Florida wild turkey nest site selection and nest success across multiple scales

MISSING IMAGE

Material Information

Title:
Florida wild turkey nest site selection and nest success across multiple scales
Physical Description:
1 online resource (71 p.)
Language:
english
Creator:
Olson,John M
Publisher:
University of Florida
Place of Publication:
Gainesville, Fla.
Publication Date:

Thesis/Dissertation Information

Degree:
Master's ( M.S.)
Degree Grantor:
University of Florida
Degree Disciplines:
Wildlife Ecology and Conservation
Committee Chair:
Giuliano, William M
Committee Members:
Ober, Holly Karina
Willcox, Emma

Subjects

Subjects / Keywords:
florida -- habitat -- nest -- osceola -- selection -- success -- turkey
Wildlife Ecology and Conservation -- Dissertations, Academic -- UF
Genre:
Wildlife Ecology and Conservation thesis, M.S.
bibliography   ( marcgt )
theses   ( marcgt )
government publication (state, provincial, terriorial, dependent)   ( marcgt )
born-digital   ( sobekcm )
Electronic Thesis or Dissertation

Notes

Abstract:
Landscapes and land-use practices in Florida continue to change and possibly degrade the quality of habitat available to Florida wild turkey hens (Meleagris gallopavo osceola) with respect to their nest site selection and subsequent success. This study attempted to understand wild turkey hen nest site selection and habitat effects on success at the microhabitat, patch, and landscape levels using logistic regression and AIC model selection at two sites in southern Florida, 2008-2010. Hens selected nest sites in dense vegetation (e.g., saw palmetto; Serenoa repens) that provided lateral and vertical cover for concealment at the microhabitat level (i.e., area within 7 m of the nest bowl), while selecting for a more open habitat at the patch level (i.e., 0.25 ha area surrounding the nest). This presumably allowed hens to survey the area for predators prior to ingress or egress, while also providing concealment. At the landscape level, hens continued this trend, selecting for areas characterized by patchy, dense, hardy vegetation, increasing possible nest locations, while allowing access to forage locations and brood rearing habitat. Areas in which vegetation was managed (i.e., areas burned or roller-chopped) were avoided. Successful hens (i.e., hatching of ?1 egg) selected for lower basal area and dense saw palmetto cover at the microhabitat level and more open habitat at the patch level. At the landscape level, nest success was associated with a greater distance from habitat edges and areas burned 0.5-2 years prior, which may have decreased the probability of predation by locating nests in the center of habitat patches, away from edge corridors. Overall, it appears that a combination of treatments, both prescribed burning and roller-chopping, may best benefit Florida wild turkey hens by creating a mosaic habitat characterized by patches of dense vegetation within an open landscape.
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 John M Olson.
Thesis:
Thesis (M.S.)--University of Florida, 2011.
Local:
Adviser: Giuliano, William M.
Electronic Access:
RESTRICTED TO UF STUDENTS, STAFF, FACULTY, AND ON-CAMPUS USE UNTIL 2012-02-29

Record Information

Source Institution:
UFRGP
Rights Management:
Applicable rights reserved.
Classification:
lcc - LD1780 2011
System ID:
UFE0043405:00001


This item is only available as the following downloads:


Full Text

PAGE 1

FLORIDA WILD TURKEY NEST SITE SELECTION AND NEST SUCCESS ACROSS MULTIPLE SCALES By JOHN M. OLSON A THESIS PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DE GREE OF MASTER OF SCIENCE UNIVERSITY OF FLORIDA 2011

PAGE 2

2 2011 J ohn M. Olson

PAGE 3

3 To my family and Rosemary, LLC

PAGE 4

4 ACKNOWLEDG MENTS I would like to extend the sincerest of thanks to my parents, family, and fianc, who have alw ays supported me and pushed me to do more. I would also like to thank Dr. William Giuliano, Dr. Holly Ober, Dr. Emma Willcox, and John Denton for their guidance and support; Mitchell Blake and the technicians who assisted in data collection for their part s in this project; and the University of Florida and the Florida Fish and Wildlife Conservation Commission for providing the financial and technical support necessary to the project.

PAGE 5

5 TABLE OF CONTENTS page ACKNOWLEDG MENTS ................................ ................................ ................................ .. 4 LIST OF TABLES ................................ ................................ ................................ ............ 6 LIST OF FIGURES ................................ ................................ ................................ .......... 7 ABSTRACT ................................ ................................ ................................ ..................... 8 CHAPTER 1 INTRODUCTION ................................ ................................ ................................ .... 10 Study Objectives ................................ ................................ ................................ ..... 12 Study Sites ................................ ................................ ................................ .............. 12 2 METHODS ................................ ................................ ................................ .............. 14 Data Collection ................................ ................................ ................................ ....... 14 Analysis ................................ ................................ ................................ .................. 19 3 RES ULTS ................................ ................................ ................................ ............... 28 Selection ................................ ................................ ................................ ................. 28 Success ................................ ................................ ................................ .................. 30 4 DISCUSSION ................................ ................................ ................................ ......... 57 Selection ................................ ................................ ................................ ................. 57 Success ................................ ................................ ................................ .................. 61 LIST OF REFERENCES ................................ ................................ ............................... 66 BIOGRAPHICAL SKETCH ................................ ................................ ............................ 71

PAGE 6

6 LIST OF TABLES Table page 2 1 Variable names, abbreviations, and definitions used in a priori models to predict nest ha bitat selection and success at microhabitat and patch levels ...... 22 2 2 Variable categories, names, abbreviations, and definitions used in a priori models to predict nest habitat selection and succes s at the landscape level ..... 23 3 1 Ranked models used to predict nest habitat selection at the microhabitat level ................................ ................................ ................................ ........................... 33 3 2 Parameter estima tes, 95% confidence intervals (CI), and odds ratios (OR) for most supported models for selection at the microhabitat level ........................... 34 3 3 Ranked models used to predict nest habitat selection at the micro habitat level ................................ ................................ ................................ ........................... 35 3 4 Parameter estimates, 95% confidence intervals (CI), and odds ratios (OR) for most supported models for selection at the patch level ................................ ...... 36 3 5 Ranked models used to predict nest habitat selection at the landscape level .... 37 3 6 P arameter estimates, 95% confidence intervals (CI), and odds ratios (OR) for most supp orted models for selection at the landscape level .............................. 43 3 7 Most supported a priori model(s) from each variable category predicting nest habitat selection at the landscape level ................................ .............................. 44 3 8 Ranked models used to predict nest success at the microhabitat level .............. 45 3 9 Parameter estimates, 95% confidence intervals (CI), and odds ratios (OR ) for most supported models for success at the microhabitat level ............................ 46 3 10 Ranked models used to predict nest success at the patch level ......................... 47 3 11 Parameter estimates, 95% confidence intervals (CI), and odds ratios (OR) for most supported models for success at the patch level ................................ ....... 48 3 12 Ranked models used to predict nest success at t he landscape level ................. 49 3 13 Parameter estimates, 95% confidence intervals (CI), and odds ratios (OR) for most supported models for success at the landscape level ................................ 55 3 14 Most supported a priori model(s) from each variable category predicting nest success at the landscape level ................................ ................................ ........... 56

PAGE 7

7 LIST OF FIGURES Figure page 2 1 Schematic of vegetation sampling plot for microhabitat level ............................. 26 2 2 Schematic of vegetation sampling plot for patch level ................................ ........ 27

PAGE 8

8 Abstract of Thesis Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Master of Science FLORIDA WILD TURKEY NEST SITE SELECTION AND NEST SUCCESS ACROSS MULTIPLE SCALES By Jo hn M. Olson August 2011 Chair: William M. Giuliano Major: Wildlife Ecology and Conservation Landscapes and land use practices in Florida continue to change and possibly degrade the quality of habitat available to Florida wild turkey hens ( Meleagris ga l lopavo o sceola ) with respect to their nest site selection and subsequent success. T his study attempted to understand wild turkey hen nest site selection and habitat effect s on success at the microhabitat, patch, and landscape levels using logistic regress ion and AIC model selection at two sites in southern Florida, 2008 2010. Hens selected nest sites in dense vegetation (e.g., saw palmetto ; Serenoa repens ) that provided lateral and vertical cover for concealment at the microhabitat level (i.e., area withi n 7 m of the nest bowl), while selecting for a more open habitat at the patch level (i.e., 0.25 ha area surrounding the nest). This presumably allowed hens to survey the area for predators prior to ingress or egress, while also providing concealment. At the landscape level, hens continued this trend, selecting for areas characterized by patchy, dense, hardy vegetation, increasing possible nest locations, while allowing access to forage locations and brood rearing habitat. Areas in which vegetation was ma naged (i.e., areas burned or roller chopped) were avoided lower basal area and dense saw palmetto cover at the microhabitat level and more open

PAGE 9

9 habitat at the patc h level. At the landscape level, nest success was associated with a greater distance from h abitat edges and areas burned 0.5 2 years prior, which may have decreased the probability of predation by locating nests in the center of habitat patches, away from edge corridors. Overall, i t appears that a combination of treatments, both prescribed burn ing and roller chopping, may best benefit Florida wild turkey hens by creating a mosaic habitat characterized by patches of dense vegetation within an open landscape.

PAGE 10

10 CHAPTER 1 INTRODUCTION In Florida, changing land use practices may degrade or destroy native habitats, through urban development, road construction, fragmentation, conversion to agriculture, fire exclusion, invasive species, and changes in natural disturbance regimes. Of particular importance is change in disturbance regimes such as the fr equency and timing and a switch to dormant season burning has led to the proliferation and ultimate dominance of woody shrubs, which, in high densities, degrades hab itat quality for many species dependent upon early successional habitats with more open understories. In much of the native rangeland and forest remaining in Florida, saw palmetto has become the dominant understory component due to changes in fire regimes ( Tanner and Marion 1990). This has resulted in a reduction of native grasses, herbaceous plants, and shrubs beneficial to wildlife as food and cover (Tanner et al. 1986). These c onditions may also make some habitat unusable or inaccessible by form ing ba rriers to wildlife movement. In recent years, many managers have sought to mitigate the proliferation and abundance of problematic woody shrub species. This type of management typically involves habitat restoration through treatments such as prescribed fire and roller chopping. These two treatments aid in opening the understory, allowing herbaceous plants and other vegetation of va (Willcox and Giuliano 2010 ) R esearch has shown that these practices can improve h abitat quality for many species, including several species of critical concern for the state of Florida such as the gopher tortoise ( Gopherus polyphemus ) and red cockaded woodpecker ( Picoides borealis ), and popular game species such as northern bobwhite

PAGE 11

11 ( C olinus virginianus ). On many public and private lands, managers have implemented habitat restoration specifically designed to benefit northern bobwhite. However, whether this type of management benefits Florida wild turkey ( Meleagris gallopavo osceola ) h ens, their nest site selection, or their nesting success has yet to be determined. L ittle is known about Florida wild turkey nest habitat selection and its effects upon nest success. Williams and Austin (1988), Williams (1991), and Dickson (1992) characte rized Florida wild turkey nests They reported Florida wild turkey hens select areas in transition zones between palmetto prairie and oak scrub where they could conceal themselves Williams and Austin (1988) reported that hens favored saw palmetto, spec that hens nesting in dense vegetation were less likely to flush, reducing detection probability. H owever much of this is only anecdotal evidence. Additionally, although it is prese ntly unknown whether prescribed burning and roller chopping benefit nesting Florida wild turkeys, these treatments may provide complex vegetation structure preferred by nesting hens (Badyaev 1995). Several projects have examined habitat selection of other wild turkey subspecies found in the United States, particularly the eastern subspecies ( Meleagris gallopavo silvestris ) but are equivocal Research indicated that eastern wild turkey hens in the S outheast selected for denser understories and more open mi dstories for nesting, though higher levels of visual obstruction due to lateral cover at the nest was the most importan t factor in selection (Godfrey and Norman 2001 ). Others have found that hens selected against bottomland hardwoods in favor of pine ( Pin us spp. ) stands, and that nests located in areas with less lateral cover, closer to roads and edges, and in forested habitats were

PAGE 12

12 more successful than those that were not (Seiss et al. 1990 ). Badyaev (1995) found that eastern wild turkey hens preferred c over types that had lower overstory densities and fewer trees of all classes, and successful nests better concealed incubating hens by having denser lateral and vertical cover while having lower densities of large trees. Seiss et al. (1990) found no selec tion for burn age by nesting hens, but Hon et al. (1978) found hens in Georgia selected for recently burned areas, while Exum et al. ( 1987) found hens in Alabama selected areas not recently burned. Research suggests n est success as the most important fac tor affec ting wild turkey population growth and ultimate size ( Seiss et al. 1990, Roberts and Porter 1996) h abitat often drives nest success and h abitat selection is a hierarchical process where birds select features at different scales (Johnson 1980, La zarus and Porter 1985, Thogmartin 1999) Therefore, t o better manage the unique Florida wild turkey subspecies, further information is needed to understand habitat determinants of nest success and how management practices such as roller chopping and presc ribed burning affect Florida wild turkey hen nest site selection and success. Study Objectives My objectives for this project were to: 1) determine what nest site characteristics ial scales, 2) discern how habitat affects nest success, and 3) evaluate how nest site selection relates to success. Study Sites I conducted this study on two sites in south central Florida from 2008 2010. The first site was Three Lakes Wildlife Management Area (WMA), located in Osceola County, Florida. Data collecti on was limited to the 6,273 ha Quail Enhancement Area where managers conducted frequent prescribed burning and roller chopping Three Lakes

PAGE 13

13 WMA consists primarily of pine flatwoods, though there are also intermin gled hammocks, swamps, and wet and dry prairies (Florida Natural Areas Inventory 2010). The state of Florida owns Three Lakes WMA, and the Florida Fish and Wildlife Conservation Commission (FWC) executes management and allow s the public to hunt the proper ty for white tailed deer ( Odocoileus virginianus ), feral hog ( Sus scrofa ), northern bobwhite, small game, and wild turkey. The second site was Longino Ranch, located in Sarasota County, Florida. Longino Ranch encompasses approximately 4,040 ha, with 2,020 ha used for the production of cattle, sod, and citrus. The remaining 2,020 ha are in pine flatwoods, wet and dry prairies, and oak cabbage palm hammocks (Florida Natural Areas Inventory 2010). Longino Ranch conducts prescribed burns and roller chopping, but not on the scale of Three Lakes WMA. Longino ranch historically managed solely for timber, but now manage s for both timber and cattle. The ranch managers operate deer, feral hog, and wild turkey hunts for the owning family.

