatistical techniques and to use these models to determine optimum trap spacing. The study was conducted in early spring 2008 and 2009 on a farm in Inverness, FL. A grid of 100 traps spaced at 15.24-m intervals in 2008 and 7.61-m intervals in 2009 was set up with an additional 30 traps interspersed randomly throughout the sample area. Inverse distance weighting and kriging produced maps with similar accuracy. The semivariogram analysis showed that traps should be spaced at least 28.8 m apart to insure spatial independence. Objective 4 sought to determine if hot spots of high thrips density were correlated with flower density. The percent of open flower data were recorded from all rows in the Inverness 2009 study each week when traps were collected. Linear regression analysis revealed a positive relationship between percent of open flowers and thrips per trap on three of the five sampling dates. Objective 5 was to examine the efficacy of several reduced-risk compounds, which were compared with malathion, SpinTorregistered trademark, and an untreated control. During the course of the trials, one of these compounds, spinetoram, was registered in Florida blueberries as DelegateTM. Rynaxypyr also reduced thrips numbers, while thrips numbers in the QRD-452 high dose treatment were higher than in the control.
General Note: In the series University of Florida Digital Collections.
General Note: Includes vita.
Bibliography: Includes bibliographical references.
Source of Description: Description based on online resource; title from PDF title page.
Source of Description: This bibliographic record is available under the Creative Commons CC0 public domain dedication. The University of Florida Libraries, as creator of this bibliographic record, has waived all rights to it worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law.
Statement of Responsibility: by Elena Rhodes.
Thesis: Thesis (Ph.D.)--University of Florida, 2010.
Local: Adviser: Liburd, Oscar E.
Electronic Access: RESTRICTED TO UF STUDENTS, STAFF, FACULTY, AND ON-CAMPUS USE UNTIL 2011-08-31

Record Information

Source Institution: UFRGP
Rights Management: Applicable rights reserved.
Classification: lcc - LD1780 2010
System ID: UFE0042089:00001


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ECOLOGY AND MANAGEMENT OF FLOWER THRIPS IN SOUTHERN HIGHBUSH
BLUEBERRIES IN FLORIDA




















By

ELENA MARION RHODES


A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL
OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT
OF THE REQUIREMENTS FOR THE DEGREE OF
DOCTOR OF PHILOSOPHY

UNIVERSITY OF FLORIDA

2010


































2010 Elena Marion Rhodes
































To the glory of our Lord Jesus Christ, to my parents, and to my brother David









ACKNOWLEDGMENTS

I thank my major professor, Dr .Oscar Liburd and all of my committee members for

their hard work and support throughout this project. I also thank all of the current and

previous staff and students of the Small Fruit and Vegetable IPM laboratory for their

help in collecting samples and harvesting many, many blueberries. I thank Gary

England for all of the hard work he did sampling the two Hernando Co. blueberry farms.

I thank Dr. Carlene Chase for identifying the plants for the plant survey. I also thank Dr.

G. B. Edwards for his help in thrips species identification.

I also thank the University of Florida, Institute of Food and Agricultural Sciences

2005-2006 Integrated Pest Management Grant for providing the funding for the project

encompassing the Hernando Co. farms. I thank Dow Agrosciences and AgraQuest for

providing funding for the insecticide efficacy trails. I thank the University of Florida

Alumni Association and graduate school for providing fellowships that funded my Ph.D.

education.

Lastly, I thank my parents, my brother, my extended family, and my friends for

providing love, support, and putting up with my bouts of inappropriate stress release. I

also thank my Lord Jesus Christ, without whose saving grace and healing touch I would

never have gotten this far.









TABLE OF CONTENTS

paae

A C K N O W LE D G M E NTS .................................. .................................... .................. 4

LIST OF TABLES ........................................ .................... .................. 8

LIST OF FIGURES................................... ................. 10

ABSTRACT .............. .... .................. ............ 13

CHAPTER

1 INTRODUCTION ............. ................................. 16

2 L IT E R A T U R E R E V IE W .................. ....................................................................... 2 1

T h rip s ................................................................. ...... .. ...... 2 1
Thrips in Blueberries............................. .......... 28
Flower Thrips Monitoring and Management............................ ............. 30
Monitoring............................. .................. 30
Chemical Control .......................... ................ ................... 32
Biological Control ............................... ................ 35
Predators .......... ........................ .................. 35
Entomopathogenic fungi .............................. .............................. 37
Entomopathogenic nematodes ............................. ............... 38
Geographic Information Systems (GISs) and Geostatistics in Pest Management.. 38

3 EXAMINING THRIPS DISPERSAL FROM ALTERNATE HOSTS INTO
SOUTHERN HIGHBUSH BLUEBERRY PLANTINGS......................................... 42

Introduction .......................................... ... .......... .......... ... 42
M materials and M ethods......................................... .................. .................. 43
Prelim inary Plant Surveys ..................................... ...... ... ...... ............ ... 43
F ie ld S tu d y ................................................... 4 5
Results.................................... ...... ............ 46
Prelim inary Plant Surveys ....................................................... .................. 46
Field Study 2009 .................... .............. ................. .................. 47
Field Study 2010 .. .................................. .................. 47
D iscussio n .................................................................... 4 8

4 EFFECTS OF BLUEBERRY VARIETY AND TREATMENT THRESHOLD ON
THRIPS PO PULATIO NS .... .. ................................... .............. ......... ......... 57

Intro d uctio n ............................................................... 5 7
Materials and Methods.................. ............ .................. 58
C itra PS R EU ......................... ....................... ..... .. ...... ... ......... ... 58









Hernando and Lake Counties............................ .......................... 59
R e s u lts ........................................................................................... ................. 6 2
Citra PSREU ............. ................................. 62
2007 .............. ........................ .... ........................... 62
Traps................................. .................. 62
Flowers ..... ....... ................................... 63
Fruit................................ .................. 63
2008 .............. ........................ .... ........................... 63
Traps................................. .................. 63
Flowers ..... ....... ................................... 63
Fruit........................................ .. .................. 64
Hernando and Lake Counties............................ .. ................ 65
2007 .............. ........................ .... ........................... 65
Traps................................. .................. 65
Flowers ..... ....... ................................... 66
Fruit................................ .................. 68
2008 .............. ........................ .... ........................... 69
Traps................................. .................. 69
Flowers ..... ....... ................................... 69
Fruit................................ .................. 70
D iscussio n .................................................................... 7 1

5 EXAMINING THE SPATIAL DISTRIBUTION OF THRIPS UTILIZING
GEOSTATSITICAL METHODS .......................................... .............. 84

Intro d uctio n ............................................................... 8 4
M materials and M ethods................................................................. 87
R e s u lts ........................................................................................... ................. 8 9
D iscussio n .................................................................... 9 3

6 EXAMINING THE RELATIONSHIP BETWEEN THRIPS SPATIAL
DISTRIBUTION AND FLOWER DENSITY .......................... ............. 115

Introduction .......................................................... ......... 115
M materials and M ethods............................................... ......... 115
Inverness Farm .......... ................................. 115
Windsor Farm .......................... ........ .......... 118
Results .............. .... .. ................................. ............ ......... 119
Inverness Farm .......... ................................. 119
Windsor Farm .......................... ..................... 120
D is c u s s io n ..................................................................... ............................. 12 0

7 THE EFFECT OF SEVERAL REDUCED RISK INSECTICIDES ON FLOWER
THRIPS POPULATIONS IN SOUTHERN HIGHBUSH BLUEBERRIES............... 130

Intro d uctio n ................................................................... 13 0
Materials and Methods......................................... 131









R e s u lts ........................................................................................... ........... ... 1 3 4
2 0 0 7 ................................................................ 13 4
2 0 0 8 ................................................................ 13 5
2 0 0 9 ................................................................ 1 3 5
SHB thrips per flower .................................... 135
RE thrips per flower ........................................ 136
Yield ................ ......... .................. 136
D iscussio n ................................................................... 13 7

8 CONCLUSIONS ...... ....... ................................. 144

LIST OF REFERENCES ........ ................................... 147

BIOGRAPHICAL SKETCH ......... .............. ............ ................... 157









LIST OF TABLES

Table page

3-1 Common and scientific names of the plants found in the blueberry planting each
month ...... .... ................................... 56

4-1 Percent of adult thrips species sampled from flowers in each treatment at the
Citra PSREU.. ......................................... .................. 83

4-2 Percent of adult thrips species sampled from flowers in each treatment on farm
1 ........ ........................................................................ 83

4-3 Percent of adult thrips species sampled from flowers in each treatment on farm
2 ........ ....................................................................... 83

5-1 Summary statistics of thrips per trap for each sample date in 2008.................... 112

5-2 Several error metrics for natural neighbor (NN), inverse distance weighting
(IDW), and ordinary kriging (OK) for each sample date in 2008..................... 112

5-3 Summary of the semivariogram analysis for each sampling week in 2008 ......... 113

5-4 Summary statistics of thrips per trap for each sample date in 2009.................... 113

5-5 Several error metrics for natural neighbor (NN), inverse distance weighting
(IDW), and ordinary kriging (OK) for each sample date in 2009..................... 113

5-6 Summary of the semivariogram analysis for each sampling week in 2009 ......... 114

6-1 Summary statistics for the thrips per trap data from each sampling week. ........... 129

6-2 Summary statistics for the percentage of open flower data from each sampling
w e e k ........................................................................... 12 9

7-1 Percent of each thrips species per treatment in 2007....................................... 141

7-2 Average number of other arthropods per flower in each treatment for the season
in 2007 .............. .... .. .......... ....................................... 142

7-3 Percent of each thrips species per treatment in 2008....................................... 142

7-4 Average number of other arthropods per flower in each treatment for the season
in 2008 .............. .... .. .......... ....................................... 142

7-5 Percent of each thrips species per treatment in the SHB blueberries in 2009 ...... 142

7-6 Average number of other arthropods per flower in each treatment for the season
in the SHB blueberries in 2009 ...................................................................... 143









7-7 Percent of each thrips species per treatment in the RE blueberries in 2009........ 143

7-8 Average number of other arthropods per flower in each treatment for the season 143









LIST OF FIGURES


Figure page

2-1 Example of an ideal semivariogram with a nugget value of zero ........................ 41

3-1 Locations of transects (arrows) in blueberry planting .......................................... 51

3-2 Numbers of each thrips species per flower collected from each plant during the
first survey ........................ ............. ................... ................... 51

3-3 Numbers of each thrips species per flower collected from each plant during the
second survey ....... ................................... .................. 52

3-4 A) Average thrips per trap in each treatment on each sampling date in 2009.
Circled data indicate significant differences. B) Average thrips per trap on
Feb. 12, 2009 ....... ............................................ ................ .. 52

3-5 Average thrips A) adults and B) larvae per flower on each sampling date in 2009 53

3-6 Average thrips per flower in the clover field on each sampling date in 2009.......... 53

3-7 Average thrips per trap A) throughout the flowering period and B) during the first
4 weeks of the flowering period (indicated by the box in A) in 2010 ................... 54

3-8 Average thrips A) adults and B) larvae per flower on each sampling date in 2010 55

3-9 Average thrips per flower in the clover field on each sampling date in 2010.......... 55

4-1 Average thrips per sticky trap recorded from each variety per week in 2007.......... 74

4-2 Proportion of injured and unmarketable fruit sampled from each variety in 2007.... 74

4-3 Average thrips per sticky trap recorded from each variety per week in 2008.......... 75

4-4 Average thrips A) larvae and B) adults per flower recorded from each variety per
week in 2008 ....... .................................... .................. 75

4-5 Average thrips per trap recorded from each treatment per week on farm 1 in
2007 ...... ...................................................... .................. 76

4-6 Average thrips per trap recorded from each variety per week on farm 1 in 2007.... 76

4-7 Average thrips per trap recorded from each variety per week on farm 2 in 2007.... 77

4-8 Average thrips A) larvae and B) adults per flower recorded from each variety per
week on farm 1 in 2007 ............................................................ ........ ........ .. 77









4-9 Average thrips A) larvae and B) adults per flower recorded from each variety per
w eek on farm 2 in 2007 .................................................... ... ...................... 78

4-10 Proportion of injured and unmarketable fruit sampled from each treatment on
farm 1 in 2007............................................................ 79

4-11 Graphs showing average thrips per flower vs. average proportion of injured
fru it ................................................... ......... ............ ....... 8 0

4-12 Average thrips per trap recorded from each variety per week on farm 1 in 2008.. 81

4-13 Average thrips A) larvae and B) adults per flower recorded from each variety
per w eek on farm 1 in 2008 ............................................. ............................. 8 1

4-14 Proportion of injured and unmarketable fruit sampled from each variety on A)
farm 1 and B) farm 2 in 2008 .. .. ............................ ... ........... ... ... ........ 82

5-1 G IS m ap of the study area in 2008............................................ ........................ 97

5-2 G IS m ap of the study area in 2009............................................ ........................ 98

5-3 Point maps of thrips per trap for each sampling week in 2008................................ 99

5-4 Natural neighbor interpolation of thrips per trap ........... ...... ... ............. 100

5-5 Inverse Distance Weighting interpolation (p = 2, # points = 20) ofthrips per trap. 101

5-6 Semivariograms (A, C, E) and Ordinary Kriging interpolation (B, D, F) of thrips
per trap .............. ...... ......... ........... ............................. 103

5-7 Point maps of thrips per trap for each sampling week in 2009........................... 105

5-8 Natural neighbor interpolation of thrips per trap ........... ...... ... ............. 107

5-9 Inverse Distance Weighting interpolation (p = 2, # points = 20) ofthrips per trap. 109

5-10 Semivariograms (A, C, E, G, I) and Ordinary Kriging interpolation (B, D, F, H,
J) of thrips per trap from .................. ................ ......... .............. 112

6-1 Graphs showing percent open flowers vs. thrips per trap .................................. 123

6-2 Maps showing the spatial similarity of number of thrips per trap (T) with percent
o f o pe n flow e rs (F).................... ..................... ............ ......... ................. ... 12 5

6-3 Graphs showing percent open flowers vs. logo thrips per trap.......................... 127

6-4 Graphs showing percent open flowers vs. thrips adults per flower................... 128

6-5 Graphs showing percent open flowers vs. thrips larvae per flower ..................... 128









7-1 Average thrips A) larvae and B) adults per flower in each treatment on each
sampling date ....... ................................... ................. 138

7-2 Average thrips A) larvae and B) adults per flower in each treatment on each
sampling date and C) adults per flower during the first three sampling weeks
as indicated by the box in B) ..................................................................... 139

7-3 Average thrips A) larvae and B) adults per flower in each treatment on each
sampling date ....... ................................... ................. 140

7-4 Average thrips A) larvae and B) adults per flower in each treatment on each
sampling date ....... ................................... ................. 141









Abstract of Dissertation Presented to the Graduate School
of the University of Florida in Partial Fulfillment of the
Requirements for the Degree of Doctor of Philosophy


ECOLOGY AND MANAGEMENT OF FLOWER THRIPS IN SOUTHERN HIGHBUSH
BLUEBERRIES IN FLORIDA

By

Elena M. Rhodes

August 2010

Chair: Oscar E. Liburd
Major: Entomology and Nematology

In Florida, southern highbush (SHB) blueberries are grown for a highly profitable

early season fresh market. Flower thrips are the key pest of these blueberries.

Frankliniella bispinosa (Morgan) is the most common species found. They injure

blueberry flowers by feeding and ovipositing in all developing tissues. These injuries can

lead to scarring of developing fruit. The overall goal of this dissertation was to improve

monitoring and management of flower thrips in southern highbush blueberries in Florida.

To this end, five specific objectives were set up.

Objective 1 was to find alternate hosts of F. bispinosa and to determine if F.

bispinosa moves into blueberry plantings from these hosts. Preliminary plant surveys

conducted in the spring of 2007 and from November 2007 until March 2008 revealed

several reproductive hosts of F. bispinosa, including: Carolina geranium (Geranium

carolinianum L.), white clover (Trifolium repens L.), and wild radish (Raphanus

raphanistum L.). Thrips population development was monitored in a blueberry planting

and neighboring white clover field on a farm in Windsor, FL during early spring 2009

and 2010. The flower thrips population in the white clover and blueberries developed at









the same time with the highest numbers of thrips recorded from the center of the

blueberry field in both years.

Objective 2 sought to determine the relationship between thrips and yield in

different SHB blueberry varieties and determine an action threshold. It involved

experiments during early spring 2007 and 2008 on three farms, two in Hernando Co.,

FL and the third at the Plant Science Research and Education Unit (PSREU) in Citra,

FL. On the Hernando Co. farms, two treatment thresholds (100 and 200 thrips per trap)

and an untreated control and four varieties (Emerald, Jewel, Millennia, and Windsor)

were compared. At the Citra PSREU, the varieties Emerald, Jewel, Millennia, and Star

were compared in 2007 and all but Star were compared in 2008. Thrips numbers

exceeded the threshold on only one farm in 2007 and although there were no

differences in thrips numbers among treatments, the threshold of 100 thrips per trap

appeared to result in a significantly lower proportion of injured and malformed fruit

compared with the control. Emerald consistently had more thrips per trap and per flower

than the other varieties on all three farms. However, this did not always lead to an

increase in fruit injury.

The third objective was to model thrips spatial distribution with geostatistical

techniques and to use these models to determine optimum trap spacing. The study was

conducted in early spring 2008 and 2009 on a farm in Inverness, FL. A grid of 100 traps

spaced at 15.24-m intervals in 2008 and 7.61-m intervals in 2009 was set up with an

additional 30 traps interspersed randomly throughout the sample area. Inverse distance

weighting and kriging produced maps with similar accuracy. The semivariogram









analysis showed that traps should be spaced at least 28.8 m apart to insure spatial

independence.

Objective 4 sought to determine if "hot spots" of high thrips density were correlated

with flower density. The percent of open flower data were recorded from all rows in the

Inverness 2009 study each week when traps were collected. Linear regression analysis

revealed a positive relationship between percent of open flowers and thrips per trap on

three of the five sampling dates.

Objective 5 was to examine the efficacy of several reduced-risk compounds, which

were compared with malathion, SpinTor, and an untreated control. During the course

of the trials, one of these compounds, spinetoram, was registered in Florida blueberries

as DelegateTM. Rynaxypyr also reduced thrips numbers, while thrips numbers in the

QRD-452 high dose treatment were higher than in the control.









CHAPTER 1
INTRODUCTION

Blueberries are a highly profitable crop in Florida. During 2009, 6.4 million kg (14.1

million Ibs) of fresh market blueberries were harvested from 1295 ha (3,200 acres) at an

average of $11.89 per kg ($5.40 per Ib) (USDA 2010). The use of low chill varieties of

Rabbiteye (Vaccinium virgatum Aiton) and the development of southern highbush (V.

corymbosum L. x V. darrowi Camp) allows Florida growers to take advantage of this

highly profitable early season market.

Rabbiteye blueberries are better suited for u-pick operations and local sales

(Williamson and Lyrene 2004). Varieties of Rabbiteye can be classified as early-, mid-,

or late season. During the early to mid 1980s, several North Florida producers

attempted to grow early-season Rabbiteye varieties on >500 acres, but yields were very

low. Improved management of insect pests, including blueberry gall midge (Dasineura

oxycoccana Johnson) and flower thrips (Frankliniella spp.), have improved yield, but

these blueberries do not ripen early enough in the season to be highly profitable.

Rabbiteye blueberries are grown exclusively for u-pick and local sales (Williamson and

Lyrene 2004).

The development of the southern highbush blueberry varieties in 1976 allowed

Florida growers to take advantage of an untapped early season market (Williamson and

Lyrene 2004). Southern highbush blueberries ripen 4-6 weeks before the early-season

rabbiteye varieties. The various varieties of southern highbush are crosses between

northern highbush blueberries (V. corymbosum) and wild blueberry species in Florida,

including rabbiteye (Childers and Lyrene 2006). All blueberry acreage grown for fresh

fruit shipping within and from Florida consists of southern highbush plantings









(Williamson and Lyrene 2004). In north Florida, frost protection is essential to avoid

damage to flowers (Williamson and Lyrene 2004).

The two major insect pests of blueberries in Florida are blueberry gall midge (aka

cranberry tipworm D. oxycoccana) and flower thrips (Frankliniella spp.) (0. E. Liburd

personal communication). Blueberry gall midge females lay their eggs in developing

blueberry buds. In Florida, they emerge in January or February and can produce up to

six generations per year (Sampson et al. 2002). The larvae develop and feed in the bud

eventually killing it (Finn 2003). Both floral and vegetative buds are attacked (Sarzymski

and Liburd 2003). An unchecked infestation can kill up to 80% of floral buds while injury

to vegetative buds distorts leaves and can reduce the number of berries a plant can

support (Sampson et al. 2002). It is a difficult pest to control although systemic

insecticides can reduce numbers (0. E. Liburd unpublished data). Blueberry gall midge

is attacked by five species of parasitoids in the Platygastridae and Tetrastichinae

(Eulophidae) families. The Tetrastichin is a species of Aprostocetus. The four

Platygastridae species include two species of Synopeas, a species of Inostemma, and

one species of Platygaster. (Sampson et al. 2006).

The blueberry bud mite {Acalitus vaccinii (Keifer)} and flea beetles are emerging

pests (0. E. Liburd personal communication). Blueberry bud mites, in the family

Eriophyidae, infest developing leaf and flower buds of both highbush and lowbush

blueberries. Feeding by the bud mites causes the buds to redden early in the season,

which prevents normal leaf and flower development. Severe infestations can cause

yield reduction. Bud mites are difficult to detect because of their small size and the









injury they cause, which closely resembles frost injury. Bud mites can be managed with

proper pruning and the use of horticultural oils (Weibelzahl and Liburd 2009).

Flea beetles are a post harvest pest of both southern highbush and rabbiteye

blueberries. Flea beetles are foliage feeders. Large numbers of them can cause a

significant reduction in photosynthetic productivity resulting in a decrease in yield for the

following season. The blueberry leaf beetle, Colaspis pseudofavosa Riley, and the red

headed flea beetle, Systena frontalis (Fabricius), are the two most common species

found in Florida blueberries (0. E. Liburd unpublished data). However, there may be

other species in the complex responsible for heavy defoliation in blueberries after

harvest.

Chili thrips, Scirtothrips dorsalis Hood, were first reported from the Florida

landscape on roses in Palm Beach Co. in October of 2005. By the end of 2005, it had

spread to 15 counties on a number of different hosts (Silagyi and Dixon 2006). There

are at least 100 recorded hosts of chili thrips (Hodges et al. 2006), and this number is

increasing. Although blueberries are not a listed host, chili thrips were reported from

blueberries in North-central Florida in the summer of 2008 (0. E. Liburd personal

communication). Chili thrips are a pale bodied thrips with dark wings. They are primarily

foliage feeders and do not feed on flower pollen (Hodges et al. 2005). Effective control

measures on blueberries have not yet been studied. However, Chlorfenapyr, spinosad,

and imidacloprid gave consistent control of chili thrips on pepper plants (Seal et al.

2006). In addition, the predatory mite Amblyseius swirskii (Athias-Henriot) maintained

chili thrips populations below 1 per terminal leaf on pepper plants in the landscape for

63 days after they were released (Arthurs et al. 2009).









Several species of flower thrips, including the Florida flower thrips {Frankliniella

bispinosa (Morgan)}, Eastern flower thrips {F. tritici (Fitch)}, Western flower thrips {F.

occidentalis (Pergande)}, and Tobacco thrips {Frankliniella fusca (Hinds)} are pests of

both rabbiteye and southern highbush blueberries in Florida and Georgia (Liburd and

Arevalo 2005). Frankliniella bispinosa is the most common thrips found in Florida, while

F. tritici is the dominant species in Georgia (Arevalo et al. 2006). They infest not only

blueberries, but a wide variety of other crop and non-crop host plants.

In general, flower thrips are very small insects (~1 mm in length) with yellowish to

orange coloration. They can be distinguished from other insect orders by their fringed

wings and punch and suck mouthparts. They have a short life cycle that can occur in 18

to 22 days under ideal conditions. Thrips progress though two actively feeding larval

instars and two inactive instars (often called pupae) before becoming adults (Lewis

1997).

Flower thrips damage flowers in two ways. Both larvae and adults feed on all parts

of the flowers including ovaries, styles, petals, and developing fruit. This feeding

damage can reduce the quality and quantity of fruit produced. Females also cause

damage to fruit when they lay their eggs inside flower tissues. The newly hatched larvae

bore holes in flower tissue when they emerge (Liburd and Arevalo 2005).

The overall goal of this project was to improve monitoring and management of

flower thrips in southern highbush blueberries in Florida. The hypothesis is that a better

understanding of flower thrips ecology in combination with the development of specific

management tactics will accomplish this goal. The objectives of this dissertation were

fivefold: 1) to examine blueberry plantings and adjacent fields for alternate hosts of









flower thrips and thrips dispersal from these host plants into blueberry plantings. 2) To

determine the relationship between populations of thrips and yield in southern highbush

blueberries and to determine an action threshold for thrips in southern highbush

blueberries. 3) To model the spatial distribution of flower thrips in a blueberry planting

utilizing geostatistical methods and to determine optimum trap spacing. 4) To determine

if "hot spots" are correlated with flower density. 5) To determine the potential of using

several experimental reduced-risk insecticides to manage flower thrips in Florida

blueberries.









CHAPTER 2
LITERATURE REVIEW

Thrips

In general, thrips are very small insects (a few mm in length) with yellowish orange

to brown coloration. They belong to the order Thysanoptera and are distinguished from

other insect orders by their fringed wings and "punch and suck" mouthparts (Lewis

1997). Thrips are unique in having only one mandible, the left one. The right one is

resorbed by the embryo (Mound 2005).

There are at least seven families of thrips, Adiheterothripidae, Aeolothripidae,

Fauriellidae, Merothripidae, Heterothripidae, Thripidae, and Phlaeothripidae, which vary

widely in their ecology. Aeolothripids are predators of mites and small insects,

Merothripids are fungus feeders, and Adiheterothripidae, Heterothripids, and Thripids

are primarily plant feeders (Lewis 1997). These six families belong to the suborder

Terebrantia (Triplehorn and Johnson 2005). The Phlaeothripidae contains mostly fungal

feeders, although a few species are predatory (Lewis 1997). Fauriellidae is a recently

described family (Triplehorn and Johnson 2005). This family falls in the suborder

Tubulifera.

Almost all of the major pest thrips belong to the family Thripidae. Major pest

species in this family belong to several genera, including: Frankliniella, Heliothrips,

Scirtothrips, Taeniothrips, and Thrips (Triplehorn and Johnson 2005). Thrips that are

crop pests tend to be polyphagous and highly adaptable (Mound 2005). However, not

all Thripidae are pests. For example, Scolothrips sexmaculstus (Pergande), the six-

spotted thrips, is an important predator of phytophagous mites (Triplehorn and Johnson

2005).









Terebrantian thrips have a short like cycle that can occur in 18 to 22 days under

ideal conditions (Lewis 1997). Females oviposit into the plant tissue on which they feed

(Terry 1991). Eggs are inserted one at a time into an incision in the plant tissue created

by the female's saw-like ovipositor (Terry 1991). Thrips progress though two actively

feeding larval instars and two inactive instars (often called the propupa and pupa)

before becoming adults (Lewis 1997). Many Terebrantian species drop off of their host

plant and pupate in the soil (Lewis 1997).

Only a little is known about the mating behavior of Terebrantian thrips because of

the ephemeral nature of their flowering host plants (Lewis 1997). Males of several

species, including Frankliniella occidentalis (Pergande), F. schultzei (Trybom), T.

fuscipennis Haliday, T. major Uzel, T. flavus Schrank, T. atratus Haliday, and T.

vulgatissimus, form aggregations on the corollas of flowers, into which females will enter

and mate (Milne et al. 2002). Females may use cues from the plants at a distance and

then find the male aggregations via a sex pheromone produced by the males when they

get closer to the plant. Milne et al. (2002) discovered that traps set among flowering

plants and baited with conspecific males attracted significantly more females than

unbaited traps placed among the plants. F. occidentalis males will fight to keep an area

clear for a female to land. Females generally mate with the first male they come into

contact with (Lewis 1997).

Many factors can influence development and reproduction, including temperature

and host plant (Lewis 1997). Tsai et al. (1995) examined Thrips palmi Karny survival,

egg production, and developmental time at three different temperatures: 15C, 260C,

and 32C. They found that T. palmi had the greatest survival and highest egg









production at 26C, but had the shortest developmental time at 32C compared to the

other temperatures. They also investigated the effect of different host plants on survival

and reproduction of T. palmi, both of which were much lower on bell pepper than on

melon, eggplant, and cucumber. Alternatively, the Japanese strain of F. occidentalis can

survive temperatures as low as 0C (Tsumuki et al. 2007) for up to 40 d in the presence

of food. Females survived longer than males (40 d vs. 30 d). Both sexes died within 48

h at temperatures below 0C.

Population density and food availability can also play a role in regulating thrips'

population growth (Nothnagi et al. 2008). Both competition for resources at high

population densities and declining food availability can lead to sharp population declines

in confined experiments. The same level of competition and declining food resources in

a greenhouse or open field situation would most likely lead to migration (Nothnagi et al.

2008).

Terebrantian thrips use both visual and chemical cues to locate their host plants.

Visual cues include floral color, shape, and size (Lewis 1997). In terms of color, blue,

white, and yellow are much more attractive than other colors to Frankliniella spp. (Finn

2003). With respect to chemical cues, anisaldehyde odor significantly increased catches

of seven polyphagous, flower inhabiting thripid species (Kirk 1985).

Although some thrips are specific to a few hosts, many are extremely polyphagous

(Lewis 1997). However, it is often difficult to determine a particular species' true host

range. Thrips will often alight, and even feed, upon many plants that they cannot

reproduce on (Mound 2005, Paini et al. 2007). For example, the pear thrips,

Taeniothrips inconsequens (Uzel), has been recorded from 242 species of plants, but









only 35 of these are breeding hosts (Teulon et al. 1994). Although F. fusca (Hinds), F.

occidentalis, and F. tritici (Fitch) are found on tomato plants in Florida and can cause

injury to these plants, only F. occidentalis reproduces on the tomato plants (Salguera

Navas et al. 1994). Adults of several species of thrips are found in native orchids in

Northern Florida and Southern Georgia, but the small numbers of larvae found indicate

that the orchids are not a reproductive host for most thrips species (Funderburk et al.

2007).

The majority of pest thrips are highly polyphagous. They can reproduce on various

weedy hosts and disperse into crops from these hosts. Frankliniella spp. prefer hosts

that are flowering, so only flowering plants should be considered as sources of

populations of these thrips (Northfield et al. 2008). In Japan, F. occidentalis reproduces

in at least eight weedy species common in and around ornamental nurseries throughout

the spring and summer (Katayama 2006). Cockfield et al. (2007b) found that native

vegetation surrounding apple orchards supported F. occidentalis populations when

apple trees were not flowering. These weedy hosts can also serve as reservoirs for

tomato spotted wilt and other tospoviruses (Kahn et al. 2005). Therefore, weed control

may be an important cultural tactic for control of pest thrips (Katayama 2006), but may

not be effective in reducing injury without the use of other tactics (Cockfield and Beers

2008).

Thrips disperse in two main ways. They fly from field to field and are frequently

transported long distances by humans moving plant material (Lewis 1997). When they

fly, terebrantian thrips lock the cilia on their wings in the "open" position (Ellington 1980).

"Open" refers to the fact that the cilia are at a much greater angle to the wing axes in









flight than at rest. This doubles the wing area. The cilia are opened by abdominal

combing. They are closed by tibial combing. The wings lie parallel over the abdomen at

rest (Ellington 1980). The distance thrips can fly is determined in large part by

temperature and humidity (Lewis 1997). They desiccate much more quickly in hot, dry

weather and thus cannot travel as far.

Because of their small size, thrips have little control over their flight and are carried

readily on wind and air currents (Arevalo-Rodriguez 2006). Yudin et al. (1991)

discovered that thrips dispersal can be hindered with mechanical barriers and that thrips

were more numerous on the side of the field corresponding to prevailing wind direction.

Thrips do, however, seem to have some control over landing (Lewis 1997). From a few

observations, it is thought that thrips land feet first, close their wings, and begin

quivering their antennae (Lewis 1997). Wingless thrips can also be dispersed by wind

(Mound 2005).

Thrips cause damage to their host plants directly through feeding and oviposition

and indirectly through the spread of tospoviruses (Arevalo-Rodriguez 2006). Thrips feed

by "punching" into the plant tissue with their single mandible and sucking out cell

contents with a pair of maxillary stylets (Lewis 1997). Both larvae and adults of F.

bispinosa (Morgan) feed on all parts of 'Naval' orange (Citrus sinensis (L) Osbeck)

flowers and on all parts of swollen buds (Childers and Achor 1991). Feeding causes

cellular evacuation, necrosis, plasmolysis, and cellular collapse, which often spreads to

nearby cells up to five cells deep. Some leaf feeding thrips can induce gall formation in

plants (Mound 2005). Feeding on inflorescences can cause drooping and discoloration

of petals (Rhainds et al. 2007).









Female F. bispinosa oviposit in all parts of the flowers and swollen buds of'Naval'

orange trees. However, Childers and Achor (1991) found that 73% of thrips larvae

emerged from the pistil-calyx units of open flowers, which indicates a preference for

these tissues. Ovipostion damage is localized and affects only cells directly adjacent to

the oviposition site (Childers and Achor 1991). Large numbers of thrips can cause

economic damage and even abortion of flowers (Arevalo-Rodriguez 2006). In contrast,

oviposition by F. occidentalis causes pansy spot, a corky, raised scar surrounded by a

pale halo, on apple (Cockfield et al. 2007a). Adult F. occidentalis are most abundant in

apple blossoms from king bloom (bloom of the first, central flower in the flower clusters)

to full bloom. Injury similar to pansy spot is caused when F. occidentalis oviposits in

grapes and tomatoes (Cockfield et al. 2007a).

Thrips also feed on pollen. Kirk (1987) found that a single T. imaginis Bagnall or T.

obscuratus (Crawford) could consume 0.2-0.7% of a kiwifruit {Actinidia deliciosa (A.

Chevalier)} flower's pollen per day. They noted that this suggests that pollen damage

could reduce crop yield or plant fitness in some cases. Ugine et al. (2006) found that

adult female F. occidentalis were more abundant in greenhouse impatiens flowers that

still contained pollen.

Tospoviruses are an extremely damaging group of plant viruses (Arevalo-

Rodriguez 2006). To date, there are 16 known Tospovirus species in the family

Bunyaviridae. Tomato Spotted Wilt Virus (TSWV) is one of the most well known

species. It was thought to be a major factor in the 35% decline of crisphead lettuce

(Lactuca sativa L.) and romaine (L. satvia var. longifolia Lam.) production in Hawaii in

the late 1980s (Yudin et al. 1991). Thus far, 11 species of thrips in the family Thripidae









are known vectors. Both the viruses and their vectors have been spread around the

world because of the difficulty of detecting both of them in plants in the process of being

transported (Arevalo-Rodriguez 2006). Only early second instar larvae can acquire

Tospoviruses (Moritz et al. 2004). During this stage of development, there is a

temporary connection between the mid-gut, visceral muscles, and salivary gland due to

the displacement of the brain into the prothoracic region by enlarged cibarial muscles

(Moritz et al. 2004). Tospoviruses are transmitted by adults when they feed and may

also be transmitted mechanically through excretion and oviposition (Moritz et al. 2004).

In tomatoes sprayed weekly with insecticides to control thrips, the main source of TSWV

was from immigrating thrips (Puche et al. 1995).

There are other microbes associated with thrips besides tospoviruses. Two groups

of bacteria, one that has a shared ancestry with Erwinia and the other that has a shared

ancestry with Escherichia coli (Migula), are found in the gut of F. occidentalis

(Chanbusarakum and Ullman 2008). Both bacteria are facultative symbionts that infect

thrips larvae. They parasitize the thrips when nutrients are abundant in the thrips' diet

and supplement the thrips' diet if it is nutrient deficient (Chanbusarakum and Ullman

2008).

Thrips can also serve beneficial functions as pollinators and predators.

Taeniothrips (Amblythrips) ericae Haliday is the major pollinator of Erica tetralix flowers

(Hagerup and Hagerup 1953). Two thripid species, F. diverse and F. insularis, pollinate

flowers of the Moraceae tree (Castilla elastica) and a new species of Thrips pollinates

Antiaropsis, a genus of Moraceae in New Guinea (Mound 2005). The flowers of

Arctostaphyllos uva-ursi are pollinated by several species of thrips, including









Ceratothrips ericae (Haliday) and Haplothrips setiger Priesner (Garcia-Fayos and

Goldarazena 2008). Though one thrips can carry only a small amount of pollen, large

numbers of thrips can transport large amounts of pollen and thrips can occur in very

high numbers in flowers (Mound 2005).

Some species of thrips, such as the six-spotted thrips (S. sexmaculatus), are

primarily predators and feed on spider mites and other pests (Triplehorn and Johnson

2005). However, some phytophagous thrips species are also facultative predators of

spider mites. Thrips imaginis Bagnall, T. tabaci Linderman, and F. Schultzei consume

twospotted spider mite (Tetranychus urticae Koch) eggs in early season cotton in

Australia, which causes mite outbreaks to occur later in the season then they would if

this did not occur (Wilson et al. 1996). In California, F. occidentalis preys on spider mite

eggs in cotton and F. tritici is listed as a predator of spider mite eggs in peanuts (Trichilo

and Leigh 1986).

Thrips in Blueberries

Various thrips species inhabit blueberry flowers, leaves, and both leaves and

flowers (Childers and Lyrene 2006). Frankliniella vaccinii Morgan and Catinathrips

kainos O'Neil are the most common pestiferous leaf inhabiting species. Flower thrips

include F. occidentalis and F. bispinosa, while F. tritici and Scirtothrips ruthveni Shull

attack both leaves and flowers (Childers and Lyrene 2006). Uncultivated Vaccinium

species in southern Georgia are host to several species of gall-forming leaf thrips

(Braman et al. 1996). These gall thrips could become pestiferous if susceptible

uncultivated Vaccinium species are bred with cultivated species (Braman et al. 1996).

Flower thrips are the key pest of early-season blueberries in Florida. Frankliniella

bispinosa is the most common species with an average of 84% of the total thrips









collected from flowers and 89% from sticky traps. The other 16% and 11% are made up

of F. fusca, F. occidentalis, and T. hawaiiensis (Morgan) in order of decreasing

abundance (Arevalo-Rodriguez et al. 2006). A similar situation exists in Florida oranges,

where F. bispinosa comprises 84 to 99% of thrips species collected from soil

emergence traps (Childers et al. 1994) and in other varieties of citrus where F.

bispinosa accounted for 92% of thrips found in closed buds and open flowers (Childers

et al. 1990).

Thrips move into crops from other cultivated plants that flower earlier, like citrus in

the case of blueberries (Childers et al. 1994), and from wild plant species (Chellemi et

al. 1994, Topanta et al. 1996). In wild plant species adjacent to tomato fields in north

Florida, F. tritici was the most abundant species in March, May and August, F. bispinosa

in June and July, and F. occidentalis in February and April. Thirty-one of the 37 plant

species examined contained thrips (Chellemi et al. 1994). Paini et al. (2007) found that

F. occidentalis used two different weedy plant species as reproductive hosts in April and

May in North Florida. Frankliniella bispinosa also used two weedy species as

reproductive hosts from May to August. In contrast, F. fusca and F. tritici used 12 and

18 weedy species as reproductive hosts respectively in April and May.

In blueberries, flower thrips tend to aggregate and this aggregation is most

pronounced when the population density is the highest (Arevalo-Rodriguez 2006).

Thrips populations tend to form one or a few "hot-spots" on blueberry farms, which are

small areas of comparatively high thrips numbers (Arevalo and Liburd 2007). These

"hot-spots" begin forming about 7-10 days after bloom initiation, peak between 12 and

15 days after initiation when the majority of the flowers are open, and decline until about









22 days after bloom initiation when most of the flowers have become fruit and the thrips

population all but disappears (Arevalo and Liburd 2007). Frankliniella occidentalis and

F. tritici tend to aggregate on tomato plants while F. fusca was aggregated one year and

randomly dispersed the next year (Salguero Navas et al. 1994).

In blueberries, the highest numbers of thrips are caught on sticky traps placed in

or just above the blueberry plant canopy (Arevalo-Rodriguez 2006). Similarly, Reitz

(2002) and Salguero et al. (1991) found more adult F. occidentalis and F. tritici in the

upper part of the tomato plant canopy, but they found more larvae in the lower part of

the canopy. In apple orchards, the density of F. occidentalis decreases with increasing

distance from the edge of the orchard (Miliczky et al. 2007).

Flower thrips both feed and reproduce in blueberry flowers. These activities can

cause the developing fruit to be scarred and misshapen (Arevalo-Rodriguez 2006). In

his experiments, Arevalo-Rodriguez (2006) found that significantly more thrips larvae

emerged from petals than from any other flower part. Also, significantly more thrips

larvae emerged from ovaries than from styles and fruits. He concluded that flower thrips

prefer these flower parts because the tissue is mature. Therefore, their eggs will not be

crushed by growing cells (Arevalo-Rodriguez 2006). There are no known Tospoviruses

that infect blueberries (Arevalo-Rodriguez 2006).

Flower Thrips Monitoring and Management

Monitoring

In blueberries, thrips are monitored using sticky traps or by directly sampling the

flowers. Finn (2003) found that more F. bispinosa were caught on white and blue sticky

traps compared to yellow and green traps. Yellow traps often caught more than green

traps. Chu et al. (2006) also found that color is an important cue for thrips, catching









more Frankliniella spp. in white and blue plastic cup traps than in yellow cup traps.

Although white, yellow, and blue traps attract thrips, white traps are the best to employ.

Yellow traps attract a large number of other insects and the dark coloring of the blue

traps can make it difficult to see the thrips that are present on them (Arevalo-Rodriguez,

2006).

Flowers can be sampled in several ways. The simplest method involves gently

tapping the flowers and allowing the thrips to fall onto a white sheet below for counting.

Flowers can also be collected in a vial or plastic bag and then dissected in the

laboratory.

Arevalo and Liburd (2007) developed a "shake and rinse" method that is as

accurate as dissecting flowers and much more efficient. This method involves collecting

the flowers in alcohol-filled vials, shaking the vials, placing the flowers on a metal

screen over a plastic cup, rinsing the flowers with water, and counting and collecting the

thrips in the rinse liquid.

Sixteen to 18 flowers were needed to estimate thrips densities at the 25%

precision level on tomato plants (Salguero Navas et al. 1994). Twenty to 25 flowers give

an accurate estimate of thrips numbers in blueberry flowers (Arevalo-Rodriguez 2006).

Arevalo and Liburd (2007) found a strong correlation (r = 0.7621) between thrips

per flower and thrips per trap in rabbiteye blueberries. Thrips per flower was estimated

from five flower clusters sampled using the "shake and rinse" method. The sticky traps

were hung inside the blueberry canopy. Rodriguez-Saona et al. (in press) found that

sticky trap data were useful for predicting thrips' flight activity and monitoring for the

timing of insecticide applications.









Economic injury levels (EILs) are an integral part of integrated pest management

(IPM) strategies. Several terms are important in understanding this concept, including:

economic damage (ED) and economic threshold (ET). Stern et al. (1959) defines ED as

"the amount of injury that will justify the cost of artificial control measures." The EIL is

"the lowest population density that will cause this damage" and the ET is "the density at

which control measures should be initiated to prevent an increasing pest population

from reaching the EIL." The EIL can be calculated using the equation EIL = C/ (V I *

D), where C is the cost of control, V is the value of the product, I is the injury per insect

value, and D is the damage per unit injured (Pedigo et al. 1986). Arevalo-Rodriguez

(2006) used this equation to determine the EILs for 'Climax' and 'Tifblue' rabbiteye

blueberries in Georgia, which are approximately 13 and 14 thrips per 10 flowers

respectively when Malathion 5EC (Micro Flo Company LLC, Memphis, TN) is used as

the control measure and 17 and 19 thrips per 10 flowers respectively using SpinTor

2SC (spinosad) (Dow Agrosciences, Indianapolis, IN). Using his regression equations,

Arevalo-Rodriguez (2006) calculated this to be 45 for Tifblue and 50 for Climax when

malathion is applied and 64 for Tifblue and 73 for Climax when SpinTor is applied.

Chemical Control

Economic Injury Levels (EILs) for flower thrips on many crops are very low

because of these thrips' ability to transmit TSWV (Arevalo-Rodriguez 2006). For this

reason, one of the main strategies used to control thrips is insecticide application

(Morishita 2001). Morishita (2001) found that the organophosphates dichlorvos,

sulprofos, profenofos, malathion, chlorpyrifos-methyl, chlorfenvinphos, fenthion, and

phenthoate, the carbamate methomyl, the insect growth regulators (IGRs) lufenuron,

chlorfluazuron, and flufenoxuron, and two other chemistries, chlorphenapir and









spinosad, caused greater than 90% mortality to F. occidentalis in the laboratory. The

other carbamates and all pyrethroid compounds were not as effective as these.

In blueberries, flower thrips are currently managed with applications of malathion

and SpinTor (Arevalo-Rodriguez 2006). Malathion is a conventional, organophosphate

insecticide with broad-spectrum activity. SpinTor is a reduced-risk insecticide. Its

active ingredient is spinosad (spinosyn), which is derived from the fermentation of the

soil bacterium Saccharopolyspora spinosa Mertz and Yao. Spinosad, which must be

ingested, kills insects via rapid excitation of the nervous system (IPM of Alaska 2003). It

shows equal toxicity towards F. bispinosa, F. occidentalis, and F. tritici (Eger 1998) in

the laboratory. However, Reitz et al. (2003) showed that it reduced F. occidentalis

numbers, but did not reduce F. tritici numbers in field grown peppers in Florida.

Spinosad has also been shown to be effective against F. occidentalis in field grown

strawberries in Australia (Broughton and Herron 2007).

