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Recruitment Dynamics of Age-0 Largemouth Bass along a Latitudinal Gradient of Florida Lakes

Permanent Link: http://ufdc.ufl.edu/UFE0021712/00001

Material Information

Title: Recruitment Dynamics of Age-0 Largemouth Bass along a Latitudinal Gradient of Florida Lakes
Physical Description: 1 online resource (121 p.)
Language: english
Creator: Rogers, Mark W
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 2007

Subjects

Subjects / Keywords: bass, florida, largemouth, latitude, recruitment
Fisheries and Aquatic Sciences -- Dissertations, Academic -- UF
Genre: Fisheries and Aquatic Sciences thesis, Ph.D.
bibliography   ( marcgt )
theses   ( marcgt )
government publication (state, provincial, terriorial, dependent)   ( marcgt )
born-digital   ( sobekcm )
Electronic Thesis or Dissertation

Notes

Abstract: Juvenile fish life history varies across large spatial gradients because of latitudinal influences on growing season length and winter severity. I evaluated recruitment processes (e.g., hatching date, growth, and mortality) affecting largemouth bass Micropterus salmoides recruitment to age-1 across a latitudinal gradient of Florida lakes and related my findings to results for this species from more northerly latitudes. I sampled the 2003 and 2004 year classes at six Florida lakes that spanned latitudes from N27o0? to N30o5?. My first objective tested whether 1) early-hatching provided a growth and survival advantage relative to later-hatching through their first summer, and 2) whether overwinter size-selective mortality strongly influenced recruitment to age-1 across Florida?s latitudinal gradient. My results did not fully conform to common hypotheses because early-hatched sub-cohorts (i.e., fish hatched at dates in the left tail of the overall hatching distribution) did not exhibit a growth and survival advantage at all lakes and I did not detect strong size-selective overwinter mortality. My second objective evaluated the relative contributions of genetic and environmental effects on spawning periodicity by rearing Florida largemouth bass M. s. floridanus from Lake Okeechobee in south Florida and intergrade largemouth bass M. s.salmoides x floridanus (or vice-versa) from Lake Seminole in north Florida in a similar environment in Gainesville, Florida. Results showed that Florida fish began spawning earlier than intergrade fish in all ponds and Florida fish had a longer spawning season than intergrade fish. Similarly, Florida fish at Lake Okeechobee began spawning earlier and had a longer spawning season than intergrade fish at Lake Seminole. Thus, environmental factors influenced spawning periodicity for both genetic stocks, but spawning periodicity in ponds also reflected characteristics of their source populations. My last objective explored implications of hatching date-dependent growth and mortality observations for age-0 largemouth bass to evaluate relative effects on recruitment to age-1. Modeling results showed that hatching date-dependent mortality could influence the contributions of differing hatching sub-cohorts to year class composition at age-1, but total age-1 and adult biomass was not largely affected. Thus, my models predicted large compensation potential and strong regulation for largemouth bass recruitment.
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 Mark W Rogers.
Thesis: Thesis (Ph.D.)--University of Florida, 2007.
Local: Adviser: Allen, Micheal S.

Record Information

Source Institution: UFRGP
Rights Management: Applicable rights reserved.
Classification: lcc - LD1780 2007
System ID: UFE0021712:00001

Permanent Link: http://ufdc.ufl.edu/UFE0021712/00001

Material Information

Title: Recruitment Dynamics of Age-0 Largemouth Bass along a Latitudinal Gradient of Florida Lakes
Physical Description: 1 online resource (121 p.)
Language: english
Creator: Rogers, Mark W
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 2007

Subjects

Subjects / Keywords: bass, florida, largemouth, latitude, recruitment
Fisheries and Aquatic Sciences -- Dissertations, Academic -- UF
Genre: Fisheries and Aquatic Sciences thesis, Ph.D.
bibliography   ( marcgt )
theses   ( marcgt )
government publication (state, provincial, terriorial, dependent)   ( marcgt )
born-digital   ( sobekcm )
Electronic Thesis or Dissertation