PAGE 14

14 CHAPTER 2 METHODS Data Collection I prepared capture sites (n = 20 35/year ; January February ) within the Quail Enhancement Area on Three Lakes WMA and within the boundaries of Longino Ranch. I baited each capture site with cracked corn or three grain scratch feed, and prepared rocket nets at sites only after confirm ing use by female turkeys through the presence of tracks and excrement I used rocket nets to capture turkeys from January to early March each year from 2008 2010 on both study sites (Bailey et al. 1980) Upon firin g nets, I secured captured turkeys, and subsequently placed each into cardboard boxes specifically designed to contain wild turkeys. Then, I fitted each captured hen with standard numbered metal leg bands and backpack style radio transmitters with mortali ty switches (ATS transmitters, model A1540, 69 80 grams [not including harness material ] : weighing <3.5% of birds body weight). I aged, weighed, and administered a dose of vitamin E to each captured hen in an attempt to offset the stress associated with c apture. Finally, I released turkeys within 45 minutes of capture at the capture location I located radioed hens remotely by triangulation (White and Garrot 1990) using radio receivers and hand held three element Yagi antennae from pre established telemet ry stations (n ~ 100 250 depending upon site and year) using the peak method (Fuller et al. 2005). To locate radioed hens, I recorded one azimuth from distinct telemetry stations within 15 minutes to reduce error associated with long distance movements of the radioed hen (Fuller et al. 2005). I entered recorded azimuths into the program Location of a Signal (LOAS; Ecological Software Solutions 2010) to map the estimated location of tracked hens and generate error polygons. I

PAGE 15

15 March until July 15 th each year 2008 2010. When observations indicated that a hen had initiated a nest and begun incubation (i.e., was fo und repeatedly in the same location), I recorded a nesting attempt if it could be confirmed by homing in on the nesting hen (Tirpak et al. 2006). I monitored active nests daily via telemetry, and when the hen was a way from her nest confirmed the status o f the nest visually careful to keep disturbance to a minimum in nesting areas. et al. 2006). Once a nest fate had been determined I measured habitat characteristics at multiple scales at nest sites and random locations. To characterize the microhabitat (i.e., the area with in 7 m of the nest bowl) I measur ed lateral cover (i.e., horizontal visual obstruction), vegetation cover, shrub height, basal area, and tree stem density in a 7 m radius plot centered on the nest bowl ( Table 1, Figure 1) I measured basal area of hardwo od, coniferous, and palm species separately from the center using a standard 10 BAF prism (Higgins et al. 2005) Only trees that measured >11.43 cm (4.5 in) diameter at brea s t height (dbh) were considered (Sparks et al. 2002). S tem counts of tree species >2.54 cm (1 in) dbh within the plot were tallied as hardwood, conifer, or palm species. I measured lateral cover by visually estimating total cover (%) of a 36 cm x 90 cm cover board placed at three equally spaced points along the perimeter of the 7 m pl ot (Higgins et al. 2005) To classify cover densities, I recorded estimates in one of six cove r classes (i.e., 1 = 0 3%, 2 = 4 12%, 3 = 1 3 2 6 5 1 7 6 100 %). Wh en estimating cover obstruction I viewed the cover board from the center point of plots at standing height (1.7 m), and used the mean of readings taken for analysis. I measured vegetation cover and shrub height below 1.5 m using three 7 m transects radiating from the plot center (Krebs

PAGE 16

16 1999) Canopy cover of saw palmetto below 1.5 m was estimated by line intercept divided by the total length of transects, expressed as a percentage (Higgins et al. 2005) To determine intercepts, I ignored intercepts <1 cm, while small openings <20 cm within individual plants or gaps <10 cm between individual plants were included as cover. On each transect, I recorded species and height of the tallest shrub (up to 150 cm) of the tallest point of shrub intercept (including shrubs and trees <2.54 cm dbh ; Higgins et al. 2005 ). For a simpl e estimate of total cover of all shrubs except saw palmetto, I summed the individual shrub estimates on all three transects for analysis. After recording the characteristics at the nest site, I recorded characteristics in a plot located a random distance and direction from the nest within the same habitat patch. To characterize vegetation at the patch level (i.e., area 0.25 ha around the nest bowl), I recorded vegetation characteristics in a circular plot 28 m in radius centered on the nest bowl (Figure 2) I measured lateral cover, vegetation cover, shrub height, basal area, and tree stem density (Table 1) Point centered ha bitat characteristics were measured in four 7 m radius circular plots, one centered on the nest site and three adjacent plots equally spaced around the center plot at 21 m, one on each of three transects run outwards at 120 (Krebs 1999) using the methods described for the microhabitat I measured vegetation cover and shrub height below 1.5 m using three 28 m transects radiating from the center plot at the same angles as the adjacent plots. Canopy cover of saw palmetto below 1.5 m was estimated by line i ntercept methods, calculated as the accumulated length intercepted by living and standing dead parts of palmetto divided by the total length of transects, expressed as a percentage (Higgins et al. 2005) In 7 m intervals along each transect, I recorded th e height (up to 150 cm) and

PAGE 17

17 species of the tallest point of shrub intercept (including shrubs and trees <2.54 cm dbh ; Higgins et al. 2005 ). I averaged all shrub measurements for analysis. To characterize nesting habitat at the landscape level, I used 95% fixed kernel home ranges generated for radioed hens using the Home Range Tools extension in ArcGIS (Environmental S ystems Research Institute 2009; Rodgers et al 2007) to obtain the median home range size for each study site and year. I censored hens with <30 locations. To define the study areas by site and year I used the Create Minimum Convex (Beyer 2004 ; Schad 2009 ) to create minimum convex polygons around all hen locations at each study site as generated b y LOAS with an estimated error of <10 ha. To delineate habitat cover types, I downloaded and imported Florida Natural Areas Inventory (FNAI) Cooperative Land Cover Map shapefiles (Florida Natural Areas Inventory 2010) into ArcGIS. I also downloaded Unite d States Geological Survey (USGS) orthophoto quadrangles to create shapefiles delineating landscape features such as roads and water features that were absent from the FNAI shapefiles. I projected the nest sites discovered during the three years of the p roject into ArcGIS and buffered each with a circular buffer equivalent to median home range size of birds for each site and year to establish landscape level use areas (Tirpak et al. 2010 ). To establish availability, I divided the study area size for each site and year by the median home range size for that site and year. This provided the number of home ranges that could fit into each study area. To obtain random points and establish availability, I arbitrarily multiplied the number of home ranges that could fit into each study area by five to increase sample and study area coverage.

PAGE 18

18 random points within each study area according to this formula and buffered each with a circular buffer equal in area to the media n home range size for each site and year (Tirpak et al. 2006) I created a total of 370 random points and corresponding buffers which ranged from 25 105 per study area per year. This method of establishing availability provided nearly total coverage of each study area. I developed four suites of variables (i.e., habitat, management, landscape, habitat habitat from the FNAI land cover shapefile and represent ed the are a of each particular habitat type found within the buffer of each nest point both random and actual (Table 2). I used the ArcGIS intersect function to merge actual and random buffers with the habitat shapefile to determine the area of each habitat within each buffer. Five habitat types (i.e., developed, bottomland forest, successional hardwood forest, sandhill, xeric hammock) r epresented less than one percent of each study area and were combined int o one category, which I labeled other. Additionally I created new categories to combine similar habitat types, including clearing and unimproved pasture into abandoned clearing, and hydric hammock and mesic hammock into hammock (Table 2). I divided management into two treatments (i.e., prescribed burning and roller chopping) and separated each treatment into five distinct age categories defined as: 1) treatment application <6 months prior to nest initiation, 2) treatment application 6 months 2 years prior to nest initiation, 3) treatment application >2 years prior to nest initiation, 4) no record of recent management, or 5) management records incomplete/unavailable ( Williams 1991; Table 2). To establish management age for random home ranges used in selection analyses I used median initiation dates for the

PAGE 19

19 r espective area and year. Study site managers provided records of management history and shapefiles were created according to these records. I then intersected this layer with buffer layers to obtain areas of different management ages within each buffer. The landscape category contained variables dealing with the area of and distance to road, habitat edge, and water (Table 2). I mapped together paved roads, dirt roads, firebreaks, and paths visible from aerial photographs, reasoning that if they were larg e enough to be detected with aerial photography, they were large enough to be travel ed by turkeys and therefore could affect turkey behavior. Then, I applied a 2.5 m buffer to all roads because this most closely resembled average width of roads present wi thin study sites as per my field experience. I intersected these landscape attributes with nest buffers to acquire areas of each within buffers. Finally, I used the near feature in ArcGIS to obtain distances from each nest to the nearest feature of each variable within this suite (Table 2). In the final landscape level variable category I combined both habitat type and management history to create habitat treatment variables that denoted particular management for several habitats. Habitats included were those that site managers targeted for management. To accomplish this, I used the identity function in ArcGIS to combine the FNAI habitat layer and the management layer into one. I intersected this new layer with the buffers around the nests and random p oints to obtain areas of habitat with treatment histories (Table 2). Analysis To analyze how Florida wild turkey hens select ed nest sites and how habitat affect ed success, I used an information theoretical approach and logistic regression in SYSTAT 12.0 (S YSTAT 2007) I used case control logistic regression to compare habitat variables present within the vegetation plots and their associated random points at the

PAGE 20

20 microhabitat level (i.e., characteristics from the 7m radius plot centered on the nest bowl; Ta ble 1). For patch level (i.e., vegetation characteristics within 0.25 ha area surrounding nest bowls; Table 1) selection analyses, I used case control logistic regression to compare habitat variables from nest plots and their respective random points To quantify selection at the landscape level (i.e., landscape attributes present within simulated circular home ranges around nest sites and random points; Table 2), I used logistic regression to compare the habitat present within simulated home ranges for s ite and year to habitat within equally sized random home ranges generated across study areas annually. I created models featuring each individual variable present at all three levels (Table 1, Table 2), models containing combinations of these variables, an d a null model. Based upon prior knowledge, project goals, and my own field experience, I also created a Information Criterion (AIC C ) adjusted for small sample size (n/ K<40 ), and considered C (Burnham and Anderson 2002) To rank model and variable importance, I used Akaike weights ( w ), and adjusted coefficients and odds ratios of competing models (Burnham and Anderson 1998). When 95% confidence intervals for va riables within supported models overlapped with zero, I considered them to have a weak effect on the dependent variable, and only indicate a trend. Finally, I examined both the best model from each landscape level category (e.g., management), and also th e best models from all landscape level categories combined to determine which had the greatest effect on wild turkey hen nest site selection.

PAGE 21

21 I used logistic regression to compare habitat of successful and unsuccessful nests at the microhabitat level (i.e. characteristics from the 7m radius plot centered on the nest bowl; Table 1), patch level (i.e., characteristics from the 0.25 ha area around each nest; Table 1) and landscape level (i.e., landscape attributes present within simulated circular home range s around nest sites; Table 2) I used methods as listed above for selection analyses to determine important factors influencing to nest success.

PAGE 22

22 Table 2 1. Variable names, abbreviations, and their definitions used in a priori models to predict nest hab itat selection and success at microhabitat and patch levels for Florida wild turkey hens in south Florida, 2008 2010. Variable Abbreviation Description Conifer basal area BAC Conifer basal area m 2 /ha Hardwood basal area BAH Hardwood basal area m 2 /ha Pa lm basal area BAP Palm basal area m 2 /ha Total basal area BAT Total basal area m 2 /ha Conifer stems STC Conifer stems no./ha Hardwood stems STH Hardwood stems no./ha Palm stems STP Palm stems no./ha Total stems STT Total stems no./ha Saw palmetto densi ty SD Saw palmetto density % Visual obstruction VO Visual obstruction % Shrub height SHT Shrub height cm

PAGE 23

23 Table 2 2 Variable categories, names, abbreviations, and their definitions used in a priori models to predict nest habitat selection and succes s at the landscape level for Florida wild turkey hens in south Florida, 2008 2010. Variable Category Variable Abbreviation Description Habitat Abandoned clearing A Ha of abandoned clearing Agriculture Ag Ha of agriculture Basin swamp BS Ha of basi n swamp Baygall BG Ha of baygall Bottomland forest BF Ha of bottomland forest Clearing C Ha of clearing Depression marsh DM Ha of depression marsh Dome swamp DS Ha of dome swamp Dry prairie DP Ha of dry prairie Hammock H Ha of hammock Hydric hammock HH Ha of hydric hammock Improved pasture IP Ha of improved pasture Mesic flatwoods MF Ha of mesic flatwoods Mesic hammock MH Ha of mesic hammock Other O Ha of other Pine plantation PP Ha of pine plantation Sand hill SH Ha of sand hill Scrub S Ha of scrub Scrubby flatwoods SF Ha of scrubby flatwoods Shrub bog SB Ha of shrub bog Successional hardwood forest SHF Ha of successional hardwoods forest Unimproved pasture UP Ha of unimproved pasture Upland hardwo od forest UHF Ha of upland hardwood forest Wet flatwoods WF Ha of wet flatwoods Wet prairie WP Ha of wet prairie Xeric hammock X Ha of xeric hammock Landscape Distance to edge DEDGE Distance to nearest habitat edge m Distance to roads DROAD D istance to nearest road m Distance to nearest edge DRD_DEDGE Distance to nearest habitat edge or road m Distance to water DWATER Distance to nearest water body m Road ROAD Total amount of road ha Edge EDGE Amount of habitat edge ha Edge tota l RD_EDGE Total amount of habitat edge and roads ha Water WATER Total amount of water ha Management Burn1 B1 Ha of area burned <6 months Burn2 B2 Ha of area burned between 6 months and 2 years Burn3 B3 Ha of area burned >2 years Burn4 B4 Ha of area with no recent burn history Burn5 B5 Ha of area with incomplete burn history Chop1 C1 Ha of area roller chopped <6 months Chop2 C2 Ha of area roller chopped between 6 months and 2 years Chop3 C3 Ha of area roller chopped >2 years Chop4 C4 Ha of area with no recent roller chopping history

PAGE 24

24 Table 2 2 Continued. Variable Category Variable Abbreviation Description Habitat Treatment Chop5 C5 Ha of area with incomplete roller chopping history Dry prairie1 DP1 Ha of dry prairie burned < 6 months Dry prairie2 DP2 Ha of dry prairie burned 6 months 2 years Dry prairie3 DP3 Ha of dry prairie burned >2 years Dry prairie4 DP4 Ha of dry prairie with no recent burn history Dry prairie5 DP5 Ha of dry prairie with incomplete burn history Mesic flatwoods1 MF1 Ha of mesic flatwoods burned <6 months Mesic flatwoods2 MF2 Ha of mesic flatwoods burned 6 months 2 years Mesic flatwoods3 MF3 Ha of mesic flatwoods burned >2 years Mesic flatwoods4 MF4 Ha of mesic flatwoods with no recent bu rn history Mesic flatwoods5 MF5 Ha of mesic flatwoods with incomplete burn history Pine plantation1 PP1 Ha of pine plantation burned <6 months Pine plantation2 PP2 Ha of pine plantation burned 6 months 2 years Pine plantation3 PP3 Ha of pine plan tation burned >2 years Pine plantation4 PP4 Ha of pine plantation with no recent burn history Pine plantation5 PP5 Ha of pine plantation with incomplete burn history Scrubby flatwoods1 SF1 Ha of scrubby flatwoods burned <6 months Scrubby flatwoods2 SF2 Ha of scrubby flatwoods burned 6 months 2 years Scrubby flatwoods3 SF3 Ha of scrubby flatwoods burned >2 years Scrubby flatwoods4 SF4 Ha of scrubby flatwoods with no recent burn history Scrubby flatwoods5 SF5 Ha of scrubby flatwoods with incom plete burn history Wet flatwoods1 WF1 Ha of wet flatwoods burned <6 months Wet flatwoods2 WF2 Ha of wet flatwoods burned 6 months 2 years Wet flatwoods3 WF3 Ha of wet flatwoods burned >2 years Wet flatwoods4 WF4 Ha of wet flatwoods with no recent burn history Wet flatwoods5 WF5 Ha of wet flatwoods with no recent burn Wet prairie1 WP1 Ha of wet prairie burned <6 months Wet prairie2 WP2 Ha of wet prairie burned 6 months 2 years Wet prairie3 WP3 Ha of wet prairie burned >2 years Wet prai rie4 WP4 Ha of wet prairie with no recent burn history Wet prairie5 WP5 Ha of wet prairie with incomplete burn history Chop_dry prairie1 DP C 1 Ha of dry prairie roller chopped <6 months Chop_dry prairie2 DP C 2 Ha of dry prairie roller chopped 6 months 2 years

PAGE 25

25 Table 2 2 Continued. Variable Category Variable Abbreviation Description Chop_dry prairie3 DP C 3 Ha of dry prairie roller chopped >2 years Chop_dry prairie4 DP C 4 Ha of dry prairie with no recent roller chopping history Chop_dry pra irie5 DP C 5 Ha of dry prairie with incomplete roller chopping history Chop_mesic flatwoods1 MF C 1 Ha of mesic flatwoods <6 months Chop_mesic flatwoods2 MF C 2 Ha of mesic flatwoods roller chopped 6 months 2 years Chop_mesic flatwoods3 MF C 3 Ha of mesic flatwoods roller chopped >2 years Chop_mesic flatwoods4 MF C 4 Ha of mesic flatwoods with no recent roller chopping history Chop_mesic flatwoods5 MF C 5 Ha of mesic flatwoods with incomplete roller chopping history Chop_scrubby flatwoods1 SF C 1 Ha of scru bby flatwoods <6 months Chop_scrubby flatwoods2 SF C 2 Ha of scrubby flatwoods roller chopped 6 months 2 years Chop_scrubby flatwoods3 SF C 3 Ha of scrubby flatwoods roller chopped >2 years Chop_scrubby flatwoods4 SF C 4 Ha of scrubby flatwoods with no r ecent roller chopping history Chop_scrubby flatwoods5 SF C 5 Ha of scrubby flatwoods with incomplete roller chopping history Chop_wet flatwoods1 WF C 1 Ha of wet flatwoods <6 months Chop_wet flatwoods2 WF C 2 Ha of wet flatwoods roller chopped 6 months 2 years Chop_wet flatwoods3 WF C 3 Ha of wet flatwoods roller chopped >2 years Chop_wet flatwoods4 WF C 4 Ha of wet flatwoods with no recent roller chopping history Chop_wet flatwoods5 WF C 5 Ha of wet flatwoods with incomplete roller chopping history Cho p_wet flatwoods2 WF C 2 Ha of wet flatwoods roller chopped 6 months 2 years

PAGE 26

26 Figur e 2 1 Schematic of vegetation sampling plot used to record vegetation characteristics and measurements to predict nest habitat selection and success at the microhabita t level for Florida wild turkey hens in south Florida, USA, 2008 2010.