On blueberry farms, these insecticides are usually applied early in the morning or

late at night to minimize the impact on pollinating bees (Arevalo-Rodriguez 2006).

However, growers still report problems with bee toxicity, especially when malathion is

applied (0. E. Liburd personal communication). Growers have also reported problems

with SpinTor relating to its poor residual activity (0. E. Liburd personal communication).

The exclusive use of only two compounds also raises questions of resistance

development. Morse and Brawler (1986) found that the citrus thrips, S. citri (Moulton),

appeared to be developing resistance to all of the insecticides they tested against them.

Resistance to acephate, chlorpyrifos, dichlorvos, dimethoate, endosulfan, fipronil,

malathion, methamidophos, methidathion, methomyl, and spinosad has been detected









in F. occidentalis populations in Australia (Herron and James 2005). However, no

resistance to abamectin, methiocarb, or pyrazophos was detected in these populations

(Herron and James 2005). Frankliniella occidentalis can rapidly develop resistance

because it has a short generation time, high fecundity, and a haplodiploid breeding

system (Jensen 2000). Spinosad resistance in F. occidentalis has been detected in

many parts of the world (DaSh and Tunc 2007, Bielza et al. 2007). Resistance in F.

occidentalis seems to be polyfactorial, involving: reduced penetration of the

exoskeleton, increased detoxification by P450-monooxygenases, esterases, and

glutathione S-transferases (GSTs), altered, insensitive, and increased AChE, and

knockdown resistance (insensitive sodium channels) (Jensen 2000). The resistance

appears to be unstable under field conditions (Bielza et al. 2008) and can be managed

by minimizing the use of insecticides and using strategies that take resistance

mechanisms and cross-resistance into consideration (Bielza 2008).

Spinetoram (Dow Agrosciences, Indianapolis, IN), a new spinosyn, was effective

in controlling F. occidentalis, F. bispinosa, and F. tritici on tomatoes in north Florida.

Like spinosad, spinetoram is a fermentation product of the soil bacterium S. spinosa. It

has very low toxicity to many beneficial insects, humans, and the environment

(Srivastava et al. 2008). During the course of this project, spinetoram was registered for

use in blueberries as Delegate T; in large part due to efficacy trials conducted as part of

the project (see chapter 7).

For these reasons, a part of the ongoing IPM research on flower thrips control in

blueberries has begun to focus on finding other effective reduced-risk insecticides.

Arevalo-Rodriguez (2006) found that Coragen 2SC (Rynaxypyr) (DuPont, Wilmington,









DE) showed some control of F. bispinosa in Florida blueberries. Rynaxypyr is a

reduced-risk insecticide with a novel mode of action. It is a ryanidine receptor agonist,

causing the release of Ca2+ from muscle cells. The insects lose the ability to regulate

muscle function and die via muscle paralysis (Ribbeck 2007). It shows no toxicity

toward non-target organisms (Marchesini et al. 2008), including bees, no phytotoxicity,

and shows some translaminar activity (Ribbeck 2007).

Surround WP (kaolin clay) (Engelhard Corporation, Iselin, NJ) has also shown

promise for flower thrips control in blueberries. It was the only compound that reduced

thrips numbers in a field study in Florida in 2003 (Liburd and Finn 2003). Spiers et al.

(2005) found that it reduced the number of flower thrips by half in rabbiteye blueberries,

was nontoxic to pollinating bees, and showed no phytotoxic effects. However, it is white

in color when it dries and this can attract large numbers of adult thrips from surrounding

areas (Arevalo-Rodriguez 2006).

Biological Control

Predators

Members of 23 families of insects distributed among 8 orders and 9 families of

mites have been reported to prey on thrips (Arevalo-Rodriguez 2006). The most

commonly studied predators are Orius insidiosus Say (Hemiptera: Anthocoridae) and

various Amblyseius spp. (Acari: Phytoseiidae). Orius insidiosus is an important predator

ofthrips on field grown peppers in Florida. It can significantly suppress populations of F.

bispinosa, F. occidentalis, and F. tritici, the three flower thrips species found in the

pepper flowers (Funderburk et al. 2000). However, 0. insidiosus at the rate of 10 adults

per plant bi-weekly did not reduce F. occidentalis numbers to economically acceptable

levels on greenhouse tomatoes (Shipp and Wang 2003). Reitz et al. (2006) determined









that although 0. insidiosus can prey on both F. bispinosa and F. occidentalis, it

preferentially captures F. occidentalis. This may be due to the fact that F. bispinosa can

evade predation better than F. occidentalis because it is smaller and more active. Onus

insidiosus is mass reared and sold by Koppert Biological Systems (Romulus, MI).

Amblyseius cucumeris (Oudemans) is also available from Koppert. In contrast to

0. insidiosus, A. cucumeris significantly reduced F. occidentalis numbers to

economically acceptable levels on greenhouse tomatoes (Shipp and Wang 2003). Hoy

and Glenister (1991) determined that inoculative releases of A. barked (Hughes) and A.

cucumeris could provide control of onion thrips, Thrips tabaci Lindeman, on field-grown

cabbage in the northwestern United States.

Other control tactics, particularly pesticide applications, can impact the

effectiveness of predators. Reitz et al. (2003) found that UV-reflective mulch reduced

both the abundance of 0. insidiosus and early season F. occidentalis adults. They also

found that spinosad was the least disruptive insecticide towards 0. insidiosus compared

to esfenvalerate and acephate. A combination of predatory mites {A. cucumeris and

Hypoaspis aculeifer (Canestrini)} and soil applied NeemAzal-U (17% azadirachtin) was

highly effective in controlling F. occidentalis on beans (Phaseolus vulgarus L.)

(Thoeming and Poehling 2006). This emphasizes the importance and need of integrated

control tactics.

Unfortunately, biological control of flower thrips in Florida blueberries with

predators has proven unsuccessful. Arevalo et al. (2009) released 0. insidiosus and A.

cucumeris singly and in combination as both preventative and curative releases. Neither

the preventative nor curative releases of any treatment reduced thrips numbers below









those found in the control. The shortness of the flowering season in blueberries may not

give these natural enemies enough time to establish and control thrips populations in

Florida blueberries.

Entomopathogenic fungi

Several species of entomopathogenic fungi have been shown to attack various

species of thrips (Ekesi et al. 1998). Beauveria bassiana (Bals.-Criv.) Vuill. is a

promising control agent for Thrips palmi (Castineiras et al. 1996), T. tabaci Lindeman

(Jung 2004), F. intonsa (Trybom), F. occidentalis (Pergande), T. coloratus Schmutz,

and T. hawaiiensis (Abe and Ikegami 2005).

Thrips are infected by B. bassiana when they come into contact with conidia.

These asexual, nonmotile spores stick to the insect's integument, where they germinate

and eventually penetrate into the insect's body cavity. The resulting infection kills the

insect in 3-7 days (Bradley et al. 1998). Under optimal temperature and relative humidity

an epizootic can occur, which is when a high percentage of a thrips population becomes

infected, causing significant reductions in the population size (Murphy et al. 1998).

Along with their ability to cause epizootics, entomopathogenic fungi like B.

bassiana have several desirable characteristics. One advantage the fungi have over

traditional biological control is that they can be applied using standard spray equipment

as long as adequate coverage is achieved (Murphy et al. 1998). Other advantages

include host specificity and low toxicity towards non-target organisms (Murphy et al.

1998, Jacobson et al. 2001). Yet another advantage is that bumble bees can vector

fungal conidia to crops in greenhouses with no adverse effects on the bumble bee

colonies (Al-mazra'awi et al. 2006).









Like all living organisms, entomopathogenic fungi have an optimum temperature

and humidity range. Optimum temperature can vary between 15C and 30C depending

on species and strain. Optimum humidity for B. bassiana is reported as 95%, but this

can vary with strain (Ekesi et al. 1999).

The use of entomopathogenic fungi for flower thrips control in blueberries is not

likely to be effective in Florida because the blueberry flowering season occurs in

January and February. The cool temperatures and lower relative humidity would most

likely prevent an epizootic from occurring.

Entomopathogenic nematodes

Frankliniella occidentalis is susceptible to several species of steinernematid and

heterorhabditid nematodes (Georgis et al. 2006). Previous studies have shown

promising results with foliar applications on ornamental plants, which target mainly

larval and adult F. occidentalis. Frequent applications utilizing an optimum spray

volume, a wetting agent, and an adjuvant are essential for suppression of the pest

(Georgis et al. 2006). However, Ruttenhuns and Shipp (2005) found that only F.

occidentalis propupae and pupae were susceptible to Steinernema feltiae (Filipjev).

Clearly, more research is needed before these nematodes become a viable control

tactic.

Geographic Information Systems (GISs) and Geostatistics in Pest Management

Until recently, studies of the spatial distribution of insect populations have been

limited to using various dispersion indices (Faris et al. 2003, Florez and Corredor 2000,

Liebhold et al. 1993, Midgarden et al. 1993, Park and Tollefson 2005, Schotzko and

O'Keeffe 1990, Wright et al. 2002). However, these dispersion indices only describe the

frequency distribution of a set of samples; they do not take into account the spatial









relationship of the points (Midgarden et al. 1993). With the advent of Geographic

Information Systems (GISs) and geostatistics into the world of insect ecology, these

spatial relationships can now be studied (Liebhold et al. 1993).

A GIS is a computer system that can assemble, store, manipulate, and display

geographically referenced data such as insect densities, crop type, soil type, soil

moisture, etc. (Liebhold et al. 1993). Each data set can be used to form a map layer or

theme. Collections of themes from similar areas form a GIS database. Thus, the GIS is

a powerful tool for analyzing spatial interactions within and among these spatially

referenced data themes (Liebhold et al. 1993).

Geostatistics provide the tools to characterize and model spatial patterns (Liebhold

et al. 1993). The cornerstone of geostatistics is called the variogram (Webster and

Oliver 2001). To construct a variogram, the semivariance of each pair of data points in a

data set must be calculated. A semivariance is defined as 2 of the average squared

difference between data values at the same separation distance. A semivariogram plots

the semivariance on the y-axis and the specified distance between sample pairs, the

lag, on the x-axis (Wright et al. 2002). Since it is very difficult to fit a model to a

semivariogram where each individual semivariance is plotted, the semivariance is

averaged for each of several lags (Webster and Oliver 2001). This is expressed

mathematically as y(h) = {1/2m(h)} Z {z(x,) z(x, + h)}2, where y(h) is the semivariance at

lag h, m(h) is the number of data point pairs separated by lag h, and z(x,) and z(x, + h)

are the data values (z) a places separated by h (Webster and Oliver 2001). The

important features of semivariograms are the sill, range, lag, and nugget (Fig. 2-1),

which are defined as the value of the semivariance when it stops increasing, the









distance at which spatial independence is reached, the distance between sample pairs,

and the semivariance value when x = 0 respectively. The nugget variance is a

combination of measurement error and variation over distances less than the shortest

lag distance sampled for all continuous variables (Webster and Oliver 2001).

Semivariograms have been used to examine and describe the spatial relationship

of several corn pests, including western corn rootworm adults on yellow sticky traps in

corn (Midgarden et al. 1993), corn rootworm injury to corn (Park and Tollefson 2005),

and European corn borer larvae and their damage in whorl stage corn (Wright et al.

2002). Semivariograms have also been used to examine and describe the spatial

relationships of three species of Xylella fastidiosa (Wells) sharpshooter vectors on citrus

(Paulo et al. 2003) and of Lygus hesperus (Knight) in lentils (Schotzko and O'Keeffe

1990). Florez and Corredor (2000) used semivariogram along with other geostatistical

analyses to examine the spatial dependence of F. occidentalis in a covered strawberry

crop at Bogota plateau. Spatial dependence was found in 3 of 12 sampling weeks. They

found that although thrips colonies were aggregated at first, over time the pattern

changed toward a random pattern. This change was caused by thrips movement to

neighboring quadrants.

The advent of geostatistics has also brought with it more sophisticated

interpolation tools. Interpolation allows researchers to estimate the continuous

properties of something in the environment from a finite number of sampled points

(Webster and Oliver 2001). Four commonly used interpolation techniques are natural or

nearest neighbor, local average, inverse distance weighting (IDW), and kriging (Ess and

Morgan 2003). Natural neighbor is the simplest interpolation method. The value at an









unknown point is set equal to the value of the nearest sample point (Ess and Morgan

2003) Local average uses a simple average of known values around the unknown point

to predict the value at the unknown point Either a fixed number of points or all of the

points within a fixed distance are used in the average (Ess and Morgan 2003) IDW is,

in effect, a weighted average Sample points closer to the unknown point are given a

higher weight than those farther away (Ess and Morgan 2003)

Kriging is a geostatistical interpolation method Semlvarlogram models are used

to predict values at unsampled locations Ordinary kriging is the most common kriging

method used in most applications (Webster and Oliver 2001) In ordinary kriging, the

overall mean of the population is assumed to be unknown Like IDW, ordinary kriging

uses a weighted average to estimate unknown values However, the weights are based

upon the semlvarlogram model


0 Range _



| 0--------------- ----------
60
50


30
E 20 Sill
0)
10


0 20 40 60 80 100
Lag (m)

Nugget = the semlvariance at 0 lag
Fig 2-1 Example of an ideal semlvarlogram with a nugget value of zero









CHAPTER 3
EXAMINING THRIPS DISPERSAL FROM ALTERNATE HOSTS INTO SOUTHERN
HIGHBUSH BLUEBERRY PLANTINGS

Introduction

A complex of flower thrips species causes injury to southern highbush (SHB)

blueberries (V. corymbosum L. x V. darrowi Camp) in Florida (Arevalo-Rodriguez 2006).

Frankliniella bispinosa (Morgan) is the most common species, accounting for

approximately 90% of the adult thrips collected from both traps and flowers (Arevalo

and Liburd 2007). Flower thrips feed and reproduce on all parts of developing blueberry

flowers. The resulting injury can be magnified into scars when the fruit form, which

make the fruit unsalable on the fresh market (Arevalo-Rodriguez 2006).

Thrips move into crops from other cultivated plants that flower earlier and from wild

plant species that also serve as hosts (Chellemi et al. 1994, Topanta et al. 1996).

Chellemi et al. (1994) found that 31 of 37 plant species adjacent to tomato fields

contained thrips. Paini et al. (2007) found that F. bispinosa used two weedy species as

reproductive hosts from May to August in north Florida. Cockfield et al. (2007b) found

that native vegetation surrounding apple orchards supported F. occidentalis populations

when the apple trees were not flowering.

It is often difficult to determine the true host range of a particular thrips species

because thrips will often alight and feed upon many plants on which they cannot

reproduce (Mound 2005, Paini et al. 2007). For example, although F. fusca (Hinds), F.

occidentalis (Pergande), and F. tritici (Fitch) are found on tomato plants in Florida and

can cause injury, only F. occidentalis reproduces on the tomato plants (Salguera Navas

et al. 1994).









The objectives of this study were twofold. 1) To examine blueberry plantings and

adjacent fields for alternate hosts of thrips. 2) To examine thrips dispersal from these

host plants into blueberry plantings. The hypotheses of this study were: 1) flowering

plants support and sustain F. bispinosa populations when blueberry plants are not

flowering and 2) thrips disperse into blueberry plantings from these flowering plants

when the blueberries begin to flower.

Materials and Methods

Preliminary Plant Surveys

In the first survey, flower samples from three of the most common flowering plants

found at the Plant Science Research and Education Unit (PSREU) in Citra, FL, were

collected, which included cutleaf evening primrose (Oenothera laciniata Hill), white

clover (Trifolium repens L.), and wild radish (Raphanus raphanistum L.). Eight primrose

flowers, 6 clover flowers, and 25 wild radish flowers were collected and placed in vials

containing 70% ethanol. Thrips adults and larvae were sampled from the flowers using

the "shake and rinse" method developed by Arevalo and Liburd (2007). In this method,

each vial was shaken vigorously for 1 min. Then the contents of the vial were emptied

onto a metal screen with 6.3 x 6.3-mm openings placed over a 300-ml white

polyethylene jar. Flowers were gently opened and rinsed with water. The rinsate was

then examined under a dissecting microscope. The numbers of thrips and other

arthropods present were recorded. The thrips and other arthropods were stored in 100-

cc glass vials. The flowers on the screen were emptied into another 300-ml

polyethylene jar containing 10 ml of water. Once the lid was placed on the jar, the jar

was shaken vigorously for 1 min. The rinse procedure was repeated as before except

that the flowers were rinsed with 70% ethanol. If thrips were found in the second rinse









water, the procedure was repeated for a third time (shaking the flowers in 70% ethanol

and rinsing with water). Thrips adults were identified to species using a key developed

for Florida SHB blueberries by Arevalo et al. (2006). Thrips that did not match the

character descriptions in the key were sent to the Division of Plant Industry in

Gainesville, FL. for identification.

In the second survey, several flowering plant species within the 0.52-ha blueberry

planting and the area surrounding it at the Citra PSREU were flagged and sampled to

determine whether or not they were suitable hosts for F. bispinosa. For the purposes of

this study, a suitable host was defined as one in which F. bispinosa reproduces and is

abundant. Plants were identified to genus and species (if possible).

Ten 27-m transects were taken. Two were on the border of the blueberry field and

eight were within the field (Fig. 3-1). Flowering plants within a 0.6-m (2-ft) radius were

sampled every 3-m (10-ft). The height and maximum width of the plants and percent

coverage were measured. Plant samples were collected in small press and seal bags

and brought back to the laboratory for identification.

Samples were taken during the first and third full week of the two months prior

flowering and during the two months of the blueberry flowering season. Twenty flowers

were collected from each plant species and placed in 50-ml plastic vials containing 70%

ethanol. If less than 20 flowers were present, then all available flowers were collected.

The samples were brought back to the laboratory at the University of Florida in

Gainesville, FL. The "shake and rinse" method was used to collect the thrips from the

flowers. Adults and larvae were counted and adults were identified to species as

detailed previously.









Flower samples were also collected from an adjacent strawberry field to the west

of the blueberry planting and from the blueberry bushes themselves. Ten strawberry

flowers were collected from each of four rows. This was done once a month in

December, January, and February. Four blueberry flower clusters (~ 20 flowers) were

collected from each plot on each sample collection date.

Field Study

This study was conducted at a farm in Windsor, Florida, during the spring of 2009

and 2010. White clover, Trifolium repens L., grows in the grassy areas all over this farm.

The study area consisted of a field of white clover and part of a large blueberry planting

on the farm that contained plants approximately 7 years in age. In 2009, six sampling

sites in a 625-m2 area in the clover and 12 sampling sites in the blueberry planting, in

four rows of three sites in a 2400-m2 area, were selected. Four traps were placed in the

corners of the clover sampling area and the other two were placed in the center 8-m

apart. Traps were spaced 10-m apart in each blueberry row and the rows were 15-m

apart. In 2010, the setup was expanded to include ten sampling sites in the clover (660-

m2) and four rows of five sites (2464-m2) in the blueberry planting. All of the traps in the

clover were spaced 10-m apart. The traps in the blueberry rows were spaced as in

2009. The rows were labeled 1 to 4, with 1 closest to the clover field and 4 farthest

away from it. Each row was considered a treatment.

In 2009, white sticky traps (Great Lakes IPM, Vestaburg, MI) were set out every

week and collected weekly for five weeks from January 31 to March 5 in the clover and

blueberries. In 2010, traps were set out every week and collected weekly for seven

weeks from Feb. 4 to March 25. When the traps were replaced, flower samples were









collected from both the clover and blueberries adjacent to traps. Three to five clover

flowers and four to five blueberry flower clusters (~20-25 flowers) were collected each

week.

The treatments, which included the clover only in the sticky trap data set, were

compared each week using a one-way analysis of variance (ANOVA) (SAS Institute

2002) and means were separated using the least significant differences (LSD) test.

Sticky trap data (x) were loglox transformed to meet the assumptions of the analysis. In

2009, the loglo(x +1) transformation was also used for thrips adults per flower (x), while

thrips larvae per flower were transformed using the equation 1/4 (thrips per flower +1).

For the 2010 flower sample data, transformation was not enough to cause the data to

meet the ANOVA assumptions. Therefore, the nonparametric Friedman, Kendall-

Babington Smith test (Hollander and Wolfe 1999) for general alternatives in a

randomized complete block design was used to analyze the data.

Results

Preliminary Plant Surveys

In the first survey, both adults and larvae were collected from the clover and wild

radish flowers, while only adults were collected from the primrose flowers (Fig. 3-2). All

adults collected were F. bispinosa.

Twelve different species of plants were found in the blueberry planting during the

second survey (Table 3-1). Of these, 8 flowered at some point during the sampling

period and thrips were sampled from 3 {Carolina geranium (Geranium carolinianum L.),

hairy indigo (Indigofera hirsuta L.), and pusley (Richardia sp.)}. Thrips were also found

in the blueberry and strawberry flowers.









Both thrips adults and larvae were found in the Carolina geranium, pusley,

strawberry, and blueberry flowers (Fig. 3-3). All of the adults found in the Carolina

geranium were F. bispinosa, while F. fusca (Hinds) and Haplothrips graminis Hood were

found in the pusley and strawberry flowers. Only H. graminis adults were found in the

hairy indigo flowers. Most of the thrips adults in the blueberry flowers were Thrips

species, either T. hawaiiensis (Morgan) or T. pini Karny. Frankliniella bispinosa, F.

fusca, Franklinothrips sp., and H. graminis were also present in the blueberry flowers in

small numbers.

Field Study 2009

On Feb. 12, there were significantly more thrips per trap in row 3 compared with

the clover, row 1, and row 4 (F = 3.92, df = 4, 17, P = 0.0267, Fig. 3-4A, B).

There were no significant differences in thrips adults (all F 5 1.51, df = 3, 11, P

0.29) or thrips larvae (all F 5 1.45, df = 3, 11, P > 0.30) per flower among rows on any

sampling date (Fig. 3-5A, B). Thrips adults and larvae were present in the clover field

throughout the blueberry flowering period (Fig. 3-6). Larval numbers remained low

throughout the flowering period, while adult numbers increased as the flowering period

progressed.

In the clover and row 2, all of the thrips sampled were F. bispinosa. In rows 1 and

4, 96% of the thrips sampled were F. bispinosa and 2% were T. pini. The remaining 2%

were T. hawaiiensis in row 1 and Franklinothrips sp. in row 4. In row 3, 98% of the thrips

sampled were F. bispinosa. The remaining 2% were Franklinothrips sp.

Field Study 2010

On Feb. 11, there were significantly higher numbers of thrips per trap in the clover

field compared with rows 1 and 4 (F = 3.12, df = 4, 29, P = 0.0327, Fig. 3-7A, B). On









Feb. 25, there were significantly more thrips per trap in rows 2 and 3 compared with the

clover field (F = 2.89, df = 4, 29, P = 0.0429). On March 11, there were significantly

higher numbers of thrips per trap in rows 2, 3, and 4 compared with row 1 and the

clover field (F = 5.95, df = 4, 29, P = 0.0017). On March 25, there were significantly

higher numbers of thrips per trap in row 3 compared with all of the other treatments and

in row 4 compared with row 1 and the clover field (F = 6.86, df = 4, 29, P = 0.0007).

There were no significant differences in thrips adults (all S' 6, k, n = 5, 4, P > 0.1,

Fig. 3-8A) or larvae (all S' < 6.43, k, n = 5, 4, P > 0.09, Fig. 3-8B) per flower on any

sampling date. Thrips adults were present in the clover flowers on Feb. 11, March 18,

and March 25 (Fig. 3-9). In contrast, only a single larva was collected from the clover

flowers on Feb. 18.

As in 2009, most of the thrips collected during the blueberry flowering period in

2010 were F. bispinosa. All of the thrips sampled from rows 2, 3, and 4, 82 % of those

sampled from row 1, and 67% of those sampled from the clover were F. bispinosa.

Several Franklinothrips sp. and a single Limnothrips sp. that was caught during the first

week of sampling made up the remaining 18% of row 1. A single F. fusca and 3

unknown thrips made up the remaining 33% found in the clover.

Discussion

Flower samples were collected from Carolina geranium, hairy indigo, narrowleaf

cudweed (Gnaphalium falcatum Lam.), oldfield toadflax (Nuttallanthus canadensis (L.)),

pusley, spurge (Euphorbia sp.), thistle (Circium spp.), white clover, and wild radish. It

appears that only Carolina geranium, white clover, and wild radish are reproductive

hosts of F. bispinosa during the sampling period due to presence of immature stages.

Northfield et al. (2008) also found that white clover and wild radish are reproductive









hosts ofF. bispinosa, especially in the spring (April June). In contrast, Paini et al.

(2007) found only adult F. bispinosa on wild radish (white clover was not sampled in this

study). Carolina geranium was not sampled in either of these studies. Cutleaf evening

primrose appears to be only a feeding host, since no larvae were found in the flowers.

Several other species of thrips were found on other plants that flowered during the

sampling period. Hairy indigo had only H. graminis adults, which are predatory and may

have been feeding on the large number of aphids also present in the flowers (data not

shown). Haplothrips graminis adults were also frequently found in the pusley flowers. A

single F. fusca adult was also found in the pusley flowers, as were a number of thrips

larvae. Whether the H. graminis were feeding on the thrips larvae or other insects

present in the flowers is not known. The same two species of adult thrips and a few

thrips larvae were also found in the strawberry flowers.

In 2009, the thrips population in the clover appeared to develop at the same time

as the population in the blueberry planting. Two extreme cold events, one in late

January and the second in early February, may have contributed to this population

growth pattern. The cold may have reduced the thrips population in both the clover and

blueberry flowers to very low levels, which then rebounded together.

The difference in thrips per trap occurred on Feb. 12, approximately 1 week after

the second extreme cold event. Row 3, which had higher numbers of thrips compared

with rows 1, 4, and the clover, is in the center of the sampled blueberry block. It is

possible that the thrips were better sheltered from the cold there.

Thrips numbers were low throughout the 2010 SHB blueberry flowering season.

Thrips adults were collected from the blueberry flowers in low numbers throughout the









flowering season, but the population did not begin to increase until March 11. In the

clover flowers, a single adult unknown was collected on Feb. 11. Thrips adults were not

found in clover flowers again until two unknowns and a F. bispinosa were collected on

March 18. All of the adults collected from the clover flowers on March 25 were F.

bispinosa with the exception of a single F. fusca. Thrips larvae were not collected from

blueberry flowers until March 18 and the only larvae collected from the clover flowers

was found on Feb. 18.

The flowering season itself began later than the average and was extended till the

end of March. Both of these factors were most likely due to the extended extreme winter

temperatures that occurred during January and February of 2010 (FAWN 2010).

Despite their low numbers, there were some statistically significant differences in

thrips per trap on Feb. 11 and 25 and March 11 and 25. As in the previous year, thrips

numbers were higher in the middle of the field. However, in 2010, they remained higher

instead of equalizing as occurred in 2009.

From these studies, it would appear that clover is not a significant source of F.

bispinosa in SHB blueberry fields. This is supported by Northfield et al. (2008) who

found that F. bispinosa uses white clover as a reproductive host in the spring,

particularly in April and May. Since they are found almost exclusively in flowers

(Northfield et al, 2008), F. bispinosa may move from one or a few hosts to different

hosts as they flower. Frankliniella occidentalis exhibits this pattern of behavior in

Washington apple orchards (Cockfield et al. 2007b).















Further research is needed to determine which plants are sources of F. bispinosa



for SHB blueberry plantings. Controlling these plants could reduce flower thrips



numbers in blueberry bushes.


-I II -

I I -

II I-i I

I- I- II
I I -


II I F- I-
I-I I- II
,E -I, II
ri 14- I: I
II I I- I-
I-I I- II


-I- II -
I I

I- I I-
-I- II -

I-I I- II
I- I I-


rabbiteye grapes


II n


I II I-I l-

I- I I: II
nI I I I-

I I ,-
'-- I I- II
PLOT 3


E=Emerald J=Jewel M=Millennia SH=Spnng High


Cutleaf evening White Clover Wild Radish
Pnmrose


Each letter represents 5 plants

FENCE

Fig. 3-1. Locations of transects (arrows) in blueberry planting. (E = Emerald, J = Jewel,

M = Millennia, S = Star, and SH = Spring High)


Fig. 3-2. Numbers of each thrips species per flower collected from each plant during the

first survey.






















51


2


1 i
rep ro


S=Star


Larsvae
*F bispinosa


3 II 1 -


I i





















oA t ,
p
& ^< 9


Fig. 3-3. Numbers of each
second survey.


larae
*T pini
T hawallensis
*H graminis
SFr sp
*F fusca
iF bispinosa


thrips species per flower collected from each plant during the


-- Clover
--Row 1
-- Row 2
-- Row 3
-- Row 4


12-Feb


40

30

20

10

0


SRow 1 0 Row 2


SRow 3 Row 4


B
Fig. 3-4. A) Average thrips per trap in each treatment on each sampling date in 2009.
Circled data indicate significant differences. B) Average thrips per trap on
Feb. 12, 2009. Means with the same letter are not significantly different from
each other at P < 0.05. Error bars indicate standard error of the mean.


SClover













-- row 1
-A-- row 2
-- row 3
-- row 4


-- row 1
-A-- row 2
x row 3
--- row 4


Fig. 3-5. Average thrips A) adults and B) larvae per flower on each sampling date in
2009. Error bars indicate standard error of the mean.


-Adults
Larvae


Date

Fig. 3-6. Average thrips per flower in the clover field on each sampling date in 2009.
Error bars indicate standard error of the mean.


--;t

























K6


T



a.


- Clover
-- Row 1
Row 2
-xRow 3
-x-Row 4


Fig. 3-7. Average thrips per trap A) throughout the flowering period and B) during the
first 4 weeks of the flowering period (indicated by the box in A) in 2010.
Treatments with the same letter are not significantly different from each other
at P = 0.05. Error bars indicate standard error of the mean.












025

02

0 15

01

0 05

0

pV pO p


- Row 1
Row 2
- Row 3
-- Row 4


Date


0 15

01

005

0 -



Date


-- Row 1
Row 2
x Row 3
- Row 4


Fig. 3-8. Average thrips A) adults and B) larvae per flower on each sampling date in
2010. Error bars indicate standard error of the mean.





06

05--
S04-
-*- adults
03I -
.= 1 larvae
M n \I -


Date


Fig. 3-9. Average thrips per flower in the clover field on each sampling date in 2010.
Error bars indicate standard error of the mean.










Table 3-1. Common and scientific
each month


November


December


names of the plants found in the blueberry planting


January


February


March


Carolina geranium Carolina geranium Carolina geranium Carolina geranium Carolina geranium
Geranium Geranium Geranium Geranium Geranium
carolinianum L carolinianum L carolinianum L carolinianum L carolinianum L
narrowleaf narrowleaf narrowleaf
coffee senna? hairy indigo cudweed cudweed cudweed
Senna Indigofera hirsuta Gnaphalium Gnaphalium Gnaphalium
occidentalis L L falcatum Lam falcatum Lam falcatum Lam
narrowleaf
hairy indigo cudweed oldfleld toadflax oldfleld toadflax oldfleld toadflax
Indigofera hirsuta Gnaphalium Nuttallanthus Nuttallanthus Nuttallanthus
L falcatum Lam canadensis (L) canadensis (L) canadensis (L)
pennywort pennywort pennywort pennywort pennywort
(dollarweed) (dollarweed) (dollarweed) (dollarweed) (dollarweed)
Hydrocotyle Hydrocotyle Hydrocotyle Hydrocotyle Hydrocotyle
umbellata L umbellata L umbellata L umbellata L umbellata L
pigweed? pusley pusley pusley pusley
Amaranthus sp Richardia sp Richardia sp Richardia sp Richardia sp
red sorrel red sorrel red sorrel
pusley thistle Rumex Acetosella Rumex Acetosella Rumex Acetosella
Richardia sp Circium spp L L L
wandering
cudweed
Gnaphalium
spurge pensylvanicum thistle thistle thistle
Euphorbia sp Willdenow Circium spp Circium spp Circium spp
wandering wandering wandering
cudweed cudweed cudweed
Gnaphalium Gnaphalium Gnaphalium
thistle pensylvanicum pensylvanicum pensylvanicum
Circium spp Willdenow Willdenow Willdenow
wandering
cudweed
Gnaphalium
pensylvanicum
Willdenow
Highlighting indicates when plants were flowering and a question mark indicates
uncertainty in identification.









CHAPTER 4
EFFECTS OF BLUEBERRY VARIETY AND TREATMENT THRESHOLD ON THRIPS
POPULATIONS

Introduction

Several species of flower thrips, including the Florida flower thrips {Frankliniella

bispinosa (Morgan)}, western flower thrips {F. occidentalis (Pergande)}, eastern flower

thrips {F. tritici (Fitch)}, and Scirtothrips ruthveni Shull, have recently become known as

pests of cultivated blueberries (Spiers et al. 2005). The three Frankliniella species are

pests of both rabbiteye (RE), Vaccinium virgatum Aiton, and southern highbush (SHB),

V. corymbosum L. x V. darrowi Camp, blueberries in Florida (Liburd and Arevalo 2005).

Frankliniella bispinosa is the key pest and by far the most abundant, while the others

are occasional pests (Arevalo et al. 2006). They infest not only blueberries, but many

other crop and non-crop host plants (Arevalo et al. 2006).

Flower thrips injure flowers in two ways. Larvae and adults feed on all parts of the

flowers including ovaries, styles, petals, and developing fruit (Arevalo-Rodriguez 2006).

This feeding injury can reduce the quality and quantity of fruit produced. Females also

cause injury to fruit when they lay their eggs inside flower tissues. The newly hatched

larvae bore holes in flower tissue when they emerge.

The objectives of this study were to: a) determine the relationship between

populations of thrips and yield in several different SHB varieties and b) to determine an

action threshold for thrips in SHB blueberries. In Florida, several SHB varieties are

grown together on the same farm. These varieties differ in fruit and flower

characteristics and in the timing and length of flowering period (Williamson and Lyrene

2004). This may lead to differences in thrips numbers and thrips injury among the









varieties. If this is the case, economic injury levels may need to be developed for each

variety or among varieties with similar flowering periods.

Materials and Methods

Citra PSREU

This experiment was conducted at the University of Florida Plant Science

Research and Education Unit in Citra, FL. There were four 0.13 ha plots of SHB

blueberries that contained eight rows of blueberry bushes. Five bushes of each variety

are planted in each row. The experimental setup was a completely randomized block

design with 12 replicates (four rows from each plot) of four (2007) or three (2008)

varieties. In 2007, four Southern Highbush varieties: Emerald, Jewel, Millennia, and Star

were sampled. In 2008, only Emerald, Jewel, and Millennia were sampled because

many of the Star plants were small and produced too few flowers to provide consistent

samples. There were five plants per variety in each replicate. The plants were

approximately four years old in 2007.

Four sticky traps (Great Lakes IPM, Vestaburg, MI) were placed in each replicate

(one per variety for a total of 48 for the experiment). The traps were hung from the

center plant in each variety and were replaced weekly. Each week, 10 flowers were

sampled from the middle bush and placed in 50-ml plastic tubes containing 15 ml of

70% ethanol. In 2007, samples were collected for seven weeks from Jan. 29 until March

12. In 2008, samples were collected for five weeks from Feb. 14 until March 14.

The traps and flower samples were brought back to the Small Fruit and Vegetable

Laboratory at the University of Florida in Gainesville where the number of thrips per trap

and per flower was counted. Flowers were sampled using the "shake and rinse" method

developed by Arevalo and Liburd (2007). Adult thrips were identified to species using a









key developed for Florida SHB blueberries by Arevalo et al. (2006). Thrips that did not

match the character descriptions in the key were sent to the Division of Plant Industry in

Gainesville, FL for identification.

At harvest time, 30 berries per variety in each replicate were examined for thrips

injury and marketability. Ten berries were taken from each of the three middle plants.

The number of total injured and unmarketable fruit was divided by 30 to give proportion

of total injured and unmarketable fruit per plant. Total injured fruit included both those

that were still marketable and the unmarketable fruit.

Average thrips per trap, average thrips larvae and adults per flower, and average

proportion of injured and unmarketable fruit were transformed as necessary to meet the

assumptions of the analysis and compared among varieties using a one way analysis of

variance (ANOVA) test (SAS Institute 2002). Means were separated using the least

significant difference (LSD) means separation test. Thrips per sticky trap, thrips larvae

per flower, and thrips adults per flower were analyzed by date.

Data were also examined for a linear relationship between numbers of thrips

(larvae and adults) per flower pooled over all dates vs. proportion of total injured fruit

per plant using lease squares regression in SAS (SAS Institute 2002).

Hernando and Lake Counties

Samples were taken from two commercial farms in Hernando Co., Florida, during

early spring 2007 and one commercial farm in Hernando and another in Lake Co. in

2008. A 5-ha area on farm 1 in Hernando Co. was sampled only in 2007. The varieties

on this farm are arranged in blocks of six to nine rows. A 2.5-ha area of the second farm

in Hernando Co., farm 2, and of the Lake Co. farm was sampled. Farm 2 had alternating









rows of different varieties. Blueberry plants at the Hernando Co. farms were four to

seven years of age, while those at the Lake Co. farm were only one year old.

On each farm, the four most popular SHB varieties: Emerald, Jewel, Millennia, and

Windsor were divided into three treatments: T100, T200, and an untreated control. If the

number of thrips per trap exceeded 200 in the T200 treatment or 100 in the T100

treatment, SpinTor 2 SC (spinosad) (Dow Agrosciences, Indianapolis, IN) was applied

at the label rate of 0.438 L / ha. The 100 thrips per trap threshold is only slightly higher

than the economic injury level calculated by Arevalo-Rodriquez (2006) for rabbiteye

blueberries when SpinTor is applied. The treatment thresholds encompassed a row of

each of the varieties. There were three replicates containing each threshold/variety

combination, which encompassed all of the samples from the beginning, middle, and

end of the rows. A sticky trap was placed in each threshold/variety combination. Five

flowers from each of two plants closest to the trap were also sampled.

In 2007, sticky trap samples were collected for six weeks beginning on Feb. 1 and

2 on farms 1 and 2, respectively. Flower samples were collected until the majority of

plants were in fruit set. On farm 1, both treatments were above threshold after the first

week of sampling. Applications of SpinTor were made on Feb. 9 and Feb. 23. Thrips

numbers on farm 2 remained below threshold throughout the sampling period, so

SpinTor was not applied.

In 2008, sticky trap samples were collected for four weeks from the Lake Co. farm

and for three weeks from the Hernando Co. farm (farm 2 from 2007) beginning on Feb.

14 and Feb. 21, respectively. Flower samples were collected until the majority of plants









were in fruit set. The number of thrips per trap did not exceed either of the treatment

threshold levels on any date, so SpinTor was not applied on either farm.

Sticky traps and flower samples were shipped to the Small Fruit and Vegetable

IPM Laboratory at the University of Florida in Gainesville, FL. The number of thrips per

trap was counted and flowers were dissected under a dissecting microscope. Adult and

larval thrips were counted and stored in 1 dram vials containing 70% ethyl alcohol.

Adults were identified to species using a key developed for Florida SHB blueberries by

Arevalo et al. (2006). Thrips that did not match the character descriptions in the key

were sent to the Division of Plant Industry in Gainesville, FL for identification.

At harvest time, 25 berries from the two previously sampled plants and from two

adjacent plants were examined for thrips injury and marketability. The number of total

injured and unmarketable fruit from each sample was divided by 25 to give proportion of

total injured and unmarketable fruit per plant. The proportions from the four samples

were then averaged. Total injured fruit included both those that were still marketable

and the unmarketable fruit.

Average thrips per trap exceeded the threshold only on farm 1 in 2007. Therefore,

average thrips per trap, average thrips larvae and adults per flower, and average

proportion of total injured and unmarketable fruit per plant were transformed as

necessary to meet the assumptions and compared among treatments and varieties

using a two-way ANOVA test (SAS Institute 2002). If no interaction was present, main

effects of both factors were compared using the LSD means separation test. If

interaction was present, then simple effects were compared for whichever factor was









significant. Thrips per sticky trap, thrips larvae per flower, and thrips adults per flower

were analyzed by week.

Numbers of thrips per trap did not reach the threshold on farm 2 in 2007 or on

either farm in 2008 so SpinTor was never applied. Therefore, the previously described

data sets were transformed as needed to meet the assumptions of ANOVA and varietal

differences were analyzed using a one-way ANOVA. Means were separated using the

LSD means separation test. Thrips per sticky trap, thrips larvae per flower, and thrips

adults per flower were analyzed by week.

Data from 2007 and 2008 were also examined for any linear relationship between

numbers of thrips (larvae and adults) per flower pooled over all dates vs. total injured

fruit per plant using Theil regression (Hollander and Wolfe 1999) in 2007 and least

squares regression (SAS Institute, 2002) in 2008. Kendall's tau, a nonparametric

correlation statistic (Hollander and Wolfe 1999) was also calculated for the 2007 data

(Wessa 2008).

Results

Citra PSREU

2007

Traps

There were significantly more thrips per trap in the Emerald and Millennia varieties

compared with the Star variety (F = 2.48, df = 3, 47, P = 0.052) on Feb. 5 (Fig. 4-1). On

Feb. 12, there were significantly more thrips per trap in the Emerald variety compared

with all of the other varieties (F= 8.33, df = 3, 47, P= 0.0003). Also, Jewel had

significantly higher thrips per trap than Star.









Flowers

There were no significant differences among either thrips larvae or thrips adults

per flower on any date (all F 5 2.47, df = 3, 47, P > 0.08). Average thrips larvae per

flower did not exceed 0.09 0.07 larvae on any date. Average thrips adults per flower

did not exceed 0.15 0.07 adults on any date.

There was a high diversity in adults thrips sampled from the flowers. The majority

of thrips were F. bispinosa (Table 1). Others species sampled included F. fusca (Hinds),

Franklinothrips sp., Haplothrips graminis Hood, Thrips hawaiiensis (Morgan), and T. pini

Karny.

Fruit

Emerald and Jewel had a significantly higher proportion of injured fruit than

Millennia and Star (F = 7.53, df = 3, 47, P = 0.0006, Fig. 4-2). Emerald also had a

significantly higher proportion of unmarketable fruit then all of the other varieties (F =

11.31, df = 3, 47, P < 0.0001).

Simple linear regression did not show any relationship between thrips per flower

and proportion of total injured fruit in any of the varieties (all R2 0.03, all t 1.13, df=

11, Psiope 0.28).

2008

Traps

On Feb. 14, there were significantly more thrips per trap in the Emerald and Jewel

varieties than in the Millennia variety (F = 3.9, df = 2, 35, P = 0.036, Fig. 4-3).

Flowers

There were significantly more thrips larvae per flower in the Emerald variety

compared with the Jewel variety on Feb. 14 (F = 3.37, df = 2, 35, P = 0.053) and









compared with both other varieties on Feb. 22 (F = 12.69, df = 2, 35, P = 0.0002) and

March 8 (F = 6.81, df = 2, 35, P = 0.0050, Fig. 4-4A).

The Emerald variety also had significantly more thrips adults per flower compared

with the other two varieties on Feb. 14 (F = 7.2, df = 2, 35, P = 0.0039, Fig. 4-4B). In

contrast, Jewel had significantly higher numbers of thrips adults per flower compared

with the other two varieties on March 14 (F = 7.99, df = 2, 35, P = 0.0025).

Percent of adult thrips species sampled from flowers was similar to 2007, with F.

bispinosa comprising the majority (> 60%) of thrips sampled (Table 4-1). Most of the

remaining adult thrips were either T. hawaiiensis or T. pini. Frankliniella fusca,

Franklinothrips sp., and H. graminis were also found.

Fruit

There were no significant differences in either proportion of injured (F = 0.18, df =

2, 35, P = 0.83) or unmarketable (F = 0.62, df = 2, 35, P = 0.55) fruit among varieties.

Emerald, Jewel, and Millennia averaged 0.14 0.02, 0.16 0.03, and 0.16 0.02

proportions of injured fruit, respectively. All three varieties averaged a 0.01 0.00

proportion of unmarketable fruit.

Simple linear regression did not show any relationship between thrips per flower

and proportion of total injured fruit in any of the varieties (all R2 0.12, all t < 1.57, df =

11, Psiope 0.15).









Hernando and Lake Counties

2007

Traps

There were no treatment*variety interactions on any date after treatments were

applied (all F < 1.23, df = 6, 35, P > 0.33). Therefore each factor was examined

separately.

There were significantly fewer thrips per trap recorded from the 200 thrips per trap

threshold treatment compared with the control on March 1 (F = 4.1, df = 2, 35, P =

0.029, Fig. 4-5).

On farm 1, Emerald had significantly higher numbers of thrips per trap compared

with at least two of the other varieties on all sampling dates. There were significantly

higher numbers of thrips per trap in the Emerald variety compared with the Jewel and

Millennia varieties (F = 7.18, df = 3, 35, P= 0.0013) on Feb. 1 (Fig. 4-6). Windsor also

had significantly higher numbers of thrips per trap compared with Millennia on this date.

Emerald had significantly higher numbers of thrips per trap than all of the other varieties

on Feb. 8 (F = 11.27, df = 3, 35, P < 0.0001), Feb. 15 (F = 5.71, df = 3, 35, P < 0.0043),

Feb. 22 (F = 22.65, df = 3, 35, P < 0.0001), and March 1 (F = 11.58, df = 3, 35, P <

0.0001). Jewel and Windsor also had significantly higher numbers of thrips per trap than

Millennia on Feb. 22. On March 8, Emerald had significantly higher numbers of thrips

per trap than Jewel and Windsor (F = 4.07, df = 3, 35, P = 0.018).

On farm 2, Emerald had significantly higher numbers of thrips per trap than all

other varieties (F = 8.53, df = 3, 35, P = 0.0003) on Feb. 9 (Fig. 4-7). On Feb. 16,

Emerald had significantly higher numbers of thrips per trap compared with Jewel and

Windsor. Also, Jewel had significantly more thrips per trap compared with Windsor (F =









16.27, df = 3, 35, P < 0.0001). On Feb. 23, both Emerald and Jewel had significantly

more thrips per trap compared with Windsor (F = 3.32, df = 3, 35, P = 0.033).