Notes

Abstract: Juvenile fish life history varies across large spatial gradients because of latitudinal influences on growing season length and winter severity. I evaluated recruitment processes (e.g., hatching date, growth, and mortality) affecting largemouth bass Micropterus salmoides recruitment to age-1 across a latitudinal gradient of Florida lakes and related my findings to results for this species from more northerly latitudes. I sampled the 2003 and 2004 year classes at six Florida lakes that spanned latitudes from N27o0? to N30o5?. My first objective tested whether 1) early-hatching provided a growth and survival advantage relative to later-hatching through their first summer, and 2) whether overwinter size-selective mortality strongly influenced recruitment to age-1 across Florida?s latitudinal gradient. My results did not fully conform to common hypotheses because early-hatched sub-cohorts (i.e., fish hatched at dates in the left tail of the overall hatching distribution) did not exhibit a growth and survival advantage at all lakes and I did not detect strong size-selective overwinter mortality. My second objective evaluated the relative contributions of genetic and environmental effects on spawning periodicity by rearing Florida largemouth bass M. s. floridanus from Lake Okeechobee in south Florida and intergrade largemouth bass M. s.salmoides x floridanus (or vice-versa) from Lake Seminole in north Florida in a similar environment in Gainesville, Florida. Results showed that Florida fish began spawning earlier than intergrade fish in all ponds and Florida fish had a longer spawning season than intergrade fish. Similarly, Florida fish at Lake Okeechobee began spawning earlier and had a longer spawning season than intergrade fish at Lake Seminole. Thus, environmental factors influenced spawning periodicity for both genetic stocks, but spawning periodicity in ponds also reflected characteristics of their source populations. My last objective explored implications of hatching date-dependent growth and mortality observations for age-0 largemouth bass to evaluate relative effects on recruitment to age-1. Modeling results showed that hatching date-dependent mortality could influence the contributions of differing hatching sub-cohorts to year class composition at age-1, but total age-1 and adult biomass was not largely affected. Thus, my models predicted large compensation potential and strong regulation for largemouth bass recruitment.
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 Mark W Rogers.
Thesis: Thesis (Ph.D.)--University of Florida, 2007.
Local: Adviser: Allen, Micheal S.

Record Information

Source Institution: UFRGP
Rights Management: Applicable rights reserved.
Classification: lcc - LD1780 2007
System ID: UFE0021712:00001


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RECRUITMENT DYNAMICS OF
AGE-0 LARGEMOUTH BASS ALONG A LATITUDINAL GRADIENT OF FLORIDA
LAKES






















By

MARK WAYNE ROGERS


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

UNIVERSITY OF FLORIDA

2007































O 2007 Mark Wayne Rogers


































To Kristin and our future together









ACKNOWLEDGMENTS

My research was funded by a grant from the Florida Fish and Wildlife Conservation

Commission (FWC) and I was supported by a UF CALS Alumni Fellowship. Other financial

support was provided by the AFS Southern Division Reservoir Committee Robert M. Jenkins

Scholarship, FL Chapter of AFS Roger Rottman Scholarship, and travel grants through: UF

IFAS, the UF Graduate Student Association, FL Chapter of AFS, and AFS Education Section

Skinner Award.

I thank my advisor, Dr. Michael Allen, and other graduate committee members, Dr. Debra

Murie, Dr. Tom Frazer, Dr. Craig Osenberg, and Dr. Ramon Littell for their service. I especially

thank Dr. Allen for his high expectations and efforts to do everything he could do to benefit my

student experiences. My research would not have been possible without the assistance of the

following FWC cooperators: John Benton, Rich Cailteaux, Jim Estes, Don Fox Beacham Furse,

Steve Gornak, Jay Holder, Bill Johnson, Earl Lundy, Charlie Mesing, Wes Porak, and Andy

Strickland. University of Florida students and staff: C. Barrientos, Mo Bennett, G. Binion, T.