PAGE 27

27 Figur e 2 2 Schematic of vegetation sampling plot used to record vegetation characteristics and measurements to predict nest habitat selection and success at the patch level for Fl orida wild turkey hens in south Florida, USA, 2008 2010

PAGE 28

28 CHAPTER 3 RESULTS During the three years of study, I captured and radioed 142 hens on the two study sites. I discovered 67 nests 27 of which were successful. At Three Lakes WMA I discovered 8, 8 and 14 nests in 2008, 2009, and 2010 respectively. I found 10, 10, and 17 nests in 2008, 2009, and 2010 respectively, at Longino Ranch. The leading cause of nest failure was depredation (n = 24), though nests also failed due to predation of the hen o n the nest (n = 8) and abandonment ( total n = 7; due to habitat management n = 3, due to observer interference n = 1, unknown cause n = 3). Habitat management efforts accounted for three cases of nest abandonment through prescribed fire (n = 2) and loggin to fate. I censored this nest and nests failing due to management or observer interference (n = 5 ) from both selection and success analyses because no data regarding veget ation characteristics could be recorded (i.e., vegetation characteristics hens selected were destroyed, or at minimum, radically changed after a prescribed fire) and these nests failed Selection At the microhabitat level of selection, I found three supported models (Table 3). The most supported model contained palm and conifer stem density and saw palmetto density. Saw palmetto density was the only variable for which the 95% confiden ce interval for the parameter estimate did not overlap with zero and indicated that hens selected nest sites with a greater amount saw palmetto (Table 4). The other models indicated that turkeys also selected for a greater density of palm stems. Trends

PAGE 29

29 s uggested that hens selected against increasing conifer and hardwood stem densities (Table 4). Six models were supported at the patch level, with the most supported model containing palm and hardwood stem densities (Table 5); however all 95% confidence inte rvals for parameter estimates overlapped with zero which limited interpretation (Table 6). T rends indicated that whil e hens selected higher densities of palm stems, they also selected for more open areas, manifested by lower hardwood, conifer, and total s tem and saw palmetto densities, and lower levels of visual obstruction. The habitat category for landscape level selection contained two supported models, with the best model containing the habitat types agriculture, dry prairie, mesic flatwoods, and wet flatwoods (Table 7). Agriculture, dry prairie, and mesic flatwoods had 95% confidence intervals of parameter estimates not containing zero, and suggested that hens selected for greater amounts of each ; while trends indicated that hens also selected for sc rubby flatwoods and wet flatwoods (Table 8). In the landscape category there were two supported models (Table 7). The best model contained the variables distance to road and distance to water. Both had 95% confidence intervals of estimates that did not overlap with zero, and suggested that hens selected sites further from roads and water (Table 8). Trends indicated that turkeys also selected sites that were located nearer to habitat edges (Table 8). There were six supported models at the landscape level of selection in the management category (Table 7). The best model contained the variables denoting areas burned 0.5 2 years ago, unburned, and unchopped; though only the 95% confidence interval of the estimate for the unchopped did not overlap with zero (Table 8). Th is

PAGE 30

30 suggested that hens selected for areas that had not received any roller chopping application. Trends for other parameter estimates indicated that hens selected against sites burned >6 months prior but selected for sites chopped >6 months prior (Table 8). The habitat treatment category had two supported models, and the most supported model contained unburned dry prairie, scrubby flatwoods, and mesic flatwoods, mesic flatwoods burned 0.5 2 years ago, mesic flatwoods chopped 0.5 2 years ago, and unchopped mesic flatwoods (Table 7). All parameters had confidence intervals containing zero except unchopped mesic flatwoods, which suggested that hens selected for greater amounts of this habitat treatment type (Table 8). Trends suggested that hen s selected for unburned scrubby flatwoods, unchopped and mesic flatwoods chopped 0.5 2 years ago, and against unburned dry prairie and mesic flatwoods and mesic flatwoods burned 0.5 2 years ago (Table 8). When I compared the best models from each of the la ndscape level categories only mod els from the management category were supported (Table 9) with the model s containing burned 0.5 2 years ago, unburned, and unchopped variables Additionally, I found that only the unchopped parameter had a 95% confidence interval of the estimate that did not overlap with zero ( Table 8, Table 9) suggesting that hens selected areas with greater amounts of unchopped vegetation Trends indicated h ens selected for areas unburned or chopped >6 months ago, while avoiding burns >6 months old. Success At the microhabitat level, nine models examining habitat differences between successful and unsuccessful nests were supported (Table 10). The most supported model contained only total basal area which had a 95% confidence interval not overlapping zero and indicated that successful nests were associated with a lower total

PAGE 31

31 basal area than unsuccessful nests (Table 11). Other supported models suggested that nest success was associated with a lower conifer basal area and higher saw pa lmetto density. Additionally, trends indicated that hens selecting areas with greater visual obstruction, hardwood basal area, and lower palm, conifer, and total stem density, and hardwood and conifer basal area were more likely to succeed (Table 11). Pat ch level nest success had three supported models (Table 12). The most supported model included only palm basal area, but parameters within all models had 95% confidence intervals that overlapped with zero limiting interpretation (Table 13). T rends indic ate d that successful nests had greater palm stem density and lower total and palm basal area than unsuccessful nests (Table 13). The habitat category at the landscape level had four supported models, with the most supported model containing scrubby flatwoo ds and wet flatwoods, but all parameter 95% confidence intervals contained zero, li miting interpretation (Table 14, Table 15). T rends suggested that when compared with unsuccessful nests, successful nests were more often associated with scrubby flatwoods, and less often with wet flatwoods and dry prairie (Table 15). In the landscape category the null model had the most support, though there were eight other supported models. As the null model had the most support, all results in this category must be int erpreted very conservatively. All parameters had 95% confidence intervals overlapping zero, but trends suggested that successful nests were located further from roads, habitat edge, and water than unsuccessful nests, while having more area of each within the home range (Table 15).

PAGE 32

32 Within the management category, I found f ive models supported at landscape level for success (Table 14). Both parameters within the most supported model had 95% confidence intervals that did not overlap with zero, and suggested that nests in areas that contained more burns 0.5 2 years old and fewer chops 0.5 2 years old were more likely to succeed (Table 15). All v ariables present in other models had 95% confidence intervals that overlapped with zero, but trends suggested that s uccessful nests had more burns 0.5 2 years old, but less area chopped >6 months ago and not chopped and burns <6 months old and unburned (Table 15). The habitat treatment category of landscape level nest success had two supported models, with the most sup ported model containing unburned dry prairie and mesic flatwoods chopped 0.5 2 years ago (Table 14). All parameters in both models had 95% confidence intervals that overlapped with zero, but trends indicated that successful nests were more often associate d with unburned dry prairie and less with dry prairie burned 0.5 2 years ago and mesic flatwoods chopped 0.5 2 years ago when compared to unsuccessful nests (Table 15). When I compared results among categories at the landscape level, six models had suppor t (Table 16). These models came from the habitat treatment, management, and habitat categories. The most supported model contained unburned dry prairie and mesic flatwoods chopped 0.5 2 years, but estimates of both parameters had 95% confidence intervals that overlapped with zero. Of the supported models, only two parameters had 95% confidence intervals not containing zero, and suggested that hens selecting areas with more burns and chops of age 0.5 2 years had greater success than those not associated w ith these treatments (Table 15).

PAGE 33

33 Table 3 1 A priori models, number of variables (K), second Information Criterion corrected for small sample size (AIC c ), distance from the lowest AIC c c ), and model weights (w i ) used to predict nest habitat selection at the microhabitat level for Florida wild turkey hen s in south Florida, 2008 2010, USA. Model K AIC C AIC C w i STP,STC,SD 3 59.18 0.00 0.31 STP,STH,SD 3 59.29 0.11 0.29 STP,SD 2 59 .52 0.34 0.26 BAT,STT,SD 3 62.01 2.83 0.07 BAT,STT,SD,VO 4 63.75 4.57 0.03 STC,STH,SD 3 65.07 5.89 0.02 STC,SD 2 66.11 6.92 0.01 STC,BAH,SD 3 67.66 8.48 4.42E 03 BAT,SD 2 68.11 8.93 3.52E 03 STT,SD 2 68.18 9.00 3.40E 03 BAC,BAH,SD,VO,STC,STH 6 71.4 1 12.22 6.79E 04 SD,VO 2 70.83 11.65 9.04E 04 SD 1 71.98 12.79 5.10E 04 BAC,BAH,SD 3 73.04 13.86 3.00E 04 BAC,SD 2 74.12 14.93 1.75E 04 BAC,STH,SD 3 74.47 15.29 1.47E 04 BAT,STT 2 74.90 15.72 1.18E 04 STP,STT 2 75.86 16.68 7.30E 05 BAT,STT,VO 3 77. 07 17.89 3.99E 05 STP,STC,STH 3 78.09 18.90 2.41E 05 STT 1 77.97 18.79 2.54E 05 STC 1 78.95 19.76 1.56E 05 STP,STC 2 79.70 20.52 1.07E 05 BAC,STC 2 79.85 20.67 9.95E 06 STT,VO 2 80.10 20.92 8.78E 06 STP,STH 2 80.58 21.40 6.90E 06 BAT 1 80.65 21.47 6.67E 06 BAH,STH 2 81.34 22.16 4.73E 06 STH 1 81.30 22.11 4.83E 06 STP 1 81.32 22.13 4.78E 06 STP,BAH 2 81.67 22.48 4.02E 06 STP,BAT 2 81.71 22.52 3.94E 06 BAC,BAH,STC,STH,SD,VO 6 83.14 23.96 1.92E 06 SHT 1 81.83 22.65 3.69E 06 BAT,BAC 2 82.01 22.8 3 3.38E 06 BAT,VO 2 82.10 22.92 3.23E 06 BAT,BAH 2 82.16 22.98 3.13E 06 BAH 1 82.05 22.87 3.31E 06 BAP 1 82.27 23.09 2.96E 06 STP,BAC 2 82.70 23.52 2.39E 06 BAC,STH 2 82.97 23.78 2.10E 06 VO 1 82.83 23.64 2.25E 06 STP,VO 2 83.16 23.97 1.91E 06 BAC 1 83.17 23.99 1.89E 06 STP,BAP 2 83.40 24.21 1.69E 06 BAC,BAH 2 83.57 24.39 1.55E 06 NULL 0 83.79 24.68 1.34E 06

PAGE 34

34 Table 3 2 Parameter estimates, 95% confidence intervals (CI), and odds ratios (OR) for variables used in supported a priori models to predict nest habitat selection at the microhabitat level of Florida wild turkey hens in south Florida, USA, 2008 2010. 95% CI Model Variable Estimate Lower Upper OR STP,STC,SD STP 0.191 0.178 0.560 1.210 STC 0.014 0.034 0.005 0.986 SD 0.066 0 .027 0.106 1.069 STP,STH,SD STP 0.293 0.050 0.535 1.340 STH 0.010 0.025 0.005 0.990 SD 0.065 0.026 0.104 1.067 STP,SD STP 0.279 0.029 0.529 1.321 SD 0.065 0.027 0.104 1.067

PAGE 35

35 Table 3 3 A priori models, number of variables (K), second order Ak Criterion corrected for small sample size (AIC c ), distance from the lowest AIC c c ), and model weights ( w i ) used to predict habitat selection at the patch level for Florida wild turkey hens in south Florida, 2008 2010, USA. Model K AIC C AIC C w i STP,STH 2 70.63 0.00 0.19 STP,STT 2 70.76 0.13 0.18 STP,STH,SD 3 71.75 1.13 0.11 STP,VO 2 72.02 1.39 0.09 STP 1 72.12 1.49 0.09 STP,STC,STH 3 72.57 1.94 0.07 STP,BAC 2 72.75 2.12 0.07 STP,BAT 2 73.15 2.52 0.05 STP,BAP 2 73.78 3.16 0. 04 STP,SD 2 74.22 3.59 0.03 STP,STC 2 74.23 3.60 0.03 STP,BAH 2 74.25 3.63 0.03 STP,STC,SD 3 76.29 5.66 0.01 STH 1 79.74 9.12 1.97E 03 BAC,STH,SD 3 80.70 10.08 1.22E 03 BAH,STH 2 80.99 10.36 1.06E 03 STC,STH,SD 3 81.48 10.86 8.26E 04 BAC,STH 2 81. 72 11.10 7.32E 04 STT 1 82.04 11.42 6.25E 04 BAP 1 82.31 11.68 5.47E 04 BAT 1 82.39 11.77 5.25E 04 BAT,STT 2 82.62 12.00 4.68E 04 VO 1 82.95 12.33 3.96E 04 BAT,VO 2 83.52 12.90 2.98E 04 STT,SD 2 83.53 12.91 2.96E 04 SD 1 83.54 12.91 2.96E 04 SHT 1 83.56 12.94 2.92E 04 BAC 1 83.61 12.98 2.85E 04 BAT,STT,SD 3 83.65 13.02 2.80E 04 BAH 1 83.70 13.07 2.73E 04 STC 1 83.72 13.09 2.71E 04 STT,VO 2 83.74 13.11 2.68E 04 NULL 1 83.86 13.24 2.52E 04 BAT,SD 2 83.87 13.24 2.51E 04 BAT,STT,VO 3 84.33 13 .70 1.99E 04 BAT,BAC 2 84.39 13.77 1.93E 04 BAT,BAH 2 84.50 13.88 1.83E 04 STC,SD 2 84.94 14.32 1.46E 04 SD,VO 2 85.10 14.47 1.36E 04 BAC,SD 2 85.21 14.58 1.28E 04 BAC,BAH 2 85.55 14.92 1.08E 04 BAC,STC 2 85.61 14.98 1.50E 04 BAT,STT,SD,VO 4 85.95 15.33 8.84E 05 BAC,BAH,STC,STH,SD,VO 6 86.28 15.65 7.51E 05 BAC,BAH,SD,VO,STC,STH 6 86.28 15.65 7.51E 05 STC,BAH,SD 3 86.97 16.34 5.32E 05 BAC,BAH,SD 3 87.23 16.60 4.67E 05