Flowers

There were no treatment*variety interactions in thrips larvae per flower on any

date after treatments were applied (both F = 0.51, df = 6, 35, P = 0.79) on farm 1.

There were no significant differences in thrips larvae per flower among treatments

on any date (all P 0.11). In the T200 treatment, thrips larvae per flower peaked at 2.2

0.3 larvae on Feb. 8, the day before SpinTor was applied. Thrips larvae per flower

peaked in both the T100 and control treatments on Feb. 15 at 2.2 0.4 and 2.4 0.4

larvae, respectively.

However, there were significantly higher numbers of thrips larvae per flower in the

Jewel variety compared with the other varieties (F = 3.57, df = 3, 35, P = 0.029) on Feb.

8 (Fig. 4-8A).

For thrips adults, there was no treatment*variety interaction on Feb. 15 (F = 0.78,

df = 6, 35, P = 0.59). However, there was treatment*variety interaction on Feb. 22 (F =

3.42, df = 6, 35, P= 0.014).

There were no significant differences in thrips adults per flower among treatments

on any date (all F < 1.42, df = 2, 35, P > 0.26). Average thrips adults per flower did not

rise above 1.2 0.3 adults on any date.

However, the varietal trends in thrips adults per flower on farm 1 were similar to

thrips per trap. On Feb. 8, there were significantly higher numbers of thrips adults per

flower in the Emerald variety compared with the Millennia and Windsor varieties (F =

5.00, df = 3, 35, P = 0.0078, Fig. 4-8B). Emerald had significantly higher numbers of

thrips adults per flower compared with all of the other varieties on Feb. 15 (F = 10.32, df









= 3, 35, P = 0.0001). Jewel had significantly higher numbers ofthrips adults per flower

compared with Millennia on both of the above dates.

On Feb. 22, main effects showed that Emerald had significantly higher numbers of

thrips adults per flower compared with Jewel and Millennia (F = 9.93, df = 3, 35, P =

0.0003). Jewel also had significantly higher numbers of thrips than Millennia. When

simple effects are examined, Emerald has significantly higher numbers of thrips adults

compared with all of the other varieties in the untreated control. There were no varietal

differences in the T100 treatment. In contrast, Millennia had significantly fewer thrips

adults per flower than all three of the other varieties in the T200 treatment.

On farm 2, there were significantly more thrips larvae per flower in the Emerald

variety compared with the Windsor variety (F = 3.70, df = 3, 35, P = 0.022) on Feb. 9

(Fig. 4-9A). On Feb. 16, both Jewel and Emerald had significantly higher numbers of

thrips larvae compared with Millennia and Windsor (F = 4.99, df = 3, 35, P = 0.0063).

Emerald had significantly higher numbers of thrips larvae per flower compared with all

of the other varieties on Feb. 23 (F = 4.76, df = 3, 35, P = 0.0079). In contrast, Jewel

and Windsor had significantly higher numbers of thrips larvae per flower compared with

Emerald and Millennia on March 2 (F = 4.54, df = 3, 35, P = 0.0097).

There were significantly more thrips adults per flower in the Emerald variety

compared with the Jewel and Windsor varieties and significantly more thrips adults per

flower in the Millennia variety compared with the Windsor variety (F = 5.35, df = 3, 35, P

= 0.0045) on Feb. 16 (Fig. 4-9B). On Feb. 23, Jewel and Windsor had significantly

higher numbers of thrips adults per flower compared with Millennia (F = 3.01, df = 3, 35,

P = 0.046).









Farm 1 had an unusually high number of T. hawaiiensis and T. pini present in the

Emerald and Windsor varieties (Table 4-2). Frankliniella bispinosa was the dominant

species found in the Jewel and Millennia varieties. A single H. graminis was found in the

Windsor variety.

In contrast, the majority of thrips adults sampled from flowers of all the varieties on

farm 2 were F. bispinosa (Table 4-3). Franklinothrips sp., H. graminis, T. hawaiiensis,

and T. pini were also present.

Fruit

There were no treatment*variety interactions in proportion of total injured (F =

0.47, df = 6, 35, P =0.82) or unmarketable (F = 0.70, df = 6, 35, P =0.66) fruit on farm 1.

Therefore, main effects were analyzed.

Interestingly, there was a significantly higher proportion of injured (F = 5.72, df = 6,

35, P =0.0093) and malformed (F = 3.53, df = 6, 35, P =0.045) fruit in the untreated

control compared with the T100 treatment (Fig. 4-10).

There were no significant differences in either proportion of total injured or

unmarketable fruit among varieties on farm 1 (injured: F = 1.05, df = 3, 35, P = 0.39;

unmarketable: F = 0.87, df = 3, 35, P = 0.57) or farm 2 (injured: F = 1.87, df = 3, 35, P =

0.16; unmarketable: F = 0.25, df = 3, 35, P = 0.86). On farm 1, there was an average

proportion of total injured fruit of 0.09 0.02 and an average proportion of unmarketable

fruit of 0.02 0.01 across varieties. On farm 2, there was an average proportion of total

injured fruit of 0.05 0.01 and an average proportion of unmarketable fruit of 0.02

0.002 across varieties.

Nonparametric regression showed a significant positive linear relationship

between thrips per flower and total injured fruit in the Emerald (T = 0.41, C = 74, n = 18,









Psiope = 0.003), Jewel (T = 0.35, C = 56, n = 18, Psiope = 0.024), Millennia (T = 0.25, C =

44, n = 18, Psiop = 0.056), and Windsor (T = 0.30, C = 56, n = 18, Psiop = 0.02) varieties

(Fig. 4-11A-D).

2008

Traps

On the Lake Co. farm, Emerald, Windsor, and Jewel had significantly higher

numbers of thrips per trap compared with Millennia (F = 4.52, df= 3, 34, P = 0.0096) on

Feb. 21 (Fig. 4-12). On Feb. 28, Windsor had significantly higher numbers of thrips per

trap compared with Millennia (F = 3.09, df = 3, 35, P = 0.041). Windsor had significantly

higher numbers of thrips per trap compared with all of the other varieties on March 6 (F

= 13.68, df = 3, 35, P < 0.0001).

On Hernando Co. farm 2, there were no significant differences in thrips per trap

among varieties on any date (all F S 2.09, df = 3, 32, P > 0.12). There were an average

of 8.3 1.8, 4.2 1.1, and 10.3 2.2 thrips per trap over variety on Feb. 21, Feb. 28,

and March 6 respectively.

Flowers

On the Lake Co. farm, there were significantly more thrips larvae per flower in the

Emerald variety compared with the Millennia variety (F = 3.4, df = 3, 34, P = 0.030) on

Feb. 14 (Fig. 4-13A).

There were significantly more thrips adults per flower in the Emerald variety

compared with all of the other varieties (F = 16.41, df = 3, 34, P < 0.0001) on Feb. 14

(Fig. 4-13B). There were significantly more thrips adults per flower in the Windsor

variety compared with the Jewel and Millennia varieties on Feb. 28 (F = 6.17, df = 2, 21,

P = 0.0086).









On Hernando Co. farm 2, there were significantly higher numbers of thrips larvae

per flower in the Emerald (0.8 0.2 larvae) and Windsor (1.1 0.2 larvae) varieties

compared with the Jewel (0.3 0.1 larvae) and Millennia (0.3 0.2 larvae) varieties (F =

5.9, df = 3, 35, P = 0.0025) on Feb. 21. On Feb. 28, Windsor (0.05 0.01 larvae) had

significantly higher numbers of thrips per flower than all of the other varieties (0 larvae)

(F = 6.32, df = 3, 22, P = 0.0037).

There were no significant differences in thrips adults per flower among varieties on

either date (both F < 1.42, df = 3, 35, P 0.25). There was an average of 0.2 0.1 and

0.01 0.008 adults per flower across varieties on Feb. 21 and 28 respectively.

All four varieties on the Lake Co. farm had high percentages of T. hawaiiensis and

T. pini adults (Table 4-2). Most of the remaining adult thrips were F. bispinosa.

Frankliniella fusca, Franklinothrips sp., and H. graminis were sampled occasionally.

In contrast, F. bispinosa was the dominant thrips species sampled from Jewel,

Millennia, and Windsor flowers on Hernando Co. farm 2 (Table 4-3). Most of the thrips

sampled from the Emerald flowers were either T. hawaiiensis or T. pini. A few

Franklinothrips sp. were found in the Windsor variety.

Fruit

On the Lake Co. farm, Jewel had a significantly higher proportion of injured fruit

compared with all of the other varieties and Windsor had a significantly higher

proportion of injured fruit compared with Emerald and Millennia (F = 15.41, df = 3, 35, P

< 0.0001, Fig. 4-14A). Jewel also had a significantly higher proportion of unmarketable

fruit compared with all the other varieties (F = 13.87, df = 3, 25, P < 0.0001).

On Hernando Co. farm 2, Jewel and Windsor had a significantly higher proportion

of injured fruit compared with Emerald and Millennia and Emerald had a significantly









higher proportion of injured fruit compared with Millennia (F = 18.43, df = 3, 35, P <

0.0001, Fig. 4-14B). Jewel also had a significantly higher proportion of unmarketable

fruit compared with all the other varieties (F = 21.77, df = 3, 25, P < 0.0001).

Simple linear regression, combining the data from both farms, did not show any

relationship between thrips per flower and proportion of total injured fruit in any of the

varieties (all R2 < 0.01, all t -1.08, df = 17, Psiope 0.30).

Discussion

The only significant difference in thrips numbers among treatment thresholds

occurred with thrips per trap on March 1, 2007 on farm 1. By this date, flowers were

only present on the Emerald variety. The lack of effectiveness of the thresholds may

have been caused by thrips from untreated areas of the farms recolonizing the treated

rows. Funderburk and Stavisky (2004) note that F. bispinosa adults can quickly

recolonize a treated area, making the application appear to be ineffective. The

proportion of injured and unmarketable fruit data suggest that 100 thrips per trap is an

effective threshold, but more research is needed to confirm this fact.

Southern highbush blueberry variety does appear to influence thrips numbers.

This was particularly true on the two Hernando Co. farms in 2007. Emerald frequently

had significantly more thrips per trap and per flower than the other varieties. Millennia

and Star tended to have the lowest numbers of thrips. This may be due to their

flowering characteristics. Emerald, Jewel, and Millennia reach 50% open flowers around

Feb. 16 in Gainesville, FL. Star and Windsor reach 50% open flowers about a week

later (Williamson and Lyrene 2004). Unlike the other varieties, Emerald flowers

uniformly. All of the varieties tested except Millennia reach petal fall around the same

time. Millennia reaches petal fall 3 to 4 days earlier (Williamson and Lyrene 2004). The









combination of flowering early and uniformly, when flower thrips are abundant, may

make Emerald more attractive to flower thrips.

The differences in thrips numbers among varieties were not as pronounced on the

Lake and Hernando Co. farms in 2008 compared with 2007. There are several possible

reasons for this difference. Firstly, sampling was initiated late and only a few weeks of

data were collected. Secondly, only the week of Feb. 14 contained a complete data set

for flowers from farm 1. Many plants had already reached petal fall by Feb. 21 on both

farms. Therefore, there were several missing data points from Feb. 21 and 28. Thirdly,

there were fewer thrips on the farms in 2008 compared with 2007.

The differences in thrips numbers among varieties were also not as pronounced

on the Citra PSREU farm compared with the Hernando and Lake Co. farms. The four

varieties at the Citra farm are distributed evenly among each other. This may be

partially masking the effect of variety on thrips numbers. In contrast, farm 1 in Hernando

Co. has large blocks of a single variety. Farm 2 has an intermediate setup, with only a

few rows of the same variety adjacent to each other. Further research is needed to

confirm the hypothesis that arrangement of blueberry varieties affects thrips numbers.

There were differences in fruit injury among varieties, but these did not appear to

be related to differences in thrips numbers. There could be several reasons for this. The

different varieties could have different levels of tolerance to flower thrips. It is also

possible that some varieties are more susceptible to diseases than others. Lastly, the

different species of thrips may differ greatly in their effect on the blueberry flowers and

subsequent fruits. It has been shown that peppers in Florida can tolerate high numbers









of F. bispinosa and F. tntici, but only a few F. occidentalis will cause significant injury

(Funderburk 2009).

The thrips complex in SHB blueberry flowers in Florida is dominated by F.

bispinosa (Arevalo et al. 2006). Frankliniella bispinosa was the most common species

sampled from all of the varieties at the Citra PSREU. A diversity of other species was

also found. This diversity of species was probably due to the wide variety of crops

grown at the research station.

The two Hernando and Lake Co. farms, however, differed from this norm. On farm

1 in 2007, the Millennia variety was the only one dominated by F. bispinosa. Jewel and

Windsor had high percentages of F. bispinosa, T. hawaiiensis, and T. pini. Emerald was

dominated by the two Thrips species. On the Lake Co. farm in 2008, all four varieties

were dominated by the two Thrips species. Farm 2 was less extreme in its differences

from the expected. In 2007, only the Jewel variety had high percentages of the two

Thrips species. Even so, the majority of thrips sampled from Jewel were F. bispinosa. In

2008, the Emerald variety was dominated by the two Thrips species, while the other

varieties were dominated by F. bispinosa. Further research is needed to determine why

the two Thrips species occurred in such high numbers on these three farms.

Significant positive linear relationships between thrips per flower and fruit injury

were found in all four varieties from the Hernando Co. farms in 2007. Neither the

Hernando and Lake Co. farms in 2008 nor the Citra PSREU in either year showed a

relationship between thrips per flower and fruit injury. This may be due to the low

numbers of thrips present at these farms during these years.











The results from these experiments show evidence that SHB blueberry varieties

may attract different numbers of thrips and may have varying tolerance to thrips injury. If

this is the case, then each variety would have a different Economic Injury Level (EIL).

Since multiple varieties are grown on the same farm, the lowest EIL could be used to

set the threshold level for the farm.


1000

800 -
c. -*-- Emr
S 60 0 -
.a- Star
400 Mill

200 -
< a
00
29-Jan 5-Feb 12-Feb 19-Feb 26-Feb 5-Mar 12-Mar 19-Mar
Date

Fig. 4-1. Average thrips per sticky trap recorded from each variety per week in 2007.
Error bars represent standard error of the mean. Means with the same letter
are not significantly different from each other at the P = 0.05 level.


03
A A
l: 025
02
02
B total inj
0- 15
o unmarketable
o 01
0. 05
0-
Emr Jwl Mill Star
Variety

Fig. 4-2. Proportion of injured and unmarketable fruit sampled from each variety in
2007. Error bars represent standard error of the mean. Means with the same
letter are not significantly different from each other at the P = 0.05 level.











100 0


--- Emr
-a- Mill
-a- Jwl


1=y


Date

Fig. 4-3. Average thrips per sticky trap recorded from each variety per week in 2008.
Error bars represent standard error of the mean. Means with the same letter
are not significantly different from each other at the P = 0.05 level.


--Emr
- -Mill
--- Jwl


S 1
14-Feb 21-Feb 28-Feb


6-Mar 13-Mar 20-Mar


Date


--Emr
- -Mill
--- Jwl


21-Feb 28-Feb 6-Mar 13-Mar 20-Mar


Date
B
Fig. 4-4. Average thrips A) larvae and B) adults per flower recorded from each variety
per week in 2008. Error bars represent standard error of the mean. Means
with the same letter are not significantly different from each other at the P =
0.05 level.

















-T100
--T200
- Control


n n


8-Feb 15-Feb 22-Feb 1-Mar


Date

Fig. 4-5. Average thrips per trap recorded from each treatment per week on farm 1 in
2007. Error bars represent standard error of the mean. Means with the same
letter are not significantly different from each other at the P = 0.05 level.
Arrows indicate the dates when SpinTor was applied.


15000


00
1-Feb


- Emr
---Jwl
-- Mill
--Win


8-Feb 15-Feb 22-Feb 1-Mar 8-Mar


Date


Fig. 4-6. Average thrips per trap recorded from each variety per week on farm 1 in 2007.
Error bars represent standard error of the mean. Means with the same letter
are not significantly different from each other at the P = 0.05 level. Arrows
indicate the dates when SpinTor was applied.


1000 -
















--Emr
--Jwl
--- Mill
-x--Win


00 0. I
2-Feb 9-Feb 16-Feb 23-Feb 2-Mar 9-Mar 16-Mar
Date


Fig. 4-7. Average thrips per trap recorded from each variety per week on farm 2 in 2007.
Error bars represent standard error of the mean. Means with the same letter
are not significantly different from each other at the P = 0.05 level.


00 *
1-Feb


-- Emr
---Jwl
-A- Mill
-x- Win


8-Feb 15-Feb 22-Feb 1-Mar


Date


nn I


1-Feb


-- Emr
---Jwl
- Mill
-x- Win


8-Feb 15-Feb 22-Feb 1-Mar


Date
B
Fig. 4-8. Average thrips A) larvae and B) adults per flower recorded from each variety
per week on farm 1 in 2007. Error bars represent standard error of the mean.
Means with the same letter are not significantly different from each other at
the P = 0.05 level. Arrows indicate the dates when SpinToro was applied.


250 0

2000

1500

1000

50 0
ci


~f~E~




















10

05

001
2-Feb


9-Feb 16-Feb 23-Feb
Date


2-Mar 9-Mar


S-Emr
--Jwl
- Mill
-x-Win


-eb 9-Feb 16-Feb 23-Feb 2-Mar


Fig. 4-9. Average thrips A) larvae and B) adults per flower recorded from each variety
per week on farm 2 in 2007. Error bars represent standard error of the mean.
Means with the same letter are not significantly different from each other at
the P= 0.05 level.


- Emr
---Jwl
- Mill
-x-Win











02

A
.- 015

.o .B Total
S 0 1 AB
oC i Unmarketable
SB
S 0 05 a
bLab

0 b
Control T100 T200
Treatment

Fig. 4-10. Proportion of injured and unmarketable fruit sampled from each treatment on
farm 1 in 2007. Error bars represent standard error of the mean. Means with
the same letter are not significantly different from each other at the P = 0.05
level.



S 16 = 0 0154x + 0 03
"u 016
S014
S 012
a 01-
0o
.= E 008
.o 0 06


S0 02 -
> 0

0 05 1 15 2 25 3 35
Average thrips per flower
A











0 12 y= 0004x+ 004


0 05 1 15 2 25 3 35


Average thrips per flower


y = 0 026x + 0 047
03


0 15


r v


Average thrips per flower


03 y = 0018x+ 0055


0 15


0 05 1 15 2 25 3


Average thrips per flower
D
Fig. 4-11. Graphs showing average thrips per flower vs. average proportion of injured
fruit, the Theil regression line, and equation for the A) Emerald, B) Jewel, C)
Millennia, and D) Windsor varieties. Data from both farms were combined.




80














75

60 --- Emr
-- Jwl

i----Mill
45 -
w 30a -x--Win



0
14-Feb 21-Feb 28-Feb 6-Mar
Date


Fig. 4-12. Average thrips per trap recorded from each variety per week on farm 1 in
2008. Error bars represent standard error of the mean. Means with the same
letter are not significantly different from each other at the P = 0.05 level.


12
18

> 08-- Emr
06 J

04 -Win
02
0,

14-Feb 21-Feb 28-Feb
Date
A

12


S08- --- Emr
0 6 -
0-- o ---- M ill
604
0
0,
02-


14-Feb 21-Feb 28-Feb
Date
B
Fig. 4-13. Average thrips A) larvae and B) adults per flower recorded from each variety
per week on farm 1 in 2008. Error bars represent standard error of the mean.
Means with the same letter are not significantly different from each other at
the P= 0.05 level.









0 25
I-
-c 02

0
' 015
= 01
0
o
o 005
0- 0


Emr Jwl


c


b



I


m total injured
* unmarketable


Variety


0 25
4-
*3 02

'5
0


c 01
o 005
a. n


c
it


Emr Jwl


a




Ib


Total injured
* unmarketable


Fig. 4-14. Proportion ofi
farm 1 and B)
mean. Means
other at the P


Variety
B
injured and unmarketable fruit sampled from each variety on A)
farm 2 in 2008. Error bars represent standard error of the
with the same letter are not significantly different from each
= 0.05 level.










Table 4-1. Percent of adult thrips
Citra PSREU
F bispinosa F fusca


2007 Emerald
Jewel
Millenla
Star
2008 Emerald
Jewel
Millenla


species sampled from flowers in each treatment at the


F occidentalis


T hawallensis


T pini Franklinothnps sp


H qraminis


733 0 33 33 133 33 33
720 40 0 40 0 80 120
912 29 0 0 0 59 0
545 91 0 303 909 303 21 2
719 06 0 78 156 31 0
533 06 0 141 239 65 1 1
769 0 0 103 26 77 26


Table 4-2. Percent of adult thrips species sampled from flowers in each treatment on
farm 1


2007 Emerald
Jewel
Millennia
Winsor
2008 Emerald
Jewel
Millenla
Winsor


F bispinosa F fusca


T hawalensis


T pini Franklinothnps sp H qraminis


151 0 362 49 0 0
500 0 202 30 0 0
71 9 0 63 22 0 0
41 2 0 271 31 0 1 2
284 27 432 162 95 0
400 0 360 240 40 0
157 0 569 255 0 20
365 1 4 392 21 6 0 1 4


Table 4-3. Percent of adult thrips species sampled from flowers in each treatment on
farm 2


2007 Emerald
Jewel
Millenia
Winsor
2008 Emerald
Jewel
Millenia
Winsor


F bispinosa T hawallensis


T pini Franklinothnps sp H qraminis


773 45 182 0 0
563 188 250 0 0
909 0 0 91 0
810 48 48 48 48
167 41 7 41 7 0 0
1000 0 0 0 0
66 7 33 3 0 0 0
700 33 200 67 0









CHAPTER 5
EXAMINING THE SPATIAL DISTRIBUTION OF THRIPS UTILIZING
GEOSTATSITICAL METHODS

Introduction

Flower thrips are the key pest of southern highbush (SHB) blueberries in Florida.

The most common species is Frankliniella bispinosa (Morgan), the Florida flower thrips

(Arevalo-Rodriguez et al. 2006). They feed on and breed in all flower tissues. Flower

thrips injure the flower tissues while feeding and laying their eggs. When the ovaries of

the flowers develop into fruit, this injury can become magnified and appear as scars on

fruit tissue. High populations of flower thrips can cause fruit to be malformed and

unmarketable (Arevalo-Rodriguez et al. 2006).

Flower thrips have a highly clumped distribution and tend to form small areas of

high population termed "hot spots" (Arevalo and Liburd 2007). If these "hot spots" can

be modeled and predicted, insecticide applications could specifically target these spots

instead of the entire field.

With the advent of geostatistics into the world of insect ecology, the spatial

relationships of insect populations can now be studied (Liebhold et al. 1993). The

cornerstone of geostatistics is called the variogram or semivariogram (Webster and

Oliver 2001). A semivariogram plots the semivariance, 2 of the average squared

difference between data values at the same separation distance, on the y-axis and the

specified distance between sample pairs, the lag, on the x-axis (Wright et al. 2002).

Since it is very difficult to fit a model to a semivariogram where each individual

semivariance is plotted, the semivariance is averaged for each of several lags (Webster

and Oliver 2001). This is expressed mathematically as y(h) = {1/2m(h)} Z {z(x,) z(x, +

h)}2, where y(h) is the semivariance at lag h, m(h) is the number of data point pairs









separated by lag h, and z(x,) and z(x, + h) are the data values (z) a places separated by

h (Webster and Oliver 2001). The important features of semivariograms are the sill,

range, lag, and nugget, which are defined as the value of the semivariance when it

stops increasing, the distance at which spatial independence is reached, the distance

between sample pairs, and the semivariance value when x = 0 respectively. The nugget

variance is a combination of measurement error and variation over distances less than

the shortest lag distance sampled for all continuous variables (Webster and Oliver

2001).

Semivariograms have been used to examine and describe the spatial relationship

of several corn pests, including western corn rootworm adults on yellow sticky traps in

corn (Midgarden et al. 1993), corn rootworm injury to corn (Park and Tollefson 2005),

and European corn borer larvae and their damage in whorl stage corn (Wright et al.

2002). Semivariograms have also been used to examine and describe the spatial

relationships of three species of Xylella fastidiosa (Wells) sharpshooter vectors on citrus

(Paulo et al. 2003) and of Lygus hesperus (Knight) in lentils (Schotzko and O'Keeffe

1990). Florez and Corredor (2000) used semivariogram along with other geostatistical

analyses to examine the spatial dependence of F. occidentalis in a covered strawberry

crop at Bogota plateau. Spatial dependence was found in 3 of 12 sampling weeks. They

found that although thrips colonies were aggregated at first, over time the pattern

changed toward a random pattern. This change was caused by thrips movement to

neighboring quadrants.

Kriging is a method that allows researchers to estimate the continuous properties

of something in the environment from a finite number of sampled points (Webster and









Oliver 2001). Ordinary kriging is the most common kriging method used in most

applications (Webster and Oliver 2001). In ordinary kriging, the overall mean of the

population is assumed to be unknown. Like IDW, ordinary kriging uses a weighted

average to estimate unknown values. However, the weights are based upon the

semivariogram model.

Two other commonly used interpolation techniques are natural or nearest neighbor

and inverse distance weighting (IDW). Natural neighbor is the simplest interpolation

method. The value at an unknown point is set equal to the value of the nearest sample

point (Ess and Morgan 2003). In IDW, a set of samples that are a given distance away

from the unknown point are used to interpolate the value at that point. Sample points

closer to the unknown point are given a higher weight than those farther away. IDW is,

in effect, a weighted average (Ess and Morgan 2003). The estimated value for the

unknown point a location j, Zj, is calculated using the equation {Z(z,/dP,)} / { Z(1/dP,)},

where d,j is the distance between known point i and unknown point j, z, is the value at

known point i, and p is an exponent defined by user that is commonly set equal to two

(Bolstad 2006).

There were two major objectives of this study. The first was to determine the best

spatial interpolation method to use to model thrips population distribution. Natural

neighbor, IDW, and ordinary kriging were compared. Ordinary kriging tends to be the

most accurate interpolation method. If thrips variation can be modeled with

semivariograms, ordinary kriging will most likely prove the most accurate in this study as

well. However, if the thrips variation cannot be modeled well with semivariograms, IDW

will be as accurate as, if not more accurate than, ordinary kriging.









The second objective was to use the semivariogram models to determine optimum

trap spacing. Traps spaced at or beyond the range of the semivariogram will monitor

populations that are spatially independent from each other.

Materials and Methods

In 2008, 100 white sticky traps (Great Lakes IPM, Vestaburg, MI) were distributed

throughout a 1.13-ha SHB blueberry planting of four to seven year old bushes in

Inverness, Florida, in a regular grid at 15.24-m increments (Fig. 5-1). An additional 30

traps were placed randomly throughout the plot to collect information on distances

shorter and longer than 15.24-m. Traps were changed out weekly over a three week

period on Feb. 14, 21, and 28, 2008. The number of thrips per trap was counted and

recorded.

Trap locations were mapped using a Trimble GeoXT GPS receiver (Trimble,

Sunnyvale, CA) in the WGS84 datum. The data were then imported into ArcMap 9.1

(ESRI 2005), projected, and interpolated using several spatial interpolation methods.

When the data was imported into ArcMap, the NAD 27 datum was automatically

attached to it. The data was redefined into the NAD 83 datum and then projected into

Albers Equal Area Conic. Natural neighbor, IDW, and Ordinary kriging (Ess and Morgan

2003) were computed in ArcMap 9.1 itself. The semivariograms for the ordinary kriging

were constructed in Space Time Information System (STIS) (Terraseer, Inc. 2007) and

then input into ArcMap for kriging. For IDW, p was set at the default 2 and the search

area was divided into 4 quadrants from which at least 5 data points per quadrant were

included. In Ordinary kriging, the search area, with a radius equal to the range of the

semivariogram, was also divided into 4 quadrants from which at least 1 point per

quadrant up to a total of 5 points was used in the interpolation.









In 2009, 100 white sticky traps were distributed throughout a 1-ha of the same

blueberry planting used in the previous year in a regular grid at 7.61-m increments (Fig.

5-2). A smaller area was used in order to identify finer scale spatial variability. An

additional 30 traps were again placed randomly throughout the plot. Traps were

changed out weekly over a five-week period on Jan. 30, Feb. 5, Feb. 13, Feb. 20, and

Feb. 26, 2009. The number of thrips per trap was counted and recorded.

Trap locations were mapped using a Trimble Pathfinder GPS receiver (Trimble,

Sunnyvale, CA) in the WGS84 datum. The data were then imported into ArcMap 9.1

(ESRI 2005), projected into universal transverse mercator (UTM), and interpolated

using several spatial interpolation methods. Again, the datum was automatically

assumed to be NAD 27, so this datum was used in the UTM projection. Natural

neighbor, IDW, and Ordinary kriging were computed in ArcMap 9.1 itself. The

semivariograms for the ordinary kriging were constructed in SGeMS (Remy 2007) and

then input into ArcMap for kriging. It was necessary to normalize the data for

semivariogram analysis using a natural logarithmic transformation for all sample dates

except Feb. 13. For IDW, p was set at the default 2 and the search area was divided

into 4 quadrants from which at least 5 data points per quadrant were included. In

ordinary kriging, the search area, with a radius equal to the range of the semivariogram,

was also divided into 4 quadrants from which at least 2 points per quadrant up to a total

of 5 points were used in the interpolation.

Cross-validation was used to assess the accuracy of the predictions from all three

interpolation methods in both years. For IDW and kriging, this was done in ArcMap. For

the natural neighbor interpolation, the cross-validation had to be done manually. Mean









prediction error (ME) was calculated using the equation ME = {Z (predicted -

measured)} / n, where n is the sample size (Webster and Oliver 2001). R2 values were

calculated using the equation R2 = Z (predicted mean measured)2 / Z (measured -

mean measured)2. The root mean square error (RMSE) was calculated using the

equation RMSE = 4 {Z (predicted measured)2 / n)}, where n is the sample size

(Bolstad 2006). The residual prediction deviation (RPD) was calculated using the

equation RPD = oval / RMSEv4 {n / (n-1)}, where val is the standard deviation of the

validation set, RMSEv is the root mean square error of the validation as calculated

above, and n is the sample size (Vasques et al. 2010).

Results

The summary data for thrips per trap was similar for all three weeks in 2008 (Table

5-1). On Feb. 14, high numbers of thrips per trap were located in the southeastern

quadrant of the northern block of rows and throughout the southern block of rows, but

more concentrated in the northern half of the southern block (Fig. 5-3A). The highest

numbers of thrips per trap on Feb. 21 were located in two rows, one in the southwest

quadrant of the sampling area and the other towards the east side of the northern block

of rows (Fig. 5-3B). The traps with the highest numbers of thrips on Feb. 28 were

located at the northern end of the southern block of rows (Fig. 5-3C).

The natural neighbor, IDW, and Ordinary kriging interpolations of thrips per trap for

each sampling week from 2008 are shown in figures 5-4, 5-5, and 5-6 B, D & F

respectively. Locations of areas of high thrips population, 'hot spots,' are similar in all

three interpolation methods. The natural neighbor method was the least accurate for all

three sample weeks (Table 5-2). IDW and ordinary kriging had very similar RMSEs,

RPDs, and R2 values on all three dates, indicating that their accuracies were similar.









However, IDW had a ME much closer to 0 than ordinary kriging on Feb. 21, indicating

that IDW was more accurate on this date.

On Feb. 14, one hotspot was distinguishable in the southwest area of the field in

the natural neighbor and IDW maps. The area of this 'hot spot' was larger in the natural

neighbor map. It was also present in the ordinary kriging map, but the estimated number

of thrips was much smaller.

On Feb. 21, three major "hot spots" had formed: two were very close to each other

in the southwest area of the field, and one was present in the northeast area of the field.

The one present on Feb. 14 was still present along with several other smaller "hot

spots". All three maps looked very similar, but the area of the "hot spots" was smaller in

the IDW map.

On Feb. 28, the remnants of the "hot spots" in the southern half of the field could

be seen. The kriging map showed fewer thrips in these 'hot spot' remnants compared

with the other two methods.

The semivariograms used for the ordinary kriging varied greatly among the weeks

(Fig. 5-6 A, C, & E, Table 5-3). The Feb. 14 semivariogram had a very large nugget and

a large range (~ 80 m). The nugget to sill ratio was also large at 1.44. The Feb. 21

semivariogram showed a distinct spatial trend with a small nugget (0.14), a very small

nugget to sill ratio (0.0000015) and a range of 11.04 m. The Feb. 28 semivariogram had

a small nugget of 0.002, a very small nugget to sill ratio (0.000000071), and a very short

range of 2.51 m.

The summary data of thrips per trap for all five sampling weeks is shown in Table

5-4. The Jan. 30 summary data was similar to that found for all three weeks in 2008.









Traps with high numbers of thrips per trap on Jan. 30 were concentrated in two rows,

the south center row and one of the southwest rows (Fig. 5-7A). There was also a trap

with high trips numbers in the southeast corner of the sampling area. All values on Feb.

5 were low because very few thrips were caught on the traps during the preceding week

(Fig. 5-7B). Summary data from the remaining three sampling weeks was very similar

except that the skewness coefficient and kertosis were much smaller on Feb. 13. The

distribution of high and low numbers of thrips per trap on Feb. 13 appeared to be

random (Fig. 5-7C), with high numbers located in several of the northern and

southwestern rows and in the southeast corner of the sampling area. The furthest

southwestern row had very low numbers of thrips per trap. On Feb. 20, high numbers of

thrips per trap were found throughout the northern rows, particularly in the central and

east rows (Fig. 5-7D). High numbers were also found in several of the southeast rows

and in one row towards the southwest. The furthest southwest row again had very low

numbers of thrips per trap. A similar pattern was seen on Feb. 26 (Fig. 5-7E) with even

higher numbers of thrips per trap.

The natural neighbor, IDW, and Ordinary kriging interpolations for each sampling

week from 2009 are shown in figures 5-8, 5-9, and 5-10 B, D, F, H, & I respectively.

Locations of areas of high thrips population, 'hot spots,' are similar in all three

interpolation methods. The natural neighbor method was the least accurate for all five

sample week according to the RMSE and RPD values, and IDW and ordinary kriging

had very similar accuracies (Table 5-5). The ME indicates that the natural neighbor

interpolation was just as accurate as the ordinary kriging interpolation on Feb. 5 while

the IDW interpolation had greater accuracy than both of them. On Jan. 30, Feb. 20, and









Feb. 26, IDW was more accurate than ordinary kriging. On Feb. 13, the reverse was

true.

On Jan. 30, "hot spots" appeared to be developing in the south center and west of

the field. They are visible in all three maps, but are less distinct and contain a smaller

number of thrips in the ordinary kriging map. There is another developing 'hot spot' in

the eastern corner of the IDW map. This spot is also present in the kriging map, but with

fewer thrips. In the natural neighbor map, high thrips numbers are found throughout the

eastern edge of the field.

On Feb. 5, the thrips population in the field had all but disappeared. The areas

where the developing "hot spots" had been the previous week had less than 50 thrips

per trap. The rest of the field had less than 15 thrips per trap.

On Feb. 13, "hot spots" reappeared in the same areas they were developing in on

Jan. 30. Also, a new 'hot spot' appeared in the northeast area of the field. The "hot

spots" were smaller in the IDW map compared with both other maps and contained less

thrips in the ordinary kriging map.

On Feb. 20, the 'hot spot' in the northeast corner of the field had expanded in all

three maps. The expansion was much less pronounced in the IDW map. Many other,

smaller "hot spots" were present in both the natural neighbor and IDW maps, but not in

the ordinary kriging map because they were smoothed out.

On Feb. 26, the 'hot spot' in the northeast corner on Feb. 13 had expanded to

cover a large part of the northeast and center of the field. Again, the expansion was less

pronounced in the IDW map and there were other, smaller "hot spots" present in both

the natural neighbor and IDW maps that were not found in the ordinary kriging map.









The semivariograms used for the ordinary kriging varied greatly among the weeks

(Fig. 5-10 A, C, E, G, I, Table 5-6). The Jan. 30 semivariogram had a fairly large nugget,

a nugget to sill ratio of 0.38, and a range of 28.75 m. The Feb. 5 semivariogram was

mostly nugget with a nugget to sill ratio of 0.71 and a range of 22.50 m. The Feb. 13

semivariogram, the only data set that could be modeled without transformation, had a

small nugget, a nugget to sill ratio of 0.2, and a range of 17.50 m. The Feb. 20

semivariogram was mostly nugget with a nugget to sill ratio of 0.73 and a range of 27.50

m. The Feb. 26 semivariogram had a very large nugget with a nugget to sill ratio of 0.67

and a range of 23.75 m.

Discussion

In 2008, the differences among the three weeks could be explained by the

flowering stage of the blueberry plants. Arevalo and Liburd (2007) documented the

close relationship between thrips numbers and blueberry flowering stage. Plants were

approaching peak flowering during the week of Feb. 7 14. The thrips population was

also increasing and "hot spots" were beginning to form. The plants were at peak

flowering during the week of Feb. 14 21. The thrips population also reached its peak

during this week. By Feb. 21, petal fall had begun and fruits were forming on some of

the varieties. By Feb. 28, most of the plants contained developing fruit and had few

remaining flowers and the thrips population had greatly diminished as well.

In 2009, both stage of flowering and temperature appeared to play major roles in

explaining the difference in the thrips population among the weeks. The blueberry plants

had reached about 70% open flowers on Jan. 30. The thrips population was increasing

and "hot spots" were beginning to form. Plants had reached peak flowering by Feb. 5

and remained at this stage until Feb. 13. In contrast, the thrips population had crashed









to very low levels on Feb. 5. This was most likely caused by an extreme cold front that

blew through Florida during the preceding week (FAWN 2009). The thrips population

was increasing dramatically by Feb. 13 and remained high throughout the next two

weeks. In contrast, the blueberry bushes had declined to 70% open flowers on Feb. 20

and then to 20% open flowers on Feb. 26. The extreme temperature event seemed to

cause the thrips population to peak well after peak flowering.

All three interpolation methods showed "hot spots" in the same areas of the

blueberry field during both years. On Feb. 14 and 28, 2008 and on all dates in 2009

except Feb. 5, the ordinary kriging maps showed a much lower number of thrips in

these "hot spots" than the natural neighbor and IDW maps. This is because there were

only a few traps with very high numbers of thrips on these dates. The 'hot spot' was

centered where the one trap with > 1000 thrips on it was located. This point on the map

is set equal to this value in both natural neighbor and IDW interpolation, but not in

ordinary kriging. This causes the kriged map to be much smoother. The combination of

setting the points at data locations to the value of the data point and using a weighted

average causes the 'bulls-eye' effect that IDW maps are known to exhibit (Bolstad

2006). This effect is not as pronounced in the natural neighbor maps, because all points

closest to a sample point are set to its exact value (Ess and Morgan 2003) producing

large areas of the same value.

On Feb. 21, 2008, the maps were very similar. The area of the three major "hot

spots" is smaller in the IDW map. This is because IDW calculates an average that is

weighted by distance whereas natural neighbor interpolation sets every unknown point

equal to the closest data point. This causes all of the points near a trap with high thrips









numbers to have that high number on the natural neighbor map, which in turn creates

"hot spots" with a large area. Because of the nature of the semivariogram for this week

(see below), the ordinary kriging map displays the same property as the natural

neighbor map.

The ordinary kriging interpolation varied among the weeks during both years

because the data varied greatly and was not always modeled well using

semivariograms. Wright et al. 2002 found that the spatial distribution of European corn

borer larvae was modeled well by semivariograms in only four out of seven data sets.

Farias et al. (2003) calculated 36 semivariograms for sharpshooters on citrus, but could

fit only nine of them with mathematical models. The semivariograms from Feb. 14,

2008, Feb. 5, 2009, Feb. 20, 2009, and Feb. 26, 2009 had large nuggets. The range of

the Feb. 28, 2008 semivariogram was so short that no traps were at a distance shorter

than the range. This caused most of the points to be weighted the same in the

interpolations for these dates. The ordinary kriging interpolation on these dates was,

thus, very similar to a local average interpolation. In contrast, the semivariogram from

Feb. 21, 2008 had a very small nugget, but leveled off very rapidly. Because of this,

only points very close to the unknown point were given a high weight in the interpolation

and the interpolation, therefore, closely resembled the natural neighbor interpolation for

this date. The semivariograms from Jan. 30, 2009 and Feb. 13, 2009 had a moderate

and small nugget, respectively and had ranges that encompassed many data points.

The resulting maps are, therefore, the best examples of ordinary kriging.

In terms of accuracy, the natural neighbor interpolation was the least accurate.

The ordinary kriging and IDW interpolations were similar in accuracy. Since natural









neighbor interpolation simply sets all unknown values to the value of the nearest sample

point, it is not surprising that it is the least accurate method. Ordinary kriging is only as

powerful as the semivariograms used to perform it. In 2008, the spatial trend in flower

thrips populations in blueberries was localized. Because of this, the semivariograms

either had a very short range (Feb. 21 and 28) or a large nugget due to a lack of sample

point pairs below the actual range (Feb. 14). The result was that, in 2008, ordinary

kriging interpolation was no more accurate than IDW interpolation. The reduced grid

spacing in 2009 resulted in better semivariograms, suggesting that the spatial variability

of thrips is high and could be better captured with the finer grid spacing used in the

2009 sampling. In both years, the shortest distance sampled was 2-m. However, in

2008, there were only two data pairs at this distance while in 2009, there were

approximately ten. The data from the weeks of Jan. 30 and Feb. 13 was modeled very

well by semivariograms resulting in kriged maps with a slightly higher accuracy then the

IDW maps from these weeks. Therefore, both IDW and kriging are reasonable

interpolation methods to use to model flower thrips distribution in blueberry fields. This

is in agreement with results presented by Roberts et al. (1993) and others. The

accuracy of kriging is dependent upon the accuracy of the semivariogram.

Semivariogram models are sensitive to many factors, including: nonnormality, outliers,

directional differences in spatial trends, inconsistency of spatial trends among different

parts of the sample area, and the placement and spacing of the sample points.

The range of the semivariograms varied greatly in 2008 from 2.51 to 79.8 m. In

2009, the ranges of the semivariograms were much more consistent, varying from 17.5









to 28 8 m Therefore, spacing white sticky traps at least 28 8 m apart should result in


sampling independent populations of flower thnps


2008 study area on
blueberry farm


a southern highbush
in Inverness, FL


o 0
t 0 0 0 ,
o O o o *o
o 0 0 0
0 0 0 0

0 a510 20 30 40

Data Source Small Fruit and Vegetable
IPM laboratory, Gainesville, FL
Date Feb 28,2008
Data collection Trmble GeoXT GPS reliever
Created by Elena M Rhodes


Legend
o grid traps
random traps
-.-... Fences
Bluebemes
- Pathways
I Sheds










N

A


Fig 5-1 GIS map of the study area in 2008

















2009 study area on a southern

highbush blueberry farm in Inverness, FL


0 0
0 0 0
0 0 0



0 0
0 *0 0


0
0 0
0 0
0.
0 0


Legend

S grid traps
o0
S 0 random traps

So ..... fences

o o bluebernes

--- Pathway


0 sheds
o o

o*
0 0
0o 0





0 0
I o
o
0.


PK


0510 20 30 40
SMeters


Data source Small Fruit and Vegetable IPM Laboratory
University of Florida, Gainesville, FL
Date Feb 26,2009
Data collection Trimble GPS reliever
Created by Elena M Rhodes


Fig 5-2 GIS map of the study area in 2009

















Flower thrips distribution in blueberries

Feb. 14, 2008 Inverness, FL

** *


*


* *




* *

*

.



* .
*
* *
o **
.*





*




0 0



o *
0 0


0510 20 30 40
Meters


Thrips per trap

8-150
150-300
300-450
o 450-600
o 600-750
750-900
900-1050
*


0. *
**,


o N




* *


Data Source Small Fruit and
Vegetable IPM laboratory, Ganesvlle FL
Date Feb 14,2008
Created by Elena M Rhodes


* *
*

o 0


* 0
8


Flower thrips distribution in blueberries

Feb. 21, 2008 Inverness, FL

*.* .


SThrips per trap
S 40-150
150-300
S300-450
450- 600
S o 600- 750
S* 750- 900
900-1050
1050-1500
S 1500-2000

*.* *


o. *
*

. .


e N

S *. A


Data Source Small Fruitand
0 510 20 30 40 Vegetable PM laboratory Gainesville FL
Date Feb 21,2008
. i Meters Created by Elena M Rhodes


* S

** .
o



Flower thrips distribution in blueberries

Feb. 28, 2008 Inverness, FL




** *



** *
Thrips per trap

10- 150
150-300
300-450
S* 450-600
o 600-750
S* 1081


* *
*

0 *
*
* *


: *
0 ** **


0510 20 30 40
Meters


Data Source Small ruitand
Vegetable IPM laboratory, Gainesville FL
Cate Feb 28,2008
Created by Elena M Rhodes


Fig. 5-3. Point maps of thrips per trap for each sampling week in 2008.






99


*


.


:*


* *


N
*













Flower thrips distribution in blueberries
Feb. 14, 2008 Inverness, FL





Thrips per trap
9 150
150 300
300 450
S 450 600
6 00 750
1750 900
900 1 050
1 050 3- 1 6










N


Data Source Small Fruit and
0 510 20 30 40 egetable PM laboratory Gaesvle F
neDai te Feb 142008
u Meters M ethod Natural Neighbor
Created by Elena M Rhodes


Flower thrips distribution in blueberries
Feb. 28, 2008 Inverness, FL





hrllips pertriap

1- u- 300o
4- 0 400

600 750
750 900
1Di 13o













Dun l e SmllFrdid
0 510 20 30 40 M*901blFl blowmeq Caitsll
Fig.5-4.Nahuiralll neiUll i llllihno l


Fig. 5-4. Natural neighbor interpolation


21,2008, and C) Feb. 28, 2008.