Bonvechio, P. Cooney, J. Dotson, D. Dutterer, P. Hall, G. Kaufman, V. Maceina, V. Politano,

and N. Tripped conducted field sampling, laboratory sample and data processing. I owe

tremendous thanks to my academic mentors: Dr. Micheal Allen, Dr. Michael Hansen, and Dr.

Richard Noble and greatly appreciate the willingness of Dr. Carl Walters to share his time to

assist with my dissertation work. I also thank Dr. Chuck Cichra, Dr. Karl Havens, Mark Hoyer,

and Dr. Bill Pine for enhancing my education.

Most importantly I thank Kristin and our family for their love and support during this

process. I appreciate Kate Lazar' s friendship to my wife and willingness to entertain Kristin on

all the weekends and nights that I was working. I also thank Trinity United Methodist Church

for helping me keep my life prioritized.












TABLE OF CONTENTS

page

LIST OF TABLES .........__.. ..... .__. ...............6....


LI ST OF FIGURE S .............. ...............7.....


AB S TRAC T ......_ ................. ............_........8


CHAPTER


1 GENERAL INTRODUCTION .............. ...............10....


2 EXPLORING THE GENERALITY OF RECRUITMENT HYPOTHESES FOR
LARGEMOUTH BASS ALONG A LATITUDINAL GRADIENT OF FLORIDA
LA K ES .............. ...............12....
M ethods .............. ...............14....
Re sults ................ ...............19.................
Discussion ................. ...............23.................


3 SEPARATING GENETIC AND ENVIRONMENTAL INFLUENCES ON
TEMPORAL SPAWNING DISTRIBUTIONS OF LARGEMOUTH BASS

(M icropterus salmoides) .............. ...............40....
M ethod s .............. ...............43....
Re sults ................ ...............46.................
Discussion ................. ...............48.................


4 SIMULATED INFLUENCES OF HATCHING DATE SPECIFIC SURVIVAL ON
RECRUITMENT OF LARGEMOUTH BASS ................. ...............61........... ...
M ethod s .............. ...............62....
Re sults ................ ...............69.................
Discussion ................. ...............73.................


5 SYNTHESIS AND FUTURE RESEARCH............... ...............88

Age-0 Largemouth Bass Recruitment .............. ...............88....
Fisheries Management ................. ...............89.................
Future Research .............. ...............90....


APPENDIX


A DIET COMPOSITION MATRICES FOR ECOPATH MODELS .............. ....................93


B SENSITIVITY ANALYSIS RESULTS FOR ECOPATH MODELS ................. ...............98


REFERENCE LIST .............. ...............108....


BIOGRAPHICAL SKETCH ................. ...............121......... ......










LIST OF TABLES


Table page

2-1 Physical and chemical characteristics of 6 Florida study lakes and genetic
characteristics of their largemouth bass populations ................. .............................30

2-2 Dates corresponding to hatching periods, median hatch dates, and water temperatures
(oC) at corresponding median hatch dates for the 2003 and 2004 largemouth bass year
classes at 6 Florida study lakes .............. ...............31....

2-3 Mean daily growth rate, and standard deviation (SD) for age-0 largemouth bass
collected in block nets during spring and summer of each year .............. ....................3

2-4 Analysis of variance results for survival comparisons among hatching periods and
regions of Florida ................ ...............33........... ....

3-1 Earliest, median, and latest hatch dates of Florida (Lake Okeechobee fish) and
intergrade (Lake Seminole fish) largemouth bass (Micropterus salmoides) translocated
to experimental ponds at Gainesville, Florida in 2004 and corresponding water
tem peratures .............. ...............56....

4-1 Species composition of non-largemouth bass fish groups..........._.._.. ......._.._........._..81

4-2 Ecopath inputs for a north Florida eutrophic lake based on data from Lakes Seminole
and Talquin collected in 2003 and 2004 .............. ...............82....