PAGE 36

36 Table 3 4 Parameter estimates, 95% confidence intervals (CI), and odds r atios (OR) for variables used in supported a priori models to predict nest habitat selection at the patch level of Florida wild turkey hens in south Florida, USA, 2008 2010. 95% CI Model Variable Estimate Lower Upper OR STP,STH STP 0.045 0.020 0.11 0 1.046 STH 0.001 0.003 0.000 0.999 STP,STT STP 0.048 0.019 0.115 1.049 STT 0.001 0.003 0.000 0.999 STO,SH,SD STP 0.043 0.022 0.108 1.044 STH 175.000 0.004 0.000 0.998 SD 0.008 0.022 0.007 0.993 STP,VO STP 0.055 0.019 0.130 1.057 VO 0.016 0.039 0.006 0.984 STP STP 0.047 0.018 0.112 1.048 STP,STC,STH STP 0.046 0.020 0.112 1.047 STC 0.001 0.003 0.002 0.999 STH 0.002 0.003 0.000 0.998

PAGE 37

37 Table 3 5 A priori models, number of variables (K), second ion Criterion corrected for small sample size (AIC c ), distance from the lowest AIC c c ), and model weights ( w i ) used to predict habitat selection at the landscape level Florida wild turkey hens in south Florida, 2008 2010, USA. Category Model K AIC C AIC C w i Habitat AG,DP,MF,WF 4 205.69 0.00 0.39 AG,DP,MF,WF,SF 5 206.58 0.89 0.25 AF,DP,MF,WF,SF,DS 6 208.32 2.63 0.11 AG,DP,DS,MF,SF,WF 6 208.32 2.63 0.11 DS,MF,SF,WF,AG 5 208.32 2.63 0.11 AG,DP,MF 3 211.76 6.07 0.02 AG,DP,DS,MF,SF,BS 6 213.26 7.57 0.01 AG,DP,DS,MF,SF 5 213.44 7.76 0.01 MF,DP,SF,WF 4 217.38 11.69 1.14E 03 DP,DS,MF,SF,WF 5 219.39 13.70 4.16E 04 AG,MF 2 219.77 14.08 3.44E 04 MF,SF,WF 3 220.53 14.85 2.37E 04 SF,S,SH,MF,WF,DP 6 221.27 15.58 1.62E 04 MF,WF 2 223.07 17. 39 6.59E 05 MF,DP 2 224.27 18.59 3.62E 05 MF,SF,DP 3 225.37 19.69 2.09E 05 DS,DP,MF 3 226.20 20.51 1.38E 05 SF,DS,DP,MF 4 227.38 21.70 7.64E 06 DS,MF 2 228.66 22.98 4.03E 06 SF,S,SH,MF,DP 5 229.17 23.48 3.13E 06 MF 1 231.33 25.65 1.06E 06 S F,S,SH,MF 4 231.96 26.27 7.76E 07 SF,DS 2 239.47 33.78 1.81E 08 DP,WF,SF 3 240.50 34.81 1.08E 08 SF,DS,DP 3 240.93 35.24 8.75E 09 DS 1 242.48 36.79 4.03E 09 DS,DP 2 242.79 37.10 3.45E 09 BS 1 247.48 41.80 3.30E 10 DP,WF 2 247.72 42.03 2.94E 1 0 SF,WF 2 248.55 42.87 1.93E 10 AG,DP 2 251.07 45.39 5.48E 11 AG,WF 2 253.01 47.32 2.08E 11 O 1 256.46 50.77 3.71E 12 WP 1 256.58 50.89 3.50E 12 DP,SF 2 257.90 52.21 1.80E 12 DM 1 264.18 58.49 7.82E 14 SF 1 266.68 60.99 2.24E 14 WF 1 270. 19 64.50 3.88E 15 DP 1 273.56 67.87 7.19E 16 PP 1 275.12 69.43 3.29E 16 AG 1 280.37 74.69 2.38E 17 IP 1 280.76 75.08 1.96E 17 H 1 281.34 75.66 1.46E 17 MH 1 283.30 77.62 5.50E 18 SB 1 283.35 77.67 5.36E 18 UHF 1 284.39 78.71 3.19E 18 BG 1 284.55 78.87 2.94E 18 A 1 284.97 79.28 2.39E 18 S 1 287.16 81.47 8.00E 19

PAGE 38

38 Table 3 5 Continued. Category Model K AIC C AIC C w i Habitat BF 1 287.43 81.74 7.00E 19 UP 1 288.25 82.56 4.64E 19 X 1 291.19 85.50 1.07E 19 C 1 292.81 87.13 4.74E 20 HH 1 294.59 88.91 1.94E 20 SH 1 295.72 90.04 1.10E 20 O 1 297.58 91.90 4.36E 21 SHF 1 301.29 95.60 6.83E 22 NULL 0 3 03.44 97.75 2.33E 22 Landscape DROAD,DWATER 2 182.59 0.00 0.69 DROAD,DEDGE,DWATER 3 184.59 2.00 0.26 RD_EDGE,DROAD 2 189.99 7.40 0.02 EDGE,DROAD 2 190.10 7.50 0.02 DRD_DEDGE,WATER 2 191.07 8.48 0.01 ROAD,DROAD 2 193.24 10.64 3.38E 03 ROAD,EDGE ,DROAD,DEDGE 4 193.98 11.39 2.33E 03 RD_EDGE,DWATER 2 194.33 11.74 1.96E 03 DRD_DEDGE,EDGE 2 196.53 13.93 6.53E 04 RD_EDGE,DRD_DEDGE 2 196.59 14.00 6.30E 04 DROAD,DEDGE 2 198.20 15.61 2.82E 04 DROAD 1 199.96 17.37 1.17E 04 ROAD,DWATER 2 202.15 19.56 3.92E 05 RD_EDGE 1 202.34 19.75 3.56E 05 EDGE 1 202.77 20.18 2.88E 05 RD_EDGE,DEDGE 2 203.07 20.48 2.48E 05 EDGE,DEDGE 2 203.25 20.65 2.27E 05 DRD_DEDGE,ROAD 2 203.79 21.20 1.72E 05 DWATER 1 204.00 21.41 1.55E 05 RD_EDGE,WATER 2 204.35 21.76 1.30E 05 ROAD,DEDGE 2 213.40 30.81 1.41E 07 ROAD 1 216.03 33.43 3.80E 08 ROAD,WATER 2 217.93 35.34 1.47E 08 DRD_DEDGE 1 218.23 35.63 1.26E 08 DEDGE 1 237.60 55.00 7.87E 13 WATER 1 284.25 101.66 5.82E 23 NULL 0 303.44 120.85 3.97E 27 Ma nagement B2,B4,C4 3 170.87 0.00 0.17 B2,C4 2 171.36 0.50 0.15 B2,B3,B4,C4 4 171.89 1.03 0.11 B2,B4,C2,C4 4 172.15 1.28 0.09 B2,B3,C4 3 172.25 1.38 0.09 B2,B3,B4,C2,C3,C4 6 172.48 1.62 0.08 B1,B2,C4 3 173.35 2.48 0.05 B3,B4,C3,C4 4 173.57 2.71 0.04 B1,B2,B3,B4,C3,C4 6 173.77 2.90 0.04 B4,C4 2 173.62 2.76 0.04 B1,B2,B3,B4,C4 5 173.85 2.99 0.04 B3,B4,C4 3 174.20 3.33 0.03 C4 1 174.64 3.78 0.03 B3,C4 2 174.79 3.92 0.02 B1,B4,C4 3 175.63 4.76 0.02 B1,C4 2 176.64 5.78 0.01 C1,C2,C3 ,C4 4 179.85 8.99 1.94E 03

PAGE 39

39 Table 3 5 Continued. Category Model K AIC C AIC C w i Management C1,C2,C3,C4,C5 5 181.72 10.85 7.65E 04 B1,B2,B3,B4,B5 5 201.78 30.91 3.37E 08 B1,B2,B3,B4 4 213.67 42.80 8.82E 11 B1,B2,B3,B4,C3 5 215.42 44.55 3.68E 11 B2,B3,B4,C3 4 222.26 51.40 2.00E 12 B1,B2,C3 3 236.23 65.37 1.11E 15 B1,B2,B3 3 236.37 65.51 1.04E 15 B5 1 238.41 67.54 3.74E 16 B1,B2,C1,C2 4 239.16 68.29 2.57E 16 B1,B2,B3,C1,C2,C3 6 240.92 70.05 1.10E 16 B4,C2 2 241.35 70.48 8.61E 17 B3,B 4,C3 3 243.60 72.73 2.79E 17 B2,C3 2 246.93 76.06 5.28E 18 B2,B3,C3 3 247.08 76.22 4.89E 18 B4,C1 2 248.66 77.80 2.22E 18 B4,C3 2 249.77 78.90 1.28E 18 B1,C2 2 250.30 79.44 9.79E 19 B2 1 252.17 81.31 3.84E 19 B2,C1 2 253.31 82.44 2.17E 19 B 2,C2 2 253.97 83.10 1.56E 19 B4 1 256.51 85.65 4.38E 20 B1 1 263.20 92.34 1.55E 21 B1,C1 2 263.46 92.60 1.36E 21 B1,C3 2 263.70 92.84 1.20E 21 B3,C2 2 265.02 94.15 6.24E 22 C1,C2,C3 3 268.55 97.68 1.07E 22 C2 1 277.89 107.03 9.98E 25 B3 1 2 79.02 108.15 5.69E 25 B3,C3 2 279.06 108.19 5.58E 25 B3,C1 2 279.58 108.72 4.29E 25 C3 1 290.82 119.95 1.56E 27 C1 1 290.91 120.05 1.48E 27 C5 1 299.39 128.53 2.14E 29 NULL 0 303.44 132.57 2.83E 030 Habitat Treatment DP4,SF4,MF2,MF4,MFC4,MFC2 6 180.61 0.00 0.27 DP4,SF4,MF2,MF4,MFC4 5 182.31 1.70 0.12 MFC4,DP2,DP4,SF4 4 183.31 2.70 0.07 DP2,DP4,SF4,MF2,MF4,MFC4 6 184.30 3.68 0.04 MFC4 1 184.33 3.71 0.04 DP3,MFC4 2 184.61 4.00 0.04 MFC2,MFC4,DP2,DP4,SF4 5 184.82 4.21 0.03 DP3,DP4,MF C4 3 185.47 4.85 0.02 MFC2,MFC4 2 185.83 5.21 0.02 DP4,MFC4 2 185.83 5.22 0.02 DP3,MFC2,MFC4 3 186.10 5.49 0.02 MF3,DP2,DP3,DP4,MFC4 5 186.15 5.54 0.02 MF2,MF3,MF4,MFC2,MFC3,MFC4 6 186.24 5.62 0.02 DP2,DP3,DP4,MFC4 4 186.27 5.66 0.02 MFC3,MFC 4 2 186.27 5.66 0.02 MF2,MF3,MFC3,MFC4 4 186.31 5.70 0.02

PAGE 40

40 Table 3 5 Continued. Category Model K AIC C AIC C w i Habitat Treatment DP2,MFC4 2 186.35 5.73 0.02 MF2,MF3,MFC2,MFC4 4 186.51 5.89 0.01 MFC2,MFC4,DP2,DP4,SF2,SF4 5 186.57 5.96 0.01 DP3, MFC3,MFC4 3 186.62 6.00 0.01 MF2,MF3,MFC4 3 186.62 6.01 0.01 MF2,MF3,DP3,DP4,MFC2,MFC4 6 186.72 6.10 0.01 DP3,DP4,MFC2,MFC4 4 186.99 6.38 0.01 MF2,MF3,MF4,MFC2,MFC4 5 187.26 6.65 0.01 MF2,MF3,DP2,DP3,DP4,MFC4 6 187.33 6.71 0.01 DP4,MFC2,MFC4 3 187.36 6.75 0.01 DP2,DP4,MFC4 3 187.46 6.85 0.01 DP3,DP4,MFC3,MFC4 4 187.50 6.88 0.01 MFC2,MFC3,MFC4 3 187.78 7.16 0.01 DP4,MFC3,MFC4 3 187.80 7.19 0.01 DP2,MFC2,MFC4 3 187.84 7.23 0.01 MF1,MF2,MF3,MFC3,MFC4 5 187.89 7.28 0.01 DP2,DP3,DP4,MFC 2,MFC4 5 188.10 7.49 0.01 DP3,MFC2,MFC3,MFC4 4 188.12 7.50 0.01 DP4,MF2,MF4,MFC4,MFC2 5 188.23 7.61 0.01 DP2,MFC3,MFC4 3 188.30 7.69 0.01 DP2,DP3,DP4,MFC3,MFC4 5 188.30 7.69 0.01 DP3,DP4,MFC2,MFC3,MFC4 5 189.03 8.42 4.04E 03 DP2,DP4,MFC2,MFC4 4 189.16 8.55 3.79E 03 DP4,MFC2,MFC3,MFC4 4 189.34 8.73 3.46E 03 DP2,DP4,MFC3,MFC4 4 189.42 8.81 3.33E 03 DP2,MFC2,MFC3,MFC4 4 189.80 9.19 2.75E 03 DP2,DP3,DP4,MFC2,MFC3,MFC4 6 190.14 9.53 2.32E 03 DP2,DP4,MFC2,MFC3,MFC4 5 191.14 10.52 1.41E 03 MFC2,MFC4,DP2,DP4,SF2 5 191.16 10.54 1.40E 03 SFC4 1 221.65 41.04 3.34E 10 MF1,MF2,MF3,MF4 4 224.41 43.80 8.38E 11 MF2,MF3,MF4 3 227.22 46.61 2.06E 11 WPC4 1 228.11 47.49 1.32E 11 DP2,DP4,SF4,MF2,MF4 5 228.26 47.64 1.23E 11 DP2,DP4,SF4,WF4,MF2, MF4 6 228.86 48.24 9.08E 12 PPC4 1 229.09 48.47 8.09E 12 MF2,MF4 6 229.59 48.98 6.29E 12 MF2,MF3,DP3 3 232.36 51.75 1.58E 12 MF2,MF3 2 233.55 52.93 8.70E 13 MF1,MF2,MF3,MFC2,MFC3 5 233.99 53.38 6.97E 13 MF2,MF3,DP2,DP3 4 234.28 53.66 6.05E 13 MF2,MF3,DP3,DP4 4 234.35 53.74 5.83E 13 MF2,MF3,MFC2 3 234.47 53.85 5.50E 13 MF2,MF3,MFC3 3 234.58 53.97 5.19E 13 MF2,MF3,DP4 3 235.25 54.64 3.71E 13 MF2,MF3,DP2 3 235.43 54.82 3.39E 13 MF2,MF3,DP2,DP3,DP4 5 236.32 55.71 2.17E 13 MF2 1 237.25 56.64 1.36E 13 MF2,MF3,DP2,DP4 4 237.29 56.67 1.34E 13 DPC4 1 239.39 58.77 4.69E 14 MFC2,DP2,DP4,SF4 4 250.52 69.91 1.79E 16 DP3,MFC2,MFC3 3 254.32 73.70 2.69E 17