Flower thrips distribution in blueberries
Feb. 21, 2008 Inverness, FL





Thrips per trap
142 /50








5102030 40 o n ,
ba 300 450
S450 -00
6 00 750
1750 900

1 050 / 863













Data Source Small Fruit and
0 510 20 30 40 VegetableIPMlaboratoryGainesvle FL
Date Feb 21.2008
Meters Method Natural Neighbor
A Created by Elena M Rhodes B



































C
of thrips per trap from A) Feb. 14, 2008, B) Feb.


















of thrips per trap from A) Feb. 14, 2008, B) Feb.













Flower thrips distribution in blueberries
Feb. 14, 2008 Inverness, FL


mTrips per trap
I8 150
I 150 300
300 450
a 450 600
600 750
S750 900
S900 1 050
1 050 1331


N
A

1 Data Source Small Fruit and
Vegetable IPM laboratory Gainesville, FL
Date Feb 14,2008
Method Inverse Distance Weighting
Created by Elena M Rhodes


Flower thrips distribution in blueberries
Feb. 21, 2008 Inverness, FL


**



t a%
0 510 20 30 40
Si Meters


Thrips per trap
1 40 150
150 300
300 450
a 450 600
600 750
S750 900
S900 1 050
1 050 1895








N

A

Data Source Small Fruit and
Vegetable IPM laboratory Gainesvlle FL
Date Feb 21,2008
Method Inverse Distance Weighting
Created by Elena M Rhodes


Flower thrips distribution in blueberries
Feb. 28, 2008 Inverness, FL


Thrips per trap
I 10 150
I 150 300
300 450
as450 600
600 750
750- 900
S900 1 050
S 050 1/081


0 510 20 30 40
S iMeters


I DataSource Small Fruitand
Vegetable IPM laboratory Gainesville FL
Date Feb 28 2008
Method Inverse Distance Weighting
Created by Elena Mi Rhodes


Fig. 5-5. Inverse Distance Weighting interpolation (p = 2, # points = 20) of thrips per trap
from A) Feb. 14, 2008, B) Feb. 21, 2008, and C) Feb. 28, 2008.


L
0 510 20 30 40
SMeters












































--...... --- --- ......--- --.....---... .--- .1.. .-.g--Hn.y -- --.... --- --- ,

a o a a V
Model Range Sill Nugget
GaLssian 1 79,71
Gemisin 2 79 77 .
ao h 86s
Lag (distance) (m)


Flower thrips distribution in blueberries
Feb. 14, 2008 Inverness, FL





Thdps p trap
8 150
S150 ai00

S450 00
C00 750

-. S 0 o 1 oso

















0 510 20 30 40 i-u.l'sti- .l / lo .. nIea itt
D.. I.. 'l an





Rower thrips distribution In blueberries
Feb. 21, 2008 Inverness, FL





Thimaips per rap
E 40 150
ISO 300
30. 451)


i~50 50





*. =:
-. *











0 510 20 30 40 ,.,e
Meters Meled cdivew
Crfd by El,,. I9i6ode,


0 e t S*

Model Range SiIl Nugget
CubIC 11 04 9561 63 014
00 L anc)
Lag (distance) (m)







































S
,e


* a ** *


Model Range SiII Nugget
Exponenlial 251 253544 00018


a Lag (distance) (m) I

Fig. 5-6. Semivariograms (A, C, E) and Ordinary Kriging interpolation (B, D, F) of thrips

per trap from A) & B) Feb. 14, 2008, C) & D) Feb. 21, 2008, and E) & F) Feb.

28, 2008.












































103


Flower thrips distribution in blueberries
Feb. 28, 2008 Inverness, FL





Thnps per trap
10 5C
S iso0 3CC
Ka 0 4EC
450 6CC
600 7EC
i /0 L
i go I lI I I










N



.I.. r 1r t nit
0 510 20 30 40 \tlbl P Ia t i ,IF
Dae Fb 2302003
Meters Mth a N
Created by Elea I Rhodes


I a
n.E

















Flower thrips distribution in blueberries
Jan 30, 2009 Inverness, FL







SThrips per trap
14- 10

S 150-300
S* 3400-450
450-600
S 0 6D00724


0 5 10 2 3D0 40 wr~bliPM rrrL 1
a Meters E7' v RnD ml-a






Flower thrips distribution in blueberries
Feb. 13, 2009 Inverness, FL


* V


S5 10 20 30 40
- W Meelrs


Thrinps pertrap
6 3- 1506
150-300(
300.460O

450-600O

"9 600 -760
7650-900
900-1050
Inifl .1 47l '


vl.bIO 1uPML

rotB F ,l }*I
Dealed bp F Rhodei


Flower thrips distribution in blueberries
Feb 5, 2009 Inverness, FL








Thrp per trap

S* '* Thbips pertrap


1630


* *


0 5 10 2 30 40 y **M urcu
-.I a --n 5 n.E1





Flower thrips distribution in blueberres
Feb 20, 2009 Inverness, FL






SThrips pertrap


*







* *



o




a .



0 *




0 5 10 20 30 10
*** ** ************- Meter


0 a 37. 150
* 50 -300
300 -450

450 -600
600-750
T60-900
900-1050
t050-1500
0 i~oC-2Uao
a

, o 200(
or









SN
smi





\'i1^P ~h r aofDT o







Onlr tagyedI dUlB
(r*ldh Lil

















Flower thrips distribution in blueberries
Feb. 26, 2009 Inverness, FL





Thrps per trap


0 5 10 20 30 40
m- Meters


'PM *,n
akIy .,.,


Fig 5-7 Point maps of thrips per trap for each sampling week in 2009












Flower thrips distribution in blueberries
Jan 30, 2009 Inverness, FL


Thnps per trap


I 300 450
450 600


0 5 10 20 30 40
m sm m ,,,,,w Iv a a


m AN
BllBJH U
Vmtoil- r~i rlhighh.
- rnflriili. R hil


Flower thrips distribution in blueberries
Feb 13. 2009 Inverness. FL


Thrips per trap
f39-150
150 -300

\ -4~ 0 6-T
600-750
-. 7I0 1
I. '000 I 4
3.* 90-50
1050-1148


*


o 10 20 30 40


S


D~p*$f UF'
0.! ~ 3
.rhd !N*I .,nhU


N
A


Flower thnps distribution in blueberries
Feb 5, 2009 Inverness, FL










Thrips per trap





-VI








Flower thrips distribution in blueberries
0I F*" 4 INEf



Flower thrips dstribution in blueberries
Feb 20, 2009 Inverness, FL




SThpspertrap


S300 -450
S 450 -600







-0 5 10 ^ 20 3: 1 750 900



c r M*

D.'i f lfe Lu
V N f-qi
Ci .


&=P'l













Flower thrips distribution in blueberries
Feb. 26, 2009 Inverness. FL


Thrips per trap



45- 6oo00
M 7"r


0 5 10 20 30 40


1-







o. ", Fb-." 6 l



Cicitidlfd I; hRuit


50 01,50
500-2000
U,000 2170


Fig 5-8 Natural neighbor interpolation of thrips per trap from A) Jan 30, 2009, B) Feb
5, 2009, C) Feb 13, 2009, D) Feb 20, 2009, and E) Feb 26, 2009











































107











Flower thrips distribution in blueberries
Jan 30, 2009 Inverness. FL


Thrps per trap
14 so

150 6300

600 24


0 5 10 20 30 40
-n;-......--Meiter


mN
=n

0r,.,Ip& I p Rmff
01 P
C-


Flower thrips distribution in blueberries
Feb 5, 2009 Inverness. FL










Thrips pr trap


N


A


I 1 20 30 40 Dur}Ssu' $Smnr.Pnjd

Crrn iMo t W RIaMlf


Flower thrips distribution in bluebernes
Feb 20, 2009 Inverness, FL


SI,-



I =


r T-- nor trap



S111 21
450-;600


1500 -1 00

2L 000- 212
j """


*d


I .. I** N
i *. t' -
Vq all IPLar ar
0 5 10 20 30 40 ""''" '" l^ F
Met hrs l.,us ysan~ rq~
M* ichrd Ifi'$- R, w.tq n
C.~~l~ Ribf&f-


Flower thrips distribution in blueberries
Feb 13, 2009 Inverness, FL



,1 r trap


*50
F .. *1 o





..,
.t
.I-






Vyntlehl IPM lahmral l
0 5 10 20 30 40 E uma ti-umlu
Cilir hr RhF n











Flower thrips distribution in blueberries
Feb. 26, 2009 Inverness, FL














0 5 10 0 o






jiid- '
.- 1500-. 43100
E

Fig 5-9 Inverse Distance Weighting interpolation (p = 2, # points = 20) of thrips per trap
from A) Jan 30, 2009, B) Feb 5, 2009, C) Feb 13, 2009, D) Feb 20, 2009,
and E) Feb 26, 2009

































109
























ii--- -IiI
I i -
-I,,~1


Distance (m)













Model Spherical
Nugget 0.50
- I 'I II





'-* *


Distance (m)


Flower thrips distribution in blueberries
Jan 30, 2009 Inverness, FL


Thrips per trap
i 14- 150
S150so 300
300 45
450 600
600 724


0 5 10 20 30 40
i11 Meter


Flower thrips distribution in blueberries
Feb. 5. 2009 Inverness. FL


Thrips per trap

m -
mHyjr


5N



*-lnriltr lrN Hliiri


MA
Vesak!e~ EI'M Letonaloly
Cpmmel VE'
|]*r j V--n













Flower thrips distribution in blueberries
Feb. 13. 2009 Inverness. FL


r,,r per trap


0 450
450 600
600- 750
-* 750 900
S900 1,05D


0500- 1,173


A .,
"' 40 '

r.r*,,md I1W


Flower thrips distribution in blueberries
Feb. 20, 2009 Inverness, FL


Thrips per trap
- 37. 150
I 50- 300
30D -450
450 600
ou 750
S750 900
90 1050
1.050- 1500
- 1500- 000
2.000-2,212





N

A


0 5 10 20 30 40


labShiute Umi Ful nId
1.ynr b, ,lao


'^
:J .:".


0 5 10 20 30


Distance (m)


Distance (m)


* .


'~"'~"T


#*


0"











Rower thrips distribution in blueberries
Feb. 26, 2009 Inverness, FL


0 5 10 20 30 40
S ........... ete


D-is frlllB 0P
WUlnii nri'I~igkq~i~
[j~aIrf1B Jr hi


Fig. 5-10. Sem
J) of
Feb.


ivariograms (A, C, E, G, I) and Ordinary Kriging interpolation (B, D, F,
thrips per trap from A) & B) Jan. 30, 2009, C) & D) Feb. 5, 2009, E) &
13, 2009, G) & H) Feb. 20, 2009, and I) & J) Feb. 26, 2009.


Table 5-1. Summar

mean
median
mln
max
Std Dev
SEM
skewness coefficient
Kurtosis


y


statistics of thrips
Feb 14 Feb 21


per trap for each sample date in 2008.
Feb 28


277 351 179
195 268 158
8 40 10
1331 1895 1081
236 303 147
21 27 13
1 36 23 275
523 1001 1490


Table 5-2. Several error metrics for natural neighbor (NN), inverse distance weighting
(IDW), and ordinary kriging (OK) for each sample date in 2008.
mean prediction root mean residual prediction


square error


deviation


Feb 14 NN 613 29956 078 1 13
IDW 478 20880 1 13 034
OK 1 61 20290 1 16 029
Feb 21 NN -21 95 397 48 0 76 0 68
IDW 010 30780 098 011
OK -711 331 80 091 037
Feb 28 NN 765 211 71 069 1 16
IDW 513 14720 0 99 020
OK 449 151 60 097 026


I.


Thrips per trap
159 50
1 50 300
300-450

- 750 900
SSo0 ,060
0 1 .050 I 500
S1 500 2.000
2 000- 124



N
A


Distance (m)


- *


Model Spherical
Nugget 0.30
Sill 0.15
Range 23.75 m .












Table 5-3. Summary of the semivariogram analysis for each sampling week in 2008.
Feb 14 Feb 21 Feb 29


model
lags
nugget
sill
nugget/sill ratio
range
root mean square error
residual prediction deviation
mean prediction error


Gausslan (1 & 2) Cubic Exponential
23 23 23
3944449 014 00018
8342 28, 19086 94 95681 63 25354 64
1 44 00000015 0000000071
7971 m, 79 77m 11 04m 2 51 m
2029 331 8 151 6
1 16 091 097
1 61 -711 449
029 037 026


Table 5-4. Summar

mean
median
mln
max
Std Dev
SEM
skewness coefficient
Kurtosis


y


statistics of thrips per trap for each sample date in 2009.
Jan 30 Feb 5 Feb 13 Feb 20 Feb 26


213 11 490 571 660
164 8 499 512 506
14 1 35 37 59
724 51 1173 2212 2184
160 9 225 361 469
14 1 20 32 41
1 35 1 53 0 22 1 33 1 27
429 561 297 592 411


Table 5-5. Several error metrics for natural neighbor (NN), inverse distance weighting
(IDW), and ordinary kriging (OK) for each sample date in 2009.


mean prediction


root mean residual prediction


square error


deviation


Jan 30 NN 795 211 20 075 1 01
IDW 1 17 16470 097 013
OK 328 15790 1 01 018
Feb 5 NN 038 1240 0768115571 1 05
IDW -002 993 0959177552 020
OK 041 943 1 010035322 020
Feb 13 NN 825 21208 1 121206761 113
IDW 1 15 18400 1 292312663 022
OK 0 54 180 90 1 31445843 0 45
Feb 20 NN -3559 45518 0809245866 1 06
IDW -2 69 367 50 1 002319818 0 30
OK 20 16 342 10 1 076739355 0 28
Feb 26 NN -39 65 634 05 0 756782043 1 06
IDW 0 30 476 40 1 0072159 024
OK 16 61 442 80 1 083644206 026










Table 5-6. Summary of the semivariogram analysis for each sampling week in 2009.


model
lags
nugget
sill
nugget/sill ratio
range
root mean square error
residual prediction deviation
mean prediction error


Jan 30 Feb 5 Feb 13 Feb 20 Feb 26
Exponential Spherical Spherical Spherical Spherical
23 23 23 23 23
025 050 9000 030 030
040 020 35000 011 015
038 071 02 073 067
28 75m 22 50m 1750m 2750m 2375m
15790 943 18090 34210 44280
10101 01 1 31 1 08 1 08
328 041 054 2016 1616
018 02 045 028 026









CHAPTER 6
EXAMINING THE RELATIONSHIP BETWEEN THRIPS SPATIAL DISTRIBUTION AND
FLOWER DENSITY

Introduction

Southern highbush blueberries are an important crop in Florida that is grown for a

highly profitable early-season fresh market (USDA 2010). Flower thrips are one of the

key pests of these blueberries. Flower thrips injure blueberry flowers both when they

feed on the flowers and when they lay their eggs in them. This injury can cause scaring

on developing fruit, which makes the fruit unsalable on the fresh market (Arevalo-

Rodriguez 2006).

Thrips populations tend to form one or a few "hot-spots" on blueberry farms, which

are small areas of comparatively high thrips numbers (Arevalo and Liburd 2007). These

"hot-spots" begin forming about 7-10 days after bloom initiation, peak between 12 and

15 days after initiation when the majority of the flowers are open, and decline until about

22 days after bloom initiation when most of the flowers have become fruit and the thrips

population all but disappears (Arevalo and Liburd 2007). The "hot-spots" often form in

different areas each year.

The objective of this study was to determine if "hot spots" of thrips are correlated

with flower density. The hypothesis of this study was that thrips population density in

space has a positive linear relationship with flower density.

Materials and Methods

Inverness Farm

In 2009, 100 white sticky traps (Great Lakes IPM, Vestaburg, MI) were distributed

throughout a 1-ha SHB blueberry planting containing four to seven year old bushes in

Inverness, FL, in a regular grid at 7.61-m increments. An additional 30 traps were again









placed randomly throughout the plot. Traps were changed out weekly over a five-week

period on Jan. 30, Feb. 5, Feb. 13, Feb. 20, and Feb. 26. Traps were taken to the Small

Fruit and Vegetable laboratory in Gainesville, FL, where the number of thrips per trap

was counted and recorded.

Along with the thrips data, the percent of open flowers present in each blueberry

row in the study was recorded each week. The sampling area was divided into 38 rows

by a dirt road in the northern part of the study area. Each row contained blueberry

plants of the same variety and age. Data were collected on Jan. 30 and Feb. 5, 13, 20,

and 26.

Linear regression analysis was used to determine if the number of thrips

(dependent variable) was related to the percent of open flowers (independent variable).

All 130 sample points were input into the analysis by assigning to each trap the

percentage of open flowers recorded from the row it was hung in. Since some of the

assumptions of least squares regression could not be met even after transformation on

several sampling dates, Theil regression (Hollander and Wolfe 1999) was used for all

sampling dates. Kendall's tau, a nonparametric correlation statistic (Hollander and

Wolfe 1999), was also calculated (Wessa 2008) for all sampling dates.

In addition, GIS layers of thrips numbers and percent of open flowers for each

sampling date were created in ArcGIS (ESRI 2005). Trap locations were mapped using

a Trimble Pathfinder GPS receiver (Trimble, Sunnyvale, CA) in the WGS 84 datum. The

data were then imported into ArcMap 9.1 (ESRI 2005), projected into universal

transverse mercator (UTM), and interpolated using inverse distance weighting (IDW).

The NAD 27 datum was automatically assigned to the data when it was imported into









ArcMap, so this datum was used to project the data. For IDW, p was set at the default 2

and the search area was divided into 4 quadrants from which at least 5 data points per

quadrant were included.

For the percent of open flowers, a point dataset was created by assigning the

percent of open flowers recorded from each row to all of the sample points in that row.

Inverse distance weighting (IDW), with p set at the default 2 and the search area divided

into 4 quadrants from which at least 5 data points per quadrant were included, was used

to create the percent of open flowers layers. Each layer was then saved as a raster with

a cell size of 1.

Each raster layer was then classified. The thrips layers were classified into groups

separated at 150 thrips per trap intervals up to 1,050 thrips. The final classification was

> 1,050 because there were only a small number of traps with thrips exceeding this

number. The one exception was week 2, which was separated at 15 thrips per trap

intervals due to the extremely low numbers that week. One hundred and fifty thrips per

trap is a commonly used action threshold. This produced five (week 1), three (week 2),

and eight (weeks 3 through 5) categories respectively. The percent of open flower data

were separated into the same number of categories as the thrips per trap data from the

same sampling week using equal interval classification.

All of the layers were then reclassified so that each category was represented by a

number from 1 to 3, 5, or 8 with 1 representing the lowest category and 3, 5, or 8

representing the highest. For each week, the reclassified percent of open flowers layer

was subtracted from the reclassified thrips per trap layer. This produced a layer showing

the qualitative relationship of the variables in space. The resulting layers were classified









as follows: 0 = high numbers of thrips per trap paired with high percentages of open

flowers or low paired with low, 1 = high thrips numbers paired with moderately low

percentages of open flowers, 2 -5 = high thrips numbers paired with low percentages of

open flowers, > 5 = high thrips numbers paired with very low percentages of open

flowers, -1 = low thrips numbers paired with moderately high percentages of open

flowers, -2 -5 = low thrips numbers paired with high percentages of open flowers, < -5

= low thrips numbers paired with very high percentages of open flowers.

Windsor Farm

This study was conducted on a farm in Windsor, FL, in Feb. and March of 2010.

Twenty white sticky traps were placed in a 2464-m2 area ofa SHB blueberry planting.

The blueberry plants were approximately seven years old. Traps were spaced 15-m

apart in each of five blueberry rows. The rows were 10-m apart. Traps were replaced

weekly and 4 5 flower clusters (20 25 flowers) were collected and placed into 50-ml

vials containing 20 ml of 70% ethanol. Traps and flower samples were taken to the

Small Fruit and Vegetable laboratory in Gainesville, FL, where the number of thrips per

trap was counted and recorded. Thrips adults and larvae were extracted from the

flowers using the "shake and rinse" method developed by Arevalo and Liburd (2007)

and counted. Percent of open flowers was also recorded from each sampled plant on

the Windsor farm in 2010. Traps, flower samples, and percent of open flower data were

collected for 6 weeks from Feb. 18 to March 25.

Least squares regression analysis was used to determine if the number of thrips

per trap (dependent variable) was related to the percent of open flowers (independent

variable). The thrips per trap (x) data had to be loglo(x + 1) transformed so that all of the

least squares regression analysis assumptions could be met.









Very few thrips were collected from the flowers until March 18. Therefore, only the

March 18 and 25 data sets were analyzed for a relationship between thrips larvae and

adults per flower (dependent variables) and percent of open flowers (independent

variable). Since some of the assumptions of least square regression could not be met

even after transformation for the thrips per flower data, Theil regression (Hollander and

Wolfe 1999) was used. Kendall's tau, a nonparametric correlation statistic (Hollander

and Wolfe 1999), was also calculated (Wessa 2008) for the thrips per flower data sets.

Results

Inverness Farm

A significant positive linear relationship between percent of open flowers and thrips

per trap occurred on Jan. 30 (T = 0.36, C > 1988, n = 130, Psiope < 0.0001, Fig. 6.1A).

There was a significant positive linear relationship between percent of open flowers and

thrips per trap on Feb. 5 (T = 0.24, C = 1734, n = 130, Psiope = 0.0002) and Feb. 20 (T =

0.21, C = 1555, n = 130, Psiope = 0.0012, Fig. 6-1 B & D). No relationship was found

between percent of open flowers and thrips per trap on Feb. 13 (T = 0.07, C = 273, n =

130, Psiope = 0.29, Fig. 6-1C) or between percent of open flowers and thrips per trap on

Feb. 26 (T= 0.08, C = 581, n = 130, Psiope = 0.12, Fig. 6-1E).

Summary data for the thrips per trap and percent of open flowers data are shown

in Tables 6-1 and 6-2 respectively. On Jan. 30, 17% of the area was covered by

pairings where either high thrips numbers were paired with high percentages of open

flowers or low numbers were paired with low percentages (Fig. 6-2A). Pairings with a

small degree of dissimilarity covered another 25% of the area. A similar pattern was

seen on Feb. 5 (Fig. 6-2B), where 4% of the pairings were the same and 36% were only

slightly dissimilar.









On Feb. 13, 68% of the sampling area was dominated by low thrips numbers

paired with high percentages of open flowers (Fig. 6-2C). The degree of dissimilarity

was much greater than that seen in the two previous weeks. Only 6% of the pairings

were the same and 25% were slightly dissimilar. A similar pattern was seen on Feb. 20

(Fig. 6-2D), with 59% of the area covered by low thrips numbers paired with high

percentages of open flowers. However, 11% of the area was covered by similar pairings

and 27% by slightly dissimilar pairings.

Feb. 26 (Fig. 6-2E) was dominated by high thrips numbers paired with low

percentages of open flowers (48%). Similar pairings encompassed 13% of the area and

slightly dissimilar pairings 32%.

Windsor Farm

There was a significant negative linear relationship between percent of open

flowers and loglothrips per trap on March 18 (R2 = 0.24, t = -2.67, df = 19, Psiope =

0.0156, Fig. 6-3E). No relationship was found between percent of open flowers and

loglothrips per trap on any of the other dates (all R2 < 0.03, all Itl 5 1.23, df = 19, Psope >

0.23, Fig. 6-3A-D & F).

No relationship was found between percent of open flowers and thrips adults (both

T 0.19, Cs 14, n = 20, Psiope 0.33, Fig. 6-4) or larvae (both T 0.02, C = 0, n = 20,

Psiope > 0.86, Fig. 6-5) per flower on either date.

Discussion

According to Arevalo-Rodriguez (2006), flower thrips population density is strongly

correlated with the percent of open flowers over time. The results from the Inverness

2009 study indicate that this relationship may exist in space as well. The differences in

percent of open flowers in space most likely exist because multiple varieties are grown









on the same farm to maximize cross pollination (Childers and Lyrene 2006). Different

varieties begin to flower at different times and flower for different periods of time.

However, there appeared to be no relationship between flower thrips density and

percent of open flowers on the Windsor farm in 2010, except on March 18 where a

relationship opposite to what was expected occurred. This may have been a result of

the unusually cold winter weather that occurred throughout January and February

(FAWN 2010). Further research is needed to determine if there are some cases where

flower thrips density decreases with increasing percentages of open flowers.

An intense cold snap that occurred from Feb. 4 Feb. 6, 2009 (FAWN 2009) may

explain some of the anomalies in the Inverness study. The extremely low thrips

numbers found on Feb. 6 are likely a direct result of this cold snap. Tsai et al. (1995)

found a 56% mortality rate when Thrips palmi Karny was held for 15h at 0C.

Development was also reduced at 26C compared with 320C.

The lack of any relationship between flower thrips numbers and percent of open

flowers on Feb. 26 may have been indirectly related to the cold snap. After the cold

snap, the thrips population began increasing and continued to do so throughout the

sampling period. In contrast, peak flowering, averaged over the whole sampling area,

occurred during the Feb. 13 sampling week. By Feb. 26, only a few rows, most likely

containing later or longer flowering varieties, had more than 20% open flowers. This

resulted in a large number of samples where high thrips numbers occurred with bushes

having a low percent of open flowers as seen in Fig. 6-2E.

The opposite trend occurred on Feb. 13, when most of the rows were at 80 100%

open flowers, while only a few rows, which probably contained later flowering varieties,









had just reached 50 65% open flowers. This resulted in a large number of samples

where low thrips numbers occurred with bushes having a high percent of open flowers

as seen in Fig. 6-2C.

The results from the Inverness study indicate that "hot spots" of flower thrips

population may be related to flower density. Further research utilizing more accurate

measures of flower density is needed.


y = 2 600x 32 00


,I
<


0 20 40 60 80
Percent of open flowers


100 120


y = 0 100x + 1 000


.3
S
* -


0 20 40 60 80
Percent of open flowers















i!


20 40 60 80 100 120
Percent of open of flowers


y = 4 013x + 211 558


20 40


Percent of open flowers


St
* *
* 0


. *
*


Percent of open flowers


Fig. 6-1. Graphs showing percent open flowers vs. thrips per trap on A) Jan. 30, B) Feb.
5, C) Feb. 13, D) Feb. 20, and E) Feb. 26. The black lines represent
regression lines fitted by Theil regression.


2500 -


2500 -


*

*












Degree of similarity between thrips per trap
and percent of open flowers on Jan 30, 2009





Similarity
MT F
=TIP
T"< F
T F
MT F

















Degree of similarity between thrps per trap
and percent of open flowers on Feb 13, 2009


Similarity
M T <<< F


S T= F










N



.-. Met \ygA,.^* ry
0 5 10 2D 0 0 tes0a re. .prte F


Degree of similarity between thrips per trap
and percent of open flowers on Feb 5, 2009





Similarity
T< T< F
T=F
T> F








N




ECrI.b 0 lhdy



Degree of similarity between thrips per trap
and percent of open flowers on Feb 20, 2009



Similarity
MT-- rF
MT <<< F
S T< T
MT>F











o Nm 20 30 40
Dl]IinS M




C l-t. 'ni L












Degree of similarity between thnps per trap
and percent of open flowers on Feb. 26, 2009







T< r F






6-2 Maps show ing the spatial smlarity of number of thps per trap T) with percent
T <4< F

T
T> F
T>> F
< T>- F














6-2 Maps showing the spatial similarity of number of thrips per trap (T) with percent
of open flowers (F) on A) Jan 30, B) Feb 5, C) Feb 13, D) Feb 20, and E)
Feb 26, 2009



















* *


0 10 20 30 40 50 60 70
Percent of open flowers


t**
** *
*
* *


Percent of open flowers


** **


Percent of open flowers












* *
.


0 10 20 30 40 50 60 70 80
Percent of open flowers


y = -0 0138x + 1 4764
# *


50 60


Percent open flowers


Percent of open flowers


Fig. 6-3. Graphs showing percent open flowers vs. logo thrips per trap on A) Feb. 18, B)
Feb. 25, C) March 4, D) March 11, E) March 18, and F) March 25. The black
lines represent regression lines fitted by least squares regression.




127



















$ $
.


20 30 40 50 60
Percent open flowers


t


10 15 20 25 30
Percent open flowers


Fig. 6-4. Graphs showing percent open flowers vs. thrips adults per flower on a) March
18 and b) March 25.


0 10 20 30 40 50 60
Percent open flowers


06
05 *
04
03
02
01
0
0 ------- --- -----------
0 5 10 15 20 25 30
Percent open flowers


Fig. 6-5. Graphs showing percent open flowers vs. thrips larvae per flower on a) March
18 and b) March 25.




128


0 10










Table 6-1. Summary statistics for the thrips per trap data from each sampling week.


mean
median
mmin
max
Std Dev
SEM
skewness coefficient
Kurtosis


30-Jan 5-Feb 13-Feb 20-Feb 26-Feb
2130538 1126923 4899692 5705923 6604154
164 8 4985 511 5 5055
14 1 35 37 59
724 51 1173 2212 2184
160 0633 9 35086 2246699 360 9792 469 2586
1403848 0820125 1970486 3165997 411567
1 35 153 022 1 33 1 27
429 561 297 592 411


Table 6-2. Summary statistics for the percentage
sampling week.


mean
median
mmin
max
Std Dev
SEM
skewness coefficient
Kurtosis


of open flower data from each


30-Jan 5-Feb 13-Feb 20-Feb 26-Feb
8026923 7676923 7723077 7284615 2726923
90 80 80 80 20
50 10 30 10 0
100 100 100 100 80
2109697 2466725 1638989 2358342 2360127
1850326 2163461 1437488 2068403 2069968
-0 35 -1 16 -1 12 -1 42 070
135 371 439 440 215









CHAPTER 7
THE EFFECT OF SEVERAL REDUCED RISK INSECTICIDES ON FLOWER THRIPS
POPULATIONS IN SOUTHERN HIGHBUSH BLUEBERRIES

Introduction

Flower thrips in blueberries are typically managed with applications of insecticides.

The two most commonly used insecticides are malathion (Micro Flo Company LLC,

Memphis, TN) and SpinTor (spinosad) (Dow Agrosciences, Indianapolis, IN) (Arevalo-

Rodriguez 2006). The recently registered DelegateTM (spinetoram) (Dow Agrosciences,

Indianapolis, IN) is beginning to be used more frequently (0. E. Liburd personal

communication). Malathion is a conventional, organophosphate insecticide with broad

spectrum activity. SpinTor is a reduced-risk insecticide. Its active ingredient, spinosad

(spinosyn), is derived from the fermentation of the soil bacterium Saccharopolyspora

spinosa Mertz and Yao. It must be ingested and kills insects via rapid excitation of the

nervous system (IPM of Alaska 2003). DelegateTM was registered for use on flower

thrips in blueberries during the course of the work presented in this chapter.

Spinetoram, the active ingredient of DelegateTM, is also a fermentation product of the

soil bacterium S. spinosa (Srivastava et al. 2008).

Toxicity to bees and other pollinators is a major concern of blueberry growers.

Therefore, insecticides are usually applied early in the morning or at night to minimize

the impact on pollinating bees (Arevalo-Rodriguez 2006). Even with this practice,

malathion still causes some pollinator mortality (0. E. Liburd personal communication).

With such a limited number of compounds, the development of resistance is also a

concern. Resistance has been reported in Frankliniella occidentalis (Pergande) from

various parts of the world to many insecticides including spinosad (Herron and James

2005, Dash and Tunc 2007, Bielza et al. 2007). Frankliniella occidentalis can rapidly









develop resistance because it has a short generation time, high fecundity, and a

haplodiploid breeding system (Jensen 2000). Frankliniella bispinosa (Morgan) share

these traits.

The objective of this study was to determine the potential of using several

reduced-risk insecticides to manage flower thrips in Florida blueberries. Compounds

tested included spinetoram, which was registered on blueberries as DelegateTM (Dow

Agrosciences, Indianapolis, IN) during the course of this study, rynaxypyr (DuPont,

Wilmington, DE), and QRD 452 (AgraQuest, Davis, CA). Rynaxypyr is a ryanidine

receptor agonist, causing the release of Ca2+ from muscle cells, which is a novel mode

of action. The insects lose the ability to regulate muscle function and die via muscle

paralysis (Ribbeck 2007). QRD 452 is an extract of Mexican Tea, Chenopodium

ambrosioides L. (AgraQuest 2008). These compounds were compared with malathion,

SpinTor, and an untreated control to determine their efficacy. The hypothesis is that

they will be at least as effective as malathion and SpinTor.

Materials and Methods

This experiment was conducted on a commercial blueberry farm in Windsor, FL, in

2007 and 2008. In 2009, it was conducted at the University of Florida Plant Science

Research and Education Unit (PSREU) near Citra, FL. The experiment was a

randomized complete block design with four replicates of six treatments in 2007 and

2009 and five replicates of five treatments in 2008.

At the Windsor farm, treatments encompassed three rows of blueberries

containing plants of approximately seven years of age. The middle row was sprayed on

both sides and the two adjacent rows were sprayed on only one side, the side facing the

middle row. Treatments were 12.2-m. long with a 3-m. buffer between them. There was









an unsprayed buffer row between each replicate. In 2007, the study area encompassed

0.78 ha and in 2008 it encompassed 0.83 ha.

There were four 0.13 ha plots of six year old southern highbush (SHB) blueberries

at the Citra PSREU and these served as blocks. The variety Jewel was used throughout

the two SHB applications. However, there were only two 0.12 ha plots of six year old

rabbiteye (RE) blueberries at the research station. Therefore, two varieties, Premier and

Brightwell, were included so that the experiment could be replicated four times. Each

treatment group consisted of a row of five plants.

Treatments for 2007 included: 1) Malathion 5 EC at a rate of 1.75 L / ha, 2)

SpinTor 2 SC at a rate of 0.438 L / ha, 3) Rynaxypyr at a rate of 89.7 g / ha, 4) XDE-

175 (spinetoram) at 131 g a. i. / ha, 5) XDE-175 (spinetoram) at 173 g a. i. / ha, and 6)

untreated control. They were applied using a CO2 sprayer three times during the

flowering season 14 days apart.

Treatments for 2008 included Malathion, SpinTor 2 SC, and Rynaxypyr at the

same rates as the previous year, spinetoram at 131 g a. i. / ha, and an untreated

control. They were applied using a CO2 sprayer twice during the flowering season 14

days apart.

In 2007 and 2008, five flower clusters (~25 flowers) were collected from the

blueberry bushes in the center of each treatment. They were collected the day of

treatment, two days post treatment, and six days post treatment except during the

second application in 2008. On this date, flowers were collected the day of treatment

and five days post treatment due to inclement weather two days post treatment. The

flower samples were brought back to the Small Fruit and Vegetable IPM laboratory in









Gainesville where the number of thrips and other arthropods per flower was counted

utilizing the "shake and rinse" method (Arevalo and Liburd 2007). Adult thrips were

identified to species using a key developed by Arevalo et al. (2006). Any thrips not

matching the characters in the key were sent to the Division of Plant Industry in

Gainesville, Florida for identification.

Treatments in 2009 included: 1) Malathion 5 EC, 2) SpinTor 2 SC, and 3)

DelegateTM (spinetoram) at the rates used in the previous years, and 4) QRD 452 at

4.68 L / ha, 5) QRD 452 at 9.35 L / ha and 6) water treated control. They were applied

using a CO2 sprayer twice in the SHB blueberries, 14 days apart and once in the RE

blueberries during the flowering season.

Four flower clusters were collected from the three blueberry bushes in the center

of each treatment. In the SHB, they were collected the day of treatment, two days post

treatment, seven days post treatment, and fourteen days post treatment. In the RE, they

were collected the day of treatment, two days post treatment, and seven days post

treatment.

The flower samples were brought back to the Small Fruit and Vegetable IPM

laboratory and sampled as in 2007 and 2008. Because a large number of adult thrips

were present in the RE flowers, a sub-sample of 60 adult thrips per treatment each

week was identified to species as described for 2007 and 2008.

Yield data were collected for both SHB and RE blueberries. In the SHB plots,

blueberries were harvested from the two largest plants in the treatment group once a

week for four weeks beginning on April 27. The yield from each week was summed and

then divided by two to give an estimate of yield per plant for each treatment group. Yield









was collected from the RE treatment groups in the same way except that it was

collected once a week for five weeks beginning on May 27.

Thrips per flower data from 2007, 2008, and the SHB blueberries in 2009 did not

meet the assumptions of a one-way analysis of variance (ANOVA) and were therefore

analyzed using the Friedman, Kendall-Babington Smith nonparametric test for a

randomized complete block design and the Wilcoxon, Nemenyi, Mcdonald-Thompson

multiple comparisons test (Hollander and Wolfe 1999). The 2009 RE blueberry thrips

per trap data and both sets of yield data were analyzed with a one-way ANOVA in SAS

and means were separated using the least significant difference (LSD) test if the

ANOVA was significant (P < 0.05).

Results

2007

Two days after the third treatment was applied, there were significantly less thrips

larvae per flower in the Rynaxypyr, SpinTor, and XDE-175 low rate treatments

compared with the control (S'= 12.36, k, n = 6, 4, P< 0.02, Fig. 7-1A). There were no

significant differences in thrips adults per flower among treatments on any date (all S' <

8.05, k, n = 6, 4, P> 0.1, Fig. 7-1 B).

The percent of each species that was present in each treatment is shown in Table

7-1. Frankliniella bispinosa was the dominant species. Other species present included

F. fusca (Hinds), F. occidentalis, Thrips hawaiiensis (Morgan), and T. pini Karny.

Very few other arthropods were recorded from the flowers (Table 7-2). Three

predatory mites and three small spiders were the main predators sampled. Four

Coleopterans were also collected and some of these may also have been predators.











There were no significant differences among average thrips larvae per flower on

any date (all S' < 4.73, k, n = 5, 5, P > 0.1, Fig. 7-2A).

However, two days after the first application, there were significantly more thrips

adults per flower in the Rynaxypyr treatment compared with the spinetoram treatment

(S'= 9.08, k, n = 6, 4, P = 0.048, Fig. 7-2B & C). Six days after the first application, the

control had significantly higher numbers of thrips adults per flower than the spinetoram

treatment (S' = 9.66, k, n = 6, 4, P = 0.036).

The percent of each species that was present in each treatment is shown in Table

7-3. Frankliniella bispinosa was the dominant species. Thrips hawaiiensis and T. pini

were the second most numerous species present in the flowers. Other species present

included F. fusca, F. occidentalis, and Franklinothrips sp.

Very few other arthropods were recorded from the flowers (Table 7-4). Eighteen

predatory mites spread among the Rynaxypyr, SpinTor and spinetoram treatments and

a small spider found in the control were the main predators sampled. The wasp that was

also recorded from the control may be a parasitoid.

2009

SHB thrips per flower

There were no significant differences in thrips larvae or adults per flower among

treatments on any date (all S' < 9.39, k, n = 6, 4, P > 0.08, Fig. 7-3).

The percent of each species that was present in each treatment is shown in Table

7-5. Frankliniella bispinosa was the dominant species. Thrips pini was the second most

numerous species. Many other thrips species were also encountered occasionally,









including F. fusca, F. occidentalis, Franklinothrips sp., Haplothrips graminis Hood, and

T. hawaiiensis.

A number of other arthropods were found in the flower samples (Table 7-6). The

only predators found were spiders, one each in the SpinTor and QRD 452 high rate

treatments. Ants, which are sometimes predatory, were found in the control, SpinTor,

and QRD 452 high rate treatments. A wasp was found in the DelegateTM treatment.

RE thrips per flower

There were no significant differences in average thrips larvae per flower among

any of the treatments on any date (all F 1.99, df= 5, 23, P 0.13, Fig.7-4A).

However, 2 days post treatment there were significantly fewer adult thrips in the

DelegateTM treatment compared with the control and both rates of the QRD 452 (F =

8.52, df = 5, 23, P = 0.0004, Fig. 7-4B). Interestingly, the high rate of the QRD 452 had

significantly more thrips adults per flower than the control.

The percent of each species that was present in each treatment is shown in Table

7-7. Nearly all of the thrips sampled were F. bispinosa. A single T. pini and H. graminis

were also sampled.

Other arthropods found in the flower samples included mostly aphids and ants

(Table 7-8). A predatory mite was found in the control treatment.

Yield

There were no significant differences in SHB (F = 0.34, df = 5, 23, P = 0.88) or RE

(F = 1.47, df = 5, 23, P = 0.25) yield among any of the treatments. The average yields

across treatments in the SHB and RE blueberries were 1.03 0.19 and 2.14 0.68 kg

per plant respectively.









Discussion

Spinetoram reduced either thrips larvae or adults below levels found in the control

after one application each year. At all other times, it was as effective as SpinTor.

Srivastava et al. (2008) found that spinetoram was effective against F. bispinosa, F.

occidentalis, and F. tritici (Fitch) in pepper at a rate of 151 g a. i. per acre. This rate is

only slightly higher than the lower rate of 131 g a. i. per ha used in this study.

Rynaxypyr reduced numbers of thrips larvae below the control in 2007, but not in

2008. It did not reduce adult numbers in either year. Rynaxypyr has been shown to be

effective against various Lepidopterous pests (Ribbeck 2007), leaf rollers in apples

(Puciennik and Olszak 2009), and several sugar cane pests including termites and early

shoot borer (Rajavel et al. 2009, Singh et al. 2009). It may prove useful against thrips,

but further research is necessary.

The QRD 452 high rate treatment had significantly higher numbers of thrips adults

than the control 2 days post treatment. QRD 452 is an extract of C. ambrosioides,

commonly called Mexican Tea. It produces an odor that is pleasing to the human nose

(E. Rhodes personal observation). It is possible that QRD 452 may contain a volatile

that is attractive to F. bispinosa. There are a number of floral volatiles that are attractive

to various species of flower thrips (Lewis 1997). However, further research is needed to

substantiate this hypothesis.

The main reason for the presence of only a few significant results is the very low

numbers of both thrips adults and larvae that were present in the SHB blueberry flowers

during all three years. Neither numbers of thrips larvae nor adults exceeded an average

of 0.5 thrips per flower in 2007 or 2009. Thrips numbers were higher before the second

application of insecticides in 2008, but a violent storm that blew through the area on










March 7 prevented sampling on that date and most likely washed the treatments off of

the blueberry plants. Numbers of thrips larvae per flower were also low in the RE

blueberries.

Overall, both spinetoram and Rynaxypyr performed as well as malathion and

SpinTor while QRD 452 appeared to cause an increase thrips numbers. Spinetoram,

now registered in blueberries as DelegateTM, has become another tool for thrips control

in blueberries. Further trials must be done before any firm conclusions on the

effectiveness of Rynaxypyr and QRD 452 against thrips in blueberries can be drawn.


- Con
--Mal
--Ryn
SpT
XDEL
-* XDEH


6-Feb 12-Feb 18-Feb 24-Feb 2-Mar 8-Mar
Date


- Con
-- Mal
- Ryn
SpT
-- XDEL
-*-XDEH


31-Jan 6-Feb 12-Feb 18-Feb 24-Feb 2-Mar 8-Mar
Date
B
Fig. 7-1. Average thrips A) larvae and B) adults per flower in each treatment on each
sampling date. Arrows indicate dates when treatments were applied. Error
bars indicate standard error of the mean. Means with the same letter are not
significantly different from each other at P = 0.05.



138











14
1 4 -
aC 12
1 0 --Con
0 -M-- Mal
o08
CL. A --Ryn
So 06
SpT
04
0 --Spm
02
00
20-Feb 26-Feb 3-Mar 9-Mar 15-Mar
Date
A

14-
08 -
I --- Con

S08- -u--Mal
S--Ryn
0 06 -
06 SpT
04
0 ---Spm
02

20-Feb 26-Feb 3-Mar 9-Mar 15-Mar
Date
B

04



---Mal
02 02 a -Ryn
0._
-b SpT
01 Spm

0
20-Feb 26-Feb
Date
C

Fig. 7-2. Average thrips A) larvae and B) adults per flower in each treatment on each
sampling date and C) adults per flower during the first three sampling weeks
as indicated by the box in B). Arrows indicate dates when treatments were
applied. Error bars indicate standard error of the mean. Means with the same
letter are not significantly different from each other at P = 0.05.











06
S05- s Con

S 04 --- Mal
-- i SpT
a. 03-
S 02
0 --- Del
SI QRDL
2 01 -*-QRDH
17-Feb 24-Feb 3-Mar 10-Mar 17-Mar
Date
A

06

C. 05 Con
.4
04 --- Mal
03 SpT
,. i 03
*.2 -x-Del
W 02 i QRDL

S01 QRDH


17-Feb 24-Feb 3-Mar 10-Mar 17-Mar
Date
B
Fig. 7-3. Average thrips A) larvae and B) adults per flower in each treatment on each
sampling date. Arrows indicate dates pesticides were applied. Error bars
indicate standard error of the mean. Means with the same letter are not
significantly different from each other at P = 0.05.




















140













W 4 -*--Con
-u--Mal
3| SpT
0 2 -x--Del
S--QRDL
1 ---QRDH
S0
31 -Mar 3-Apr 6-Apr 9-Apr
Date
A

60

50 --- Con
40 -U-- Mal
3o0 SpT
S. c -x-- Del
20
20 -- QRDL
E 10 --- QRDH
< 0
31-Mar 3-Apr 6-Apr 9-Apr
Date
B
Fig. 7-4. Average thrips A) larvae and B) adults per flower in each treatment on each
sampling date. The Arrow indicates the date the pesticides were applied.
Error bars represent standard error of the mean. Means with the same letter
are not significantly different from each other at P < 0.05.