PAGE 41

41 Table 3 5 Continued. Category Model K AIC C AIC C w i Habitat Treatment DP3,DP4,MFC2,MFC3 4 256.24 75.63 1.01E 17 MF3,DP2,DP3,DP4.MFC2 5 256.74 76.12 8.02E 18 DP2,DP3,DP4,MFC2,MFC3 5 258.07 77.45 4.13E 18 DP3,MFC2 2 259.56 78.95 1.95E 18 MFC2,MFC3 2 259.57 78.96 1.94E 18 DP4,MFC2,MFC3 3 26 0.97 80.36 9.66E 19 DP2,MFC2,MFC3 3 261.10 80.48 9.07E 19 DP3,DP4,MFC2 3 261.46 80.85 7.56E 19 DP2,DP3,DP4,MFC2 4 263.42 82.80 2.84E 19 MFC2 1 266.28 85.67 6.79E 20 DP4,MFC2 2 267.56 86.95 3.58E 20 MF3,MF4 2 267.85 87.24 3.09E 20 DP2,MFC2 2 2 67.95 87.33 2.95E 20 DP2,DP4,MFC2 3 269.59 88.97 1.30E 20 PP2 1 272.56 91.95 2.94E 21 MF1 1 276.72 96.10 3.68E 22 DP2,DP4,SF4,WF4,DP3 5 277.10 96.48 3.04E 22 WF5 1 279.10 98.49 1.11E 22 WP2 1 279.37 98.75 9.79E 23 MF3 1 280.78 100.17 4.82E 23 SF4 1 283.27 102.65 1.39E 23 DP1,DP2,DP3,DP4 4 285.07 104.45 5.66E 24 DP2,DP4,SF4,WF4 4 285.15 104.53 5.44E 24 SFC2 1 285.57 104.95 4.41E 24 DP2,DP3,DP4,MFC3 4 285.62 105.00 4.30E 24 MF4 1 285.95 105.34 3.63E 24 DP1 1 286.03 105.42 3.49E 24 DP3,MFC3 2 286.90 106.29 2.260E 24 DP3,DP4,MFC3 3 288.11 107.50 1.23E 24 WF2 1 288.85 108.23 8.54E 25 MF5 1 288.85 108.23 8.54E 25 WP3 1 288.87 108.26 8.43E 25 SF1 1 290.26 109.65 4.21E 25 WFC4 1 290.28 109.67 4.17E 25 DP2,MFC3 2 290.49 109 .87 3.76E 25 PP5 1 290.65 110.03 3.47E 25 PPC5 1 291.99 111.37 1.78E 25 DP2,DP4,MFC3 3 292.48 111.87 1.39E 25 WP4 1 292.53 111.91 1.36E 25 WF1 1 292.97 112.36 1.09E 25 DM4 1 293.42 112.80 8.69E 26 PP1 1 293.68 113.07 7.61E 26 DP2,DP3 2 293. 84 113.23 7.02E 26 MFC3 1 294.02 113.40 6.44E 26 PP3 1 294.27 113.65 5.69E 26 DP4,MFC3 2 294.34 113.72 5.49E 26 PPC2 1 294.83 114.21 4.30E 26 DP2,DP3,DP4 3 295.51 114.89 3.06E 26 DP3 1 296.25 115.63 2.11E 26 DP3,DP4 2 297.36 116.74 1.21E 26

PAGE 42

42 Table 3 5 Continued. 1 304.62 124.00 3.21E 28 Category Model K AIC C AIC C w i Habitat Treatment SF2 1 299.19 118.57 4.86E 27 SFC1 1 300.06 119.44 3.14E 27 SF3 1 300.31 119.69 2.77E 27 WFC5 1 300.44 119.82 2.60E 27 DPC1 1 301.47 120.86 1.54E 27 WF3 1 302.02 121.40 1.18E 27 WP5 1 302.91 122.30 7.53E 28 DP2 1 303.02 122.41 7.14E 28 WPC2 1 303.95 123.33 4.49E 28 DPC5 1 304.00 123.38 4.38E 28 WF4 1 304.68 124.07 3.11E 28 SFC5 1 304.71 124.10 3.06E 28 DP2,DP4 2 305.04 124.42 2.60E 28 SFC3 1 306.01 125.40 1.60E 28 MFC1 1 306.13 125.52 1.50E 28 WPC1 1 306.14 125.52 1.50E 28 NULL 1 306.22 125.61 1.44E 28 DP4 1 306.28 125.66 1.40E 28 WPC3 1 306.45 125.84 1.28E 28 MH4 1 306.73 126.12 1.11E 28 SF5 1 306.84 126.22 1.06E 28 P PC3 1 307.84 127.23 6.41E 29 MFC5 1 307.99 127.37 5.96E 29 WPC5 1 308.22 127.61 5.30E 29 DPC2 1 308.22 127.61 5.30E 29

PAGE 43

43 Table 3 6 Parameter estimates, 95% confidence intervals (CI), and odds ratios (OR) for variables used in supported a priori models to predict nest habitat selection at the landscape level of Florida wild turkey hens in south Florida, USA, 2008 2010. 95% CI Category Model Variable Estimate Lower Upper OR Habitat AG,DP,MF,WF AG 0.017 0.005 0.030 0.017 DP 0.016 0.003 0 .028 0.016 MF 0.005 0.003 0.006 0.005 WF 0.188 0.006 0.382 0.188 AG,DP,MF,WF,SF AG 0.018 0.005 0.030 1.018 DP 0.013 0.000 0.026 1.013 MF 0.004 0.002 0.006 1.004 WF 0.185 0.008 0.378 1.203 SF 0.027 0.027 0.081 1.027 Management B2,B4, C4 B2 0.003 0.006 0.000 0.997 B 4 0.002 0.001 0.005 1.002 C 4 0.008 0.006 0.011 1.008 B2,C4 B2 0.003 0.006 0.000 0.997 C4 0.009 0.007 0.011 1.009 B2,B3,B4,C4 B2 0.003 0.006 0.000 0.997 B3 0.003 0.008 0.002 0.997 B4 0.002 0.001 0. 005 1.002 C4 0.009 0.006 0.011 1.009 B2,B4,C2,C4 B2 0.004 0.007 0.000 0.996 B4 0.002 0.001 0.005 1.002 C2 0.006 0.008 0.021 1.006 C4 0.008 0.006 0.011 1.008 B2,B3,C4 B2 0.003 0.006 0.000 0.997 B3 0.003 0.008 0.002 0.997 C4 0.0 09 0.007 0.012 1.009 B2,B3,B4,C2,C3,C4 B2 0.004 0.007 0.000 0.996 B3 0.006 0.012 0.000 0.994 B4 0.002 0.001 0.005 1.002 C2 0.009 0.007 0.025 1.009 C3 0.013 0.004 0.029 1.013 C4 0.009 0.006 0.012 1.009 Landscape DROAD,DWATER DROAD 0 .008 0.004 0.012 1.008 DWATER 0.001 0.000 0.001 1.001 DROAD,DEDGE, DWATER DROAD 0.008 0.004 0.012 1.008 DEDGE 0.001 0.008 0.006 0.999 DWATER 0.001 0.000 0.001 1.001 Habitat Treatment DP4,SF4,MF2,MF4, MFC4, MFC2 DP4 0.022 0.096 0.051 0.978 SF4 6.791E+0 6 5.620E+11 5.620E+11 N/A MF2 0.007 0.015 0.000 0.993 MF4 0.010 0.020 0.001 0.990 MFC4 0.021 0.013 0.028 1.021 MFC2 0.023 0.002 0.049 1.023 DP4,SF4,MF2,MF4, MFC4 DP4 0.022 0.096 0.051 0.978 SF4 6.783E+0 6 5.360E+11 5.360E+01 1 N/A

PAGE 44

44 Table 3 6 Continued. 95% CI Category Model Variable Estimate Lower Upper OR Habitat Treatment DP4,SF4,MF2,MF4, MFC4 MF2 0.003 0.008 0.002 0.997 MF4 0.008 0.018 0.002 0.992 MFC4 0.020 0.013 0.027 1.020 Table 3 7 Best a priori model(s) from each variable category predicting nest habitat selection at the landscape level of Florida wild turkey hens in south Florida, USA, 2008 2010. Category Model K AIC c c wi Management B2,B4,C4 3 170.87 0.00 0.26 Management B2,C4 2 171.36 0.50 0.20 Management B2,B3,B4,C4 4 171.89 1.03 0.15 Management B2,B4,C2,C4 4 172.15 1.28 0.14 Management B2,B3,C4 3 172.25 1.38 0.13 Management B2,B3,B4,C2,C3,C4 6 172.48 1.62 0.12 Habitat Treatment DP4,SF4,MF2,MF4,MFC4,MFC2 6 180.61 9.75 0.00 Habitat Treatment DP4,SF4,MF2,MF4,MFC4 5 182.31 11.45 0.00 Landscape DROAD,DWATER 2 182.59 11.73 0.00 Landscape DROAD,DEDGE,DWATER 3 184.59 13.72 0.00 Habitat AG,DP,MF,WF 4 205.69 34. 82 0.00 Habitat AG,DP,MF,WF,SF 5 206.58 35.71 0.00

PAGE 45

45 Table 3 8 A priori models, number of variables (K), second Criterion corrected for small sample size (AIC c ), distance from the lowest AIC c c ), and model weights ( w i ) used to predict nest success at the microhabitat level for Florida wild turkey hens in south Florida, 2008 2010, USA. Model K AIC C AIC C w i BAT 1 82.66 0.00 0.09 BAC,SD 2 82.95 0.30 0.08 BAT,SD 2 83.04 0.38 0.07 STP,BA C 2 83.19 0.53 0.07 STP,BAT 2 83.44 0.78 0.06 BAT,STT 2 84.01 1.35 0.04 STP,STC 2 84.15 1.50 0.04 BAT,BAH 2 84.48 1.82 0.04 BAT,BAC 2 84.64 1.99 0.03 BAT,VO 2 84.69 2.04 0.03 BAC 1 84.85 2.20 0.03 STP,STC,SD 3 84.90 2.25 0.03 BAT,STT,SD 3 85.04 2 .38 0.03 BAC,BAH,SD 3 85.04 2.38 0.03 BAC,STH,SD 3 85.16 2.51 0.02 STC,SD 2 85.22 2.56 0.02 STP 1 85.43 2.78 0.02 BAC,BAH 2 85.79 3.13 0.02 BAC,STC 2 85.89 3.24 0.02 STC 1 85.94 3.28 0.02 STP,STT 2 86.01 3.35 0.02 STT 1 86.01 3.35 0.02 STP,STC,ST H 3 86.09 3.43 0.02 BAT,STT,VO 3 86.20 3.54 0.01 BAC,STH 2 86.27 3.61 0.01 BAP 1 86.82 4.16 0.01 NULL 0 86.92 4.26 0.01 STP,BAP 2 87.05 4.39 0.01 STP,SD 2 87.07 4.41 0.01 BAT,STT,SD,VO 4 87.30 4.64 0.01 STC,BAH,SD 3 87.36 4.70 0.01 STC,STH,SD 3 8 7.41 4.76 0.01 STP,VO 2 87.47 4.81 0.01 STP,STH 2 87.51 4.86 0.01 STP,BAH 2 87.56 4.90 0.01 SD 1 87.56 4.90 0.01 STT,SD 2 87.71 5.05 0.01 STT,VO 2 88.13 5.47 0.01 BAH 1 88.27 5.61 0.01 STH 1 88.83 6.17 3.98E 03 VO 1 88.89 6.23 3.86E 03 SHT 1 88.9 8 6.33 3.69E 03 STP,STH,SD 3 89.27 6.61 3.20E 03 BAH,SD 2 89.52 6.86 2.82E 03 STH,SD 2 89.66 7.01 2.62E 03 SD,VO 2 89.69 7.03 2.59E 03 BAC,BAH,STC,STH,SD,VO 6 90.31 7.66 1.89E 03 BAH,STH 2 90.40 7.74 1.82E 03

PAGE 46

46 Table 3 8 Continued Model K AIC C A IC C w i SD,STH,BAH 3 91.56 8.90 1.01E 03 BAH,SD,VO 3 91.73 9.07 9.36E 04 STH,SD,VO 3 91.87 9.21 8.71E 04 SD,STH,BAH,VO 4 93.85 11.19 3.24E 04 Table 3 9 Parameter estimates, 95% confidence intervals (CI), and odds ratios (OR) for variables used in s upported a priori models to predict nest success at the microhabitat level of Florida wild turkey hens in south Florida, USA, 2008 2010. 95% CI Model Variable Estimate Lower Upper OR BAT BAT 0.105 0.021 0.004 0.900 BAC,SD BAC 0.151 0.285 0.01 6 0.860 SD 0.016 0.000 0.031 1.016 BAT,SD BAT 0.115 0.220 0.009 0.892 SD 0.010 0.005 0.025 1.010 STP,BAC STP 0.606 6.737E+06 6.737E+06 0.546 BAC 0.109 0.225 0.006 0.896 STP,BAT STP 0.582 9.157E+06 9.157E+06 0.559 BAT 0.098 0.202 0.00 6 0.906 BAT,STT BAT 0.093 0.195 0.009 0.912 STT 0.001 0.003 0.001 0.999 STP,STC STP 0.727 3.594E+08 3.594E+08 0.484 STC 0.004 0.009 0.001 0.996 BAT,BAH BAT 0.121 0.239 0.003 0.886 BAH 0.616 1.469 2.702 1.852 BAT,BAC BAT 0.072 0.229 0.070 0.924 BAC 0.037 0.220 0.145 0.963

PAGE 47

47 Table 3 10 A priori models, number of variables (K), second Criterion corrected for small sample size (AIC c ), distance from the lowest AIC c c ), and model weights ( w i ) used to predict nest success at the patch level for Florida wild turkey hens in south Florida, 2008 2010, USA. Model K AIC C AIC C w i BAP 1 83.63 0.00 0.16 BAT 1 85.44 1.81 0.07 STP,BAP 2 85.62 1.99 0.06 BAC,STH 2 86.11 2.48 0.05 BAT,BAH 2 86.31 2.68 0.04 BAC 1 86.65 3.02 0.03 BAT,VO 2 86.71 3.08 0.03 BAT,SD 2 86.80 3.17 0.03 NULL 0 86.92 3.28 0.03 BAT,STT 2 87.06 3.43 0.03 BAT,BAC 2 87.27 3.64 0.03 BAT,STT,VO 3 87.33 3.70 0.03 STP,BAT 2 87.41 3.78 0.02 STT,VO 2 87.43 3.80 0.02 STH 1 87.48 3.85 0.02 STP,BAC 2 87.68 4.04 0.02 VO 1 87.70 4.07 0.02 BAC,STH,SD 2 87.96 4.33 0.02 BAC,SD 2 88.08 4.45 0.02 STT 1 88.18 4.55 0.02 BAT,STT,SD 3 88.26 4.63 0.02 STP 1 88.39 4.76 0.02 SD 1 88.53 4.90 0.01 BAC,BAH 2 88.56 4.93 0.01 BAC,STC 2 88.72 5.09 0.01 STP,VO 2 88.77 5.14 0.01 STC 1 88.92 5.29 0.01 SHT 1 88.94 5.30 0.01 BAH 1 88.97 5.34 0.01 BAT,STT,SD,VO 3 89.17 5.54 0.01 STP,STH 2 89.39 5.76 0.01 STH,SD 2 89.52 5.89 0.01 BAH,STH 2 89.60 5.97 0.01 STT ,SD 2 89.69 6.06 0.01 SD,VO 2 89.80 6.17 0.01 BAC,BAH,SD 3 89.94 6.31 0.01 STP,SD 2 90.14 6.51 0.01 STP,STT 2 90.15 6.52 0.01 STH,SD,VO 3 90.33 6.70 0.01 STP,STC 2 90.50 6.87 0.01 STP,BAH 2 90.52 6.89 0.01 BAH,SD 2 90.64 7.01 4.89E 03 STC,SD 2 90. 66 7.02 4.85E 03 STP,STH,SD 3 91.50 7.87 3.18E 03 STP,STC,STH 3 91.60 7.97 3.03E 03 STC,STH,SD 3 91.72 8.08 2.85E 03 SD,STH,BAH 3 91.72 8.09 2.84E 03 BAH,SD,VO 3 91.98 8.34 2.50E 03 STP,STC,SD 3 92.33 8.70 2.10E 03

PAGE 48

48 Table 3 10. Continued. Mode l K AIC C AIC C w i SD,STH,BAH,VO 4 92.61 8.98 1.82E 03 STC,BAH,SD 3 92.84 9.21 1.62E 03 BAC,BAH,STC,STH,SD,VO 6 94.43 10.80 7.33E 04 Table 3 1 1 Parameter estimates, 95% confidence intervals (CI), and odds ratios (OR) for variables used in supported a priori models to predict nest success at the patch level of Florida wild turkey hens in south Florida, USA, 2008 2010. 95% CI Model Variable Estimate Lower Upper OR BAP BAP 1.551 3.750 0.648 0.212 BAT BAT 0.109 0.237 0.020 0.897 STP,BAP STP 0.002 0 .007 0.010 1.002 BAP 1.571 3.720 0.578 0.208