Table 7-1. Percent of each thrips species per treatment in 2007
F bispinosa F fusca F occidentalis T hawallensis T pins
Con 780 122 24 49 24
Mal 745 43 21 191 0
Ryn 700 100 67 10 33
SpT 806 56 28 56 56
XDEL 739 43 130 0 87
XDEH 643 0 36 71 250
The total thrips sampled from each treatment were: control (Con) 41, malathion (Mal)
47, Rynaxypyr (Ryn) 30, SpinTor (SpT) 36, XDE-175 low rate (XDEL) 23, XDE-175
high rate (XDEH) 28.










Table 7-2. Average number of other arthropods per flower in each treatment for the
season in 2007


Acarl
Phytoseildae


Araneae Coleoptera Diptera


Hemlptera other Lepidoptera
Aphidae Hemlptera larvae


0002 0002 0002 0080 0 0 0002
0 0002 0 0135 0006 0 0
0 0 0004 0117 0 0 0002
0002 0 0 0191 0057 0 0
0 0 0 0135 0002 0002 0
0002 0002 0002 0178 0013 0 0


Percent of
F bispinosa


each thrips species per treatment in 2008
F fusca F occidentalis T hawaslensis T pmin


Franklinothnps sp


658 07 0 154 148 34
649 15 07 60 269 0
669 0 0 70 242 1 9
695 0 0 156 143 06
683 1 6 0 122 179 0


The total thrips sampled from each treatment were: control (Con) 149, malathion (Mal)
134, Rynaxypyr (Ryn) 157, SpinToro (SpT) 154, spinetoram (Spm) 123.

Table 7-4. Average number of other arthropods per flower in each treatment for the
season in 2008
Acar Hemlptera other Lepidoptera Coleoptera


Phytosenldae Araneae Hymenoptera Diptera Aphidae


Hemiptera


larvae Curcullonidae


Con 0 00019 0 0018 00983 0 0 0 0019 00019
Mal 0 0 0 00624 0 0 0 0
Ryn 00052 0 0 01138 0 0 0018 0 0
SpT 00067 0 0 00871 00047 0 00027 0
Spm 00204 0 0 01722 0 0 0 0

Table 7-5. Percent of each thrips species per treatment in the SHB blueberries in 2009
F bispinosa F fusca F occidentalis T hawaslensis T pins Franklinothnps sp H graminis


Con
Mal
SpT
Del
QRDL
QRDH


792 0 42 0 0 167 0
800 0 0 0 133 0 67
900 0 0 0 50 0 5
786 71 0 0 143 0 0
783 43 0 43 43 87 0
857 48 0 0 95 0 0


The total thrips sampled from each
15, SpinTor (SpT) 20, DelegateTM
high rate (QRDH) 21.


treatment were: control (Con) 24, malathion (Mal)
(Del) 14, QRD-452 low rate (QRDL) 23, QRD-452


Table 7-3

Con
Mal
Ryn
SpT
Spm


.










Table 7-6. Average number of other arthropods per flower in each treatment for the
season in the SHB blueberries in 2009


Hymenoptera


Araneae Hymenoptera


Hemlptera other


Formicidae Diptera Aphidae Hemlptera


0 0 04241 0 42633 00588
0 0 0 01566 1 0122 0
00385 0 005 0 1 8689 0
0 00333 0 00357 72433 0
0 0 0 0 1 3130 0
00345 0 01934 00476 1 3707 0


Percent of
F bispinosa


each thrips species per treatment in the RE blueberries in 2009
T pin H graminis


100 0 0
100 0 0
100 0 06
994 0 0
100 0 0
100 0 0


A total of 180 thrips were sampled from each treatment.


Table 7-8. Average number of other arthropods per flower in each treatment for the
season


Acar Hymenoptera
Phytoselldae Formicidae


Hemlptera


other


Diptera Aphidae Hemlptera


00588 05278 00625 025 00625
0 00625 0 04 0
0 00546 0 0203219 0
0 0 05 02223 0
0 06 0 02889 00556
0 0 0 01111 0


Table 7-7

Con
Mal
SpT
Del
QRDL
QRDH


.









CHAPTER 8
CONCLUSIONS

Results related to 5 objectives were presented in this dissertation. The objective

were: 1) to examine southern highbush blueberry plantings and adjacent fields for

alternate hosts of flower thrips and thrips dispersal from these host plants into blueberry

plantings, 2) to determine the relationship between populations of thrips and yield in

southern highbush blueberries and to determine an action threshold for thrips in

southern highbush blueberries, 3) to model the spatial distribution of flower thrips in a

blueberry planting utilizing geostatistical methods and to determine optimum trap

spacing, 4) to determine if "hot spots" are correlated with flower density, and 5) to

determine the potential of using several experimental reduced-risk insecticides to

manage flower thrips in Florida blueberries.

In the preliminary plant surveys, several reproductive hosts of Frankliniella

bispinosa were found. However, F. bispinosa developed in a white clover field and

blueberry planting simultaneously. Also, the highest numbers of thrips were often found

in the center of the blueberry planting. Other reproductive hosts still need to be

examined as sources of flower thrips in blueberry plantings, but results suggest that

thrips persist and overwinter in blueberry plantings.

The studies performed to examine objective 2 revealed that different varieties will

attract significantly different numbers of thrips. Varieties like Emerald, which flower early

and uniformly, appear to attract high numbers of thrips. However, this does not

necessarily lead to a significant difference in yield among varieties. Because of these

differences, economic injury levels may have to be developed for individual varieties or

for groups of varieties with similar flowering characteristics. Observations indicate that









varietal differences are minimized when different varieties are interplanted evenly

among each other, but further research is needed to substantiate this hypothesis.

The spatial distribution study conducted for objective 3 revealed that both inverse

distance weighting and kriging can be used to model flower thrips spatial distribution in

blueberries. The choice between the two would depend upon the objectives of a

particular study and the number of sample points to be taken. Semivariogram analysis

showed that white sticky traps should be spaced at least 28.8 m apart to ensure that all

samples are spatially independent from each other.

The correlation study, objective 4, conducted on the Inverness farm provided

evidence that "hot spots" may be correlated with flower density. Further research

incorporating more accurate measures of flower density is needed to confirm these

findings. Further research is also needed to determine if Incorporating temperature and

other environmental factors would prove beneficial.

In the efficacy trials conducted for objective 5, spinetoram was the most effective

of the reduced-risk compounds tested in reducing flower thrips numbers. In 2008, it was

registered for use in southern highbush blueberries as DelegateTM. Rynaxypyr showed

some promise and should be tested further. Trials examining the efficacy of QRD-452

are ongoing.

The overall goal of this project was to improve monitoring and management of

flower thrips in southern highbush blueberries in Florida. Awareness of the potential

effects of variety, flower density, and temperature on thrips density and spacing traps at

least 28.8 m apart should improve monitoring. Management can be improved by

planting no more than two consecutive rows of the same variety and by making proper









use of DelegateTM. Further research into the various topics addressed in this

dissertation will bring more improvement to flower thrips management in Florida

blueberries.










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BIOGRAPHICAL SKETCH

Born and raised in Miami, Elena Marion Rhodes has lived all of her 29 years in

Florida. She earned her bachelor's degree in biology at New College of Florida in

Sarasota in May of 2003. During her seventh semester, she interned in the Invertebrate

Laboratory of Archbold Biological Station in Lake Placid, Florida. While there, she

completed a project on backswimmer population ecology, which became her senior

thesis project. She graduated with a master's degree in entomology from the University

of Florida in 2005. Her thesis investigated predator-prey relationships in an attempt to

control twospotted spider mites in strawberries. With the completion of this dissertation,

she received her Ph.D. from the University of Florida in August of 2010. This

dissertation is the culmination of her work on the ecology and management of flower

thrips in Florida blueberries. She is a member of the Gamma Sigma Delta honors

society of agriculture and the Talking Gators Toastmasters club.





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ECOLOGY AND MANAGEMENT OF FLOWER THRIPS IN SOUTHERN HIGHBUSH BLUEBERRIES IN FLORIDA By ELENA MARION RHODES A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORID A IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY UNIVERSITY OF FLORIDA 2010 1

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2010 Elena Marion Rhodes 2

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To the glory of our Lord Jesus Christ, to my parents, and to my brother David 3

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ACKNOWLEDGMENTS I thank my major professor, Dr .Oscar Liburd and all of my committee members for their hard work and support throughout this proj ect. I also thank all of the current and previous staff and students of the Small Fr uit and Vegetable IPM laboratory for their help in collecting samples and harvesting many, many blueberries. I thank Gary England for all of the hard work he did sampling the two Hernando Co. blueberry farms. I thank Dr. Carlene Chase for identifying the plants for the plant survey. I also thank Dr. G. B. Edwards for his help in thrips species identification. I also thank the University of Florida, Institute of Food and Agricultural Sciences 2005-2006 Integrated Pest Management Grant for providing the funding for the project encompassing the Hernando Co. farms. I thank Dow Agrosciences and AgraQuest for providing funding for the insecticide efficacy trails. I thank the University of Florida Alumni Association and graduate school for providing fellowships that funded my Ph.D. education. Lastly, I thank my parents, my brother my extended family, and my friends for providing love, support, and putting up with my bouts of inappropriate stress release. I also thank my Lord Jesus Christ, without whose saving grace and healing touch I would never have gotten this far. 4

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TABLE OF CONTENTS page ACKNOWLEDGMENTS ..................................................................................................4 LIST OF TABLES ............................................................................................................8 LIST OF FIGURES ........................................................................................................10 ABSTRACT ...................................................................................................................13 CHAPTER 1 INTRODUC TION....................................................................................................16 2 LITERATURE REVIEW..........................................................................................21 Thrips ......................................................................................................................21 Thrips in Blueberries ...............................................................................................28 Flower Thrips Monitoring and Management ............................................................30 Monitoring .........................................................................................................30 Chemical Control ..............................................................................................32 Biological Control .............................................................................................35 Predators ...................................................................................................35 Entomopathogenic fungi ............................................................................37 Entomopathogenic nematodes ..................................................................38 Geographic Information Systems (GISs) and Geostatistics in Pest Management ..38 3 EXAMINING THRIPS DISPERSAL FR OM ALTERNATE HOSTS INTO SOUTHERN HIGHBUSH BL UEBERRY PLAN TINGS............................................42 Introduction .............................................................................................................42 Materials and Methods ............................................................................................43 Preliminary Plant Surveys ................................................................................43 Field Study .......................................................................................................45 Results ....................................................................................................................46 Preliminary Plant Surveys ................................................................................46 Field Study 2009 ..............................................................................................47 Field Study 2010 ..............................................................................................47 Discussion ..............................................................................................................48 4 EFFECTS OF BLUEBERRY VARIETY AND TREATMENT THRESHOLD ON THRIPS POPULATIONS........................................................................................57 Introduction .............................................................................................................57 Materials and Methods ............................................................................................58 Citra PSREU ....................................................................................................58 5

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Hernando and Lake Counties ...........................................................................59 Results ....................................................................................................................62 Citra PSREU ....................................................................................................62 2007 ...........................................................................................................62 Traps ..........................................................................................................62 Flowers ......................................................................................................63 Fruit ............................................................................................................63 2008 ...........................................................................................................63 Traps ..........................................................................................................63 Flowers ......................................................................................................63 Fruit ............................................................................................................64 Hernando and Lake Counties ...........................................................................65 2007 ...........................................................................................................65 Traps ..........................................................................................................65 Flowers ......................................................................................................66 Fruit ............................................................................................................68 2008 ...........................................................................................................69 Traps ..........................................................................................................69 Flowers ......................................................................................................69 Fruit ............................................................................................................70 Discussion ..............................................................................................................71 5 EXAMINING THE SPATIAL DISTRIBUTION OF THRIPS UTILIZING GEOSTATSITICAL METHOD S..............................................................................84 Introduction .............................................................................................................84 Materials and Methods ............................................................................................87 Results ....................................................................................................................89 Discussion ..............................................................................................................93 6 EXAMINING THE RELATIONSHIP BETWEEN THRIPS SPATIAL DISTRIBUTION AND FL OWER DENS ITY...........................................................115 Introduction ...........................................................................................................115 Materials and Methods ..........................................................................................115 Inverness Farm ..............................................................................................115 Windsor Farm .................................................................................................118 Results ..................................................................................................................119 Inverness Farm ..............................................................................................119 Windsor Farm .................................................................................................120 Discussion ............................................................................................................120 7 THE EFFECT OF SEVERAL REDUCED RISK INSECTICIDES ON FLOWER THRIPS POPULATIONS IN SOUTHERN HIGHBUSH BLUE BERRIES...............130 Introduction ...........................................................................................................130 Materials and Methods ..........................................................................................131 6

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Results ..................................................................................................................134 2007 ...............................................................................................................134 2008 ...............................................................................................................135 2009 ...............................................................................................................135 SHB thrips per flower ...............................................................................135 RE thrips per flower .................................................................................136 Yield .........................................................................................................136 Discussion ............................................................................................................137 8 CONCLUSION S...................................................................................................144 LIST OF REFERENCES .............................................................................................147 BIOGRAPHICAL SKETCH ..........................................................................................157 7

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LIST OF TABLES Table page 3-1 Common and scientific names of the plant s found in the blueberry planting each month .................................................................................................................56 4-1 Percent of adult thrips species sampl ed from flowers in each treatment at the Citra PSREU .......................................................................................................83 4-2 Percent of adult thrips species sampl ed from flowers in each treatment on farm 1 .........................................................................................................................83 4-3 Percent of adult thrips species sampl ed from flowers in each treatment on farm 2 .........................................................................................................................83 5-1 Summary statistics of thrips per trap for each sample date in 2008. .....................112 5-2 Several error metrics for natural nei ghbor (NN), inverse distance weighting (IDW), and ordinary kriging (OK) for each sample date in 2008. ......................112 5-3 Summary of the semivariogram anal ysis for each sampling week in 2008. ..........113 5-4 Summary statistics of thrips per trap for each sample date in 2009. .....................113 5-5 Several error metrics for natural nei ghbor (NN), inverse distance weighting (IDW), and ordinary kriging (OK) for each sample date in 2009. ......................113 5-6 Summary of the semivariogram anal ysis for each sampling week in 2009. ..........114 6-1 Summary statistics for the thrips per trap data from each sampling week. ...........129 6-2 Summary statistics for the percent age of open flower data from each sampling week. ................................................................................................................129 7-1 Percent of each thrips species per treatment in 2007 ...........................................141 7-2 Average number of other ar thropods per flower in each treatment for the season in 2007 ..............................................................................................................142 7-3 Percent of each thrips species per treatment in 2008 ...........................................142 7-4 Average number of other ar thropods per flower in each treatment for the season in 2008 ..............................................................................................................142 7-5 Percent of each thrips species per treatment in the SHB blueberries in 2009 ......142 7-6 Average number of other ar thropods per flower in each treatment for the season in the SHB blueberries in 2009 .........................................................................143 8

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7-7 Percent of each thrips species per tr eatment in the RE blueberries in 2009 .........143 7-8 Average number of other ar thropods per flower in each treatment for the season 143 9

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LIST OF FIGURES Figure page 2-1 Example of an ideal semivariogram with a nugget value of zero ............................41 3-1 Locations of transects (a rrows) in blueberry planting ..............................................51 3-2 Numbers of each thrips species per fl ower collected from each plant during the first survey ..........................................................................................................51 3-3 Numbers of each thrips species per fl ower collected from each plant during the second survey ....................................................................................................52 3-4 A) Average thrips per trap in each treatment on each sampling date in 2009. Circled data indicate significant differences. B) Average thrips per trap on Feb. 12, 2009 .....................................................................................................52 3-5 Average thrips A) adults and B) larv ae per flower on each sampling date in 2009 .53 3-6 Average thrips per flower in the cl over field on each sampling date in 2009 ...........53 3-7 Average thrips per trap A) throughout t he flowering period and B) during the first 4 weeks of the flowering period (i ndicated by the box in A) in 2010 ...................54 3-8 Average thrips A) adults and B) larv ae per flower on each sampling date in 2010 .55 3-9 Average thrips per flower in the cl over field on each sampling date in 2010 ...........55 4-1 Average thrips per sticky trap recorded from each variety per week in 2007 ..........74 4-2 Proportion of injured and unmarketable fruit sampled from ea ch variety in 2007 ....74 4-3 Average thrips per sticky trap recorded from each variety per week in 2008 ..........75 4-4 Average thrips A) larvae and B) adults per flower recorded from each variety per week in 2008 ......................................................................................................75 4-5 Average thrips per trap recorded from ea ch treatment per week on farm 1 in 2007 ...................................................................................................................76 4-6 Average thrips per trap recorded from ea ch variety per week on farm 1 in 2007 ....76 4-7 Average thrips per trap recorded from ea ch variety per week on farm 2 in 2007 ....77 4-8 Average thrips A) larvae and B) adults per flower recorded from each variety per week on farm 1 in 2007 ......................................................................................77 10

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4-9 Average thrips A) larvae and B) adults per flower recorded from each variety per week on farm 2 in 2007 ......................................................................................78 4-10 Proportion of injured and unmarketable fruit sampl ed from each treatment on farm 1 in 2007 .....................................................................................................79 4-11 Graphs showing average thrips per fl ower vs. average proportion of injured fruit .....................................................................................................................80 4-12 Average thrips per trap recorded from each variety per week on farm 1 in 2008 ..81 4-13 Average thrips A) larvae and B) adults per flower recorded from each variety per week on farm 1 in 2008 ................................................................................81 4-14 Proportion of injured and unmarketable fruit sampled from each variety on A) farm 1 and B) farm 2 in 2008 ..............................................................................82 5-1 GIS map of t he study area in 2008 ..........................................................................97 5-2 GIS map of t he study area in 2009 ..........................................................................98 5-3 Point maps of thrips per trap for each sampling week in 2008 ................................99 5-4 Natural neighbor interpol ation of thrips per trap ....................................................100 5-5 Inverse Distance Weighting interpolation (p = 2, # points = 20) of thrips per trap .101 5-6 Semivariograms (A, C, E) and Ordinary Kriging interpolation (B, D, F) of thrips per trap .............................................................................................................103 5-7 Point maps of thrips per trap for each sampling week in 2009 ..............................105 5-8 Natural neighbor interpol ation of thrips per trap ....................................................107 5-9 Inverse Distance Weighting interpolation (p = 2, # points = 20) of thrips per trap .109 5-10 Semivariograms (A, C, E, G, I) and Ordinary Kriging in terpolation (B, D, F, H, J) of thrips per trap from ...................................................................................112 6-1 Graphs showing percent open flowers vs. thrips per trap .....................................123 6-2 Maps showing the spatial similarity of number of thrips per trap (T) with percent of open flowers (F) ............................................................................................125 6-3 Graphs showing percent open flowers vs. log10 thrips per trap .............................127 6-4 Graphs showing percent open flower s vs. thrips adults per flower ........................128 6-5 Graphs showing percent open flowers vs. thrips larvae per flower .......................128 11

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7-1 Average thrips A) larvae and B) adults per flower in each treatment on each sampling date ...................................................................................................138 7-2 Average thrips A) larvae and B) adults per flower in each treatment on each sampling date and C) adults per flower during the first three sampling weeks as indicated by the box in B) ............................................................................139 7-3 Average thrips A) larvae and B) adults per flower in each treatment on each sampling date ...................................................................................................140 7-4 Average thrips A) larvae and B) adults per flower in each treatment on each sampling date ...................................................................................................141 12

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Abstract of Dissertation Pr esented to the Graduate School of the University of Florida in Partial Fulf illment of the Requirements for t he Degree of Doctor of Philosophy ECOLOGY AND MANAGEMENT OF FLOWER THRIPS IN SOUTHERN HIGHBUSH BLUEBERRIES IN FLORIDA By Elena M. Rhodes August 2010 Chair: Oscar E. Liburd Major: Entomology and Nematology In Florida, southern highbush (SHB) bluebe rries are grown for a highly profitable early season fresh market. Flower thri ps are the key pest of these blueberries. Frankliniella bispinosa (Morgan) is the most common s pecies found. They injure blueberry flowers by feeding and ovipositing in all developing tissues. These injuries can lead to scarring of developing fruit. The overall goal of this dissertation was to improve monitoring and management of fl ower thrips in southern highbush blueberries in Florida. To this end, five specific objectives were set up. Objective 1 was to find alternate hosts of F. bispinosa and to determine if F. bispinosa moves into blueberry plantings from these hosts. Preliminary plant surveys conducted in the spring of 2007 and from No vember 2007 until March 2008 revealed several reproductive hosts of F. bispinosa including: Carolina geranium ( Geranium carolinianum L.), white clover ( Trifolium repens L.), and wild radish ( Raphanus raphanistum L.). Thrips population development wa s monitored in a blueberry planting and neighboring white clover field on a farm in Windsor, FL during early spring 2009 and 2010. The flower thrips population in the white clover and blueberries developed at 13

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the same time with the highest numbers of th rips recorded from the center of the blueberry field in both years. Objective 2 sought to determine the rela tionship between thrips and yield in different SHB blueberry varieties and dete rmine an action threshold. It involved experiments during early spring 2007 and 20 08 on three farms, two in Hernando Co., FL and the third at the Plant Science Res earch and Education Unit (PSREU) in Citra, FL. On the Hernando Co. farms, two treatment thresholds (100 and 200 thrips per trap) and an untreated control and four varieties (Emerald, Jewel, Millennia, and Windsor) were compared. At the Citra PSREU, the varieties Emerald, Jewel, Millennia, and Star were compared in 2007 and all but Star were compared in 2008. Thrips numbers exceeded the threshold on only one farm in 2007 and although there were no differences in thrips numbers among treatment s, the threshold of 100 thrips per trap appeared to result in a significantly lower proportion of injured and malformed fruit compared with the control. Emerald consistently had more thrips per trap and per flower than the other varieties on all three farms. Ho wever, this did not always lead to an increase in fruit injury. The third objective was to model thrips spatial distribution with geostatistical techniques and to use these models to deter mine optimum trap spacing. The study was conducted in early spring 2008 and 2009 on a farm in Inverness, FL. A grid of 100 traps spaced at 15.24-m intervals in 2008 and 7. 61-m intervals in 2009 was set up with an additional 30 traps interspersed randomly throughout the sample area. Inverse distance weighting and krigi ng produced maps with similar a ccuracy. The semivariogram 14

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analysis showed that traps should be spaced at least 28.8 m apart to insure spatial independence. Objective 4 sought to determine if hot spot s of high thrips density were correlated with flower density. The percent of open flower data were recorded from all rows in the Inverness 2009 study each week when traps were collected. Linear regression analysis revealed a positive relationship between percent of open flowers and thrips per trap on three of the five sampling dates. Objective 5 was to examine the efficacy of several reduced-risk compounds, which were compared with malathion, SpinTor and an untreated control. During the course of the trials, one of these compounds, spinetoram, was regi stered in Florida blueberries as Delegate TM Rynaxypyr also reduced thrips num bers, while thrips numbers in the QRD-452 high dose treatment were higher than in the control. 15

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CHAPTER 1 INTRODUCTION Blueberries are a highly profitable crop in Florida. During 2009, 6.4 million kg (14.1 million lbs) of fresh market blueberries were harvested from 1295 ha (3 ,200 acres) at an average of $11.89 per kg ($5.40 per lb) (USDA 2010). The use of low chill varieties of Rabbiteye ( Vaccinium virgatum Aiton) and the development of southern highbush ( V. corymbosum L. x V. darrowi Camp) allows Florida growers to take advantage of this highly profitable early season market. Rabbiteye blueberries are better suit ed for u-pick operations and local sales (Williamson and Lyrene 2004). Varieties of Rabbi teye can be classified as early-, mid-, or late season. During the early to mi d 1980s, several North Florida producers attempted to grow early-season Rabbiteye vari eties on >500 acres, but yields were very low. Improved management of insect pests, including blueberry gall midge ( Dasineura oxycoccana Johnson) and flower thrips ( Frankliniella spp.), have improved yield, but these blueberries do not ripen early enough in the season to be highly profitable. Rabbiteye blueberries are grown exclusiv ely for u-pick and local sales (Williamson and Lyrene 2004). The development of the s outhern highbush blueberry va rieties in 1976 allowed Florida growers to take advantage of an untapped early season market (Williamson and Lyrene 2004). Southern highbush blueberries ripen 4-6 weeks before the early-season rabbiteye varieties. The various varieties of southern highbush are crosses between northern highbush blueberries ( V. corymbosum ) and wild blueberry species in Florida, including rabbiteye (Childer s and Lyrene 2006). All blueberry acreage grown for fresh fruit shipping within and from Florida consists of southern highbush plantings 16

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(Williamson and Lyrene 2004). In north Florida, frost protection is essential to avoid damage to flowers (Williamson and Lyrene 2004). The two major insect pests of blueberries in Florida are blueberry gall midge (aka cranberry tipworm D. oxycoccana ) and flower thrips ( Frankliniella spp.) (O. E. Liburd personal communication). Blueberry gall midge females lay their eggs in developing blueberry buds. In Florida, they emerge in January or February and can produce up to six generations per year (S ampson et al. 2002). The larvae develop and feed in the bud eventually killing it (Finn 2003). Both flor al and vegetative buds are attacked (Sarzymski and Liburd 2003). An unchecked infestation can k ill up to 80% of floral buds while injury to vegetative buds distorts leaves and can reduce the number of berries a plant can support (Sampson et al. 2002). It is a diffi cult pest to control although systemic insecticides can reduce numbers (O. E. Liburd unpublished data). Blueberry gall midge is attacked by five species of parasitoids in the Platygastridae and Tetrastichinae (Eulophidae) families. The Tetrastichin is a species of Aprostocetus. The four Platygastridae species include two species of Synopeas a species of Inostemma and one species of Platygaster (Sampson et al. 2006). The blueberry bud mite { Acalitus vaccinii (Keifer)} and flea beetles are emerging pests (O. E. Liburd personal communication ). Blueberry bud mites, in the family Eriophyidae, infest developing leaf and fl ower buds of both hi ghbush and lowbush blueberries. Feeding by the bud mites causes the buds to redden early in the season, which prevents normal leaf and flower devel opment. Severe infestations can cause yield reduction. Bud mites are difficult to detect because of their small size and the 17

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injury they cause, which closely resemble s frost injury. Bud mites can be managed with proper pruning and the use of horticultural oils (Weibel zahl and Liburd 2009). Flea beetles are a post harvest pest of both southern highbu sh and rabbiteye blueberries. Flea beetles are foliage feeder s. Large numbers of them can cause a significant reduction in photosynthetic productivi ty resulting in a decrease in yield for the following season. The blueberry leaf beetle, Colaspis pseudofavosa Riley, and the red headed flea beetle, Systena frontalis (Fabricius), are the two most common species found in Florida blueberries (O. E. Libur d unpublished data). However, there may be other species in the complex responsible for heavy defoliation in blueberries after harvest. Chili thrips, Scirtothrips dorsalis Hood, were first repor ted from the Florida landscape on roses in Palm Beach Co. in Oct ober of 2005. By the end of 2005, it had spread to 15 counties on a number of differ ent hosts (Silagyi and Dixon 2006). There are at least 100 recorded hosts of chili thri ps (Hodges et al. 2006), and this number is increasing. Although blueberries are not a lis ted host, chili thrips were reported from blueberries in North-central Florida in the summer of 2008 ( O. E. Liburd personal communication). Chili thrips are a pale bodied thrips with dark wings. They are primarily foliage feeders and do not feed on flower pollen (Hodges et al 2005). Effective control measures on blueberries have not yet been st udied. However, Chlorfenapyr, spinosad, and imidacloprid gave consist ent control of chili thrips on pepper plants (Seal et al. 2006). In addition, the predatory mite Amblyseius swirskii (Athias-Henriot) maintained chili thrips populations below 1 per termi nal leaf on pepper plants in the landscape for 63 days after they were released (Arthurs et al. 2009). 18

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Several species of flower thrips, in cluding the Florida flower thrips {Frankliniella bispinosa (Morgan)}, Eastern flower thrips { F. tritici (Fitch)}, Western flower thrips { F. occidentalis (Pergande)}, and Tobacco thrips { Frankliniella fusca (Hinds)} are pests of both rabbiteye and southern highbush bluebe rries in Florida and Georgia (Liburd and Arvalo 2005). Frankliniella bispinosa is the most common thrips found in Florida, while F. tritici is the dominant species in Georgia (Arvalo et al. 2006). They infest not only blueberries, but a wide variety of other crop and noncrop host plants. In general, flower thrips are very small inse cts (~1 mm in length) with yellowish to orange coloration. They can be distinguished fr om other insect orders by their fringed wings and punch and suck mouthparts. They have a short life cycle that can occur in 18 to 22 days under ideal conditions. Thrips progr ess though two actively feeding larval instars and two inactive instars (often ca lled pupae) before becoming adults (Lewis 1997). Flower thrips damage flowers in two ways. Both larvae and adults feed on all parts of the flowers including ovaries, styles, petals, and dev eloping fruit. This feeding damage can reduce the quality and quantity of fruit produced. Females also cause damage to fruit when they lay their eggs inside flower tissues. The newly hatched larvae bore holes in flower tissue when they emerge (Liburd and Arvalo 2005). The overall goal of this project was to improve monitoring and management of flower thrips in southern highbush blueberries in Florida. The hypothesis is that a better understanding of flower thrips ecology in co mbination with the devel opment of specific management tactics will accomplish this goal. The objectives of this dissertation were fivefold: 1) to examine bluebe rry plantings and adjacent fiel ds for alternate hosts of 19

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flower thrips and thrips dispersal from these host plants into blueberry plantings. 2) To determine the relationship bet ween populations of thrips and yield in southern highbush blueberries and to determine an action thre shold for thrips in southern highbush blueberries. 3) To model the spatial distributi on of flower thrips in a blueberry planting utilizing geostatistical methods and to determi ne optimum trap spacing. 4) To determine if hot spots are correlated with flower density. 5) To det ermine the potential of using several experimental reduced-risk insecticid es to manage flower thrips in Florida blueberries. 20

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CHAPTER 2 LITERATURE REVIEW Thrips In general, thrips are very small insect s (a few mm in length) with yellowish orange to brown coloration. They belong to the order Thysanoptera and are distinguished from other insect orders by t heir fringed wings and punch and suck mouthparts (Lewis 1997). Thrips are unique in having only one m andible, the left one. The right one is resorbed by the embryo (Mound 2005). There are at least seven families of thrips, Adiheterothripidae, Aeolothripidae, Fauriellidae, Merothripidae, Heterothripidae, Thripidae, and Phlaeothripidae, which vary widely in their ecology. Aeolothripids ar e predators of mites and small insects, Merothripids are fungus feeder s, and Adiheterothripidae, He terothripids, and Thripids are primarily plant feeders ( Lewis 1997). These six families belong to the suborder Terebrantia (Triplehorn and Johnson 2005). The Ph laeothripidae contains mostly fungal feeders, although a few species are predatory (Lewis 1997). F auriellidae is a recently described family (Triplehorn and Johnson 2005) This family falls in the suborder Tubulifera. Almost all of the major pes t thrips belong to the fam ily Thripidae. Major pest species in this family belong to several genera, including: Frankliniella Heliothrips Scirtothrips Taeniothrips and Thrips (Triplehorn and Johnson 2005). Thrips that are crop pests tend to be polyph agous and highly adaptable (Mound 2005). However, not all Thripidae are pests. For example, Scolothrips sexmaculstus (Pergande), the sixspotted thrips, is an important predator of phytophagous mites (Triplehorn and Johnson 2005). 21

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Terebrantian thrips have a short like cycle that can occur in 18 to 22 days under ideal conditions (Lewis 1997). Females oviposit into the plant tissue on which they feed (Terry 1991). Eggs are inserted one at a time into an incision in the plant tissue created by the females saw-like ovipositor (Te rry 1991). Thrips progr ess though two actively feeding larval instars and two inactive instars (often called the propupa and pupa) before becoming adults (Lewis 1997). Many Terebrantian species drop off of their host plant and pupate in the soil (Lewis 1997). Only a little is known about the mating behavi or of Terebrantian thrips because of the ephemeral nature of their flowering hos t plants (Lewis 1997). Males of several species, including Frankliniella occidentalis (Pergande), F. schultzei (Trybom), T. fuscipennis Haliday, T. major Uzel, T. flavus Schrank, T. atratus Haliday, and T. vulgatissimus form aggregations on the corollas of flowers, into which females will enter and mate (Milne et al. 2002). Females may use cues from the plants at a distance and then find the male aggregations via a sex pheromone produced by the males when they get closer to the plant. Milne et al. (2002) discovered that traps set among flowering plants and baited with conspecific males attr acted significantly more females than unbaited traps placed among the plants. F. occidentalis males will fight to keep an area clear for a female to land. Females generally mate with the first male they come into contact with (Lewis 1997). Many factors can influence development and reproduction, including temperature and host plant (Lewis 1997). Tsai et al. (1995) examined Thrips palmi Karny survival, egg production, and developmental time at thr ee different temperatures: 15C, 26C, and 32C. They found that T. palmi had the greatest survival and highest egg 22

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production at 26C, but had the shortest developmental time at 32C compared to the other temperatures. They also investigated the effect of diffe rent host plants on survival and reproduction of T. palmi both of which were much lower on bell pepper than on melon, eggplant, and cucumber. Alter natively, the Japanese strain of F. occidentalis can survive temperatures as low as 0C (Tsumuki et al. 2007) for up to 40 d in the presence of food. Females survived longer than males ( 40 d vs. 30 d). Both sexes died within 48 h at temperatures below 0C. Population density and food ava ilability can also play a role in regulating thrips population growth (Nothnagi et al. 2008). Both competiti on for resources at high population densities and declining food availability can lead to sharp population declines in confined experiments. The same level of competition and declining food resources in a greenhouse or open field situation would most likely lead to migration (Nothnagi et al. 2008). Terebrantian thrips use both visual and chemical cues to locate their host plants. Visual cues include floral color, shape, and si ze (Lewis 1997). In terms of color, blue, white, and yellow are much more a ttractive than other colors to Frankliniella spp. (Finn 2003). With respect to chemical cues, anisal dehyde odor significantly increased catches of seven polyphagous, flower inhabiti ng thripid species (Kirk 1985). Although some thrips are specific to a few hosts, many are extremely polyphagous (Lewis 1997). However, it is often difficult to determine a particular species true host range. Thrips will often alight, and even f eed, upon many plants that they cannot reproduce on (Mound 2005, Paini et al. 2007). For example, the pear thrips, Taeniothrips inconsequens (Uzel), has been recorded from 242 species of plants, but 23

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only 35 of these are breeding hosts (Teulon et al. 1994). Although F. fusca (Hinds), F. occidentalis and F. tritici (Fitch) are found on tomato pl ants in Florida and can cause injury to these plants, only F. occidentalis reproduces on the tomato plants (Salguera Navas et al. 1994). Adults of several species of thrips are found in native orchids in Northern Florida and Southern G eorgia, but the small numbers of larvae found indicate that the orchids are not a r eproductive host for most thrips species (Funderburk et al. 2007). The majority of pest thrips are highly polyphagous. They can reproduce on various weedy hosts and disperse into crops from these hosts. Frankliniella spp. prefer hosts that are flowering, so onl y flowering plants should be considered as sources of populations of these thrips (Nor thfield et al. 2008). In Japan, F. occidentalis reproduces in at least eight weedy species common in and around ornamental nurseries throughout the spring and summer (Katayama 2006). Cockf ield et al. (2007b) found that native vegetation surrounding apple orchards supported F. occidentalis populations when apple trees were not flowering. These weedy hosts can also serve as reservoirs for tomato spotted wilt and other tospoviruses (Kahn et al. 2005). Therefore, weed control may be an important cultural tactic for control of pest thri ps (Katayama 2006), but may not be effective in reducing injury without t he use of other tactics (Cockfield and Beers 2008). Thrips disperse in two main ways. They fly from field to field and are frequently transported long distances by humans moving plant material (Lewis 1997). When they fly, terebrantian thrips lock the cilia on thei r wings in the open position (Ellington 1980). Open refers to the fact that the cilia are at a much greater angle to the wing axes in 24

PAGE 25

flight than at rest. This doubles the wi ng area. The cilia are opened by abdominal combing. They are closed by tibial combing. The wings lie parallel over the abdomen at rest (Ellington 1980). The distance thrips can fly is determined in large part by temperature and humidity (Lewis 1997). They desiccate much more quickly in hot, dry weather and thus cannot travel as far. Because of their small size, thrips have littl e control over their flight and are carried readily on wind and air curr ents (Arvalo-Rodriguez 2006). Yudin et al. (1991) discovered that thrips dispersal can be hind ered with mechanical barri ers and that thrips were more numerous on the side of the field corresponding to prevailing wind direction. Thrips do, however, seem to have some cont rol over landing (Lewis 1997). From a few observations, it is thought that thrips l and feet first, close their wings, and begin quivering their antennae (Lewis 1997). Wingle ss thrips can also be dispersed by wind (Mound 2005). Thrips cause damage to their host plants directly through feeding and oviposition and indirectly through the spread of tospovir uses (Arvalo-Rodriguez 2006). Thrips feed by punching into the plant tissue with their single mandible and sucking out cell contents with a pair of maxi llary stylets (Lewis 1997). Both larvae and adults of F. bispinosa (Morgan) feed on all parts of Naval orange ( Citrus sinensis (L) Osbeck) flowers and on all parts of swollen buds (Childers and Achor 1991). Feeding causes cellular evacuation, necrosis, plasmolysis, and cellular collapse, which often spreads to nearby cells up to five cells deep. Some leaf feeding thrips can induce gall formation in plants (Mound 2005). Feeding on inflorescenc es can cause drooping and discoloration of petals (Rhainds et al. 2007). 25

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Female F. bispinosa oviposit in all parts of the flowers and swollen buds of Naval orange trees. However, Childers and Achor ( 1991) found that 73% of thrips larvae emerged from the pistil-calyx units of open fl owers, which indicates a preference for these tissues. Ovipostion damage is localized and affects only cells directly adjacent to the oviposition site (Childers and Achor 1991). Large numbers of thrips can cause economic damage and even abortion of flower s (Arvalo-Rodriguez 2006). In contrast, oviposition by F. occidentalis causes pansy spot, a corky, raised scar surrounded by a pale halo, on apple (Cockfield et al. 2007a). Adult F. occidentalis are most abundant in apple blossoms from king bloom (bloom of the fi rst, central flower in the flower clusters) to full bloom. Injury similar to pansy spot is caused when F. occidentalis oviposits in grapes and tomatoes (Cockfield et al. 2007a). Thrips also feed on pollen. Ki rk (1987) found that a single T. imaginis Bagnall or T. obscuratus (Crawford) could consume 0. 2-0.7% of a kiwifruit { Actinidia deliciosa (A. Chevalier)} flowers pollen per day. They noted that this suggests that pollen damage could reduce crop yield or plant fitness in so me cases. Ugine et al. (2006) found that adult female F. occidentalis were more abundant in greenhouse impatiens flowers that still contained pollen. Tospoviruses are an extremely damaging group of plant viruses (ArvaloRodriguez 2006). To date, there are 16 known Tospovirus species in the family Bunyaviridae. Tomato Spotted Wilt Viru s (TSWV) is one of the most well known species. It was thought to be a major factor in the 35% decline of crisphead lettuce ( Lactuca sativa L.) and romaine ( L. satvia var. longifolia Lam.) production in Hawaii in the late 1980s (Yudin et al. 1991). Thus far, 11 species of thrips in the family Thripidae 26

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are known vectors. Both the viruses and their vectors have been spread around the world because of the difficulty of detecting both of t hem in plants in the process of being transported (Arvalo-Rodriguez 2006). Only ear ly second instar larvae can acquire Tospoviruses (Moritz et al. 2004). During this stage of development, there is a temporary connection between t he mid-gut, visceral muscles, and salivary gland due to the displacement of the brain into the prothoracic region by enlarged cibarial muscles (Moritz et al. 2004). Tospoviruses are transmitted by adults when they feed and may also be transmitted mechanically through excreti on and oviposition (Mori tz et al. 2004). In tomatoes sprayed weekly with insecticides to control thrips, the main source of TSWV was from immigrating thrips (Puche et al. 1995). There are other microbes associated with thrips besides tospoviruses. Two groups of bacteria, one that ha s a shared ancestry with Erwinia and the other that has a shared ancestry with Escherichia coli (Migula), are found in the gut of F. occidentalis (Chanbusarakum and Ullman 2008). Both bacteria are facultative symbionts that infect thrips larvae. They parasitize the thrips when nutrients are abundant in the thrips diet and supplement the thrips diet if it is nutrient deficient (Chanbusarakum and Ullman 2008). Thrips can also serve beneficial f unctions as pollinators and predators. Taeniothrips ( Amblythrips ) ericae Haliday is the major pollinator of Erica tetralix flowers (Hagerup and Hagerup 1953). Two thripid species, F. diversa and F. insularis pollinate flowers of the Moraceae tree ( Castilla elastica ) and a new species of Thrips pollinates Antiaropsis a genus of Moraceae in New Guinea (Mound 2005). The flowers of Arctostaphyllos uva-ursi are pollinated by several s pecies of thrips, including 27

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Ceratothrips ericae (Haliday) and Haplothrips setiger Priesner (Garca-Fayos and Goldarazena 2008). Though one thrips can carry only a small amount of pollen, large numbers of thrips can transpor t large amounts of pollen and thrips can occur in very high numbers in flowers (Mound 2005). Some species of thrips, such as the six-spotted thrips ( S. sexmaculatus ), are primarily predators and feed on spider mi tes and other pests (Triplehorn and Johnson 2005). However, some phytophagous thrips s pecies are also facultative predators of spider mites. Thrips imaginis Bagnall, T. tabaci Linderman, and F. Schultzei consume twospotted spider mite ( Tetranychus urticae Koch) eggs in early season cotton in Australia, which causes mite outbreaks to o ccur later in the season then they would if this did not occur (Wilson et al. 1996). In California, F. occidentalis preys on spider mite eggs in cotton and F. tritici is listed as a predator of spider mite eggs in peanuts (Trichilo and Leigh 1986). Thrips in Blueberries Various thrips species inhabit bluebe rry flowers, leaves, and both leaves and flowers (Childers and Lyrene 2006). Frankliniella vaccinii Morgan and Catinathrips kainos ONeil are the most common pestiferous l eaf inhabiting species. Flower thrips include F. occidentalis and F. bispinosa while F. tritici and Scirtothrips ruthveni Shull attack both leaves and flowers (Childers and Lyrene 2006). Uncultivated Vaccinium species in southern Georgia are host to seve ral species of gall-forming leaf thrips (Braman et al. 1996). These gall thrips c ould become pestiferous if susceptible uncultivated Vaccinium species are bred with cu ltivated species (Braman et al. 1996). Flower thrips are the key pest of early-season blueberries in Florida. Frankliniella bispinosa is the most common species with an average of 84% of the total thrips 28

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collected from flowers and 89% from sticky traps. The other 16% and 11% are made up of F. fusca F. occidentalis, and T. hawaiiensis (Morgan) in order of decreasing abundance (Arvalo-Rodriguez et al. 2006). A simila r situation exists in Florida oranges, where F. bispinosa comprises 84 to 99% of thrips species collected from soil emergence traps (Childers et al. 1994) and in other varieties of citrus where F. bispinosa accounted for 92% of thrips found in closed buds and open flowers (Childers et al. 1990). Thrips move into crops from other cultivated plants that flower earlier, like citrus in the case of blueberries (Childers et al. 1994) and from wild plant species (Chellemi et al. 1994, Topanta et al. 1996). In wild plant spec ies adjacent to tomato fields in north Florida, F. tritici was the most abundant species in March, May and August, F. bispinosa in June and July, and F. occidentalis in February and April. Th irty-one of the 37 plant species examined contained thrips (Chellemi et al. 1994). Paini et al. (2007) found that F. occidentalis used two different weedy plant species as reproductive hosts in April and May in North Florida. Frankliniella bispinosa also used two weedy species as reproductive hosts from Ma y to August. In contrast, F. fusca and F. tritici used 12 and 18 weedy species as reproductive hosts respectively in April and May. In blueberries, flower thrips tend to aggregate and this aggregation is most pronounced when the population density is the highest (Arvalo-Rodriguez 2006). Thrips populations tend to form one or a few hot-spots on blueberry farms, which are small areas of comparatively high thrips numbers (Arvalo and Liburd 2007). These hot-spots begin forming about 7-10 days after bloom initiation, peak between 12 and 15 days after initiation when the majority of the flowers are open, and decline until about 29

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22 days after bloom initiation when mo st of the flowers have become fruit and the thrips population all but disappears (Arvalo and Liburd 2007). Frankliniella occidentalis and F. tritici tend to aggregate on tomato plants while F. fusca was aggregated one year and randomly dispersed the next year (Salguero Navas et al. 1994). In blueberries, the highest numbers of thrips are caught on sticky traps placed in or just above the blueberry plant canopy (A rvalo-Rodriguez 2006). Similarly, Reitz (2002) and Salguero et al. (1991) found more adult F. occidentalis and F. tritici in the upper part of the tomato plant canopy, but t hey found more larvae in the lower part of the canopy. In apple orchards, the density of F. occidentalis decreases with increasing distance from the edge of the orchard (Miliczky et al. 2007). Flower thrips both feed and reproduce in bl ueberry flowers. These activities can cause the developing fruit to be scarr ed and misshapen (Arvalo-Rodriguez 2006). In his experiments, Arvalo-Rodriguez (2006) found that significantly more thrips larvae emerged from petals than from any other flower part. Also, significantly more thrips larvae emerged from ovaries than from styles and fruits. He concluded that flower thrips prefer these flower parts because the tissue is mature. Therefore, t heir eggs will not be crushed by growing cells (Arvalo-Rodriguez 2006). There are no known Tospoviruses that infect blueberries (Arvalo-Rodriguez 2006). Flower Thrips Monitoring and Management Monitoring In blueberries, thrips are monitored using sticky traps or by directly sampling the flowers. Finn (2003) found that more F. bispinosa were caught on white and blue sticky traps compared to yellow and green traps. Ye llow traps often caught more than green traps. Chu et al. (2006) also found that colo r is an important cue for thrips, catching 30