PAGE 49

49 Table 3 12 A priori models, number of variables (K), second Criterion corrected for small sample size (AIC c ), distance from the lowest AIC c c ), and model weights ( w i ) used to predict nest success at the landscape level for Florida wild turkey hens in south Florida, 2008 2010, USA. Category Model K AIC C AIC C w i Habitat SF,WF 2 38.70 0.00 0.14 SF 1 39.34 0.64 0.10 DP,WF,SF 3 39.61 0.90 0.09 DP,SF 2 40.17 1.47 0.07 IP 1 40.72 2.02 0.05 SB 1 40.72 2.02 0.05 DP 1 40.78 2.08 0.05 MF,SF,WF 3 40.90 2.20 0.05 SF,DS 2 41.17 2.47 0.04 MF,DP,SF,WF 4 41.73 3.03 0.03 SF,DS,DP 3 42.01 3.31 0.03 UP 1 42.11 3.41 0.02 MF,SF,DP 3 42.31 3.61 0.02 DS,DP 2 42.50 3.80 0.02 DP,WF 2 42.56 3.86 0.02 PP 1 42.64 3.94 0.02 AG,DP 2 42.90 4.20 0.02 MF,DP 2 42.92 4.22 0.02 DS,MF,SF,WF,AG 5 43.17 4.47 0.01 DS,DP,MF 3 43.33 4.63 0.01 DS,MF 2 43.39 4.68 0.01 A 1 43.43 4.73 0.01 AG,DP,MF,WF,SF 5 43.77 5.07 0.01 DP,DS,MF,SF,WF 5 43.80 5.10 0.01 SF,DS,DP,MF 4 43.90 5.19 0.01 DS 1 43.90 5.20 0.01 AG,DP,MF 3 45.11 6.41 0.01 AF,DP,MF,WF,SF,DS 6 45.15 6.44 0.01 AG,DP,DS,MF,SF,WF 6 45.15 6.44 0.01 SF,S,SH,MF 4 45.19 6.49 0.01 DS,MF,SF,WF,BG,DM 6 45.51 6.81 4.53E 03 NULL 0 45.59 6.89 4.36E 03 SF,S,SH,MF,WF,DP 5 45.72 7.02 4.08E 03 AG,DP,DS,MF,SF 5 45.72 7.02 4.08E 03 BG 1 45.75 7.05 4.01E 03 X 1 46.09 7.39 3.39E 03 S 1 46.13 7.43 3.32E 03 SH 1 46.27 7.57 3.1 0E 03 AG,DP,DS,MF,SF,BS 5 46.33 7.63 3.01E 03 SF,S,SH,MF,DP 5 46.46 7.76 2.82E 03 MH 1 46.47 7.76 2.81E 03 DM 1 46.98 8.28 2.17E 03 AG,DP,MF,WF 4 47.01 8.30 2.14E 03 C 1 47.08 8.38 2.06E 03 WP 1 47.09 8.39 2.05E 03 BS 1 47.09 8.39 2.05E 03 UHF 1 47.14 8.43 2.01E 03 MF 1 47.47 8.76 1.70E 03 WF 1 47.54 8.84 1.64E 03

PAGE 50

50 Table 3 12 Continued. Category Model K AIC C AIC C w i Habitat HH 1 47.58 8.87 1.61E 03 AG 1 47.64 8.93 1.56E 03 BF 1 47.66 8.95 1.55E 03 H 1 47.66 8.95 1.55E 03 SHF 1 47.66 8.95 1.55E 03 MF,WF 2 49.56 10.86 5.98E 04 AG,MF 2 49.60 10.90 5.86E 04 AG,WF 2 49.67 10.97 5.67E 04 H 1 52.12 13.42 1.66E 04 O 1 52.89 14.19 1.13E 04 Landscape NULL 0 45.59 0.00 0.09 DWATER 1 45.93 0.35 0.08 ROAD 1 45.98 0.39 0.07 DEDGE 1 46.48 0.89 0.06 RD_EDGE 1 46.65 1.06 0.05 EDGE 1 46.77 1.18 0.05 DROAD 1 46.91 1.32 0.05 WATER 1 47.02 1.44 0 .04 DRD_DEDGE,ROAD 2 47.40 1.81 0.04 DRD_DEDGE 1 47.48 1.89 0.04 DRD_DEDGE,DWATER 2 47.76 2.17 0.03 ROAD,DWATER 2 47.87 2.28 0.03 ROAD,DROAD 2 47.97 2.38 0.03 RD_EDGE,DWATER 2 48.06 2.47 0.03 DROAD,DWATER 2 48.07 2.48 0.03 ROAD,WATER 2 48.0 8 2.49 0.03 ROAD,DEDGE 2 48.09 2.50 0.03 RD_EDGE,DEDGE 2 48.50 2.91 0.02 RD_EDGE,DRD_DEDGE 2 48.53 2.94 0.02 EDGE,DEDGE 2 48.54 2.95 0.02 DROAD,DEDGE 2 48.55 2.96 0.02 RD_EDGE,WATER 2 48.65 3.06 0.02 DRD_DEDGE,EDGE 2 48.73 3.15 0.02 RD_EDGE ,DROAD 2 48.78 3.19 0.02 EDGE,DROAD 2 48.88 3.30 0.02 ROAD,EDGE,WATER 3 48.98 3.39 0.02 ROAD,EDGE,DWATER 3 48.98 3.39 0.02 RD_EDGE,DRD_DEDGE,DWATER 3 49.72 4.13 0.01 DROAD,DEDGE,DWATER 3 50.06 4.47 0.01 DROAD,DEDGE,WATER 3 50.55 4.97 0.01 RD_ EDGE,DRD_DEDGE,WATER 3 50.67 5.08 0.01 ROAD,EDGE,DROAD,DEDGE 4 51.25 5.66 0.01 Management B2,C2 2 37.53 0.00 0.23 B2,C2,C3 3 38.32 0.79 0.15 B2,C2,C4 3 38.83 1.30 0.12 B2,B4,C2 3 39.28 1.76 0.10 B1,B2,C2 3 39.44 1.92 0.10 B2,B3,B4,C2 4 40.48 2 .95 0.05 B1,B2,B3,C2 4 40.69 3.17 0.05 B2,B4,C2,C4 4 40.77 3.24 0.05 B1,B2,C1,C2 4 41.42 3.89 0.03 B1,B2,B3,B4,B5 5 42.18 4.66 0.02 B2,B3,B4,C2,C3,C4 6 43.76 6.24 0.01

PAGE 51

51 Table 3 12 Continued. Category Model K AIC C AIC C w i Management B2 1 43.85 6.33 0.01 B1,B2,B3,C1,C2,C3 6 44.25 6.72 0.01 B1,B2,B3 3 44.73 7.20 0.01 C5 1 44.84 7.31 0.01 B2,B3,C3 3 45.44 7.92 4.39E 03 NULL 0 45.59 8.06 4.08E 03 B2,C1 2 45.60 8.08 4.06E 03 B2,C4 2 45.67 8.15 3.92E 03 B2,C3 2 45.95 8.43 3.40E 03 B2,B3,C4 3 45.99 8.47 3.34E 03 B1 1 46.56 9.03 2.52E 03 C1 1 46.84 9.31 2.18E 03 B1,B2,B3,B4 4 46.93 9.41 2.08E 03 C2 1 46.98 9.46 2.03E 03 B2,B4,C4 3 47.00 9.47 2.01E 03 B2,B4,C4 3 47.00 9.47 2.01E 03 B5 1 47.1 7 9.64 1.85E 03 B3 1 47.36 9.83 1.68E 03 B1,B2,C4 3 47.40 9.88 1.65E 03 B4 1 47.43 9.90 1.62E 03 C3 1 47.54 10.01 1.54E 03 C4 1 47.64 10.11 1.46E 03 B2,B3,B4,C3 4 47.71 10.18 1.41E 03 B2,B3,B4,C3 4 47.71 10.18 1.41E 03 B3,C1 2 47.77 10.24 1 .37E 03 B2,C3,C4 3 47.86 10.34 1.30E 03 B1,C2 2 47.94 10.41 1.26E 03 B2,B3,B4,C4 4 47.94 10.42 1.25E 03 B1,B2,C3 3 47.98 10.46 1.23E 03 B4,C4 2 48.06 10.53 1.18E 03 B1,C4 2 48.26 10.73 1.07E 03 C1,C2,C3,C4,C5 5 48.38 10.85 1.01E 03 B1,B2,B3 ,B4,C3 5 48.53 11.00 9.39E 04 B1,C1 2 48.53 11.01 9.38E 04 B1,C3 2 48.68 11.16 8.69E 04 B4,C1 2 48.78 11.25 8.29E 04 B3,C3 2 48.83 11.30 8.08E 04 B4,C2 2 48.84 11.32 8.04E 04 B1,B2,B3,B4,C4 5 48.98 11.45 7.51E 04 B3,B4 2 49.06 11.53 7.21E 04 B3,C2 2 49.08 11.55 7.14E 04 B1,B4,C4 3 49.33 11.80 6.30E 04 B1,B3,C2 3 49.33 11.80 6.29E 04 B3,C4 2 49.48 11.96 5.82E 04 B4,C3 2 49.50 11.98 5.77E 04 B3,B4,C4 3 50.15 12.62 4.18E 04 C1,C2,C3 3 50.50 12.98 3.50E 04 B3,B4,C3 3 50.65 13.13 3. 25E 04 B3,B4,C2 3 50.90 13.38 2.86E 04 B3,B4,C3,C4 4 52.19 14.67 1.50E 04 C1,C2,C3,C4 4 52.79 15.26 1.11E 04

PAGE 52

52 Table 3 12 Continued. Category Model K AIC C AIC C w i Habitat Treatment DP4,MFC2 2 37.20 0.00 0.15 DP2,DP4,MFC2 3 38.27 1.07 0.09 DP4,MFC2,MFC3 3 39.30 2.10 0.05 DP4,MFC2,MFC4 3 39.39 2.19 0.05 DP3,DP4,MFC2 3 39.41 2.21 0.05 DP4 1 39.51 2.30 0.05 DP4,MF3,MF2,MFC2 4 40.36 3.16 0.03 DP2 ,DP4 2 40.46 3.25 0.03 DP2,DP4,MFC2,MFC3 4 40.48 3.27 0.03 DP2,DP4,MFC2,MFC4 4 40.51 3.30 0.03 DP2,DP3,DP4,MFC2 4 40.56 3.36 0.03 MFC2,DP2,DP4,SF4 4 40.56 3.36 0.03 DP4,MFC3 2 41.54 4.33 0.02 DP4,MFC2,MFC3,MFC4 4 41.56 4.36 0.02 DP3,DP4,MFC2, MFC3 4 41.59 4.39 0.02 DP4,MFC4 2 41.60 4.40 0.02 DP3,DP4 2 41.65 4.44 0.02 DP3,DP4,MFC2,MFC4 4 41.68 4.48 0.02 DP4,MF3,MF2,MFC2,MFC4 5 42.02 4.82 0.01 DP2,DP4,MFC4 3 42.53 5.32 0.01 DPC4 1 42.54 5.33 0.01 DP2,DP4,MFC3 3 42.59 5.39 0.01 DP2 ,DP3,DP4 3 42.67 5.46 0.01 DP4,MF3,MF2,MFC2,SF4 5 42.73 5.53 0.01 DP2,DP4,MFC2,MFC3,MFC4 5 42.78 5.58 0.01 MF3,DP2,DP3,DP4.MFC2 5 42.80 5.59 0.01 DP2,DP3,DP4,MFC2,MFC3 5 42.85 5.64 0.01 DP2,DP3,DP4,MFC2,MFC4 5 42.88 5.67 0.01 MFC2,MFC4,DP2,DP4, SF4 5 42.88 5.67 0.01 MF2,MF3,DP4 3 43.10 5.89 0.01 MF5 1 43.22 6.01 0.01 DP5 1 43.29 6.09 0.01 MFC2,MFC4,DP2,DP4,SF2 5 43.51 6.31 0.01 DP2,DP4,SF4,WF4 4 43.57 6.37 0.01 DP4,MF3,MF2,MFC2,SF4,WF4 6 43.66 6.45 0.01 DP4,MFC3,MFC4 3 43.69 6.49 0. 01 DP3,DP4,MFC3 3 43.75 6.54 0.01 DP3,DP4,MFC4 3 43.81 6.61 0.01 DP4,SF3,MFC4 3 43.81 6.61 0.01 DP3,DP4,MFC2,MFC3,MFC4 5 43.93 6.73 0.01 MF2,MF3,DP2,DP4 4 44.33 7.12 4.28E 03 DP2,DP4,MFC3,MFC4 4 44.72 7.52 3.51E 03 DP2,DP3,DP4,MFC4 4 44.82 7. 61 3.35E 03 MFC4,DP2,DP4,SF4 4 44.82 7.61 3.35E 03 DP2,DP3,DP4,MFC3 4 44.88 7.68 3.24E 03 DP1,DP2,DP3,DP4 4 44.95 7.74 3.14E 03 WP5 1 44.99 7.78 3.07E 03 PPC2 1 45.08 7.88 2.93E 03 DP2,DP3,DP4,MFC2,MFC3,MFC4 6 45.24 8.03 2.72E 03 PP2 1 45.27 8.06 2.67E 03 DPC2 1 45.38 8.18 2.52E 03 MF2,MF3,DP3,DP4 4 45.38 8.18 2.52E 03 AG3 1 45.43 8.22 2.47E 03

PAGE 53

53 Table 3 12 Continued. Category Model K AIC C AIC C w i Habitat Treatment SFC1 1 45.49 8.29 2.39E 03 WFC4 1 45.49 8.29 2.39E 03 DPC5 1 45 .62 8.42 2.24E 03 DP2,DP4,SF4,WF4,DP3 5 45.94 8.74 1.91E 03 MFC2,MFC4,DP2,DP4,SF2,SF4 6 45.97 8.76 1.88E 03 DP3,DP4,MFC3,MFC4 4 45.98 8.78 1.87E 03 PP4 1 46.03 8.82 1.83E 03 SFC2 1 46.07 8.86 1.79E 03 DP2 1 46.20 8.99 1.68E 03 NULL 1 46.36 9. 15 1.55E 03 MF2,MF3,DP2,DP3,DP4 5 46.70 9.49 1.30E 03 WPC5 1 46.79 9.58 1.25E 03 WF3 1 46.88 9.68 1.19E 03 MF4 1 46.97 9.76 1.14E 03 DP1 1 46.97 9.77 1.14E 03 DP3 1 46.97 9.77 1.14E 03 WF4 1 46.97 9.77 1.14E 03 PPC3 1 46.97 9.77 1.14E 03 SFC3 1 46.97 9.77 1.14E 03 DP2,DP3,DP4,MFC3,MFC4 5 47.09 9.89 1.07E 03 WP3 1 47.11 9.91 1.06E 03 MF3,DP2,DP3,DP4,MFC4 5 47.14 9.93 1.05E 03 MFC4 1 47.18 9.98 1.02E 03 DP2,MFC4 2 47.19 9.99 1.02E 03 PPC5 1 47.28 10.08 9.76E 04 SF1 1 47.30 10.0 9 9.70E 04 DP2,MFC2 2 47.43 10.23 9.07E 04 PP5 1 47.45 10.25 8.98E 04 PPC4 1 47.45 10.25 8.98E 04 SFC4 1 47.63 10.43 8.21E 04 WP2 1 47.67 10.46 8.05E 04 MFC2 1 47.72 10.52 7.83E 04 SF3 1 47.76 10.56 7.68E 04 SF2 1 47.81 10.61 7.49E 04 WP4 1 47.81 10.61 7.49E 04 WPC4 1 47.83 10.62 7.45E 04 DP2,MFC3 2 47.85 10.65 7.34E 04 DP3,MFC4 2 47.93 10.73 7.07E 04 WP1 1 47.97 10.77 6.93E 04 MF1 1 48.04 10.84 6.67E 04 DP2,MFC2,MFC4 3 48.05 10.84 6.66E 04 MFC3 1 48.05 10.85 6.66E 04 DP2,D P3 1 48.12 10.92 6.42E 04 MF2 1 48.13 10.92 6.40E 04 SFC5 1 48.15 10.95 6.33E 04 WPC2 1 48.18 10.98 6.24E 04 WPC3 1 48.20 10.99 6.18E 04 MFC5 1 48.22 11.02 6.11E 04 WPC1 1 48.23 11.03 6.07E 04 MFC2,MFC4 2 48.25 11.04 6.03E 04 WF2 1 48.30 11 .09 5.88E 04

PAGE 54

54 Table 3 12 Continued. Category Model K AIC C AIC C w i Habitat Treatment WF5 1 48.30 11.10 5.87E 04 MF3 1 48.30 11.10 5.86E 04 PP3 1 48.32 11.12 5.80E 04 MFC1 1 48.36 11.15 5.71E 04 SF4 1 48.36 11.15 5.71E 04 DPC3 1 48.36 11.15 5.71E 04 WFC5 1 48.36 11.15 5.71E 04 DP3,MFC2 2 48.48 11.27 5.38E 04 MF2,MF4 2 48.67 11.46 4.89E 04 DP3,MFC3 2 48.80 11.60 4.57E 04 DP2,MFC2,MFC3 3 49.04 11.84 4.05E 04 DP2,MFC3,MFC4 3 49.07 11.86 4.00E 04 DP3,MFC2,MFC4 3 49.07 11.87 3.99E 04 MF3,MF4 2 49.10 11.90 3.93E 04 MFC3,MFC4 2 49.12 11.92 3.89E 04 MF2,MF3,DP2,DP3,DP4.MFC4 6 49.15 11.94 3.84E 04 MF2,MF3,DP2 3 49.87 12.67 2.67E 04 DP2,MFC2,MFC3,MFC4 4 49.91 12.70 2.63E 04 DP3,MFC3,MFC4 3 49.95 12.74 2.57E 04 MF2,MF3 2 49.96 12.76 2.56E 04 MFC2,MFC3,MFC4 3 50.23 13.03 2.23E 04 DP3,MFC2,MFC3 3 50.32 13.12 2.13E 04 MF2,MF3,MFC4 3 50.42 13.21 2.04E 04 MF2,MF3,MF4 3 50.70 13.50 1.76E 04 MF2,MF3,DP3 3 50.78 13.58 1.70E 04 DP3,MFC2,MFC3,MFC4 4 51.13 13.93 1.42E 04 MF1 ,MF2,MF3,MFC4 4 51.51 14.31 1.17E 04 MF1,MF2,MF3,MF4 4 51.92 14.72 9.61E 05 MF2,MF3,MFC3 3 52.00 14.79 9.24E 05 MF2,MF3,DP2,DP3 4 52.10 14.89 8.80E 05 MF2,MF3,MFC3,MFC4 4 52.56 15.36 6.98E 05 MF1,MF2,MF3,MFC3,MFC4 5 53.79 16.58 3.78E 05 MF1,MF2 ,MF3,MFC3,MFC4 5 53.85 16.65 3.66E 05 MF1,MF2,MF3,MFC2,MFC3 5 55.27 18.07 1.80E 05

PAGE 55

55 Table 3 13 Parameter estimates, 95% confidence intervals (CI), and odds ratios (OR) for variables used in supported a priori models to predict nest success at the landscape level of Florida wild turkey hens in south Florida, USA, 2008 2010. 95% CI Category Model Variable Estimate Lower Upper OR Habitat SF,WF SF 14.814 4.132E+09 4.132E+09 2.710e+06 WF 25.996 2.238E+10 2.238E+10 0.000 DP,WF,SF DP 0.126 0.397 0.145 0.882 WF 30.377 7.599E+10 7.599E+10 0.000 SF 17.405 3.238E+09 3.238E+09 3 620E+21 SF SF 14.804 4.071E+09 4.071E+09 2.690E+06 DP,SF DP 0.126 0.397 0.145 0.882 SF 17.405 3.237E+09 3.237E+09 3.620E+07 Management B2,C2 B2 0.009 0.001 0.017 1.009 C2 0.048 0.093 0.003 0.953 B2,C2,C3 B2 0.013 0.000 0.026 1.013 C2 0.061 0.119 0.003 0.941 C3 0.025 0.067 0.017 0.975 B2,C2,C4 B2 0.011 0.000 0.021 0.011 C2 0.053 0.103 0.003 0.053 C4 0.002 0.006 0.002 0.002 B2,B4,C2 B2 1.010 0.001 0.020 1.010 B4 0.002 0.008 0.004 0.998 C2 0.052 0.099 0.004 0.950 B1,B2,C2 B1 0.006 0.027 0.015 0.994 B2 0.011 0.000 0.022 1.011 C2 0.053 0.102 0.003 0.949 Landscape NULL NULL 0.000 0.000 0.00 0 0.000 DWATER DWATER 0.000 0.000 0.001 1.000 ROAD ROAD 0.060 0.033 0.152 1.062 DEDGE DEDGE 0.005 0.005 0.014 1.005 RD_EDGE RD_EDGE 0.008 0.008 0.023 1.008 EDGE EDGE 0.009 0.010 0.027 1.009 DROAD DROAD 0.003 0.004 0.010 1.003 WATER WATER 0.108 0.174 0.391 1.114 DRD_DEDGE, ROAD DRD_DEDGE 0.009 0.029 0.012 0.991 ROAD 0.105 0.038 0.248 1.111 DRD_DEDGE DRD_DEDGE 0.003 0.011 0.016 1.003 Habitat Treatment DP4,MFC2 DP4 1.369 0.879 3.616 13.786 MFC2 0.001 0.014 0.011 0.872 DP 2,DP4,MFC2 DP2 0.090 0.302 0.121 0.914 DP4 3.228 0.554 7.011 25.240 MFC2 0.103 0.351 0.146 0.903

PAGE 56

56 Table 3 14 Best a priori model(s) from each variable category predicting nest success at the landscape level of Florida wild turkey hens in so uth Florida, USA, 2008 2010. Category Model K AIC c c w i Habitat Treatment DP4,MFC2 2 37.20 0.00 0.18 Treatment B2,C2 2 37.53 0.32 0.15 Habitat Treatment DP2,DP4,MFC2 3 38.27 1.07 0.11 Treatment B2,C2,C3 3 38.32 1.11 0.10 Habitat SF,WF 2 38.70 1.5 0 0.08 Treatment B2,C2,C4 3 38.83 1.63 0.08 Habitat SF 1 39.34 2.13 0.06 Management B1,B2,C2 3 39.44 2.24 0.06 Habitat DP,WF,SF 3 39.61 2.40 0.05 Habitat DP,SF 2 40.17 2.96 0.04 NULL NULL 0 45.59 8.38 0.00 Landscape DWATER 1 45.93 8.73 0.00 Landsca pe ROAD 1 45.98 8.77 0.00 Landscape DEDGE 1 46.48 9.27 0.00 Landscape RD_EDGE 1 46.65 9.44 0.00 Landscape EDGE 1 46.77 9.57 0.00 Landscape DROAD 1 46.91 9.70 0.00 Landscape WATER 1 47.02 9.82 0.00 Landscape DRD_DEDGE,ROAD 2 47.40 10.20 0.00 Landscap e DRD_DEDGE 1 47.48 10.28 0.00

PAGE 57

57 CHAPTER 4 DISCUSSION Selection Florida wild turkey hen m icrohabitat selection was for the presence of dense lateral cover in the form of shrubs, which has been widely documented by many, including Day et al. (1991) with e astern wild turkeys. This manifested not in increased visual obstruction, but in saw palmetto density. H ens selected for higher levels of saw palmetto density most likely to aid in concealing incubating hens as saw palmetto can provide significant latera l cover. Saw palmetto also provides hens with cover overhead, which reduces the probability of detection by avian predators such as the red tailed hawk ( Buteo jamaicensis ; Lehman et al. 2002 Nguyen et al. 2004). It may also function to shade hens, decre F indings for other wild turkey subspecies (e.g., Meleagris gallopavo silvestris, Meleagris gallopavo merriami, Meleagris gallopavo intermedia ) corroborate the importance or presence of dense cover featuring low shrubs and slash at nest sites ( Logan 1973 Lutz and Crawford 1987, Schmutz et al. 1989, Eichler and Whiting 2004 Shields and Flake 2004, Palmer et al. 1996). Other r esearchers have also observed heavy vegetation and saw palm etto at Florida wild turkey nests (e.g., Williams et al. 1968, Williams 1991, Dickson 1992 ). Additionally, hens preferred areas with higher densities of palm stems, which can also provide a great degree of lateral and overhead cover through standing vege tation and litter, decreasing predator efficiency by providing visual, auditory, and olfactory obstruction at nest sites ( Bowman and Harris 1980 Redmond et al. 1982, Crabtree et al. 1989, Badyaev 1995, Shiel ds and Flake 2004). Dense cover such as this ma y also provide greater numbers of locations where hens could establish nests, decreasing

PAGE 58

58 predator efficiency by making predators search in more areas to find nests. Similar findings include those of Thomas and Litvaitis (1993), who reported that eastern w ild turkey hens selected for more stems near the nest site. At the patch level, hens selected for higher densities of palm stems, but trends show ed selection for more open areas with fewer stems overall, lower densities of saw palmetto, and lower levels of visual obstruction. The dense cover selected for nest sites may conceal hens well but it does not allow hens to move easily through it Therefore hens may choose to have more open habitat adjacent to nest sites for easy ingress and egress. Other re search has found similar results for nesting hens, suggesting that they prefer to be concealed on the nest, while being able to survey the area for threats in coming from and going to the nest (Logan 1973, Speake et al. 1975). Additionally, though specie s such as saw palmetto afford dense understory cover to conceal hens while nesting, it inhibits growth of other vegetation used as forage by wild turkeys (Williams and Austin 1988 Willcox and Giuliano 2010). By selecting for patches of dense cover within more open habitats, hens may be selecting for areas with more food potential nearby. When they must leave to water and feed, they do not have to travel far, and while foraging, they can readily see threats around them due to the lower levels of lateral a nd overhead cover. Foraging in more open habitat such as this decreases the risk of predation (Williams 1991). Moreover, when hens are successful, newly hatched poults do not have to travel far to reach areas suitable for forage and movement (Lazar us and Porter 1985, Haegen et al. 1991). Trends evident at the finer scales continued at the landscape level. Birds typically selected for areas characterized by patchy dense and rank vegetation in more open

PAGE 59

59 habitats. Hens selected against areas that had been recently treated with either prescribed fire or roller chopping. Burn ed areas were especially avoided, possibly because of the clean nature of the landscape after a burn, in addition to the typical magnitude of the burns at Three Lakes WMA. These tended to be very large scale, leaving behind sizeable open areas avoided by turkeys. Though no data concerning the date, these findings concur with those of Exum et al. (1 987) in Alabama ; however, Sisson et al. (1990) and Eichler and Whiting (2004) found eastern wild turkey hens selected nest sites in upland pine stands burned on a three to five year basis. Seiss et al. (1990) found no selection based upon burn regime for eastern wild turkey nest site selection. In addition to areas unburned and unchopped, hens selected areas not recently chopped (0.5 2 years and >2 years). This suggest s that roller chopping may be a more suitable management technique for turkeys in refer ence to nest site selection, possibly because it can produce landscapes less open and clean than does prescribed fire, affording incubating hens concealment within an open landscape (Willcox and Giuliano 2010). Hens also selected sites further from roads a nd water, possibly because these landscape features provide corridors for movement and foraging for many potential nest predators (Gates and Gysel 1978 Dickson 1992). Hens selected nest sites closer to habitat edges, which also provide corridors for move ment of predators. However, this for the nest site Habitats are typically denser near edges, so hens probably selected for areas nearer habitat edges because of their preference for lat eral and overhead cover at the nest bowl. While Florida wild turkey hen nest site proximity to habitat edges has not been

PAGE 60

60 examined Seiss et al. (1990) and Swanson et al. (1996) both reported eastern wild turkey hens selected nest sites within sixty meter s of habitat edge, while Porter (1978) found that nearly 75% of eastern wild turkey nests were located within edge habitat. Further, Thogmartin (1999) reported that although most captured eastern wild turkey hens selected nest sites in edge habitat, adult hens chose less edge habitat than expected, indicating that wild turkeys may learn edge avoidance as they mature. Continuing the trend s found at finer scales, hens selected for agriculture, dry prairie, and flatwoods at the landscape level These habitat types feature more open understories, while providing scrubby patches of shrubs and dense vegetation used for escape and nesting, which allow hens to detect approaching predators without impeding escape paths ( Day et al. 1991, Lopez et al. 1997). The agr icultural habitat selected was primarily citrus groves in which grasses grew tall, particularly near the bases of trees, affording cover and forage potential while also allowing hens improved visibility and movement. Others, such as Lopez (1997) and Shie lds and Flake (2004), have reported nests adjacent to protective barrier s such as the tree bases present in the citrus grove and other habitats Obstruction such as the bases of trees give s hens a 180 protective barrier against predation and a clear line of escape (Williams 2006) In dry prairie, grasses may provide a structure that can be very beneficial for nesting birds. Bunch grass es abound and allow hens to conceal themselves and their broods, but also allow for the easy movement of poults and prov ide areas for bugging and foraging (Lazarus and Porter 1985 Metzler and Speake 1985 Day et al. 1991, Dickson 1992). Hens are also able to stand up at the nest bowl and survey the surrounding area for predators before leaving the nest in these habitats. Saw palmetto can also grow in dry prairie, and in areas

PAGE 61

61 not dominated by it, saw palmetto grows in patches, affording heavy cover in a relatively open habitat why may explain why hens selected these areas while searching for nesting areas The same is t rue of flatwoods, be they mesic, scrubby, or wet. The y present open landscapes with varying densities of woody vegetation, allowing turkeys to select at multiple scales. In summary, trends for selection remained consistent through all spatial scales. Hen s selected for dense cover at the nest bowl, while selecting for more open habitats at the patch level. This trend remained at the landscape, with hens selecting for scrubby, patchy habitats that were open with some small, intermingled clumps of more dens e cover within them. This selection provided concealment at the nest site and habitat suitable for foraging and brooding nearby. Success Turkey n ests failed for several different reasons, but the most significant cause s of failure were nest depredation or predation of the incubating hen. Many have cited depredation or predation of the hen as the leading cause s of failure for wild turkey nests and a limiting factor on population size (Williams 1991 Roberts and Porter 1996, Thogmartin 1999 ). Others such a s Seiss et al. (1990), Badyaev (1995) and Thogmartin (1999) have reported habitat effects on nest success, and have even suggested that depredation may influence selection of nest sites by causing avoidance of certain habitats or habitat features H oweve r, no such data exists for Florida wild turkeys. At the patch level, when compared to unsuccessful nests, successful nests had lower densities of hardwood, total stems, and saw palmetto and decreased visual obstruction, as reported with eastern wild turk eys by Badyaev (1995). Hens that selected for these indicators of more open habitats at the patch level were more likely to succeed,

PAGE 62

62 possibly because high basal areas manifested themselves in habitat edges. Trees in edge habitat may function as den sites for some potential nest predators such as snakes, opossums, and raccoons proximity to which may decrease nest success (Williams et al. 1980 Dickson 1992 Frey and Conover 2006). Additionally, open habitat selected at the patch level allows hens to see around the nest, quickly access forage locations, or escape danger. At the landscape level, h ens selected scrubby habitats that may have provided open areas for movement (i.e., scrubby flatwoods) or open habitats with scru bby patches (i.e., dry prairie) a t the landscape level, while using dense cover for nesting at the microhabitat. Hens also selected for habitat edge at the landscape level which provided dense cover at the microhabitat that functioned to incubating enefit However, by selecting nest sites closer to habitat edge (characterized by denser understory cover and higher basal areas), hens may have exposed their nests to higher number s of predators (Gates and Gysel 1978, Wilcove 1985) I found that habitat edges may function as somewha t of an ecological trap because hens selected nest sites closer to habitat edges presumably for the benefit gained in concealment at the microhabitat level yet success rose as distance to habitat edge roads, and water increased. Landscape category succ ess results must be interpreted conservatively, but trends, as well as habitat characteristics, indicated that edge habitat may have decreased the likelihood of nest success. P otential nest predators use these features such as edge, roads, and water not o nly as residences, but also as travel and forage corridors (Gates and Gysel 1978). So turkey s searching for dense cover in more open habitat types (i.e., dry prairie, etc.) may have selected for the edge of habitat where