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more Frankliniella spp. in white and blue plastic cup traps than in yellow cup traps. Although white, yellow, and blue traps attract thrips, white traps are the best to employ. Yellow traps attract a large number of other insects and the dark coloring of the blue traps can make it difficult to see the thrips that are present on them (Arvalo-Rodriguez, 2006). Flowers can be sampled in several ways. The simplest method involves gently tapping the flowers and allowing the thrips to fall onto a white sheet below for counting. Flowers can also be collected in a vial or plastic bag and then dissected in the laboratory. Arvalo and Liburd (2007) developed a shak e and rinse method that is as accurate as dissecting flowers and much more efficient. This met hod involves collecting the flowers in alcohol-filled vials, shaking the vials, placing the flowers on a metal screen over a plastic cup, rinsing the flow ers with water, and count ing and collecting the thrips in the rinse liquid. Sixteen to 18 flowers were needed to esti mate thrips densities at the 25% precision level on tomato plants (Salguero Na vas et al. 1994). Twenty to 25 flowers give an accurate estimate of thri ps numbers in blueberry flow ers (Arvalo-Rodriguez 2006). Arvalo and Liburd (2007) found a strong correlation (r = 0.7621) between thrips per flower and thrips per trap in rabbiteye blueberries. Thrips per flower was estimated from five flower clusters sampled using the shake and rinse method. The sticky traps were hung inside the blueberry canopy. Rodri guez-Saona et al. (in press) found that sticky trap data were useful for predicting th rips flight activity and monitoring for the timing of insecticide applications. 31

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Economic injury levels (EILs) are an in tegral part of inte grated pest management (IPM) strategies. Several terms are important in understanding this concept, including: economic damage (ED) and economic threshold (E T). Stern et al. (1959) defines ED as the amount of injury that will justify the cost of artificial control measures. The EIL is the lowest population density t hat will cause this damage and the ET is the density at which control measures should be initia ted to prevent an increasing pest population from reaching the EIL. The EIL can be calculated using t he equation EIL = C/ (V I D), where C is the cost of cont rol, V is the value of the produc t, I is the injury per insect value, and D is the damage per unit injur ed (Pedigo et al. 1986). Arvalo-Rodriguez (2006) used this equation to determine the EI Ls for Climax and Tifblue rabbiteye blueberries in Georgia, which are approximately 13 and 14 thrips per 10 flowers respectively when Malathion 5EC (Micro Flo Company LLC, Memphis, TN) is used as the control measure and 17 and 19 thrips per 10 flowers respectively using SpinTor 2SC (spinosad) (Dow Agrosciences, Indianapolis, IN). Using his regression equations, Arvalo-Rodriguez (2006) calculated this to be 45 for Tifblue and 50 for Climax when malathion is applied and 64 for Tifblue and 73 for Climax when SpinTor is applied. Chemical Control Economic Injury Levels (EILs) for flower thrips on many crops are very low because of these thrips ability to transmit TSWV (Arvalo-Rodriguez 2006). For this reason, one of the main strategies used to control thrips is insecticide application (Morishita 2001). Morishita (2001) found that the organophosphates dichlorvos, sulprofos, profenofos, malathion, chlorpyr ifos-methyl, chlorfenvinphos, fenthion, and phenthoate, the carbamate methomyl, the insect growth regulators (IGRs) Iufenuron, chlorfluazuron, and flufenoxuron, and two other chemistries, chlorphenapir and 32

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spinosad, caused greater than 90% mortality to F. occidentalis in the laboratory. The other carbamates and all pyreth roid compounds were not as effective as these. In blueberries, flower thrips are current ly managed with applications of malathion and SpinTor (Arvalo-Rodriguez 2006). Malathion is a conventional, organophosphate insecticide with broad-spectrum activity. SpinTor is a reduced-risk insecticide. Its active ingredient is spinosad (spinosyn), wh ich is derived from t he fermentation of the soil bacterium Saccharopolyspora spinosa Mertz and Yao. Spinosad, which must be ingested, kills insects via rapid excitation of the nervous system (IPM of Alaska 2003). It shows equal toxicity towards F. bispinosa F. occidentalis, and F. tritici (Eger 1998) in the laboratory. However, Reitz et al. (2003) showed that it reduced F. occidentalis numbers, but did not reduce F. tritici numbers in field grown peppers in Florida. Spinosad has also been shown to be effective against F. occidentalis in field grown strawberries in Australia (Broughton and Herron 2007). On blueberry farms, these insecticides are usually applied early in the morning or late at night to minimize the impact on pollinating bees (Arvalo-Rodriguez 2006). However, growers still report problems with bee toxicity, especially when malathion is applied (O. E. Liburd personal communication ). Growers have also reported problems with SpinTor relating to its poor residual activity (O. E. Liburd personal communication). The exclusive use of only two compounds also raises questions of resistance development. Morse and Brawler (1986) found that the citrus thrips, S. citri (Moulton), appeared to be developing resistance to all of t he insecticides they tested against them. Resistance to acephate, chlorpyrifos, dich lorvos, dimethoate, endosulfan, fipronil, malathion, methamidophos, methidathion, me thomyl, and spinosad has been detected 33

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in F. occidentalis populations in Australia (Herron and James 2005). However, no resistance to abamectin, methiocarb, or py razophos was detected in these populations (Herron and James 2005). Frankliniella occidentalis can rapidly develop resistance because it has a short generation time, hi gh fecundity, and a haplodiploid breeding system (Jensen 2000). Spinosad resistance in F. occidentalis has been detected in many parts of the world (Da h and Tunc 2007, Bielza et al. 2007). Resistance in F. occidentalis seems to be polyfactorial, involv ing: reduced penetration of the exoskeleton, increased detoxification by P450-monooxygenases, esterases, and glutathione S -transferases (GSTs), altered, in sensitive, and increased AChE, and knockdown resistance (insensitive sodium channels) (Jensen 2000). The resistance appears to be unstable under field condition s (Bielza et al. 2008) and can be managed by minimizing the use of insecticides and using strategies that take resistance mechanisms and cross-resistance into consideration (Bielza 2008). Spinetoram (Dow Agroscienc es, Indianapolis, IN), a new spinosyn, was effective in controlling F. occidentalis, F. bispinosa and F. tritici on tomatoes in north Florida. Like spinosad, spinetoram is a fermentation product of the soil bacterium S. spinosa It has very low toxicity to many beneficial insects, humans, and the environment (Srivastava et al. 2008). During the course of th is project, spinetoram was registered for use in blueberries as Delegate TM ; in large part due to efficacy trials conducted as part of the project (see chapter 7). For these reasons, a part of the ongoing IPM research on fl ower thrips control in blueberries has begun to focus on finding ot her effective reduced-risk insecticides. Arvalo-Rodriguez (2006) found that Coragen 2SC (Rynaxypyr) (DuPont, Wilmington, 34

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DE) showed some control of F. bispinosa in Florida blueberries. Rynaxypyr is a reduced-risk insecticide with a novel mode of ac tion. It is a ryanidine receptor agonist, causing the release of Ca 2+ from muscle cells. The insect s lose the ability to regulate muscle function and die via muscle paralysis (Ribbeck 2007). It shows no toxicity toward non-target organisms (Marchesini et al. 2008), including bees, no phytotoxicity, and shows some translaminar activity (Ribbeck 2007). Surround WP (kaolin clay) (Engelhard Corporat ion, Iselin, NJ) has also shown promise for flower thrips control in bl ueberries. It was the only compound that reduced thrips numbers in a field study in Flori da in 2003 (Liburd and Finn 2003). Spiers et al. (2005) found that it reduced the number of flower thrips by half in rabbiteye blueberries, was nontoxic to pollinating bees, and showed no phy totoxic effects. However, it is white in color when it dries and this can attract la rge numbers of adult thrips from surrounding areas (Arvalo-Rodriguez 2006). Biological Control Predators Members of 23 families of insects dist ributed among 8 orders and 9 families of mites have been reported to prey on thrips (Arvalo-Rodriguez 2006). The most commonly studied predators are Orius insidiosus Say (Hemiptera: Anthocoridae) and various Amblyseius spp. (Acari: Phytoseiidae). Orius insidiosus is an important predator of thrips on field grown peppers in Florida. It can significantly suppress populations of F. bispinosa, F. occidentalis, and F. tritici the three flower thrips species found in the pepper flowers (Funderburk et al. 2000). However, O. insidiosus at the rate of 10 adults per plant bi-weekly did not reduce F. occidentalis numbers to economically acceptable levels on greenhouse tomatoes (Shipp and W ang 2003). Reitz et al. (2006) determined 35

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that although O. insidiosus can prey on both F. bispinosa and F. occidentalis it preferentially captures F. occidentalis. This may be due to the fact that F. bispinosa can evade predation better than F. occidentalis because it is smaller and more active. Orius insidiosus is mass reared and sold by Koppert Biological Systems (Romulus, MI). Amblyseius cucumeris (Oudemans) is also available from Koppert. In contrast to O. insidiosus, A. cucumeris significantly reduced F. occidentalis numbers to economically acceptable levels on gr eenhouse tomatoes (Shipp and Wang 2003). Hoy and Glenister (1991) determined that inoculative releases of A. barkeri (Hughes) and A. cucumeris could provide control of onion thrips, Thrips tabaci Lindeman, on field-grown cabbage in the northwest ern United States. Other control tactics, particularly pesticide applications, can impact the effectiveness of predators. Reitz et al. ( 2003) found that UV-reflective mulch reduced both the abundance of O. insidiosus and early season F. occidentalis adults. They also found that spinosad was the leas t disruptive insecticide towards O. insidiosus compared to esfenvalerate and acephate. A co mbination of predatory mites { A. cucumeris and Hypoaspis aculeifer (Canestrini)} and soil applied NeemAzal-U (17% azadirachtin) was highly effective in controlling F. occidentalis on beans ( Phaseolus vulgarus L.) (Thoeming and Poehling 2006). This emphasizes the importance and need of integrated control tactics. Unfortunately, biological control of flow er thrips in Florida blueberries with predators has proven unsuccessful. Arvalo et al. (2009) released O. insidiosus and A. cucumeris singly and in combination as both prev entative and curative releases. Neither the preventative nor curative releases of any treatment reduced thrips numbers below 36

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those found in the control. The shortness of the flowering season in blueberries may not give these natural enemies enou gh time to establish and control thrips populations in Florida blueberries. Entomopathogenic fungi Several species of entom opathogenic fungi have been shown to attack various species of thrips (Ekesi et al. 1998). Beauveria bassiana ( Bals.-Criv. ) Vuill. is a promising control agent for Thrips palmi (Castineiras et al. 1996), T. tabaci Lindeman (Jung 2004), F. intonsa (Trybom), F. occidentalis (Pergande), T. coloratus Schmutz, and T. hawaiiensis (Abe and Ikegami 2005). Thrips are infected by B. bassiana when they come into contact with conidia. These asexual, nonmotile spores stick to th e insects integument, where they germinate and eventually penetrate into the insects body cavity. The resulting infection kills the insect in 3-7 days (Bradley et al. 1998). Unde r optimal temperature and relative humidity an epizootic can occur, which is when a high percentage of a thrips population becomes infected, causing significant reductions in the population size (Murp hy et al. 1998). Along with their ability to cause epi zootics, entomopathogenic fungi like B. bassiana have several desirable characteristics. One advantage the fungi have over traditional biological control is that they can be applied using standard spray equipment as long as adequate coverage is achieved (Murphy et al. 1998). Other advantages include host specificity and low toxicity to wards non-target organisms (Murphy et al. 1998, Jacobson et al. 2001). Yet another adv antage is that bumble bees can vector fungal conidia to crops in greenhouses with no adverse effe cts on the bumble bee colonies (Al-mazraawi et al. 2006). 37

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Like all living organisms, entomopathogenic fungi have an optimum temperature and humidity range. Optimum temperature can vary between 15C and 30C depending on species and strain. Optimum humidity for B. bassiana is reported as 95%, but this can vary with strain (Ekesi et al. 1999). The use of entomopathogenic fungi for flower thrips control in blueberries is not likely to be effective in Florida because t he blueberry flowering season occurs in January and February. The cool temperatures and lower relative humidity would most likely prevent an epizootic from occurring. Entomopathogenic nematodes Frankliniella occidentalis is susceptible to several species of steinernematid and heterorhabditid nematodes (Georgis et al 2006). Previous studies have shown promising results with foliar applications on ornamental plants, wh ich target mainly larval and adult F. occidentalis Frequent applications ut ilizing an optimum spray volume, a wetting agent, and an adjuvant are essential for suppression of the pest (Georgis et al. 2006). However, Rutt enhuns and Shipp (2005) found that only F. occidentalis propupae and pupae were susceptible to Steinernema feltiae (Filipjev). Clearly, more research is needed before these nematodes become a viable control tactic. Geographic Information Systems (GISs) a nd Geostatistics in Pest Management Until recently, studies of the spatial dist ribution of insect populations have been limited to using various dispersion indices (F aris et al. 2003, Florez and Corredor 2000, Liebhold et al. 1993, Midgarden et al. 1993, Park and Tollefson 2005, Schotzko and OKeeffe 1990, Wright et al. 2002). However, these dispersion indices only describe the frequency distribution of a set of samples; t hey do not take into account the spatial 38

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relationship of the points (Midgarden et al. 1993). With the advent of Geographic Information Systems ( GISs) and geostatistics into the wo rld of insect ecology, these spatial relationships can now be studied (Liebhold et al. 1993). A GIS is a computer system that can assemble, stor e, manipulate, and display geographically referenced data such as insect densities, crop type, soil type, soil moisture, etc. (Liebhold et al. 1993). Each data set can be used to form a map layer or theme. Collections of themes from similar areas form a GIS database. Thus, the GIS is a powerful tool for analyzing spatial interactions within and am ong these spatially referenced data themes (Liebhold et al. 1993). Geostatistics provide the tools to charac terize and model spatial patterns (Liebhold et al. 1993). The cornerstone of geostatist ics is called the variogram (Webster and Oliver 2001). To construct a variogram, the semivariance of each pair of data points in a data set must be calculated. A semivariance is defined as of the average squared difference between data values at the same separation distance. A semivariogram plots the semivariance on the y-axis and the spec ified distance between sample pairs, the lag, on the x-axis (Wright et al. 2002). Since it is very difficult to fit a model to a semivariogram where each indi vidual semivariance is plott ed, the semivariance is averaged for each of several lags (Webste r and Oliver 2001). This is expressed mathematically as (h) = {1/2m(h)} {z(x i ) z(x i + h)} 2 where (h) is the semivariance at lag h, m(h) is the number of data point pairs separ ated by lag h, and z(x i ) and z(x i + h) are the data values (z) a places separated by h (Webster and Oliver 2001). The important features of semivariograms ar e the sill, range, lag, and nugget (Fig. 2-1), which are defined as the value of the semi variance when it stops increasing, the 39

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distance at which spatial independence is reached, the distance between sample pairs, and the semivariance value when x = 0 respectively. The nugget variance is a combination of measurement error and variat ion over distances less than the shortest lag distance sampled for all continuous variables (Webster and Oliver 2001). Semivariograms have been used to examine and describe the spatial relationship of several corn pests, including western co rn rootworm adults on yellow sticky traps in corn (Midgarden et al. 1993), corn rootworm in jury to corn (Park and Tollefson 2005), and European corn borer larvae and their dam age in whorl stage corn (Wright et al. 2002). Semivariograms have also been used to examine and describe the spatial relationships of three species of Xylella fastidiosa (Wells) sharpshooter vectors on citrus (Paulo et al. 2003) and of Lygus hesperus (Knight) in lentils (Schotzko and OKeeffe 1990). Florez and Corredor (2000) used semivariogram along with other geostatistical analyses to examine the spatial dependence of F. occidentalis in a covered strawberry crop at Bogota plateau. Spat ial dependence was found in 3 of 12 sampling weeks. They found that although thrips col onies were aggregated at first, over time the pattern changed toward a random pattern. This change was caused by thrips movement to neighboring quadrants. The advent of geostatistics has also brought with it more sophisticated interpolation tools. Inter polation allows researchers to estimate the continuous properties of something in the environment from a finite number of sampled points (Webster and Oliver 2001). Four commonly used interpolation techniques are natural or nearest neighbor, local average, inverse distance weighting (IDW), and kriging (Ess and Morgan 2003). Natural neighbor is the simplest interpolation method. The value at an 40

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unknown point is set equal to the value of the nearest sample point (Ess and Morgan 2003). Local average uses a simple average of known values around the unknown point to predict the value at the unknown point. Eit her a fixed number of points or all of the points within a fixed distanc e are used in the average (E ss and Morgan 2003). IDW is, in effect, a weighted average. Sample point s closer to the unknown point are given a higher weight than those fart her away (Ess and Morgan 2003). Kriging is a geostatistical interpolation method. Semivariogram models are used to predict values at unsampled locations. Or dinary kriging is the most common kriging method used in most applications (Webster and Oliver 2001). In ordinary kriging, the overall mean of the populati on is assumed to be unknown. Like IDW, ordinary kriging uses a weighted average to estimate unknow n values. However, the weights are based upon the semivariogram model. Fig. 2-1. Example of an ideal semiva riogram with a nugget value of zero. 41

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CHAPTER 3 EXAMINING THRIPS DISPERSAL FROM ALTERNATE HOSTS INTO SOUTHERN HIGHBUSH BLUEBERRY PLANTINGS Introduction A complex of flower thrips species caus es injury to southe rn highbush (SHB) blueberries ( V. corymbosum L. x V. darrowi Camp) in Florida (Arvalo-Rodriguez 2006). Frankliniella bispinosa (Morgan) is the most common species, accounting for approximately 90% of the adult thrips colle cted from both traps and flowers (Arvalo and Liburd 2007). Flower thrips feed and reproduc e on all parts of developing blueberry flowers. The resulting injury can be magnified into scars when the fruit form, which make the fruit unsalable on the fresh ma rket (Arvalo-Rodriguez 2006). Thrips move into crops from other cultiv ated plants that flower earlier and from wild plant species that also serve as hosts (Chellemi et al. 1994, Topanta et al. 1996). Chellemi et al. (1994) found that 31 of 37 plant species adjacent to tomato fields contained thrips. Paini et al. (2007) found that F. bispinosa used two weedy species as reproductive hosts from May to August in nor th Florida. Cockfield et al. (2007b) found that native vegetation surrounding apple orchards supported F. occidentalis populations when the apple trees were not flowering. It is often difficult to det ermine the true host range of a particular thrips species because thrips will often alight and feed upon many plants on which they cannot reproduce (Mound 2005, Paini et al. 2007). For example, although F. fusca (Hinds), F. occidentalis (Pergande), and F. tritici (Fitch) are found on tomato plants in Florida and can cause injury, only F. occidentalis reproduces on the tomato plants (Salguera Navas et al. 1994). 42

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The objectives of this study were twofol d. 1) To examine blueberry plantings and adjacent fields for alternate hosts of thrips. 2) To examine thrips dispersal from these host plants into blueberry plantings. The hypot heses of this study were: 1) flowering plants support and sustain F. bispinosa populations when blueberry plants are not flowering and 2) thrips disperse into bl ueberry plantings from these flowering plants when the blueberries begin to flower. Materials and Methods Preliminary Plant Surveys In the first survey, flower samples from three of the mo st common flowering plants found at the Plant Science Research and Educ ation Unit (PSREU) in Citra, FL, were collected, which included cutleaf evening primrose ( Oenothera laciniata Hill), white clover (Trifolium repens L.), and wild radish (Raphanus raphanistum L.). Eight primrose flowers, 6 clover flowers, and 25 wild radish flowers were collected and placed in vials containing 70% ethanol. Thrips adults and larv ae were sampled from the flowers using the shake and rinse method developed by Ar valo and Liburd (2007). In this method, each vial was shaken vigorously for 1 min. T hen the contents of the vial were emptied onto a metal screen with 6.3 x 6.3-mm open ings placed over a 300-ml white polyethylene jar. Flowers were gently open ed and rinsed with water. The rinsate was then examined under a dissecting microsc ope. The numbers of thrips and other arthropods present were recorded. The thrips and other arthropods were stored in 100cc glass vials. The flowers on the screen were emptied into another 300-ml polyethylene jar containing 10 ml of water. On ce the lid was placed on the jar, the jar was shaken vigorously for 1 min. The ri nse procedure was repeated as before except that the flowers were rinsed with 70% ethanol. If thrips were found in the second rinse 43

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water, the procedure was repeat ed for a third time (shaking the flowers in 70% ethanol and rinsing with water). Thrips adults were i dentified to species using a key developed for Florida SHB blueberries by Arvalo et al. (2006). Thrips that did not match the character descriptions in the key were sent to the Division of Plant Industry in Gainesville, FL. fo r identification. In the second survey, several flowering pl ant species within the 0.52-ha blueberry planting and the area su rrounding it at the Citra PSREU were flagged and sampled to determine whether or not they were suitable hosts for F. bispinosa For the purposes of this study, a suitable host was defined as one in which F. bispinosa reproduces and is abundant. Plants were identified to genus and species (if possible). Ten 27-m transects were taken. Two were on the border of the blueberry field and eight were within the field (Fig. 3-1). Floweri ng plants within a 0.6-m (2-ft) radius were sampled every 3-m (10-ft). The height and ma ximum width of the plants and percent coverage were measured. Plant samples were collected in small press and seal bags and brought back to the laboratory for identification. Samples were taken during the first and th ird full week of the two months prior flowering and during the two m onths of the blueberry flower ing season. Twenty flowers were collected from each plant species and placed in 50-ml plastic vials containing 70% ethanol. If less than 20 flowers were present, t hen all available flowers were collected. The samples were brought back to the labora tory at the University of Florida in Gainesville, FL. The shake and rinse method was used to collect the thrips from the flowers. Adults and larvae were counted and adults were identified to species as detailed previously. 44

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Flower samples were also collected from an adjacent strawberry field to the west of the blueberry planting and from the blueberry bushes themselves. Ten strawberry flowers were collected from each of f our rows. This was done once a month in December, January, and February. Four blueberry flower clusters (~ 20 flowers) were collected from each plot on each sample collection date. Field Study This study was conducted at a farm in Winds or, Florida, during the spring of 2009 and 2010. White clover, Trifolium repens L., grows in the grassy areas all over this farm. The study area consisted of a fi eld of white clover and part of a large blueberry planting on the farm that contained plants approximat ely 7 years in age. In 2009, six sampling sites in a 625-m 2 area in the clover and 12 sampling sites in the blueberry planting, in four rows of three sites in a 2400-m 2 area, were selected. Four traps were placed in the corners of the clover sampling area and the other two were placed in the center 8-m apart. Traps were spaced 10-m apart in each blueberry row and the rows were 15-m apart. In 2010, the setup was expanded to include ten sampling sites in the clover (660m 2 ) and four rows of five sites (2464-m 2 ) in the blueberry planting. All of the traps in the clover were spaced 10-m apart. The traps in the blueberry rows were spaced as in 2009. The rows were labeled 1 to 4, with 1 closest to the clover field and 4 farthest away from it. Each row was considered a treatment. In 2009, white sticky traps (Great Lakes IPM, Vestaburg, MI) were set out every week and collected weekly for five weeks from January 31 to March 5 in the clover and blueberries. In 2010, traps were set out ev ery week and collected weekly for seven weeks from Feb. 4 to March 25. When the traps were repl aced, flower samples were 45

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collected from both the clover and blueberries adjacent to traps. Three to five clover flowers and four to five bluebe rry flower clusters (~20-25 flowers) were collected each week. The treatments, which included the clover only in the sticky trap data set, were compared each week using a one-way analysi s of variance (ANOVA) (SAS Institute 2002) and means were separated using the leas t significant differences (LSD) test. Sticky trap data (x) were log 10 x transformed to meet the assu mptions of the analysis. In 2009, the log 10 (x +1) transformation was also used for thrips adults per flower (x), while thrips larvae per flower were transformed using the equation 1/ (thrips per flower +1). For the 2010 flower sample data, transfo rmation was not enough to cause the data to meet the ANOVA assumptions. Therefor e, the nonparametric Friedman, KendallBabington Smith test (Hollander and Wolfe 1999) for general alternatives in a randomized complete block design was used to analyze the data. Results Preliminary Plant Surveys In the first survey, both adults and larvae were collected from the clover and wild radish flowers, while only adults were collect ed from the primrose fl owers (Fig. 3-2). All adults collected were F. bispinosa Twelve different species of plants were found in the blueberry planting during the second survey (Table 3-1). Of these, 8 fl owered at some point during the sampling period and thrips were sampled from 3 {Carolina geranium ( Geranium carolinianum L.), hairy indigo ( Indigofera hirsuta L.), and pusley ( Richardia sp.)}. Thrips were also found in the blueberry and strawberry flowers. 46

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Both thrips adults and larvae were found in the Carolina geranium, pusley, strawberry, and blueberry flowers (Fig. 3-3). All of the adults found in the Carolina geranium were F. bispinosa while F. fusca (Hinds) and Haplothrips graminis Hood were found in the pusley and stra wberry flowers. Only H. graminis adults were found in the hairy indigo flowers. Most of the thrips adults in the blueberry flowers were Thrips species, either T. hawaiiensis (Morgan) or T. pini Karny. Frankliniella bispinosa F. fusca Franklinothrips sp., and H. graminis were also present in the blueberry flowers in small numbers. Field Study 2009 On Feb. 12, there were significantly more thrips per trap in row 3 compared with the clover, row 1, and row 4 ( F = 3.92, df = 4, 17, P = 0.0267, Fig. 3-4A, B). There were no significant differences in thrips adults (all F 1.51, df = 3, 11, P 0.29) or thrips larvae (all F 1.45, df = 3, 11, P 0.30) per flower among rows on any sampling date (Fig. 3-5A, B). Thrips adults and larvae were present in the clover field throughout the blueberry flowering period (Fig. 3-6). Larval numbers remained low throughout the flowering period, while adult numbers increased as the flowering period progressed. In the clover and row 2, all of the thrips sampled were F. bispinosa In rows 1 and 4, 96% of the thrips sampled were F. bispinosa and 2% were T. pini The remaining 2% were T. hawaiiensis in row 1 and Franklinothrips sp. in row 4. In row 3, 98% of the thrips sampled were F. bispinosa The remaining 2% were Franklinothrips sp. Field Study 2010 On Feb. 11, there were significantly higher numbers of thrips per trap in the clover field compared with rows 1 and 4 ( F = 3.12, df = 4, 29, P = 0.0327, Fig. 3-7A, B). On 47

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Feb. 25, there were significant ly more thrips per trap in rows 2 and 3 compared with the clover field ( F = 2.89, df = 4, 29, P = 0.0429). On March 11, t here were significantly higher numbers of thrips per trap in rows 2, 3, and 4 compared with row 1 and the clover field ( F = 5.95, df = 4, 29, P = 0.0017). On March 25, t here were significantly higher numbers of thrips per trap in row 3 compared with all of t he other treatments and in row 4 compared with row 1 and the clover field ( F = 6.86, df = 4, 29, P = 0.0007). There were no significant differences in thrips adults (all S 6, k, n = 5, 4, P > 0.1, Fig. 3-8A) or larvae (all S 6.43, k, n = 5, 4, P 0.09, Fig. 3-8B) per flower on any sampling date. Thrips adults were present in the clover flowers on Feb. 11, March 18, and March 25 (Fig. 3-9). In cont rast, only a single larva was collected from the clover flowers on Feb. 18. As in 2009, most of the thrips collected during the blueberry flowering period in 2010 were F. bispinosa All of the thrips sampled from rows 2, 3, and 4, 82 % of those sampled from row 1, and 67% of thos e sampled from the clover were F. bispinosa Several Franklinothrips sp. and a single Limnothrips sp. that was caught during the first week of sampling made up the rema ining 18% of row 1. A single F. fusca and 3 unknown thrips made up the remaining 33% found in the clover. Discussion Flower samples were collected from Ca rolina geranium, hairy indigo, narrowleaf cudweed ( Gnaphalium falcatum Lam.), oldfield toadflax ( Nuttallanthus canadensis (L.)), pusley, spurge ( Euphorbia sp.), thistle (Circium spp.), white clover, and wild radish. It appears that only Carolina geranium, white clover, and wild radish are reproductive hosts of F. bispinosa during the sampling period due to presence of immature stages. Northfield et al. (2008) also found that white clover and wild radish are reproductive 48

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hosts of F. bispinosa especially in the spring (April June). In contrast, Paini et al. (2007) found only adult F. bispinosa on wild radish (white clover was not sampled in this study). Carolina geranium was not sampled in either of these studies. Cutleaf evening primrose appears to be only a feeding host, since no larvae were found in the flowers. Several other species of thrips were f ound on other plants that flowered during the sampling period. Hairy indigo had only H. graminis adults, which are predatory and may have been feeding on the large number of aphids also present in the flowers (data not shown). Haplothrips graminis adults were also frequently found in the pusley flowers. A single F. fusca adult was also found in the pusley flow ers, as were a number of thrips larvae. Whether the H. graminis were feeding on the thrips larvae or other insects present in the flowers is not known. The same two species of adult thrips and a few thrips larvae were also found in the strawberry flowers. In 2009, the thrips population in the clov er appeared to develop at the same time as the population in the blueberry planting. Two extreme cold events, one in late January and the second in early February, may have contributed to this population growth pattern. The cold may have reduced the thrips population in both the clover and blueberry flowers to very low le vels, which then rebounded together. The difference in thrips per trap occurr ed on Feb. 12, approximately 1 week after the second extreme cold event. Row 3, whic h had higher numbers of thrips compared with rows 1, 4, and the clover, is in the cent er of the sampled blue berry block. It is possible that the thrips were better sheltered from the cold there. Thrips numbers were low throughout the 2010 SHB blueberry flowering season. Thrips adults were collected from the blueber ry flowers in low numbers throughout the 49

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flowering season, but the population did not begin to increase until March 11. In the clover flowers, a single adult unknown was colle cted on Feb. 11. Thrips adults were not found in clover flowers again until two unknowns and a F. bispinosa were collected on March 18. All of the adults collected from the clover flowers on March 25 were F. bispinosa with the exception of a single F. fusca Thrips larvae were not collected from blueberry flowers until March 18 and the only larvae collected from the clover flowers was found on Feb. 18. The flowering season itself began later t han the average and was extended till the end of March. Both of these factors were mo st likely due to the extended extreme winter temperatures that occurred during January and February of 2010 (FAWN 2010). Despite their low numbers, there were some statistically significant differences in thrips per trap on Feb. 11 and 25 and March 11 and 25. As in the previous year, thrips numbers were higher in the middle of the fiel d. However, in 2010, they remained higher instead of equalizing as occurred in 2009. From these studies, it woul d appear that clover is no t a significant source of F. bispinosa in SHB blueberry fields. This is s upported by Northfield et al. (2008) who found that F. bispinosa uses white clover as a reproductive host in the spring, particularly in April and May. Since they are found almost exclusively in flowers (Northfield et al, 2008), F. bispinosa may move from one or a few hosts to different hosts as they flower. Frankliniella occidentalis exhibits this pattern of behavior in Washington apple orchards (Cockfield et al. 2007b). 50

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Further research is needed to determi ne which plants are sources of F. bispinosa for SHB blueberry plantings. Controlling these plants could reduce flower thrips numbers in blueberry bushes. 6 8MSSHEJ8SHMSJE 12 7SEMJSH7JSEMSH 6ESHSHMJ6SHEJSM 5 5JMESSH5JSHMES 11 4MSSHEJ4SHMSJE 3SEMJSH3JSEMSH 4 2ESHSMJ 2SHEJSM 10 1JMESSH1JSHMES row PLOT 2 row PLOT 4 rabbiteyegrapes 8ESMJSH8JESMSH 3 7SMSHEJ7SHSMJE 9 6MJESSH6JMSHES 5SHESMJ5SHJESM 2 4ESJSHM 4JESMSH 8 3SMSHEJ3SHSMJE 2MJESSH2JMSHES 1 1SHESMJ1SHJESM 7 rep row PLOT 1 row PLOT 3 rep E=EmeraldJ=JewelM=MillenniaSH=Spring HighS=Star Each letter represents 5 plants FENCE Fig. 3-1. Locations of transects (arrows) in blueberry planting. (E = Emerald, J = Jewel, M = Millennia, S = Star, and SH = Spring High) 0 0.5 1 1.5 2 2.5 3 3.5 4 Cutleaf evening Primrose White CloverWild RadishNumber of thrips per flower Larvae F. bispinosa Fig. 3-2. Numbers of each thrips species pe r flower collected from each plant during the first survey. 51

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0 0.02 0.04 0.06 0.08 0.1 0.12 0.14blue b erry g e r a nium h air y i n dig o pusley s t r a wb er r yThrips per flower larvae T. pini T. hawaiiensis H. graminis Fr. sp. F. fusca F. bispinosa Fig. 3-3. Numbers of each thrips species pe r flower collected from each plant during the second survey. 0 50 100 150 200 2506-Feb 13-Feb 2 0 -F e b 27F e b 6-MarDateAverage thrips per trap Clover Row 1 Row 2 Row 3 Row 4 A 12-Feb0 10 20 30 40 1Average thrips per trap Clover Row 1 Row 2 Row 3 Row 4a ab b b b B Fig. 3-4. A) Average thrips per trap in each treatment on each sampling date in 2009. Circled data indicate significant diffe rences. B) Average thrips per trap on Feb. 12, 2009. Means with the same letter are not significantly different from each other at P < 0.05. Error bars indicate standard error of the mean. 52

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0 0.5 1 1.5 26-Fe b 13 Feb 20-Feb 2 7-Fe b 6-M a rDateAverage thrips adults per flower row 1 row 2 row 3 row 4 A 0 0.5 1 1.5 26-Fe b 13-Feb 20 F e b 2 7 -Feb 6-Ma rDateAverage thrips larvae per flower row 1 row 2 row 3 row 4 B Fig. 3-5. Average thrips A) adults and B) larvae per flower on each sampling date in 2009. Error bars indicate st andard error of the mean. 0 1 2 3 46-Feb 13-Feb 20-Feb 27-Feb 6-MarDateAverage thrips per flower Adults Larvae Fig. 3-6. Average thrips per flower in t he clover field on each sampling date in 2009. Error bars indicate standar d error of the mean. 53

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0 10 20 30 40 5011-F e b 18-F e b 25 Fe b 4-Ma r 11-Mar 18 Mar 25 MarDateAverage thrips per trap Clover Row 1 Row 2 Row 3 Row 4a a a a b b b c c bc A 0 2 4 6 8 1011 F e b 18 F e b 25-Fe b 4 M arDateAverage thrips per trap Clover Row 1 Row 2 Row 3 Row 4 a ab ab b b a a ab ab b B Fig. 3-7. Average thrips per trap A) thr oughout the flowering period and B) during the first 4 weeks of the flowering period (indicated by the box in A) in 2010. Treatments with the same letter are not si gnificantly different from each other at P = 0.05. Error bars indicate standard error of the mean. 54

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0 0.05 0.1 0.15 0.2 0.2511-Feb 1 8 -Feb 2 5-F eb 4-Mar 1 1-M a r 1 8-M a r 25-MarDateAverage thrips adults per flower Row 1 Row 2 Row 3 Row 4 A 0 0.05 0.1 0.15 0.2 0.251 1-Feb 18-Feb 2 5 -Feb 4-M ar 1 1-Ma r 18-Mar 2 5-M arDateAverage thrips larvae per flower Row 1 Row 2 Row 3 Row 4 B Fig. 3-8. Average thrips A) adults and B) larvae per flower on each sampling date in 2010. Error bars indicate st andard error of the mean. 0 0.1 0.2 0.3 0.4 0.5 0.611 F eb 18 F eb 25-Feb 4 Mar 11-Mar 1 8-M ar 25-M a rDateAverage thrips per flower adults larvae Fig. 3-9. Average thrips per flower in t he clover field on each sampling date in 2010. Error bars indicate standar d error of the mean. 55

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Table 3-1. Common and scientific names of the plants found in the blueberry planting each month NovemberDecemberJanuaryFebruary March Carolina geranium Geranium carolinianum L. Carolina geranium Geranium carolinianum L. Carolina geranium Geranium carolinianum L. Carolina geranium Geranium carolinianum L. Carolina geranium Geranium carolinianum L. coffee senna? Senna occidentalis L. hairy indigo Indigofera hirsuta L. narrowleaf cudweed Gnaphalium falcatum Lam. narrowleaf cudweed Gnaphalium falcatum Lam. narrowleaf cudweed Gnaphalium falcatum Lam. hairy indigo Indigofera hirsuta L. narrowleaf cudweed Gnaphalium falcatum Lam. oldfield toadflax Nuttallanthus canadensis (L.) oldfield toadflax Nuttallanthus canadensis (L.) oldfield toadflax Nuttallanthus canadensis (L.) pennywort (dollarweed) Hydrocotyle umbellata L. pennywort (dollarweed) Hydrocotyle umbellata L. pennywort (dollarweed) Hydrocotyle umbellata L. pennywort (dollarweed) Hydrocotyle umbellata L. pennywort (dollarweed) Hydrocotyle umbellata L. pigweed? Amaranthus sp. pusley Richardia sp. pusley Richardia sp. pusley Richardia sp. pusley Richardia sp. pusley Richardia sp. thistle Circium spp. red sorrel Rumex Acetosella L. red sorrel Rumex Acetosella L. red sorrel Rumex Acetosella L. spurge Euphorbia sp. wandering cudweed Gnaphalium pensylvanicum Willdenow thistle Circium spp. thistle Circium spp. thistle Circium spp. thistle Circium spp. wandering cudweed Gnaphalium pensylvanicum Willdenow wandering cudweed Gnaphalium pensylvanicum Willdenow wandering cudweed Gnaphalium pensylvanicum Willdenow wandering cudweed Gnaphalium pensylvanicum Willdenow Highlighting indicates when plants were flowering and a question mark indicates uncertainty in identification. 56

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CHAPTER 4 EFFECTS OF BLUEBERRY VARIETY AND TREATMENT THRESHOLD ON THRIPS POPULATIONS Introduction Several species of flower thrips, in cluding the Florida flower thrips {Frankliniella bispinosa (Morgan)}, western flower thrips { F. occidentalis (Pergande)}, eastern flower thrips {F. tritici (Fitch)}, and Scirtothrips ruthveni Shull, have recently become known as pests of cultivated blueberries (Spiers et al. 2005). The three Frankliniella species are pests of both rabbiteye (RE), Vaccinium virgatum Aiton, and southern highbush (SHB), V. corymbosum L. x V. darrowi Camp, blueberries in Florid a (Liburd and Arvalo 2005). Frankliniella bispinosa is the key pest and by far the most abundant, while the others are occasional pests (Arvalo et al. 2006). They infest not only blueberries, but many other crop and non-crop host plant s (Arvalo et al. 2006). Flower thrips injure flowers in two wa ys. Larvae and adults feed on all parts of the flowers including ovaries, styles, petals, and developing fruit (Arvalo-Rodriguez 2006). This feeding injury can reduce the quality and quantity of fruit produced. Females also cause injury to fruit when they lay their eggs inside flower tissues. The newly hatched larvae bore holes in flower tissue when they emerge. The objectives of this study were to : a) determine the relationship between populations of thrips and yield in several diffe rent SHB varieties and b) to determine an action threshold for thrips in SHB blueberries. In Florida, several SHB varieties are grown together on the same farm. These varieties differ in fruit and flower characteristics and in the timing and length of flowering period (Williamson and Lyrene 2004). This may lead to differences in th rips numbers and thrips injury among the 57

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varieties. If this is the case, economic in jury levels may need to be developed for each variety or among varieties wit h similar flowering periods. Materials and Methods Citra PSREU This experiment was conducted at the Un iversity of Florida Plant Science Research and Education Unit in Citra, FL There were four 0.13 ha plots of SHB blueberries that contained eight rows of bl ueberry bushes. Five bushes of each variety are planted in each row. The experimental setup was a completely randomized block design with 12 replicates (four rows from each plot) of four (2007) or three (2008) varieties. In 2007, four Southern Highbush varieties: Emerald, Jewel, Millennia, and Star were sampled. In 2008, only Emerald, Jewel, and Millennia were sampled because many of the Star plants were small and prod uced too few flowers to provide consistent samples. There were five plants per variety in each replicate. The plants were approximately four years old in 2007. Four sticky traps (Great Lakes IPM, Vest aburg, MI) were placed in each replicate (one per variety for a total of 48 for the experiment). The traps were hung from the center plant in each variety and were repl aced weekly. Each week, 10 flowers were sampled from the middle bush and placed in 50-ml plastic tubes containing 15 ml of 70% ethanol. In 2007, samples were collected for seven weeks from Jan. 29 until March 12. In 2008, samples were collected for fi ve weeks from Feb. 14 until March 14. The traps and flower samples were brought back to the Small Fruit and Vegetable Laboratory at the University of Florida in Gai nesville where the number of thrips per trap and per flower was counted. Flowers were sa mpled using the shake and rinse method developed by Arvalo and Liburd (2007). Adult thrips were identified to species using a 58

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key developed for Florida SHB blueberries by Ar valo et al. (2006). Thrips that did not match the character descriptions in the key were sent to the Division of Plant Industry in Gainesville, FL for identification. At harvest time, 30 berries per variety in each replicate were examined for thrips injury and marketability. Ten berries were tak en from each of the three middle plants. The number of total injured and unmarketable fruit was divided by 30 to give proportion of total injured and unmarketable fruit per pl ant. Total injured fruit included both those that were still marketable and the unmarketable fruit. Average thrips per trap, average thrips larvae and adults per flower, and average proportion of injured and unmarke table fruit were transformed as necessary to meet the assumptions of the analysis and compared among varieties using a one way analysis of variance (ANOVA) test (SAS Institute 2002) Means were separated using the least significant difference (LSD) means separation te st. Thrips per sticky trap, thrips larvae per flower, and thrips adults per flower were analyzed by date. Data were also examined for a linear re lationship between numbers of thrips (larvae and adults) per flower pooled over all dates vs. proportion of total injured fruit per plant using lease squares regression in SAS (SAS Institute 2002). Hernando and Lake Counties Samples were taken from two commercial farms in Hernando Co., Florida, during early spring 2007 and one commercial farm in Hernando and another in Lake Co. in 2008. A 5-ha area on farm 1 in Hernando Co. wa s sampled only in 2007. The varieties on this farm are arranged in blocks of six to nine rows. A 2.5-ha area of the second farm in Hernando Co., farm 2, and of the Lake Co. farm was samp led. Farm 2 had alternating 59

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rows of different varieties. Blueberry plant s at the Hernando Co. farms were four to seven years of age, while those at the Lake Co. farm were only one year old. On each farm, the four most popular SHB varieties: Emerald, Jewel, Millennia, and Windsor were divided into three treatments: T100, T200, and an untr eated control. If the number of thrips per trap exceeded 200 in the T200 treatment or 100 in the T100 treatment, SpinTor 2 SC (spinosad) (Dow Agrosciences, Indianapolis, IN) was applied at the label rate of 0.438 L / ha. The 100 thrips per trap thre shold is only slightly higher than the economic injury level calculated by Arvalo-Rodriquez (2006) for rabbiteye blueberries when SpinTor is applied. The treatment thresholds encompassed a row of each of the varieties. There were three r eplicates containing each threshold/variety combination, which encompassed all of t he samples from the beginning, middle, and end of the rows. A sticky trap was placed in ea ch threshold/variety combination. Five flowers from each of two plants closes t to the trap were also sampled. In 2007, sticky trap samples were collect ed for six weeks beginning on Feb. 1 and 2 on farms 1 and 2, respectively Flower samples were collected until the majority of plants were in fruit set. On farm 1, both tr eatments were above threshold after the first week of sampling. Applications of SpinTor were made on Feb. 9 and Feb. 23. Thrips numbers on farm 2 remained below thres hold throughout the sampling period, so SpinTor was not applied. In 2008, sticky trap samples were collected for four weeks from the Lake Co. farm and for three weeks from the Hernando Co. fa rm (farm 2 from 2007) beginning on Feb. 14 and Feb. 21, respectively. Flower samples were collected until the majority of plants 60

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were in fruit set. The number of thrips per trap did not exceed ei ther of the treatment threshold levels on any date, so SpinTor was not applied on either farm. Sticky traps and flower samples were sh ipped to the Small Fruit and Vegetable IPM Laboratory at the University of Florida in Gainesville, FL. The number of thrips per trap was counted and flowers were dissected under a dissecting microscope. Adult and larval thrips were counted and stored in 1 dram vials containing 70% ethyl alcohol. Adults were identified to species using a key developed for Florida SHB blueberries by Arvalo et al. (2006). Thrips that did not match the charac ter descriptions in the key were sent to the Division of Plant Industr y in Gainesville, FL for identification. At harvest time, 25 berries from the two previously sa mpled plants and from two adjacent plants were examined for thrips in jury and marketability. The number of total injured and unmarketable fruit from each samp le was divided by 25 to give proportion of total injured and unmarketable fruit per plan t. The proportions from the four samples were then averaged. Total injured fruit in cluded both those that were still marketable and the unmarketable fruit. Average thrips per trap exceeded the thre shold only on farm 1 in 2007. Therefore, average thrips per trap, average thrips larvae and adults per flower, and average proportion of total injured and unmarketabl e fruit per plant were transformed as necessary to meet the assumptions and compared among treatments and varieties using a two-way ANOVA test (SAS Institute 2002). If no interaction was present, main effects of both factors were compared using the LSD means separation test. If interaction was present, then simple effects were compared for whichever factor was 61