PAGE 63

63 vegetation becomes more dense Ho wever these edges also mean predators were more likely to come upon the nest, resulting in elevated levels of nest predation (Gates and Gysel 1978 Wilcove 1985 Niemuth and Boyce 1997). For this reason, hens that selected areas with lower basal areas an d areas further from roads, habitat edges, and water were more likely to be successful; presumably because hens selected these areas with dense cover close to the center of habitat patches, not near habitat edge (Thogmartin 1999). Thogmartin (1999) had si milar findings, though Seiss et al. (1990) found that successful nests were located closer to features such as habitat edge and roads. Another factor associated with selection of dense vegetation (e.g., saw pal metto) for nest sites at the microhabitat level, as observed by Williams et al. (1968) and Williams and Austin (1988) in Florida wild turkey hens and Badyaev (1995) and Lazarus and Porter (1985) researching other subspecies. In Florida, saw palmetto has become a dominant component of the understory, providing dense cover selected for by nesting hens Because it is readily available and conceals hens, it may increase the probability of nest success. Saw palmetto exists both in the interior of habitat pat ches and at habitat edges, but its presence in the center of habitat patches may allow Florida wild turkey hens to select dense cover further from edge habitat, aiding success. At the landscape level, selection of habitat and habitat treatment type was not associated with success. Successful nests were associated with greater areas burned 0.5 2 year s ago which hens selected against, and with smaller areas of 0.5 2 year old chops, which hens selected. Sisson et al. (1990) reported that eastern wild turkey hens

PAGE 64

64 selected for areas with similar burn histories, however, Eichler and Whiting (2004) reported that eastern wild turkey hens in Texas avoided burns 0.5 2 years old. Ultimately, it appears a management strategy providing many different features through different treatment applications and ages may be best for wild turkeys in Florida. Smaller scale patchwork burning and chopping programs will create a mosaic of habitat, allowing hens to seek out patches of dense cover not available to them when large bu rn and chop units are used. Though large tracts dominated by saw palmetto may not benefit turkeys, small patches of dense shrubs such as saw palmetto does benefit the Florida wild turkey hen in her selection of a nest site and the probability of its succe ss. This type of management may also decrease hen predation and increase brood survival because it provides more open habitat available for foraging and brood rearing near dense patches of brush where nests may be located. In summary, Florida wild turkey hens seemed to select nesting sites based on features that would decrease the probability of detection by predators. These features provided enhanced concealment and decreased predator efficiency by increasing possible nesting locations as reported for e astern wild turkeys (Badyaev 1995). However, hens selected for areas closer to edge habitat, presumably for the increase in cover available near habitat edges, although predation may sometimes increase within edge habitat (Gates and Gysel 1978, Niemuth an d Boyce 1997). Habitat managers may be successful in mitigating depredation through treatments to habitat edge or by allowing and even encouraging the growth and persistence of small clumps of dense vegetation located throughout habitat patches If edge is made less appealing to nesting hens, they may choose to nest in vegetation clumps more internally located in habitat patches. This

PAGE 65

65 may be accomplished using prescribed fire and roller chopping along the edges of habitats, reducing the dense vegeta tion characteristic s of ecotones selected by nesting Florida wild turkey hens. Additionally, h ens selected for untreated areas and for areas roller chopped >6 months prior, but they experienced more success in areas burned 0.5 2 years prior. A combination of these treatments may satisfy needs for nest site selection, while simultaneously benefitting success rates. In some areas of Florida where saw palmetto dominates shrub removal may be necessary H owever, this study demonstrates its benefits to wild turke saw palmetto should be allowed to remain throughout habitat patches to aid in concealing incubating hens and increasing the area that predators must search to find nests while kept in low enough densitie s that turkeys can easily move through the area while foraging

PAGE 66

66 L IST OF REFERENCES Badyaev, A. V. 1995. Nesting habitat and nesting success of eastern wild turkeys in the Arkans as Ozark Highlands. Condor 97: 221 232. Bailey, W., D. Bennett Jr., H. Go re, J. Pack, R. Simpson and G. Wright. 1980. Basic considerations and general recommendations for trapping wild turkeys. Proc eedings of the Nat ional Wild Turkey Symp osium 4:251 261. Beyer, H. L. 2004. Hawt h's Analysis Tools for ArcGIS. http://www.s patialecology.com/htools. Bowman, G. B., and L. D. Harris. 1980. Effect of spatial heterogeneity on ground nest depredation. Jou rnal of Wildlife Management 44: 807 813. Burnham, K. P., and D. R. Anderson. 1998. Model selection and inference: a practi cal information theoretic approach. Springer Verlag, New York, New York, USA. Burnham, K. P. and D. R. Anderson. 2002. Model selection and inference: a practical information theoretic approach. Second edition. Springer Verlag, New York, New York, USA. Crabtree, R. L., L. S. Broome, and M. L. Wolfe 1989. Effects of habitat characteristics on Gadwall nest predators and nest site selection. Jou rnal of Wildlife Management 53: 129 137. Day K. S., L. D. Flake, and W. L. Tucker. 1991. Characteristic s of wild turkey nest sites in a mixed grass prairie oak woodland mosaic in the northern great plain, South Dakota. Canadian Journal of Zoology 69 :2840 2845. Dickson, J. G., editor. 1992. The wild turkey: b iology and management. Stackpole Books, Harris burg, Pennsylvania USA Ecological Software Solutions. 2005. Location of a Signal, version 3.0.4. Ecological Software Solutions, Urnsch, Switzerland. Eichler, B. G. and R. M. Whiting, Jr. 2004. Nesting habitat of eastern wild turkeys in east Tex as. Texas Journal of Scie nce 56: 405 414. Environmental Systems Research Institute. 2009. ArcGIS Desktop 9.3.1: ArcMap 9.3.1. ESRI, Redlands, California USA Exum, J. H., J. A. McGlincy, D. W. Speake, J. L. Buckner, and F. M. Stanley. 1987. Tall Tim bers Research Station 23:76.

PAGE 67

67 Florida Natural Areas Inventory (FNAI). 2010. Guide to the natural communities of Florida: 2010 edition. Florida Natural Areas Inventory, Tallahassee, FL USA. http://www.fnai.org/LandCover.cmf. Frey, S. N. and M. R. Conover. 2006. Habitat use by meso predators in a corridor environment. The Journal of Wildlife Management 70:1111 1118. Fuller, M.R., J. J. Millspaugh, K. E. Church, and R. E. Kenward. 2005. Wildlife radiotelemetry. Pages 377 417 in C. E Braun, editor. Techniques for Wildlif e Investigations and Management Sixth edition. The Wildlife Society, Bethesda, Maryland, USA. Gates, J. E. and L. W. Gysel. 1978. Avian nest dispersion and fledgling success in field forest ecotones. Ecology 59:871 883. Godfrey, C. L., and G. W. Norman. 2001. Reproductive ecology and nesting habitat of eastern wild turkeys in western Virginia. Proceedings of the National Wild Turkey Symposium 8:203 210. Haegen, W. M. V., M. W. Sayre, and J. E. Cardoza. 19 91. Nesting and brood rearing habitat use in a northern wild turkey population. Transactions of the Northeast Sect ion of the Wildlife Society 48: 113 119. Higgins, K. F., K. J. Jenkins, G. K. Clambey, D. W. Uresk, D. E. Naugle, J. E. Norland, and W. T Ba rker. 2005. Vegetation sampling and measurement. Pages 524 553 in C. E. Braun, editor. Techniques for Wildlife Investigations and Management. Sixth edition. The Wildlife Society, Bethesda, Maryland, USA. Hon, T., D. P. Belcher, B. Mullis, and J. R. Monroe. 1978. Nesting, b rood range, and r eproductive s uccess of an i nsular t urkey p opulation Proceedings of the Annual Conference of the Southeastern Association of Fish and Wildlife Agencies 32:137 149. Johnson, D. H. 1980. The comparison of usage and availability measurements for evaluating resource preference. Ecology 61:65 71. Krebs, C. J. 1999. Ecological Methodology Addison Wesley Educational Publishers, Inc. Menlo Park, California USA Lazarus, J. E. and W. F. Porter. 1985. Nest ha bitat selection by wild turkeys in Minnesota. Proceedings of the Na tional Wild Turkey Symposium 5: 67 81. Lehman, C. P., L. D. Flake, and D. J. Thompson. 2002. Comparison of microhabitat conditions at nest sites between eastern and R io G rand e wild turke ys in northeastern S outh Dakota. American M idland Naturalist Journal 149 :192 200.

PAGE 68

68 Logan, T. H. 1973. Seasonal behavior of Rio Grande wild turkeys in western Oklahoma. Proceedings of the Annual Conference of Southeastern Association of Game and Fish Co mmissioners 27:74 91. Lopez, R. R., C. K. Feuerbacher, M. A. Sternberg, J. W. Gainey, N. J. Silvy, and J. D. Burke. 1997. Nest site characteristics of relocated eastern wild turkeys in Texas. Proceedings of the Annual Conference of the Southeastern Ass ociation of Fish and Wildlife Agencies 51:449 456. Lutz, S. R. and J. A. Crawford. 1987. Reproductive success and nesting habitat of 787. Metzler, R. and D. W. Speake. 1985. W ild turkey poult mortality rates and their relationship to brood habitat structure in Northeast Alabama. Proceedings of the National Wild Turkey Symposium 5:103 111. Nguyen, L. P., J. Hamr, and G. H. Parker. 2004. Nest site characteristics of eastern w ild turkeys in central Ontario. Northeastern Naturalist 11: 255 260. Niemuth, N. D. and M. S. Boyce. 1997. Edge related nest losses in Wisconsin pine barrens. Journal of Wildlife Management 61:1234 1239. Palmer, W. E., G. A. Hurst, K. D. Godwin, and D. A. Miller. 1996. Effects of prescribed burning on wild turkeys. Transactions of t he North American Wildlife and N atural Resources Conference 61:228 236. Porter, W. F. 1978. The ecology and behavior of the wild turkey ( Meleagris gallopavo ) in south eastern Minnesota. D issertation, University of Minnesota, Minneapolis MN USA Redmond, G. W., D. M. Keppie, and P. W. Herzog. 1982. Vegetative structure, concealment, and success at nests of two races of Spruce Grouse. Canadian Journal of Zoology 60 :670 675. Roberts, S. D., and W. F. Porter. 1996. Importance of demographic parameters to annual changes in Wild Turkey abundance. Proceedings of the National Wild Turkey Symposium 7:15 20. Schad, B. J., 2009. Reproductive ecology of resident and tra nslocated bobwhites on South Florida rangelands. T hesis, University of Florida, Gainesville, FL, USA. Schmutz, J. A., C. E. Braun, and W. F. Andelt. 1989. Nest habitat use of Rio Grande wild turkeys. The Wilson Bulletin 101:591 598. Seiss, R. S., P. S. Phalen, and G. A. Hurst. 1990. Wild Turkey nesting habitat and success rates. Proceedings of the National Wild Turkey Symposium 6:18 24.

PAGE 69

69 Shields, R. D. and L. D. Flake. 2004. Nest site characteristics of eastern wild turkey in northeastern South Dakota. Prairie Naturalist 36:161 175. Sisson, D. C., D. W. Speake, J. L. Landers, and J. L. Buckner. 1990. Effects of prescribed burning on wild turkey habitat preference and nest site selection in south Georgia. Proceedings of the National Wild Turk ey Symposium 6:44 50. Sparks, J. C., R. E. Masters, and M. E. Payton. 2002. Comparative evaluation of accuracy and efficiency of six forest sampling methods. Proceedings of the Oklahoma Academy of Science 82:49 56. Speake, D. W., T. E. Lynch, W. J. Fl eming, G. A. Wright, and W. J. Hamrick. 1975. Habitat use and seasonal mo vements of wild turkeys in the S outheast. Proceedings of the National Wild Turkey Symposium 3:122 129. Swanson, D. A., J. C. Pack, C. I. Taylor, D. E. Samuel, and P. W. Brown. 19 96. Selective timber harvesting and wild turkey reproduction in West Virginia. Proceedings of the National Wild Turkey Symposium 7:81 88. SYSTAT Software, Inc. 2008. SYSTAT 12 Statistics IV. SYSTAT Software, Inc. San Jose, California USA Tanner, G. W. and W. R. Marion. 1990. Wildlife habitat considerations when burning and chopping Florida range. Cooperative Florida Extension Service Fact Sheet WRS 6. Tanner, G. W., R. S. Kalmbacher, and J. W. Prevatt. 1986. Saw palmetto control in Florida. Cooperative Florida Extension Service Circular 668. Thogmartin, W. E. 1999. Landscape attributes and nest site selection in wild turkeys. AUK 116 :912 923. Thomas, G. E. and J. A. Litvaitis. 1993. Nesting ecology of wild turkeys in New Hampshire Transactions of the Northeast Section of the Wildlife Society 50:119 126. Tirpak, J. M., W. M. Giuliano, T. J. Allen, S. Bittner, J. W. Edwards, S. Friedhof, C. A. Harper, W. K. Igo, D. F. Stauffer, and G. W. Norman. 2010. Ruffed grouse habitat prefe rence in the central and southern Appalachians. For est Ecology and Management 260: 1525 1538. Tirpak, J. M., W. M. Giuliano, C. A. Miller, T. J. Allen, and S. Bittner. 2006. Ruffed grouse nest success and habitat selection in the Central and Southern Ap palachians. Jo urnal of Wildlife Management 70 :138 144.

PAGE 70

70 White, G. C. and R. A. Garrott. 1990. Analysis of wildlife radio tracking data. Harcort Brace Jovanovich, New York USA Wilcove, D. S. 1985. Nest predation in forest tracts and the decline of migratory songbirds. Ecology 66:1211 1214. Willcox, E. V. and W. M. Giuliano. 2010. Seasonal effects of prescribed burning and roller chopping on saw palmetto in flatwoods. For est Ecology and Management 259 :1580 1585. Williams, L. E., Jr., D. H. Aus tin, N. F. Eichholz, T. E. Peoples, and R. W. Phillips. 1968. A study of nesting turkeys in southern Florida. Proceedings of the Annual Conference of Southeastern Association of Game and Fish Commissioners 22:16 30. Williams, L. E. Jr. D. H. Austin, and T. E. Peoples. 1980. Turkey nesting success on a Florida study area. Proceedings of the National Wild Turkey Symposium 4:102 107. Williams, L. E., Jr., and D. H. Austin. 1988. Studies of t he wild turkey in Florida. Technical Bulletin 10. Univer sity of Florida Press. Gainesville FL, USA Williams, L. E., Jr. 1991. Managing for wild turkeys in Florida. Real Turkey Publishers, Gainesville, FL, USA Williams, L. E., Jr. 2006. Wild Turkey Hunting and Management Real Turkey Publishers, Cedar Key, FL, USA

PAGE 71

71 BIOGRAPHICAL SKETCH Johnny Olson was raised in Thomasville, Georgia and the surrounding Red Hills Region. He has worked as a trapper, hired hand, and tractor driver on South Georgia quail plantations and as a technician tracking quail and capturing broods at Tall Timbers Research Station. Upon graduating from Furman University with a B achelor of the A rts degree in h istory in 2009, he moved to Gainesville, Florida to attend the University of Florida for his m d egree in w ildlife e colo gy and c onservation, researching Florida wild turkeys. He intends to further his education by continuing at the U niversity for his PhD, studying white tailed deer and coyotes in North Florida and South Georgia. He enjoys hunting, reloading, archery fish ing, fly tying, and being outdoors.