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significant. Thrips per sticky trap, thrips larvae per flower and thrips adults per flower were analyzed by week. Numbers of thrips per trap did not reach the threshold on farm 2 in 2007 or on either farm in 2008 so SpinTor was never applied. Therefor e, the previously described data sets were transformed as needed to meet the assumptions of ANOVA and varietal differences were analyzed using a one-way ANOVA. Means were separated using the LSD means separation test. Thrips per sticky trap, thrips larvae per flower, and thrips adults per flower were analyzed by week. Data from 2007 and 2008 were also examin ed for any linear relationship between numbers of thrips (larvae and adu lts) per flower pooled over all dates vs. total injured fruit per plant using Theil regression (Hollander and Wolfe 1999) in 2007 and least squares regression (SAS Institute, 2002) in 2008. Kendalls tau, a nonparametric correlation statistic (Hollander and Wolfe 1999) was also calculated for the 2007 data (Wessa 2008). Results Citra PSREU 2007 Traps There were significantly more thrips per trap in the Emerald and Millennia varieties compared with the Star variety ( F = 2.48, df = 3, 47, P = 0.052) on Feb. 5 (Fig. 4-1). On Feb. 12, there were signific antly more thrips per trap in the Emerald variety compared with all of the other varieties ( F = 8.33, df = 3, 47, P = 0.0003). Also, Jewel had significantly higher thrips per trap than Star. 62

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Flowers There were no significant differences among either thrips larvae or thrips adults per flower on any date (all F 2.47, df = 3, 47, P 0.08). Average thrips larvae per flower did not exceed 0.09 0.07 larvae on any date. Average thrips adults per flower did not exceed 0.15 0.07 adults on any date. There was a high diversity in adults thrips sampled from the flow ers. The majority of thrips were F. bispinosa (Table 1). Others spec ies sampled included F. fusca (Hinds), Franklinothrips sp., Haplothrips graminis Hood, Thrips hawaiiensis (Morgan), and T. pini Karny. Fruit Emerald and Jewel had a significantly higher proportion of injured fruit than Millennia and Star (F = 7.53, df = 3, 47, P = 0.0006, Fig. 4-2). Emerald also had a significantly higher proportion of unmarketable fruit then all of the other varieties ( F = 11.31, df = 3, 47, P < 0.0001). Simple linear regression did not show any relationship between thrips per flower and proportion of total injured fruit in any of the varieties (all R 2 0.03, all t 1.13, df = 11, P slope 0.28). 2008 Traps On Feb. 14, there were significantly more thrips per trap in the Emerald and Jewel varieties than in the Millennia variety ( F = 3.9, df = 2, 35, P = 0.036, Fig. 4-3). Flowers There were significantly more thrips la rvae per flower in the Emerald variety compared with the Jewel variety on Feb. 14 ( F = 3.37, df = 2, 35, P = 0.053) and 63

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compared with both other varieties on Feb. 22 ( F = 12.69, df = 2, 35, P = 0.0002) and March 8 ( F = 6.81, df = 2, 35, P = 0.0050, Fig. 4-4A). The Emerald variety also had significantly more thrips adults per flower compared with the other two varieties on Feb. 14 ( F = 7.2, df = 2, 35, P = 0.0039, Fig. 4-4B). In contrast, Jewel had significant ly higher numbers of thrips adults per flower compared with the other two varieties on March 14 ( F = 7.99, df = 2, 35, P = 0.0025). Percent of adult thrips species sampled from flowers was similar to 2007, with F. bispinosa comprising the majority (> 60%) of thri ps sampled (Table 4-1). Most of the remaining adult thrips were either T. hawaiiensis or T. pini Frankliniella fusca Franklinothrips sp., and H. graminis were also found. Fruit There were no significant differences in either proportion of injured ( F = 0.18, df = 2, 35, P = 0.83) or unmarketable ( F = 0.62, df = 2, 35, P = 0.55) fruit among varieties. Emerald, Jewel, and Millennia averaged 0. 14 0.02, 0.16 0.03 and 0.16 0.02 proportions of injured fruit, respectively. All three varieties averaged a 0.01 0.00 proportion of unmarketable fruit. Simple linear regression did not show any relationship between thrips per flower and proportion of total injured fruit in any of the varieties (all R 2 0.12, all t 1.57, df = 11, P slope 0.15). 64

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Hernando and Lake Counties 2007 Traps There were no treatment*variety interactions on any date after treatments were applied (all F 1.23, df = 6, 35, P 0.33). Therefore each factor was examined separately. There were significantly fewer thrips per trap recorded from t he 200 thrips per trap threshold treatment compared with the control on March 1 ( F = 4.1, df = 2, 35, P = 0.029, Fig. 4-5). On farm 1, Emerald had significantly higher numbers of thrips per trap compared with at least two of the other varieties on a ll sampling dates. There were significantly higher numbers of thrips per trap in the Em erald variety compared with the Jewel and Millennia varieties ( F = 7.18, df = 3, 35, P = 0.0013) on Feb. 1 (Fig. 4-6). Windsor also had significantly higher numbers of thrips per trap compared with Millennia on this date. Emerald had significantly higher numbers of thrips per trap t han all of the other varieties on Feb. 8 ( F = 11.27, df = 3, 35, P < 0.0001), Feb. 15 ( F = 5.71, df = 3, 35, P < 0.0043), Feb. 22 ( F = 22.65, df = 3, 35, P < 0.0001), and March 1 ( F = 11.58, df = 3, 35, P < 0.0001). Jewel and Windsor also had significantly higher numbers of thrips per trap than Millennia on Feb. 22. On Ma rch 8, Emerald had significant ly higher numbers of thrips per trap than Jewel and Windsor ( F = 4.07, df = 3, 35, P = 0.018). On farm 2, Emerald had significantly higher numbers of thri ps per trap than all other varieties ( F = 8.53, df = 3, 35, P = 0.0003) on Feb. 9 (Fig 4-7). On Feb. 16, Emerald had significantly higher numbers of thrips per trap compared with Jewel and Windsor. Also, Jewel had significantly more thrips per trap compared with Windsor ( F = 65

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16.27, df = 3, 35, P < 0.0001). On Feb. 23, both Emerald and Je wel had significantly more thrips per trap compared with Windsor ( F = 3.32, df = 3, 35, P = 0.033). Flowers There were no treatment*variety interacti ons in thrips larvae per flower on any date after treatments were applied (both F = 0.51, df = 6, 35, P = 0.79) on farm 1. There were no significant differences in thrips larvae per flower among treatments on any date (all P 0.11). In the T200 treatment, thri ps larvae per flower peaked at 2.2 0.3 larvae on Feb. 8, the day before SpinTor was applied. Thrips larvae per flower peaked in both the T100 and control treatment s on Feb. 15 at 2.2 0.4 and 2.4 0.4 larvae, respectively. However, there were significantly higher nu mbers of thrips larvae per flower in the Jewel variety compared with the other varieties ( F = 3.57, df = 3, 35, P = 0.029) on Feb. 8 (Fig. 4-8A). For thrips adults, there was no treatm ent*variety interaction on Feb. 15 (F = 0.78, df = 6, 35, P = 0.59). However, there was treatm ent*variety interaction on Feb. 22 (F = 3.42, df = 6, 35, P = 0.014). There were no significant differences in thrips adults per flower among treatments on any date (all F 1.42, df = 2, 35, P 0.26). Average thrips adults per flower did not rise above 1.2 0.3 adults on any date. However, the varietal trends in thrips adul ts per flower on farm 1 were similar to thrips per trap. On Feb. 8, there were signi ficantly higher numbers of thrips adults per flower in the Emerald variety compared with the Millennia and Windsor varieties ( F = 5.00, df = 3, 35, P = 0.0078, Fig. 4-8B). Emerald had significantly higher numbers of thrips adults per flower compared with a ll of the other varieties on Feb. 15 ( F = 10.32, df 66

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= 3, 35, P = 0.0001). Jewel had signifi cantly higher numbers of thrips adults per flower compared with Millennia on both of the above dates. On Feb. 22, main effects showed that Em erald had significantly higher numbers of thrips adults per flower compared with Jewel and Millennia ( F = 9.93, df = 3, 35, P = 0.0003). Jewel also had significantly higher numbers of thrips than Millennia. When simple effects are examined, Emerald has si gnificantly higher numbers of thrips adults compared with all of the other varieties in t he untreated control. There were no varietal differences in the T100 treatment. In contra st, Millennia had significantly fewer thrips adults per flower than all th ree of the other varieties in the T200 treatment. On farm 2, there were significantly more thrips larvae per flower in the Emerald variety compared with t he Windsor variety ( F = 3.70, df = 3, 35, P = 0.022) on Feb. 9 (Fig. 4-9A). On Feb. 16, both Jewel and Em erald had significantly higher numbers of thrips larvae compared with Millennia and Windsor ( F = 4.99, df = 3, 35, P = 0.0063). Emerald had significantly higher numbers of thrips larvae per flower compared with all of the other varieties on Feb. 23 ( F = 4.76, df = 3, 35, P = 0.0079). In contrast, Jewel and Windsor had significantly hi gher numbers of thrips larv ae per flower compared with Emerald and Millenn ia on March 2 ( F = 4.54, df = 3, 35, P = 0.0097). There were significantly more thrips adul ts per flower in the Emerald variety compared with the Jewel and Windsor varieties and significantly more thrips adults per flower in the Millennia variety compared with the Windsor variety ( F = 5.35, df = 3, 35, P = 0.0045) on Feb. 16 (Fig. 4-9B). On Feb. 23, Jewel and Windsor had significantly higher numbers of thrips adults per flower compared with Millennia ( F = 3.01, df = 3, 35, P = 0.046). 67

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Farm 1 had an unusually high number of T. hawaiiensis and T. pini present in the Emerald and Windsor varieties (Table 4-2). Frankliniella bispinosa was the dominant species found in the Jewel and Millennia varieties. A single H. graminis was found in the Windsor variety. In contrast, the majority of thrips adults sampled from flowers of all the varieties on farm 2 were F. bispinosa (Table 4-3). Franklinothrips sp., H. graminis T. hawaiiensis, and T. pini were also present. Fruit There were no treatment*variety interactions in proportion of total injured ( F = 0.47, df = 6, 35, P =0.82) or unmarketable ( F = 0.70, df = 6, 35, P =0.66) fruit on farm 1. Therefore, main effects were analyzed. Interestingly, there was a signific antly higher proportion of injured ( F = 5.72, df = 6, 35, P =0.0093) and malformed ( F = 3.53, df = 6, 35, P =0.045) fruit in the untreated control compared with the T 100 treatment (Fig. 4-10). There were no significant differences in either proportion of total injured or unmarketable fruit among vari eties on farm 1 (injured: F = 1.05, df = 3, 35, P = 0.39; unmarketable: F = 0.87, df = 3, 35, P = 0.57) or farm 2 (injured: F = 1.87, df = 3, 35, P = 0.16; unmarketable: F = 0.25, df = 3, 35, P = 0.86). On farm 1, there was an average proportion of total injured frui t of 0.09 0.02 and an average proportion of unmarketable fruit of 0.02 0.01 across varieties. On farm 2, there was an aver age proportion of total injured fruit of 0.05 0.01 and an average proportion of unmar ketable fruit of 0.02 0.002 across varieties. Nonparametric regression showed a signi ficant positive linear relationship between thrips per flower and total injured fruit in the Emerald ( = 0.41, C = 74, n = 18, 68

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P slope = 0.003), Jewel ( = 0.35, C = 56, n = 18, P slope = 0.024), Millennia ( = 0.25, C = 44, n = 18, P slope = 0.056), and Windsor ( = 0.30, C = 56, n = 18, P slope = 0.02) varieties (Fig. 4-11A-D). 2008 Traps On the Lake Co. farm, Emerald, Windsor and Jewel had significantly higher numbers of thrips per tr ap compared with Millennia ( F = 4.52, df= 3, 34, P = 0.0096) on Feb. 21 (Fig. 4-12). On Feb. 28, Windsor had significantly higher numbers of thrips per trap compared with Millennia ( F = 3.09, df = 3, 35, P = 0.041). Windsor had significantly higher numbers of thrips per trap compared with all of the other varieties on March 6 ( F = 13.68, df = 3, 35, P < 0.0001). On Hernando Co. farm 2, there were no sign ificant differences in thrips per trap among varieties on any date (all F 2.09, df = 3, 32, P 0.12). There were an average of 8.3 1.8, 4.2 1. 1, and 10.3 2.2 thrips per trap ov er variety on Feb. 21, Feb. 28, and March 6 respectively. Flowers On the Lake Co. farm, there were significant ly more thrips larvae per flower in the Emerald variety compared with the Millennia variety ( F = 3.4, df = 3, 34, P = 0.030) on Feb. 14 (Fig. 4-13A). There were significantly more thrips adul ts per flower in the Emerald variety compared with all of t he other varieties ( F = 16.41, df = 3, 34, P < 0.0001) on Feb. 14 (Fig. 4-13B). There were significantly more thrips adults per flower in the Windsor variety compared with the Jewel and Millennia varieties on Feb. 28 ( F = 6.17, df = 2, 21, P = 0.0086). 69

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On Hernando Co. farm 2, there were signifi cantly higher numbers of thrips larvae per flower in the Emerald (0.8 0.2 larvae) and Windsor (1.1 0.2 larvae) varieties compared with the Jewel (0.3 0.1 larvae) and Millennia (0.3 0.2 larvae) varieties ( F = 5.9, df = 3, 35, P = 0.0025) on Feb. 21. On Feb. 28, Windsor (0.05 0.01 larvae) had significantly higher numbers of thrips per flower than all of the other varieties (0 larvae) ( F = 6.32, df = 3, 22, P = 0.0037). There were no significant differences in thrips adults per flower among varieties on either date (both F 1.42, df = 3, 35, P 0.25). There was an av erage of 0.2 0.1 and 0.01 0.008 adults per flower across varieties on Feb. 21 and 28 respectively. All four varieties on the Lake Co. farm had high percentages of T. hawaiiensis and T. pini adults (Table 4-2). Most of t he remaining adult thrips were F. bispinosa Frankliniella fusca, Franklinothrips sp., and H. graminis were sampled occasionally. In contrast, F. bispinosa was the dominant thrips species sampled from Jewel, Millennia, and Windsor flowers on Hernando Co. farm 2 (Table 4-3). Mo st of the thrips sampled from the Emerald flowers were either T. hawaiiensis or T. pini A few Franklinothrips sp. were found in the Windsor variety. Fruit On the Lake Co. farm, Jewel had a signific antly higher proportion of injured fruit compared with all of the ot her varieties and Windsor had a significantly higher proportion of injured fruit com pared with Emerald and Millennia ( F = 15.41, df = 3, 35, P < 0.0001, Fig. 4-14A). Jewel also had a signi ficantly higher proportion of unmarketable fruit compared with all the other varieties ( F = 13.87, df = 3, 25, P < 0.0001). On Hernando Co. farm 2, Jewel and Windsor had a significantly higher proportion of injured fruit compared with Emerald and Millennia and Emerald had a significantly 70

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higher proportion of injured fr uit compared with Millennia ( F = 18.43, df = 3, 35, P < 0.0001, Fig. 4-14B). Jewel also had a signi ficantly higher propor tion of unmarketable fruit compared with all the other varieties ( F = 21.77, df = 3, 25, P < 0.0001). Simple linear regression, combining t he data from both farms, did not show any relationship between thrips per flower and proportion of total injured fruit in any of the varieties (all R 2 0.01, all t -1.08, df = 17, P slope 0.30). Discussion The only significant difference in thri ps numbers among treatment thresholds occurred with thrips per trap on March 1, 2007 on farm 1. By this date, flowers were only present on the Emerald variety. The la ck of effectiveness of the thresholds may have been caused by thrips from untreated areas of the farms recolonizing the treated rows. Funderburk and Stavisky (2004) note that F. bispinosa adults can quickly recolonize a treated area, making the app lication appear to be ineffective. The proportion of injured and unmarke table fruit data suggest that 100 thrips per trap is an effective threshold, but more research is needed to confirm this fact. Southern highbush blueberry variety does appear to influence thrips numbers. This was particularly true on the two Her nando Co. farms in 2007. Emerald frequently had significantly more thrips per trap and per flower than the other varieties. Millennia and Star tended to have the lo west numbers of thrips. This may be due to their flowering characteristics. Emerald, Jewel, and Millennia reach 50% open flowers around Feb. 16 in Gainesville, FL. Star and Wi ndsor reach 50% open flowers about a week later (Williamson and Lyrene 2004). Unlike t he other varieties, Emerald flowers uniformly. All of the varieties tested except Millennia reach petal fall around the same time. Millennia reaches petal fall 3 to 4 days earlier (Williamson and Lyrene 2004). The 71

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combination of flowering early and unifo rmly, when flower thrips are abundant, may make Emerald more attracti ve to flower thrips. The differences in thrips numbers among varieties were not as pronounced on the Lake and Hernando Co. farms in 2008 compared with 2007. There are several possible reasons for this difference. Firstly, sampli ng was initiated late and only a few weeks of data were collected. Secondly, only the week of Feb. 14 contained a complete data set for flowers from farm 1. M any plants had already reached petal fall by Feb. 21 on both farms. Therefore, there were several missing data points from Feb. 21 and 28. Thirdly, there were fewer thrips on the farms in 2008 compared with 2007. The differences in thrips numbers among varieties were also not as pronounced on the Citra PSREU farm compared with the Hernando and Lake Co. farms. The four varieties at the Citra farm are distri buted evenly among each other. This may be partially masking the effect of variety on thri ps numbers. In contrast, farm 1 in Hernando Co. has large blocks of a single variety. Fa rm 2 has an intermediate setup, with only a few rows of the same variety adjacent to each other. Further research is needed to confirm the hypothesis that arrangement of blueberry varieties affects thrips numbers. There were differences in fruit injury among varieties, but these did not appear to be related to differences in thrips numbers. There could be several reasons for this. The different varieties could have different levels of tolerance to flower thrips. It is also possible that some varieties are more susceptible to diseases than others. Lastly, the different species of thrips may differ greatly in their effect on the blueberry flowers and subsequent fruits. It has been shown that pepper s in Florida can tolerate high numbers 72

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of F. bispinosa and F. tritici but only a few F. occidentalis will cause significant injury (Funderburk 2009). The thrips complex in SHB blueberry flowers in Florida is dominated by F. bispinosa (Arvalo et al. 2006). Frankliniella bispinosa was the most common species sampled from all of the varieties at the Cit ra PSREU. A diversity of other species was also found. This diversity of species wa s probably due to the wide variety of crops grown at the research station. The two Hernando and Lake Co. farms, however differed from this norm. On farm 1 in 2007, the Millennia variety was the only one dominated by F. bispinosa Jewel and Windsor had high percentages of F. bispinosa T. hawaiiensis, and T. pini Emerald was dominated by the two Thrips species. On the Lake Co. farm in 2008, all four varieties were dominated by the two Thrips species. Farm 2 was less extreme in its differences from the expected. In 2007, only the Jewel variety had high percentages of the two Thrips species. Even so, the majority of thrips sampled from Jewel were F. bispinosa In 2008, the Emerald variety was dominated by the two Thrips species, while the other varieties were dominated by F. bispinosa Further research is needed to determine why the two Thrips species occurred in such high numbers on these three farms. Significant positive linear re lationships between thrips per flower and fruit injury were found in all four varieties from the Hernando Co. farms in 2007. Neither the Hernando and Lake Co. farms in 2008 nor the Cit ra PSREU in either year showed a relationship between thrips per flower and fr uit injury. This may be due to the low numbers of thrips present at t hese farms during these years. 73

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The results from these experiments show evidence that SHB blueberry varieties may attract different numbers of thrips and may have varying tolerance to thrips injury. If this is the case, then each variety would hav e a different Economic Injury Level (EIL). Since multiple varieties are grown on the sa me farm, the lowest EIL could be used to set the threshold level for the farm. 0.0 20.0 40.0 60.0 80.0 100.0 29-Jan5-Feb12-Feb19-Feb26-Feb5-Mar12-Mar19-Mar DateAverage thrips per trap Emr Star Mill Jwl a a ab b a b bc c Fig. 4-1. Average thrips per sticky trap re corded from each variety per week in 2007. Error bars represent standard error of the mean. Means with the same letter are not significantly differ ent from each other at the P = 0.05 level. 0 0.05 0.1 0.15 0.2 0.25 0.3 EmrJwlMillStar VarietyProportion of injured fruit total inj unmarketablea b b b A A B B Fig. 4-2. Proportion of inju red and unmarketable fruit sa mpled from each variety in 2007. Error bars represent standard error of the mean. Means with the same letter are not significantly diffe rent from each other at the P = 0.05 level. 74

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0.0 20.0 40.0 60.0 80.0 100.0 14-Feb24-Feb5-Mar15-Mar DateAverage thrips per trap Emr Mill Jwl a a b Fig. 4-3. Average thrips per sticky trap re corded from each variety per week in 2008. Error bars represent standard error of the mean. Means with the same letter are not significantly differ ent from each other at the P = 0.05 level. 0 0.1 0.2 0.3 0.4 0.5 14-Feb21-Feb28-Feb6-Mar13-Mar20-Mar DateAverage thrips larvae per flower Emr Mill Jwl a ab b a b b a b b A 0 0.1 0.2 0.3 0.4 0.5 14-Feb21-Feb28-Feb6-Mar13-Mar20-Mar DateAverage thrips adults per flower Emr Mill Jwl a b b a b b B Fig. 4-4. Average thrips A) larvae and B) adul ts per flower recorded from each variety per week in 2008. Error bars represent standard error of the mean. Means with the same letter are not significantly different from each other at the P = 0.05 level. 75

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0 200 400 600 800 1000 1-Feb8-Feb15-Feb22-Feb1-Mar8-Mar DateAverage thrips per trap T100 T200 Control a ab b Fig. 4-5. Average thrips per trap recorded from each treatment per week on farm 1 in 2007. Error bars represent standard error of the mean. Means with the same letter are not significantly diffe rent from each other at the P = 0.05 level. Arrows indicate the dates when SpinTor was applied. 0.0 500.0 1000.0 1500.0 1-Feb8-Feb15-Feb22-Feb1-Mar8-Mar DateAverage thrips per trap Emr Jwl Mill Win ab a bc c a b b b a a a b b b c b b b b b a b b ab Fig. 4-6. Average thrips per trap recorded from each variety per week on farm 1 in 2007. Error bars represent standard error of the mean. Means with the same letter are not significantly differ ent from each other at the P = 0.05 level. Arrows indicate the dates when SpinTor was applied. 76

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0.0 50.0 100.0 150.0 200.0 250.0 2-Feb9-Feb16-Feb23-Feb2-Mar9-Mar16-Mar DateAverage thrips per trap Emr Jwl Mill Win b b b a a ab b c a a ab b Fig. 4-7. Average thrips per trap recorded from each variety per week on farm 2 in 2007. Error bars represent standard error of the mean. Means with the same letter are not significantly differ ent from each other at the P = 0.05 level. 0.0 5.0 10.0 15.0 1-Feb8-Feb15-Feb22-Feb1-Mar DateAverage thrips larvae per flower Emr Jwl Mill Win a b b b A 0.0 3.0 6.0 9.0 12.0 15.0 1-Feb8-Feb15-Feb22-Feb1-Mar DateAverage thrips adults per flower Emr Jwl Mill Win a bc ab c a bc c b b ab a c B Fig. 4-8. Average thrips A) larvae and B) adul ts per flower recorded from each variety per week on farm 1 in 2007. Error bars represent standard error of the mean. Means with the same letter are not signifi cantly different from each other at the P = 0.05 level. Arrows indicate the dates when SpinTor was applied. 77

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0.0 0.5 1.0 1.5 2.0 2.5 3.0 2-Feb9-Feb16-Feb23-Feb2-Mar9-Mar DateAverage thrips larvae per flower Emr Jwl Mill Win a ab ab b a a b b a b b b a a b b A 0.0 0.5 1.0 1.5 2.0 2.5 3.0 2-Feb9-Feb16-Feb23-Feb2-Mar9-Mar DateAverage thrips adults per flower Emr Jwl Mill Win a ab bc c a a b ab B Fig. 4-9. Average thrips A) larvae and B) adul ts per flower recorded from each variety per week on farm 2 in 2007. Error bars represent standard error of the mean. Means with the same letter are not signifi cantly different from each other at the P = 0.05 level. 78

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0 0.05 0.1 0.15 0.2 ControlT100 T200 TreatmentAverage proportion of injured fruit Total Unmarketablea A ab AB b B Fig. 4-10. Proportion of injured and unmarketable fruit samp led from each treatment on farm 1 in 2007. Error bars represent st andard error of the mean. Means with the same letter are not significantly different from ea ch other at the P = 0.05 level. y = 0.0154x + 0.03 0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16 00.511.522.533.5 Average thrips per flowerAverage proportion of injured fruit A 79

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y = 0.004x + 0.04 0 0.02 0.04 0.06 0.08 0.1 0.12 00.511.522.533.5 Average thrips per flowerAverage proportion of injured fruit B y = 0.026x + 0.047 0 0.05 0.1 0.15 0.2 0.25 0.3 00.511.522. Average thrips per flowerAverage proportion of injured fruit 5 C y = 0.018x + 0.055 0 0.05 0.1 0.15 0.2 0.25 0.3 00.511.522.53 Average thrips per flowerAverage proportion of injured fruit D Fig. 4-11. Graphs showing av erage thrips per flower vs. average proportion of injured fruit, the Theil regression li ne, and equation for the A) Emerald, B) Jewel, C) Millennia, and D) Windsor varieties. Data from both farms were combined. 80

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0 15 30 45 60 75 90 14-Feb21-Feb28-Feb6-Mar DateAverage thrips per trap Emr Jwl Mill Win a a a b a b ab ab a b b b Fig. 4-12. Average thrips per trap recor ded from each variety per week on farm 1 in 2008. Error bars represent standard error of the mean. Means with the same letter are not significantly diffe rent from each other at the P = 0.05 level. 0 0.2 0.4 0.6 0.8 1 1.2 14-Feb21-Feb28-Feb DateAverage thrips larvae per flower Emr Jwl Mill Win a ab ab b A 0 0.2 0.4 0.6 0.8 1 1.2 14-Feb21-Feb28-Feb DateAverage thrips adults per flower Emr Jwl Mill Win a b b b a b b B Fig. 4-13. Average thrips A) larvae and B) adul ts per flower recorded from each variety per week on farm 1 in 2008. Error bars represent standard error of the mean. Means with the same letter are not signifi cantly different from each other at the P = 0.05 level. 81

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0 0.05 0.1 0.15 0.2 0.25 EmrJwlMillWin VarietyProportion of injured fruit total injured unmarketable a b c c a b b b A 0 0.05 0.1 0.15 0.2 0.25 EmrJwlMillWin VarietyProportion of injured fruit total injured unmarketable a a a b b b b c B Fig. 4-14. Proportion of injured and unmarketable fruit sampl ed from each variety on A) farm 1 and B) farm 2 in 2008. Error bar s represent standard error of the mean. Means with the same letter are not significantly different from each other at the P = 0.05 level. 82

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Table 4-1. Percent of adult thrips species sa mpled from flowers in each treatment at the Citra PSREU F. bispinosaF. fuscaF. occidentalisT. hawaiiensisT. pini Franklinothrips sp. H. graminis 2007Emerald73.303.3 3.313.33.3 3.3 Jewel72.04.00 4.00 8.0 12.0 Millenia91.22.90 00 5.9 0 Star54.59.10 3.039.093.03 21.2 2008Emerald71.90.60 7.815.63.1 0 Jewel53.30.60 14.123.96.5 1.1 Millenia76.90 0 10.32.67.7 2.6 Table 4-2. Percent of adult thrips species sampled from flowers in each treatment on farm 1 F. bispinosaF. fuscaT. hawaiiensisT. pini Franklinothrips sp. H. graminis 2007Emerald15.1036.249 0 0 Jewel50.0020.230 0 0 Millennia71.906.322 0 0 Winsor41.2027.131 0 1.2 2008Emerald28.42.743.216.29.5 0 Jewel40.0036.024.04.0 0 Millenia15.7056.925.5 0 2.0 Winsor36.51.439.221.6 0 1.4 Table 4-3. Percent of adult thrips species sampled from flowers in each treatment on farm 2 F. bispinosaT. hawaiiensisT. pini Franklinothrips sp. H. graminis 2007Emerald77.34.518.2 0 0 Jewel56.318.825.0 0 0 Millenia90.9 00 9.1 0 Winsor81.04.84.84.8 4.8 2008Emerald16.741.741.7 0 0 Jewel100.0 00 0 0 Millenia66.733.30 0 0 Winsor70.03.320.06.7 0 83

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CHAPTER 5 EXAMINING THE SPATIAL DISTRIBUTION OF THRIPS UTILIZING GEOSTATSITICAL METHODS Introduction Flower thrips are the key pest of souther n highbush (SHB) blueberries in Florida. The most common species is Frankliniella bispinosa (Morgan), the Florida flower thrips (Arvalo-Rodriguez et al. 2006). They feed on and breed in all flower tissues. Flower thrips injure the flower tissues while feed ing and laying their eggs. When the ovaries of the flowers develop into fruit, this injury can become magnified and appear as scars on fruit tissue. High populations of flower thrips can caus e fruit to be malformed and unmarketable (Arvalo-Rodriguez et al. 2006). Flower thrips have a highly clumped distribution and tend to form small areas of high population termed hot spots (Arvalo and Liburd 2007). If thes e hot spots can be modeled and predicted, insectic ide applications could specif ically target these spots instead of the entire field. With the advent of geostatistics into t he world of insect ecology, the spatial relationships of insect populations can now be studied (Liebhold et al. 1993). The cornerstone of geostatistics is called the variogram or semivariogram (Webster and Oliver 2001). A semivariogram plots the semivariance, of the average squared difference between data values at the same separation distance, on the y-axis and the specified distance between sample pairs, the lag, on the x-axis (Wright et al. 2002). Since it is very difficult to fit a mode l to a semivariogram where each individual semivariance is plotted, the semivariance is averaged for each of several lags (Webster and Oliver 2001). This is expressed mathematically as (h) = {1/2m(h)} {z(x i ) z(x i + h)} 2 where (h) is the semivariance at lag h, m(h) is the number of data point pairs 84

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separated by lag h, and z(x i ) and z(x i + h) are the data values (z) a places separated by h (Webster and Oliver 2001). The important feat ures of semivariograms are the sill, range, lag, and nugget, which are defined as t he value of the semivariance when it stops increasing, the distance at which spatial independence is r eached, the distance between sample pairs, and the semivariance va lue when x = 0 respectively. The nugget variance is a combination of measurement error and variation over distances less than the shortest lag distance sampled for a ll continuous variables (Webster and Oliver 2001). Semivariograms have been used to examine and describe the spatial relationship of several corn pests, including western co rn rootworm adults on yellow sticky traps in corn (Midgarden et al. 1993), corn rootworm in jury to corn (Park and Tollefson 2005), and European corn borer larvae and their dam age in whorl stage corn (Wright et al. 2002). Semivariograms have also been used to examine and describe the spatial relationships of three species of Xylella fastidiosa (Wells) sharpshooter vectors on citrus (Paulo et al. 2003) and of Lygus hesperus (Knight) in lentils (Schotzko and OKeeffe 1990). Florez and Corredor (2000) used semivariogram along with other geostatistical analyses to examine the spatial dependence of F. occidentalis in a covered strawberry crop at Bogota plateau. Spat ial dependence was found in 3 of 12 sampling weeks. They found that although thrips col onies were aggregated at first, over time the pattern changed toward a random pattern. This change was caused by thrips movement to neighboring quadrants. Kriging is a method that allows researc hers to estimate the continuous properties of something in the environment from a fi nite number of sampled points (Webster and 85

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Oliver 2001). Ordinary krig ing is the most common kriging method used in most applications (Webster and Oliver 2001). In or dinary kriging, the overall mean of the population is assumed to be unknown. Like IDW, ordinary kriging uses a weighted average to estimate unknown values. Ho wever, the weights are based upon the semivariogram model. Two other commonly used interpolation te chniques are natural or nearest neighbor and inverse distance weighting (IDW). Natura l neighbor is the simplest interpolation method. The value at an unknown point is set equal to the value of the nearest sample point (Ess and Morgan 2003). In ID W, a set of samples that are a given distance away from the unknown point are used to interpolate the value at that point. Sample points closer to the unknown point are given a high er weight than those farther away. IDW is, in effect, a weighted average (Ess and Mo rgan 2003). The estimated value for the unknown point a location j, Z j is calculated using the equation { (z i /d p ij )} / { (1/d p ij )}, where d ij is the distance between known point i and unknown point j, z i is the value at known point i, and p is an exp onent defined by user that is commonly set equal to two (Bolstad 2006). There were two major objective s of this study. The first was to determine the best spatial interpolation method to use to model thrips population distribution. Natural neighbor, IDW, and ordinary kriging were compared. Ordinary kriging tends to be the most accurate interpolation method. If thrips variation can be modeled with semivariograms, ordinary kriging will most likely prove the most accurate in this study as well. However, if the thrips variation cannot be modeled we ll with semivariograms, IDW will be as accurate as, if not more accurate than, ordinary kriging. 86

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The second objective was to use the semivariogram models to determine optimum trap spacing. Traps spaced at or beyond th e range of the semivariogram will monitor populations that are spatially independent from each other. Materials and Methods In 2008, 100 white sticky traps (Great Lakes IPM, Vestabur g, MI) were distributed throughout a 1.13-ha SHB bluebe rry planting of four to seven year old bushes in Inverness, Florida, in a regular grid at 15.24-m increments (Fig. 5-1). An additional 30 traps were placed randomly throughout the plot to collect information on distances shorter and longer than 15.24-m. Traps were changed out weekly over a three week period on Feb. 14, 21, and 28, 2008. The number of thrips per trap was counted and recorded. Trap locations were mapped using a Trimble GeoXT GPS receiver (Trimble, Sunnyvale, CA) in the WGS 84 datum. The data were then imported into ArcMap 9.1 (ESRI 2005), projected, and in terpolated using several spatial interpolation methods. When the data was imported into ArcMap, the NAD 27 datum was automatically attached to it. The data was redefined into the NAD 83 datum and then projected into Albers Equal Area Conic. Natural neighbor, ID W, and Ordinary kriging (Ess and Morgan 2003) were computed in ArcMap 9.1 itself. The semivariogram s for the ordinary kriging were constructed in Space Time Information System (STIS) (Terraseer, Inc. 2007) and then input into ArcMap for kriging. For IDW, p was set at the default 2 and the search area was divided into 4 quadrants from which at least 5 data points per quadrant were included. In Ordinary kriging, the search area, with a radius equa l to the range of the semivariogram, was also divided into 4 quadrants from which at least 1 point per quadrant up to a total of 5 points was used in the interpolation. 87

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In 2009, 100 white sticky traps were distributed throughout a 1-ha of the same blueberry planting used in the previous year in a regular grid at 7.61-m increments (Fig. 5-2). A smaller area was used in order to i dentify finer scale spatial variability. An additional 30 traps were again placed r andomly throughout the plot. Traps were changed out weekly over a five-week period on J an. 30, Feb. 5, Feb. 13, Feb. 20, and Feb. 26, 2009. The number of thrips per trap was counted and recorded. Trap locations were mapped using a Trimbl e Pathfinder GPS receiver (Trimble, Sunnyvale, CA) in the WGS 84 datum. The data were then imported into ArcMap 9.1 (ESRI 2005), projected into universal tr ansverse mercator (UTM), and interpolated using several spatial interpolation met hods. Again, the datum was automatically assumed to be NAD 27, so this datum was used in the UTM projection. Natural neighbor, IDW, and Ordinary kriging were computed in ArcMap 9.1 itself. The semivariograms for the ordi nary kriging were construct ed in SGeMS (Remy 2007) and then input into ArcMap for kriging. It was necessary to normalize the data for semivariogram analysis using a natural logarit hmic transformation for all sample dates except Feb. 13. For IDW, p was set at the default 2 and the s earch area was divided into 4 quadrants from which at least 5 data points per quadrant were included. In ordinary kriging, the search area, with a radius equal to th e range of the semivariogram, was also divided into 4 quadrants from which at least 2 points per quadrant up to a total of 5 points were used in the interpolation. Cross-validation was used to assess the accu racy of the predicti ons from all three interpolation methods in both years. For IDW and kriging, this was done in ArcMap. For the natural neighbor interpol ation, the cross-validati on had to be done manually. Mean 88

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prediction error (ME) was calcul ated using the equation ME = { (predicted measured)} / n, where n is the samp le size (Webster and Oliver 2001). R 2 values were calculated using the equation R 2 = (predicted mean measured) 2 / (measured mean measured) 2 The root mean square error (RMSE) was calculated using the equation RMSE = { (predicted measured) 2 / n)}, where n is the sample size (Bolstad 2006). The residual prediction deviation (RPD) was calculated using the equation RPD = val / RMSE v {n / (n-1)}, where val is the standard deviation of the validation set, RMSE v is the root mean square error of the validatio n as calculated above, and n is the sample size (Vasques et al. 2010). Results The summary data for thrips per trap was si milar for all three weeks in 2008 (Table 5-1). On Feb. 14, high numbers of thrips per trap were located in the southeastern quadrant of the northern block of rows and throughout the southern block of rows, but more concentrated in the northern half of the southern block (Fig. 5-3A). The highest numbers of thrips per trap on Feb. 21 were located in two rows, one in the southwest quadrant of the sampling area and the other towards the east side of the northern block of rows (Fig. 5-3B). The traps with the highest numbers of thri ps on Feb. 28 were located at the northern end of the southern block of rows (Fig. 5-3C). The natural neighbor, IDW, and Ordinary kriging interpolations of thrips per trap for each sampling week from 2008 are shown in figures 5-4, 5-5, and 5-6 B, D & F respectively. Locations of areas of high thrips population, hot spots, are similar in all three interpolation methods. The natural nei ghbor method was the least accurate for all three sample weeks (Table 5-2). IDW and ordinary kriging had very similar RMSEs, RPDs, and R 2 values on all three dates, indicating that their accuracies were similar. 89

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However, IDW had a ME much closer to 0 t han ordinary kriging on Feb. 21, indicating that IDW was more accurate on this date. On Feb. 14, one hotspot was distinguishable in the southwest area of the field in the natural neighbor and IDW maps. The area of th is hot spot was larger in the natural neighbor map. It was also present in the or dinary kriging map, but the estimated number of thrips was much smaller. On Feb. 21, three major hot spots had fo rmed: two were very close to each other in the southwest area of the fi eld, and one was present in the nor theast area of the field. The one present on Feb. 14 was still present along with several other smaller hot spots. All three maps looked ve ry similar, but the area of t he hot spots was smaller in the IDW map. On Feb. 28, the remnants of the hot spots in the southern half of the field could be seen. The kriging map showed fewer thri ps in these hot spot remnants compared with the other two methods. The semivariograms used for the ordinary kriging varied greatly among the weeks (Fig. 5-6 A, C, & E, Table 5-3). The Feb. 14 semivariogram had a very large nugget and a large range (~ 80 m). The nugget to sill ratio was also large at 1.44. The Feb. 21 semivariogram showed a distinct spatial trend with a small nugget (0.14), a very small nugget to sill ratio (0.0000015) and a range of 11.04 m. The Feb. 28 semivariogram had a small nugget of 0.002, a very small nugge t to sill ratio (0.000000071), and a very short range of 2.51 m. The summary data of thrips per trap for a ll five sampling weeks is shown in Table 5-4. The Jan. 30 summary data was similar to that found for all three weeks in 2008. 90

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Traps with high numbers of thrips per trap on Jan. 30 were concentrated in two rows, the south center row and one of the southwest rows (Fig. 5-7A). There was also a trap with high trips numbers in the southeast corner of the sampling area. All values on Feb. 5 were low because very few thrips were caught on the traps during the preceding week (Fig. 5-7B). Summary data from the remain ing three sampling weeks was very similar except that the skewness coefficient and kert osis were much smaller on Feb. 13. The distribution of high and low numbers of thrips per trap on Feb. 13 appeared to be random (Fig. 5-7C), with high numbers lo cated in several of the northern and southwestern rows and in the southeast cor ner of the sampling area. The furthest southwestern row had very low numbers of thrips per trap. On Feb. 20, high numbers of thrips per trap were found throughout the nort hern rows, particularly in the central and east rows (Fig. 5-7D). High numbers were al so found in several of the southeast rows and in one row towards the southwest. The fu rthest southwest row again had very low numbers of thrips per trap. A similar pattern was seen on Feb. 26 (Fig. 5-7E) with even higher numbers of thrips per trap. The natural neighbor, IDW, and Ordinary krig ing interpolations for each sampling week from 2009 are shown in figures 5-8, 5-9, and 5-10 B, D, F, H, & I respectively. Locations of areas of high thrips population, hot spots, are similar in all three interpolation methods. The natur al neighbor method was the l east accurate for all five sample week according to the RMSE and RPD values, and IDW and ordinary kriging had very similar accuracies (Table 5-5). Th e ME indicates that the natural neighbor interpolation was just as accurate as the or dinary kriging interpolation on Feb. 5 while the IDW interpolation had greater accuracy t han both of them. On Jan. 30, Feb. 20, and 91

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Feb. 26, IDW was more accurate than ordina ry kriging. On Feb. 13, the reverse was true. On Jan. 30, hot spots appeared to be developing in the south center and west of the field. They are visible in all three maps but are less distinct and contain a smaller number of thrips in the ordi nary kriging map. There is anot her developing hot spot in the eastern corner of the IDW map. This spot is also present in t he kriging map, but with fewer thrips. In the natural neighbor map, high thrips numbers are found throughout the eastern edge of the field. On Feb. 5, the thrips population in the field had all but disappeared. The areas where the developing hot s pots had been the previous week had less than 50 thrips per trap. The rest of the field had less than 15 thrips per trap. On Feb. 13, hot spots reappeared in the same areas they were developing in on Jan. 30. Also, a new hot spot appeared in the northeast area of the field. The hot spots were smaller in the IDW map com pared with both other maps and contained less thrips in the ordinary kriging map. On Feb. 20, the hot spot in the nort heast corner of the fi eld had expanded in all three maps. The expansion was much less pronounced in the IDW map. Many other, smaller hot spots were present in both the natural neighbor and ID W maps, but not in the ordinary kriging map because they were smoothed out. On Feb. 26, the hot spot in the northeast corner on Feb. 13 had expanded to cover a large part of the northeast and center of the field. Again, t he expansion was less pronounced in the IDW map and there were other smaller hot spots present in both the natural neighbor and IDW maps that were not found in the ordinary kriging map. 92

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The semivariograms used for the ordinary kriging varied greatly among the weeks (Fig. 5-10 A, C, E, G, I, T able 5-6). The Jan. 30 semivariogram had a fairly large nugget, a nugget to sill ratio of 0.38, and a range of 28.75 m. The Feb. 5 semivariogram was mostly nugget with a nugget to sill ratio of 0.71 and a range of 22.50 m. The Feb. 13 semivariogram, the only data set that could be modeled without transformation, had a small nugget, a nugget to sill ratio of 0.2, and a range of 17.50 m. The Feb. 20 semivariogram was mostly nugget with a nugget to sill ratio of 0.73 and a range of 27.50 m. The Feb. 26 semivariogram had a very large nugget with a nugget to sill ratio of 0.67 and a range of 23.75 m. Discussion In 2008, the differences among the thr ee weeks could be explained by the flowering stage of the blueb erry plants. Arvalo and Li burd (2007) documented the close relationship between thrips numbers and blueberry flowering stage. Plants were approaching peak flowering during the week of Feb. 7 14. The thrips population was also increasing and hot spots were beginn ing to form. The plants were at peak flowering during the week of Feb. 14 21. The thrips population also reached its peak during this week. By Feb. 21, petal fall had begun and fruits were forming on some of the varieties. By Feb. 28, most of t he plants contained developing fruit and had few remaining flowers and the thrips populat ion had greatly diminished as well. In 2009, both stage of flower ing and temperature appeared to play major roles in explaining the difference in the thrips population among the weeks. The blueberry plants had reached about 70% open flowers on Jan. 30. The thrips population was increasing and hot spots were beginning to form. Plants had reached peak flowering by Feb. 5 and remained at this stage until Feb. 13. In contrast, the thrips population had crashed 93

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to very low levels on Feb. 5. This was most likely caused by an extreme cold front that blew through Florida during the precedi ng week (FAWN 2009). The thrips population was increasing dramatically by Feb. 13 and remained high throughout the next two weeks. In contrast, the blueberry bushes had declined to 70% open flowers on Feb. 20 and then to 20% open flowers on Feb. 26. The extreme temperature event seemed to cause the thrips population to peak well after peak flowering. All three interpolation methods showed hot spots in the same areas of the blueberry field during both years. On F eb. 14 and 28, 2008 and on all dates in 2009 except Feb. 5, the ordinary kriging maps showed a much lo wer number of thrips in these hot spots t han the natural neighbor and IDW maps This is because there were only a few traps with very high numbers of thrips on these dates. The hot spot was centered where the one trap with > 1000 thrips on it was located. This point on the map is set equal to this value in both natural neighbor and IDW interpolation, but not in ordinary kriging. This causes the kriged m ap to be much smoother. The combination of setting the points at data locati ons to the value of the data point and using a weighted average causes the bulls-eye effect that IDW maps ar e known to exhibit (Bolstad 2006). This effect is not as pronounced in t he natural neighbor maps, because all points closest to a sample point are set to its exact value (Ess and Morgan 2003) producing large areas of the same value. On Feb. 21, 2008, the maps were very sim ilar. The area of the three major hot spots is smaller in the IDW map. This is because IDW calculates an average that is weighted by distance whereas natural neighbor interpolation sets every unknown point equal to the closest data point. This causes a ll of the points near a trap with high thrips 94

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numbers to have that high number on the natural neighbor map, which in turn creates hot spots with a large area. Because of the nature of the semivariogram for this week (see below), the ordinary kr iging map displays the same property as the natural neighbor map. The ordinary kriging interpolation va ried among the weeks during both years because the data varied greatly and was not always modeled well using semivariograms. Wright et al. 2002 found that the spatial distribution of European corn borer larvae was modeled well by semivariograms in only four out of seven data sets. Farias et al. (2003) calculated 36 semivariog rams for sharpshooters on citrus, but could fit only nine of them with mathematical models. The se mivariograms from Feb. 14, 2008, Feb. 5, 2009, Feb. 20, 2009, and Feb. 26, 2009 had large nugget s. The range of the Feb. 28, 2008 semivariogram was so short that no traps were at a distance shorter than the range. This caused most of the points to be weighted the same in the interpolations for these dates The ordinary kriging interpolation on these dates was, thus, very similar to a local average interpol ation. In contrast, th e semivariogram from Feb. 21, 2008 had a very small nugget, but leveled off very rapidly. Because of this, only points very close to the unknown point were given a high weight in the interpolation and the interpolation, t herefore, closely resembled the nat ural neighbor interpolation for this date. The semivariograms from J an. 30, 2009 and Feb. 13, 2009 had a moderate and small nugget, respectively and had ranges that encompassed many data points. The resulting maps are, t herefore, the best examples of ordinary kriging. In terms of accuracy, the natural neighbor interpolation was the least accurate. The ordinary kriging and IDW interpolations were similar in accuracy. Since natural 95

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neighbor interpolation simply se ts all unknown values to the value of the nearest sample point, it is not surprising that it is the leas t accurate method. Ordinary kriging is only as powerful as the semivariograms used to perform it. In 2008, the spatial trend in flower thrips populations in blueberries was localiz ed. Because of this, the semivariograms either had a very short range (Feb. 21 and 28) or a large nugget due to a lack of sample point pairs below the actual range (Feb. 14) The result was that, in 2008, ordinary kriging interpolation was no more accurate than IDW interpolation. The reduced grid spacing in 2009 resulted in better semivariogra ms, suggesting that t he spatial variability of thrips is high and could be better captured with the finer grid spacing used in the 2009 sampling. In both years, the shortest distance sampled was 2-m. However, in 2008, there were only two data pairs at th is distance while in 2009, there were approximately ten. The data from the weeks of Jan. 30 and Feb. 13 was modeled very well by semivariograms resulting in kriged maps with a slightly higher accuracy then the IDW maps from these wee ks. Therefore, both IDW and kriging are reasonable interpolation methods to use to model flower thrips distribut ion in blueberry fields. This is in agreement with result s presented by Roberts et al. (1993) and others. The accuracy of kriging is dependent upon t he accuracy of the semivariogram. Semivariogram models are sensitive to many factors, including: nonnormality, outliers, directional differences in spatial trends, inconsistency of spatia l trends among different parts of the sample area, and the placem ent and spacing of the sample points. The range of the semivariograms varied great ly in 2008 from 2.51 to 79.8 m. In 2009, the ranges of the semivariograms were much more consisten t, varying from 17.5 96

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to 28.8 m. Therefore, spacing white sticky traps at leas t 28.8 m apart should result in sampling independent populations of flower thrips. Fig. 5-1. GIS map of the study area in 2008. 97

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Fig. 5-2. GIS map of the study area in 2009. 98

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A B C Fig. 5-3. Point maps of thrips pe r trap for each sampling week in 2008. 99

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A B C Fig. 5-4. Natural neighbor interpolation of thri ps per trap from A) Feb. 14, 2008, B) Feb. 21, 2008, and C) Feb. 28, 2008. 100

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A B C Fig. 5-5. Inverse Distance Weighting interpolat ion (p = 2, # points = 20) of thrips per trap from A) Feb. 14, 2008, B) Feb. 21, 2008, and C) Feb. 28, 2008. 101

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A B C D 102

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E F Fig. 5-6. Semivariograms (A, C, E) and Ordinary Kriging interpolation (B D, F) of thrips per trap from A) & B) Feb. 14, 2008, C) & D) Feb. 21, 2008, and E) & F) Feb. 28, 2008. 103

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A B C D 104

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E Fig. 5-7. Point maps of thrips pe r trap for each sampling week in 2009. 105

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A B C D 106

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E Fig. 5-8. Natural neighbor interpolation of th rips per trap from A) Jan. 30, 2009, B) Feb. 5, 2009, C) Feb. 13, 2009, D) Feb. 20, 2009, and E) Feb. 26, 2009. 107

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A B C D 108

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E Fig. 5-9. Inverse Distance Weighting interpolat ion (p = 2, # points = 20) of thrips per trap from A) Jan. 30, 2009, B) Feb. 5, 2009, C) Feb. 13, 2009, D) Feb. 20, 2009, and E) Feb. 26, 2009. 109

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A B C D 110

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E F G H 111

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I J Fig. 5-10. Semivariograms (A, C, E, G, I) and Ordi nary Kriging interpolat ion (B, D, F, H, J) of thrips per trap from A) & B) Jan. 30, 2009, C) & D) Feb. 5, 2009, E) & F) Feb. 13, 2009, G) & H) Feb. 20, 2009, and I) & J) Feb. 26, 2009. Table 5-1. Summary statistics of thrips per trap for each sample date in 2008. Feb. 14Feb. 21Feb. 28 mean277351179 median195268158 min84010 max133118951081 Std. Dev.236303147 SEM212713 skewness coefficient1.362.32.75 Kurtosis5.2310.0114.90 Table 5-2. Several error metrics for natural neighbor (NN), invers e distance weighting (IDW), and ordinary kriging (OK) for each sample date in 2008. mean prediction error root mean square error residual prediction deviationR2Feb. 14NN 6.13 299.56 0.781.13 IDW4.78 208.80 1.130.34 OK 1.61 202.90 1.160.29 Feb. 21NN-21.95397.48 0.760.68 IDW0.10 307.80 0.980.11 OK -7.11 331.80 0.910.37 Feb. 28NN 7.65 211.71 0.691.16 IDW5.13 147.20 0.990.20 OK 4.49 151.60 0.970.26 112

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Table 5-3. Summary of the semivariogram analysis for each sampling week in 2008. Feb. 14Feb. 21Feb. 29 modelGaussian (1 & 2)CubicExponential lags23 23 23 nugget39444.490.140.0018 sill8342.28, 19086.9495681.6325354.64 nugget/sill ratio1.440.00000150.000000071 range79.71 m, 79.77 m11.04 m2.51 m root mean square error202.9331.8151.6 residual prediction deviation1.16 0.910.97 mean prediction error1.61 -7.114.49 R20.29 0.370.26 Table 5-4. Summary statistics of thrips per trap for each sample date in 2009. Jan. 30Feb. 5Feb. 13Feb. 20Feb. 26 mean21311490571660 median1648499512506 min141353759 max72451117322122184 Std. Dev.1609225361469 SEM141203241 skewness coefficient1.351.530.221.331.27 Kurtosis4.295.612.975.924.11 Table 5-5. Several error metrics for natural neighbor (NN), invers e distance weighting (IDW), and ordinary kriging (OK) for each sample date in 2009. mean prediction error root mean square error residual prediction deviationR2Jan. 30NN 7.95 211.20 0.751.01 IDW1.17 164.70 0.970.13 OK 3.28 157.90 1.010.18 Feb. 5NN 0.38 12.400.7681155711.05 IDW-0.02 9.930.9591775520.20 OK 0.41 9.431.0100353220.20 Feb. 13NN 8.25 212.081.1212067611.13 IDW1.15 184.001.2923126630.22 OK 0.54 180.901.314458430.45 Feb. 20NN-35.59455.180.8092458661.06 IDW-2.69 367.501.0023198180.30 OK20.16 342.101.0767393550.28 Feb. 26NN-39.65634.050.7567820431.06 IDW0.30 476.401.00721590.24 OK16.61 442.801.0836442060.26 113

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Table 5-6. Summary of the semivariogram analysis for each sampling week in 2009. Jan. 30Feb. 5Feb. 13Feb. 20Feb. 26 modelExponentialSphericalSphericalSphericalSpherical lags2323232323 nugget0.250.5090000.300.30 sill0.400.20350000.110.15 nugget/sill ratio0.380.710.20.730.67 range28.75 m22.50 m17.50 m27.50 m23.75 m root mean square error157.909.43180.90342.10442.80 residual prediction deviati on1.011.011.311.081.08 mean prediction error3.280.410.5420.1616.16 R20.180.20.450.280.26 114

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CHAPTER 6 EXAMINING THE RELATIONSHIP BETWEEN THRIPS SPATIAL DISTRIBUTION AND FLOWER DENSITY Introduction Southern highbush blueberries ar e an important crop in Flor ida that is grown for a highly profitable early-season fr esh market (USDA 2010). Flow er thrips are one of the key pests of these blueberries. Flower thrips injure blueberry flowers both when they feed on the flowers and when they lay their eggs in them. This injury can cause scaring on developing fruit, which makes the fruit unsalable on the fresh market (ArvaloRodriguez 2006). Thrips populations tend to form one or a few hot-spots on blueberry farms, which are small areas of comparatively high thrips numbers (Arvalo and Liburd 2007). These hot-spots begin forming about 7-10 days after bloom initiation, peak between 12 and 15 days after initiation when the majority of the flowers are open, and decline until about 22 days after bloom initiation when mo st of the flowers have become fruit and the thrips population all but disappears (A rvalo and Liburd 2007). The hot-spots often form in different areas each year. The objective of this study was to determine if hot spots of thrips are correlated with flower density. The hypothesis of this study was that thrips population density in space has a positive linear relationship with flower density. Materials and Methods Inverness Farm In 2009, 100 white sticky traps (Great Lakes IPM, Vestabur g, MI) were distributed throughout a 1-ha SHB blueberry planting containing four to seven year old bushes in Inverness, FL, in a regular grid at 7.61-m increments. An additional 30 traps were again 115

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placed randomly throughout the plot. Traps were changed out weekly over a five-week period on Jan. 30, Feb. 5, Feb. 13, Feb. 20, and Feb. 26. Traps were taken to the Small Fruit and Vegetable laboratory in Gainesville, FL, where the number of thrips per trap was counted and recorded. Along with the thrips data, the percent of open flowers present in each blueberry row in the study was recorded each week. The sampling area was divided into 38 rows by a dirt road in the northern part of the study area. Each row contained blueberry plants of the same variety and age. Data we re collected on Jan. 30 and Feb. 5, 13, 20, and 26. Linear regression analysis was used to determine if the number of thrips (dependent variable) was related to the percent of open flowers (independent variable). All 130 sample points were input into the analysis by assigning to each trap the percentage of open flowers reco rded from the row it was hung in. Since some of the assumptions of least squares regression coul d not be met even after transformation on several sampling dates, Theil regression (H ollander and Wolfe 1999) was used for all sampling dates. Kendalls tau, a nonparamet ric correlation statistic (Hollander and Wolfe 1999), was also calculated (W essa 2008) for all sampling dates. In addition, GIS layers of thrips numbers and percent of open flowers for each sampling date were created in ArcGIS ( ESRI 2005). Trap locations were mapped using a Trimble Pathfinder GPS receiver (Trimble Sunnyvale, CA) in the WGS 84 datum. The data were then imported into ArcMap 9.1 (ESRI 2005), projected into universal transverse mercator (UTM), and interpolated using inverse distance weighting (IDW). The NAD 27 datum was automatically assig ned to the data when it was imported into 116

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ArcMap, so this datum was used to project th e data. For IDW, p was set at the default 2 and the search area was divide d into 4 quadrants from which at least 5 data points per quadrant were included. For the percent of open flowers, a point dataset was created by assigning the percent of open flowers recorded from each row to all of the sample points in that row. Inverse distance weighting (IDW), with p set at the default 2 and t he search area divided into 4 quadrants from which at least 5 data points per quadrant were included, was used to create the percent of open flowers layers. Each layer was then saved as a raster with a cell size of 1. Each raster layer was then classified. The thrips layers were classified into groups separated at 150 thrips per trap intervals up to 1,050 thrips. The final classification was > 1,050 because there were only a small number of traps with thrips exceeding this number. The one exception was week 2, wh ich was separated at 15 thrips per trap intervals due to the extremely low numbers that week. One hundred and fifty thrips per trap is a commonly used action threshold. Th is produced five (week 1), three (week 2), and eight (weeks 3 through 5) categories respec tively. The percent of open flower data were separated into the same number of cat egories as the thrips per trap data from the same sampling week using equal interval classification. All of the layers were then reclassified so that each category was represented by a number from 1 to 3, 5, or 8 with 1 representing the lowe st category and 3, 5, or 8 representing the highest. For eac h week, the reclassified percent of open flowers layer was subtracted from the reclassified thrips per trap layer. This produced a layer showing the qualitative relationship of the variables in space. The resulting layers were classified 117

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as follows: 0 = high numbers of thrips per trap paired with high percentages of open flowers or low paired with low, 1 = high thrips numbers paired with moderately low percentages of open flowers, 2 -5 = high thri ps numbers paired with low percentages of open flowers, > 5 = high thrips numbers pai red with very low percentages of open flowers, -1 = low thrips numbers pair ed with moderately high percentages of open flowers, -2 -5 = low thrips numbers pair ed with high percentages of open flowers, < -5 = low thrips numbers paired with very high percentages of open flowers. Windsor Farm This study was conducted on a farm in Windsor, FL, in Feb. and March of 2010. Twenty white sticky traps were placed in a 2464-m 2 area of a SHB blueberry planting. The blueberry plants were approximately seven years old. Traps were spaced 15-m apart in each of five blueberry rows. The ro ws were 10-m apart. Traps were replaced weekly and 4 5 flower clusters (20 25 fl owers) were collected and placed into 50-ml vials containing 20 ml of 70% ethanol. Traps and flower samples were taken to the Small Fruit and Vegetable laboratory in Gaine sville, FL, where the number of thrips per trap was counted and recorded. Thrips adults and larvae were extracted from the flowers using the shake and rinse met hod developed by Arv alo and Liburd (2007) and counted. Percent of open flowers was al so recorded from each sampled plant on the Windsor farm in 2010. Traps, flower sa mples, and percent of open flower data were collected for 6 weeks from Feb. 18 to March 25. Least squares regression analysis was used to determine if the number of thrips per trap (dependent variable) wa s related to the percent of open flowers (independent variable). The thrips per trap (x) data had to be log 10 (x + 1) transformed so that all of the least squares regression analysis assumptions could be met. 118

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Very few thrips were collected from the fl owers until March 18. Th erefore, only the March 18 and 25 data sets were analyzed for a relationship between thrips larvae and adults per flower (dependent variables) and percent of open flowers (independent variable). Since some of the assumptions of least square regression could not be met even after transformation for the thrips per fl ower data, Theil regr ession (Hollander and Wolfe 1999) was used. Kendalls tau, a nonparam etric correlation statistic (Hollander and Wolfe 1999), was also calculated (Wessa 2008) for the thrips per flower data sets. Results Inverness Farm A significant positive linear relationship between percent of open flowers and thrips per trap occurred on Jan. 30 ( = 0.36, C > 1988, n = 130, P slope < 0.0001, Fig. 6.1A). There was a significant positive linear rela tionship between percent of open flowers and thrips per trap on Feb. 5 ( = 0.24, C = 1734, n = 130, P slope = 0.0002) and Feb. 20 ( = 0.21, C = 1555, n = 130, P slope = 0.0012, Fig. 6-1B & D). No relationship was found between percent of open flowers and thrips per trap on Feb. 13 ( = 0.07, C = 273, n = 130, P slope = 0.29, Fig. 6-1C) or between percent of open flowers and thrips per trap on Feb. 26 ( = 0.08, C = 581, n = 130, P slope = 0.12, Fig. 6-1E). Summary data for the thrips per trap and percent of open flowers data are shown in Tables 6-1 and 6-2 respectively. On J an. 30, 17% of the area was covered by pairings where either high thrips number s were paired with high percentages of open flowers or low numbers were paired with low percentages (Fig. 6-2A). Pairings with a small degree of dissimilarity covered another 25% of the area. A similar pattern was seen on Feb. 5 (Fig. 6-2B), where 4% of t he pairings were the same and 36% were only slightly dissimilar. 119

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On Feb. 13, 68% of the sampling ar ea was dominated by low thrips numbers paired with high percent ages of open flowers (Fig. 6-2C). The degree of dissimilarity was much greater than that seen in the two previous weeks. Only 6% of the pairings were the same and 25% were slightly dissimila r. A similar pattern was seen on Feb. 20 (Fig. 6-2D), with 59% of the area cover ed by low thrips numbers paired with high percentages of open flowers. However, 11% of the area was covered by similar pairings and 27% by slightly dissimilar pairings. Feb. 26 (Fig. 6-2E) was dominated by high thrips numbers paired with low percentages of open flowers (48% ). Similar pairings encompa ssed 13% of the area and slightly dissimilar pairings 32%. Windsor Farm There was a significant negative linear relationship between percent of open flowers and log 10 thrips per trap on March 18 (R 2 = 0.24, t = -2.67, df = 19, P slope = 0.0156, Fig. 6-3E). No relationship was found between percent of open flowers and log 10 thrips per trap on any of the other dates (all R 2 < 0.03, all | t | 1.23, df = 19, P slope > 0.23, Fig. 6-3A-D & F). No relationship was found between percent of open flowers and thrips adults (both 0.19, C 14, n = 20, P slope 0.33, Fig. 6-4) or larvae (both 0.02, C = 0, n = 20, P slope > 0.86, Fig. 6-5) per flower on either date. Discussion According to Arvalo-Rodriguez (2006), flow er thrips population density is strongly correlated with the percent of open flowers ov er time. The results from the Inverness 2009 study indicate that this relationship may exist in space as well. The differences in percent of open flowers in space most likely exist because multiple varieties are grown 120

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on the same farm to maximize cross po llination (Childers and Lyrene 2006). Different varieties begin to flower at different time s and flower for differ ent periods of time. However, there appeared to be no relati onship between flower thrips density and percent of open flowers on the Windsor farm in 2010, except on March 18 where a relationship opposite to what was expected occurred. This may have been a result of the unusually cold winter weather that occurred throughout January and February (FAWN 2010). Further research is needed to det ermine if there are some cases where flower thrips density decreases with increasing percentages of open flowers. An intense cold snap that occurred from Feb. 4 Feb. 6, 2009 (FAWN 2009) may explain some of the anomalie s in the Inverness study. The extremely low thrips numbers found on Feb. 6 are likely a direct result of this cold snap. Tsai et al. (1995) found a 56% mortality rate when Thrips palmi Karny was held for 15h at 0C. Development was also reduced at 26C compared with 32C. The lack of any relationship between flow er thrips numbers and percent of open flowers on Feb. 26 may have been indirectly related to the cold snap. After the cold snap, the thrips population began increasin g and continued to do so throughout the sampling period. In contrast, peak flowering, averaged over the whole sampling area, occurred during the Feb. 13 sampling week. By Feb. 26, only a few rows, most likely containing later or longer flowering variet ies, had more than 20% open flowers. This resulted in a large number of samples w here high thrips numbers occurred with bushes having a low percent of open flower s as seen in Fig. 6-2E. The opposite trend occurred on Feb. 13, when mo st of the rows were at 80 100% open flowers, while only a few rows, which pr obably contained later flowering varieties, 121

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had just reached 50 65% open flowers. This resulted in a large number of samples where low thrips numbers occurred with bushe s having a high percent of open flowers as seen in Fig. 6-2C. The results from the Inverne ss study indicate that hot spots of flower thrips population may be related to flower density. Further research utilizing more accurate measures of flower density is needed. 0 100 200 300 400 500 600 700 800 02040608010012 Percent of open flowersThrips per trapy = 2.600x 32.00 0 A 0 10 20 30 40 50 60 02 04 06 08 01 Percent of open flowersThrips per trapy = 0.100x + 1.000 0 0 B 122

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0 200 400 600 800 1000 1200 1400 02040608010012 Percent of open of flowersThrips per trap 0 C 0 500 1000 1500 2000 2500 0204060801 Percent of open flowersThrips per trapy = 4.013x + 211.558 00 D 0 500 1000 1500 2000 2500 0204060801 Percent of open flowersThrips per trap 00 E Fig. 6-1. Graphs showing percent open flowers vs. thrips per trap on A) Jan. 30, B) Feb. 5, C) Feb. 13, D) Feb. 20, and E) Feb. 26. The black lines represent regression lines fitted by Theil regression. 123

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A B C D 124

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E Fig. 6-2. Maps showing the spatial similarity of number of thrips pe r trap (T) with percent of open flowers (F) on A) Jan. 30, B) Feb. 5, C) Feb. 13, D) Feb. 20, and E) Feb. 26, 2009. 125

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0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 010203040506070 Percent of open flowerslog10thrips per trap A 0 0.2 0.4 0.6 0.8 1 1.2 1.4 0204 0608 01 Percent of open flowerslog10thrips per trap 0 0 B 0 0.2 0.4 0.6 0.8 1 0204 0608 01 Percent of open flowerslog10thrips per trap 0 0 C 126

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0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 01020304050607080 Percent of open flowerslog10thrips per trap D y = -0.0138x + 1.4764 0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 010203040506 Percent open flowerslog10thrips per trap 0 E 0 0.5 1 1.5 2 051015202530 Percent of open flowerslog10thrips per trap F Fig. 6-3. Graphs showing percent open flowers vs. log 10 thrips per trap on A) Feb. 18, B) Feb. 25, C) March 4, D) March 11, E) March 18, and F) March 25. The black lines represent regression lines fi tted by least squares regression. 127

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0 0.05 0.1 0.15 0.2 0.25 0102030405060 Percent open flowersThrips adults per flower a 0 0.05 0.1 0.15 0.2 0.25 0.3 051015202530 Percent open flowersThrips adults per flower b Fig. 6-4. Graphs showing percent open flowers vs. thrips adults per flower on a) March 18 and b) March 25. 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0102030405060 Percent open flowersThrips larvae per flower a 0 0.01 0.02 0.03 0.04 0.05 0.06 051015202530 Percent open flowersAverage thrips larvae per flower b Fig. 6-5. Graphs showing percent open flowers vs. thrips larvae per flower on a) March 18 and b) March 25. 128

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Table 6-1. Summary statistics for the thri ps per trap data from each sampling week. 30-Jan5-Feb13-Feb20-Feb26-Feb mean213.053811.26923489.9692570.5923660.4154 median1648498.5511.5505.5 min141353759 max72451117322122184 Std. Dev.160.06339.35086224.6699360.9792469.2586 SEM14.038480.82012519.7048631.6599741.1567 skewness coefficient1. 351.530.221.331.27 Kurtosis4.295.612.975.924.11 Table 6-2. Summary statistics for the percentage of open flower data from each sampling week. 30-Jan5-Feb13-Feb20-Feb26-Feb mean80.2692376.7692377.2307772.8461527.26923 median9080808020 min501030100 max10010010010080 Std. Dev.21.0969724.6672516.3898923.5834223.60127 SEM1.8503262.1634611.4374882.0684032.069968 skewness coefficient-0.35-1.16-1.12-1.420.70 Kurtosis1.353.714.394.402.15 129

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CHAPTER 7 THE EFFECT OF SEVERAL REDUCED RISK INSECTICIDES ON FLOWER THRIPS POPULATIONS IN SOUTHERN HIGHBUSH BLUEBERRIES Introduction Flower thrips in blueberries are typicall y managed with applications of insecticides. The two most commonly used insecticides are malathion (Micro Flo Company LLC, Memphis, TN) and SpinTor (spinosad) (Dow Agrosciences, Indianapolis, IN) (ArvaloRodriguez 2006). The recent ly registered Delegate TM (spinetoram) (Dow Agrosciences, Indianapolis, IN) is beginning to be used more frequently (O. E. Liburd personal communication). Malathion is a conventional, organophos phate insecticide with broad spectrum activity. SpinTor is a reduced-risk insecticide. It s active ingredient, spinosad (spinosyn), is derived from the fe rmentation of the soil bacterium Saccharopolyspora spinosa Mertz and Yao. It must be ingested and ki lls insects via rapid excitation of the nervous system (IPM of Alaska 2003). Delegate TM was registered for use on flower thrips in blueberries during the course of the work presented in this chapter. Spinetoram, the active ingredient of Delegate TM is also a fermentation product of the soil bacterium S. spinosa (Srivastava et al. 2008). Toxicity to bees and other pollinators is a major concern of blueberry growers. Therefore, insecticides are usually applied early in the morning or at night to minimize the impact on pollinating bees (Arvalo-Rodriguez 2006). Even with this practice, malathion still causes some pollinator morta lity (O. E. Liburd personal communication). With such a limited number of compounds, the development of resistance is also a concern. Resistance has been reported in Frankliniella occidentalis (Pergande) from various parts of the world to many insectic ides including spinosad (Herron and James 2005, Da h and Tunc 2007, Bielza et al. 2007). Frankliniella occidentalis can rapidly 130

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develop resistance because it has a s hort generation time, high fecundity, and a haplodiploid breeding system (Jensen 2000). Frankliniella bispinosa (Morgan) share these traits. The objective of this study was to determine the potential of using several reduced-risk insecticides to manage flower thrips in Florida blueberries. Compounds tested included spinetoram, which was registered on blueberries as Delegate TM (Dow Agrosciences, Indianapolis, IN) during the cour se of this study, rynaxypyr (DuPont, Wilmington, DE), and QRD 452 (AgraQuest, Davis, CA). Rynaxypyr is a ryanidine receptor agonist, causing the release of Ca 2+ from muscle cells, which is a novel mode of action. The insects lose the ability to regulate muscle function and die via muscle paralysis (Ribbeck 2007). QRD 452 is an extract of Mexican Tea, Chenopodium ambrosioides L. (AgraQuest 2008). These compounds were compared with malathion, SpinTor and an untreated control to determine thei r efficacy. The hypothesis is that they will be at least as effective as malathion and SpinTor Materials and Methods This experiment was conducted on a commerc ial blueberry farm in Windsor, FL, in 2007 and 2008. In 2009, it was conducted at t he University of Florida Plant Science Research and Education Unit (PSREU) near Citra, FL. The experiment was a randomized complete block design with four replicates of six treatments in 2007 and 2009 and five replicates of five treatments in 2008. At the Windsor farm, tr eatments encompassed three rows of blueberries containing plants of approximately seven y ears of age. The middle row was sprayed on both sides and the two adjacent rows were sprayed on only one side, the side facing the middle row. Treatments were 12.2-m. long with a 3-m. buffer between them. There was 131

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an unsprayed buffer row between each replicat e. In 2007, the study area encompassed 0.78 ha and in 2008 it encompassed 0.83 ha. There were four 0.13 ha plots of six y ear old southern highbush (SHB) blueberries at the Citra PSREU and these served as bloc ks. The variety Jewel was used throughout the two SHB applications. Howe ver, there were only two 0. 12 ha plots of six year old rabbiteye (RE) blueberries at the research station. Therefor e, two varieties, Premier and Brightwell, were included so that the experiment could be replicated four times. Each treatment group consisted of a row of five plants. Treatments for 2007 included: 1) Malathion 5 EC at a rate of 1.75 L / ha, 2) SpinTor 2 SC at a rate of 0.438 L / ha, 3) Rynax ypyr at a rate of 89.7 g / ha, 4) XDE175 (spinetoram) at 131 g a. i. / ha, 5) XDE-175 (spinetoram) at 173 g a. i. / ha, and 6) untreated control. They were applied using a CO 2 sprayer three times during the flowering season 14 days apart. Treatments for 2008 included Malathion, SpinTor 2 SC, and Rynaxypyr at the same rates as the previous year, spinet oram at 131 g a. i. / ha, and an untreated control. They were applied using a CO 2 sprayer twice during the flowering season 14 days apart. In 2007 and 2008, five flower clusters (~ 25 flowers) were collected from the blueberry bushes in the center of each tr eatment. They were collected the day of treatment, two days post tr eatment, and six days post treatment except during the second application in 2008. On this date, flowers were co llected the day of treatment and five days post treatment due to inclem ent weather two days post treatment. The flower samples were brought back to the Sm all Fruit and Vegetable IPM laboratory in 132

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Gainesville where the number of thrips and other arthropods per flower was counted utilizing the shake and rinse method (Arva lo and Liburd 2007). Adult thrips were identified to species using a key developed by Arvalo et al. (2006). Any thrips not matching the characters in the key were s ent to the Division of Plant Industry in Gainesville, Florida fo r identification. Treatments in 2009 included: 1) Malathion 5 EC, 2) SpinTor 2 SC, and 3) Delegate TM (spinetoram) at the rates used in the previous years, and 4) QRD 452 at 4.68 L / ha, 5) QRD 452 at 9.35 L / ha and 6) water treated control. They were applied using a CO 2 sprayer twice in the SHB blueberries, 14 days apart and once in the RE blueberries during the flowering season. Four flower clusters were collected from the three blueberry bushes in the center of each treatment. In the SHB, they were collected the day of treatment, two days post treatment, seven days post treatment, and four teen days post treatment. In the RE, they were collected the day of treatment, tw o days post treatment, and seven days post treatment. The flower samples were brought back to the Small Fruit and Vegetable IPM laboratory and sampled as in 2007 and 2008. Be cause a large number of adult thrips were present in the RE flowers, a sub-sa mple of 60 adult thrips per treatment each week was identified to species as described for 2007 and 2008. Yield data were collected for both SHB and RE blueberries. In the SHB plots, blueberries were harvested from the two lar gest plants in the treatment group once a week for four weeks beginning on April 27. The yield from each week was summed and then divided by two to give an estimate of yi eld per plant for each treatment group. Yield 133

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was collected from the RE treatment groups in the same way except that it was collected once a week for five weeks beginning on May 27. Thrips per flower data fr om 2007, 2008, and the SHB blu eberries in 2009 did not meet the assumptions of a one-way analysis of variance (ANOVA) and were therefore analyzed using the Friedman, Kendall-Babington Smith nonparametric test for a randomized complete block design and the Wilcoxon, Nemenyi, Mcdonald-Thompson multiple comparisons test (Hollander and Wolfe 1999). The 2009 RE blueberry thrips per trap data and both sets of yield data were analyzed with a one-way ANOVA in SAS and means were separated using the least si gnificant difference (LSD) test if the ANOVA was significant ( P < 0.05). Results 2007 Two days after the third treatment was applie d, there were significantly less thrips larvae per flower in the Rynaxypyr, SpinTor and XDE-175 low rate treatments compared with the control ( S = 12.36, k, n = 6, 4, P < 0.02, Fig. 7-1A). There were no significant differences in thrips adults per flower among treatments on any date (all S 8.05, k, n = 6, 4, P > 0.1, Fig. 7-1B). The percent of each species that was pres ent in each treatment is shown in Table 7-1. Frankliniella bispinosa was the dominant species. Other species present included F. fusca (Hinds), F. occidentalis, Thrips hawaiiensis (Morgan), and T. pini Karny. Very few other arthropods were recor ded from the flowers (Table 7-2). Three predatory mites and three sma ll spiders were the main predators sampled. Four Coleopterans were also collected and some of these may also have been predators. 134

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2008 There were no significant differences among average thrips larvae per flower on any date (all S 4.73, k, n = 5, 5, P > 0.1, Fig. 7-2A). However, two days after the first application, there were significa ntly more thrips adults per flower in the Rynaxypyr treatm ent compared with the sp inetoram treatment ( S = 9.08, k, n = 6, 4, P = 0.048, Fig. 7-2B & C). Six da ys after the first application, the control had significantly higher numbers of thrips adults per flower than the spinetoram treatment ( S = 9.66, k, n = 6, 4, P = 0.036). The percent of each species that was pres ent in each treatment is shown in Table 7-3. Frankliniella bispinosa was the dominant species. Thrips hawaiiensis and T. pini were the second most numerous species present in the flowers. Other species present included F. fusca F. occidentalis and Franklinothrips sp. Very few other arthropods were recorded from the flowers (Table 7-4). Eighteen predatory mites spread among the Rynaxypyr, SpinTor and spinetoram treatments and a small spider found in the control were the main predators sampled. The wasp that was also recorded from the cont rol may be a parasitoid. 2009 SHB thrips per flower There were no significant differences in thrips larvae or adults per flower among treatments on any date (all S 9.39, k, n = 6, 4, P 0.08, Fig. 7-3). The percent of each species that was pres ent in each treatment is shown in Table 7-5. Frankliniella bispinosa was the dominant species. Thrips pini was the second most numerous species. Many other thrips s pecies were also encountered occasionally, 135

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including F. fusca F. occidentalis Franklinothrips sp., Haplothrips graminis Hood, and T. hawaiiensis. A number of other arthropods were found in the flower samples (Table 7-6). The only predators found were spiders, one each in the SpinTor and QRD 452 high rate treatments. Ants, which are sometimes predatory, were found in the control, SpinTor and QRD 452 high rate treatments. A wasp was found in the Delegate TM treatment. RE thrips per flower There were no significant differences in average thrips larvae per flower among any of the treatm ents on any date (all F 1.99, df = 5, 23, P 0.13, Fig.7-4A). However, 2 days post treatment there were significantly fewer adult thrips in the Delegate TM treatment compared wit h the control and both rates of the QRD 452 ( F = 8.52, df = 5, 23, P = 0.0004, Fig. 7-4B). Interestingl y, the high rate of the QRD 452 had significantly more thrips adults per flower than the control. The percent of each species that was pres ent in each treatment is shown in Table 7-7. Nearly all of the thrips sampled were F. bispinosa A single T. pini and H. graminis were also sampled. Other arthropods found in the flower sa mples included mostly aphids and ants (Table 7-8). A predatory mite was found in the control treatment. Yield There were no significant differences in SHB ( F = 0.34, df = 5, 23, P = 0.88) or RE ( F = 1.47, df = 5, 23, P = 0.25) yield among any of the treatments. The average yields across treatments in the SHB and RE blueberries were 1.03 0.19 and 2.14 0.68 kg per plant respectively. 136

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Discussion Spinetoram reduced either thrips larvae or adults below levels found in the control after one application each year. At all other times, it was as effective as SpinTor Srivastava et al. (2008) found that spinetoram was effective against F. bispinosa F. occidentalis and F. tritici (Fitch) in pepper at a rate of 151 g a. i. per acre. This rate is only slightly higher than the lower rate of 131 g a. i. per ha used in this study. Rynaxypyr reduced numbers of thrips larvae below the cont rol in 2007, but not in 2008. It did not reduce adult numbers in eit her year. Rynaxypyr has been shown to be effective against various Lepidopterous pests (Ribbeck 2007), leaf rollers in apples (Puciennik and Olszak 2009), and several sugar cane pests including termites and early shoot borer (Rajavel et al. 2009, Singh et al 2009). It may prove useful against thrips, but further research is necessary. The QRD 452 high rate treatment had signific antly higher numbers of thrips adults than the control 2 days post treat ment. QRD 452 is an extract of C. ambrosioides commonly called Mexican Tea. It produces an odor that is pleasing to the human nose (E. Rhodes personal observation). It is po ssible that QRD 452 may contain a volatile that is attractive to F. bispinosa There are a number of floral volatiles that are attractive to various species of flower thrips (Lewis 1997). However, further research is needed to substantiate this hypothesis. The main reason for the presence of only a few significant results is the very low numbers of both thrips adults and larvae that we re present in the SHB blueberry flowers during all three years. Neit her numbers of thrips larvae nor adults exceeded an average of 0.5 thrips per flower in 2007 or 2009. Thri ps numbers were higher before the second application of insecticides in 2008, but a violent storm t hat blew through the area on 137

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March 7 prevented sampling on that date and mo st likely washed the treatments off of the blueberry plants. Numbers of thrips larv ae per flower were also low in the RE blueberries. Overall, both spinetoram and Rynaxypyr performed as well as malathion and SpinTor while QRD 452 appeared to cause an in crease thrips numbers. Spinetoram, now registered in blueberries as Delegate TM has become another tool for thrips control in blueberries. Further trials must be done before any firm conclusions on the effectiveness of Rynaxypyr and QRD 452 against th rips in blueberries can be drawn. 0 0.2 0.4 0.6 0.8 1 31-Jan6-Feb12-Feb18-Feb24-Feb2-Mar8-Mar DateAverage thrips larvae per flower Con Mal Ryn SpT XDEL XDEH a ab ab b b b A 0 0.2 0.4 0.6 0.8 1 31-Jan6-Feb12-Feb18-Feb24-Feb2-Mar8-Mar DateAverage thrips adults per flower Con Mal Ryn SpT XDEL XDEH B Fig. 7-1. Average thrips A) larvae and B) adults per flower in each treatment on each sampling date. Arrows i ndicate dates when treatments were applied. Error bars indicate standard error of the mean. Means with the same letter are not significantly different from each other at P = 0.05. 138

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0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 20-Feb26-Feb3-Mar9-Mar15-Mar DateAverage thrips larvae per flower Con Mal Ryn SpT Spm A 0 0.2 0.4 0.6 0.8 1 1.2 1.4 20-Feb26-Feb3-Mar9-Mar15-Mar DateAverage thrips adults per flower Con Mal Ryn SpT Spm B 0 0.1 0.2 0.3 0.4 20-Feb 26-Feb DateAverage thrips adults per flower Con Mal Ryn SpT Spm a b ab ab ab b a ab ab ab C Fig. 7-2. Average thrips A) larvae and B) adults per flower in each treatment on each sampling date and C) adults per flower during the first three sampling weeks as indicated by the box in B). Arrows indicate dates when treatments were applied. Error bars indicate standard erro r of the mean. Means with the same letter are not significantly di fferent from each other at P = 0.05. 139

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0 0.1 0.2 0.3 0.4 0.5 0.6 17-Feb24-Feb3-Mar10-Mar17-Mar DateAverage thrips larvae per flower Con Mal SpT Del QRDL QRDH A 0 0.1 0.2 0.3 0.4 0.5 0.6 17-Feb24-Feb3-Mar10-Mar17-Mar DateAverage thrips adults per flower Con Mal SpT Del QRDL QRDH B Fig. 7-3. Average thrips A) larvae and B) adults per flower in each treatment on each sampling date. Arrows indicate date s pesticides were applied. Error bars indicate standard error of the mean. Means with the same letter are not significantly different from each other at P = 0.05. 140

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0 1 2 3 4 5 31-Mar3-Apr 6-Apr 9-Apr DateAverage thrips larvae per flower Con Mal SpT Del QRDL QRDH A 0 10 20 30 40 50 60 31-Mar3-Apr 6-Apr9-Apr DateAverage thrips larvae per flower Con Mal SpT Del QRDL QRDH a ab bc cd cd d B Fig. 7-4. Average thrips A) larvae and B) adults per flower in each treatment on each sampling date. The Arrow indicates the date the pesticides were applied. Error bars represent standard error of the mean. Means with the same letter are not significantly diffe rent from each other at P 0.05. Table 7-1. Percent of each thri ps species per treatment in 2007 F. bispinosaF. fuscaF. occidentalisT. hawaiiensisT. piniCon78.012.22.4 4.92.4 Mal74.54.32.1 19.10 Ryn70.010.06.7 103.3 SpT80.65.62.8 5.65.6 XDEL73.94.313.0 0 8.7 XDEH64.30 3.6 7.125.0 The total thrips sampled from each treatment were: control (Con) 41, malathion (Mal) 47, Rynaxypyr (Ryn) 30, SpinTor (SpT) 36, XDE-175 low rate (XDEL) 23, XDE-175 high rate (XDEH) 28. 141

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Table 7-2. Average number of other arthropods per flower in each treatment for the season in 2007 Acari:: Phytoseiidae Araneae ColeopteraDiptera Hemiptera: Aphidae other Hemiptera Lepidoptera larvae Con0.0020.0020.0020.080000.002 Mal00.00200.1350.0060 0 Ryn0 00.0040.117000.002 Sp0.002000.1910.0570 0 XDEL0 000.1350.0020.0020 XDEH0.0020.0020.0020.1780.0130 0 Table 7-3. Percent of each thri ps species per treatment in 2008 F. bispinosaF. fuscaF. occidentalisT. hawaiiensisT. pini Franklinothrips sp. Con65.80.7 0 15.414.8 3.4 Mal64.91.50.7 6.026.9 0 Ryn66.90 0 7.024.2 1.9 SpT69.50 0 15.614.3 0.6 Spm68.31.6 0 12.217.9 0 The total thrips sampled from each treatment were: control (Con) 149, malathion (Mal) 134, Rynaxypyr (Ryn) 157, SpinTor (SpT) 154, spinet oram (Spm) 123. Table 7-4. Average number of other arthropods per flower in each treatment for the season in 2008 Acari: Phytoseiidae AraneaeHymenoptera Diptera Hemiptera: Aphidae other Hemiptera Lepidoptera larvae Coleoptera: Curculionidae Con00.00190.00180.0983000.00190.0019 Mal0000.062400 0 0 Ryn0.0052000.113800.00180 0 SpT0.0067000.08710.004700.00270 Spm0.0204000.172200 0 0 Table 7-5. Percent of each thrips species pe r treatment in the SHB blueberries in 2009 F. bispinosaF. fuscaF. occidentalisT. hawaiiensisT. pini Franklinothrips sp.H. graminisCon79.204.2 00 16.7 0 Mal80.00 0 013.3 0 6.7 SpT90.00 0 05.0 0 5 Del78.67.10 014.3 0 0 QRDL78.34.30 4.34.38.7 0 QRDH85.74.80 09.5 0 0 The total thrips sampled from each treatment were: control (Con) 24, malathion (Mal) 15, SpinTor (SpT) 20, Delegate TM (Del) 14, QRD-452 low rate (QRDL) 23, QRD-452 high rate (QRDH) 21. 142

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Table 7-6. Average number of other arthropods per flower in each treatment for the season in the SHB blueberries in 2009 AraneaeHymenoptera Hymenoptera: Formicidae Diptera Hemiptera: Aphidae other Hemiptera Con0 0 0.424104.26330.0588 Mal0 0 00.15661.01220 SpT0.03850 0.0501.86890 Del00.0333 00.03577.24330 RqL0 0 0 01.31300 RqH0.03450 0.19340.04761.37070 Table 7-7. Percent of each thrips species per treatment in the RE blueberries in 2009 F. bispinosaT. piniH. graminis Con10000 Mal10000 SpT10000.6 Del99.400 QRDL10000 QRDH10000 A total of 180 thrips were sampled from each treatment. Table 7-8. Average number of other arthropods per flower in each treatment for the season Acari: Phytoseiidae Hymenoptera: FormicidaeDiptera Hemiptera: Aphidae other Hemiptera Con0.05880.52780.06250.250.0625 Mal0 0.062500.40 SpT0 0.054600.2032190 Del0 0 0.50.22230 RqL0 0.6 00.28890.0556 RqH0 0 00.11110 143

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CHAPTER 8 CONCLUSIONS Results related to 5 objectives were pres ented in this dissertation. The objective were: 1) to examine souther n highbush blueberry plantings and adjacent fields for alternate hosts of flower thri ps and thrips dispersal from these host plants into blueberry plantings, 2) to determine the relationship bet ween populations of thrips and yield in southern highbush blueberries and to determine an action threshold for thrips in southern highbush blueberries, 3) to model the s patial distribution of flower thrips in a blueberry planting utilizing geostatistic al methods and to determine optimum trap spacing, 4) to determine if hot spots are correlated with flower density, and 5) to determine the potential of using several expe rimental reduced-risk insecticides to manage flower thrips in Florida blueberries. In the preliminary plant surve ys, several reproductive hosts of Frankliniella bispinosa were found. However, F. bispinosa developed in a white clover field and blueberry planting simultaneously. Also, the highest numbers of thrips were often found in the center of the blueberry planting. Other reproductive hosts still need to be examined as sources of flower thrips in blueberry planti ngs, but results suggest that thrips persist and overwinter in blueberry plantings. The studies performed to examine objective 2 revealed that different varieties will attract significantly different numbers of thrips. Varieties like Emerald, which flower early and uniformly, appear to attract high numbers of thrips. However, this does not necessarily lead to a significant difference in yield among varieties. Because of these differences, economic injury levels may have to be developed for individual varieties or for groups of varieties with similar flowering characteristics. Observations indicate that 144

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varietal differences are minimized when di fferent varieties are interplanted evenly among each other, but further research is needed to substantiate this hypothesis. The spatial distribution study conducted for objective 3 revealed that both inverse distance weighting and kriging can be used to model flower thrips spatial distribution in blueberries. The choice between the tw o would depend upon the objectives of a particular study and the number of sample poi nts to be taken. Semivariogram analysis showed that white sticky traps should be spaced at least 28.8 m apart to ensure that all samples are spatially i ndependent from each other. The correlation study, objective 4, conducted on the Inverness farm provided evidence that hot spots may be correlat ed with flower density. Further research incorporating more accurate measures of flower density is needed to confirm these findings. Further research is also needed to determine if Incorporating temperature and other environmental factors would prove beneficial. In the efficacy trials conducted for objecti ve 5, spinetoram was the most effective of the reduced-risk compounds tested in reducing flower thrips number s. In 2008, it was registered for use in southern hi ghbush blueberries as Delegate TM Rynaxypyr showed some promise and should be tested further. Tr ials examining the efficacy of QRD-452 are ongoing. The overall goal of this project was to improve monitoring and management of flower thrips in southern highbush blueberries in Florida. Awareness of the potential effects of variety, flower density, and temperature on thrips density and spacing traps at least 28.8 m apart should improve monito ring. Management can be improved by planting no more than two cons ecutive rows of the same variety and by making proper 145

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use of Delegate TM Further research into the various topics addressed in this dissertation will bring more improvement to flower thrips management in Florida blueberries. 146

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BIOGRAPHICAL SKETCH Born and raised in Miami, Elena Marion Rh odes has lived all of her 29 years in Florida. She earned her bachelor's degree in biology at New College of Florida in Sarasota in May of 2003. During her seventh semester, she interned in the Invertebrate Laboratory of Archbold Biological Station in Lake Placid, Florida. While there, she completed a project on backswimmer popula tion ecology, which became her senior thesis project. She graduated with a masters degree in entomology fr om the University of Florida in 2005. Her thesis investigated pr edator-prey relationships in an attempt to control twospotted spider mites in strawberries With the completion of this dissertation, she received her Ph.D. from the Universi ty of Florida in August of 2010. This dissertation is the culmination of her wo rk on the ecology and management of flower thrips in Florida blueberries. She is a member of the Ga mma Sigma Delta honors society of agriculture and the Talking Gators Toastmasters club. 157