4-3 Ecopath inputs for a south Florida eutrophic lake based on data from Lakes Istokpoga
and Okeechobee collected in 2003 and 2004 .............. ...............83....

4-4 Ecopath estimates of diet niche overlap among age-0 largemouth bass hatching sub-
cohorts .............. ...............8 4....

A-1 Diet composition inputs for north region Ecopath model .............. .....................9

A-2 Diet composition inputs for south region Ecopath model .............. .....................9

B-1 Sensitivity analysis for north region Ecopath model .............. ...............99....

B-2 Sensitivity analysis for south region Ecopath model ................ ......_.._........._.._.. 103










LIST OF FIGURES
Figure page

2-1 Selected north region, central region, and south region study lakes .............. ..................34

2-2 Relative frequency distributions of age-0 largemouth bass hatching at north, central,
and south study lakes in 2003 .............. ...............35....

2-3 Relative frequency distributions of age-0 largemouth bass hatching at north, central,
and south study lakes in 2004 .............. ...............36....

2-4 Relationship between mean daily growth rates and average temperatures from the 40-
day period following the median hatch date for early, middle, and late hatching
periods from 6 Florida lakes during 2003 and 2004 .............. ...............37....

2-5 Length frequency distributions for 2003 fall and spring (~age-1) samples of age-0
largemouth bass collected by electrofishing at north, central, and south study lakes..........38

2-6 Length frequency distributions for 2004 fall and spring (~age-1) samples of age-0
largemouth bass collected by electrofishing at north, central, and south study lakes..........39

3-1 Locations and latitudes for Lake Seminole, Lake Okeechobee and Gainesville, Florida,
US A. ......... ........ ........ ........ ............_ ........_ ............ .......57

3-2 Five-day cohort percent hatching distribution for age-0 largemouth bass hatched in
research ponds at Gainesville, Florida .............. ...............58....

3-3 Five-day cohort percent hatching distribution for age-0 largemouth bass collected at
Lakes Okeechobee and Seminole .............. ...............59....

3-4 Semi-monthly hatching distributions for age-0 largemouth bass reared in research
ponds in Gainesville, Florida and in source populations at Lake Seminole and Lake
Okeechobee in 2004 .............. ...............60....

4-1 Representation of ecosystem flows for components of an Ecopath model consisting of
three consumer groups and a detritus group such that predation on a group results in
production for their predators ................. ...............85................

4-2 Representation of vulnerable and invulnerable states of prey functional group biomass
and predator consumption .............. ...............85....

4-3 Percent biomass change imposed by simulating variable hatch-date specific mortality
relative to baseline Ecopath values for a north Florida lake .............. ....................8

4-4 Percent biomass change imposed by simulating variable hatch-date specific mortality
relative to baseline Ecopath values for a south Florida lake ................ .......................87









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

RECRUITMENT DYNAMICS OF
AGE-0 LARGEMOUTH BASS ALONG A LATITUDINAL GRADIENT OF FLORIDA
LAKES

By

Mark Wayne Rogers

December 2007

Chair: Micheal Allen
Major: Fisheries and Aquatic Sciences

Juvenile fish life history varies across large spatial gradients because of latitudinal

influences on growing season length and winter severity. I evaluated recruitment processes (e.g.,

hatching date, growth, and mortality) affecting largemouth bass M~icropterus salmoides

recruitment to age-1 across a latitudinal gradient of Florida lakes and related my findings to

results for this species from more northerly latitudes. I sampled the 2003 and 2004 year classes

at six Florida lakes that spanned latitudes from N27o0' to N30o5'. My first objective tested

whether 1) early-hatching provided a growth and survival advantage relative to later-hatching

through their first summer, and 2) whether overwinter size-selective mortality strongly

influenced recruitment to age-1 across Florida' s latitudinal gradient. My results did not fully

conform to common hypotheses because early-hatched sub-cohorts (i.e., Eish hatched at dates in

the left tail of the overall hatching distribution) did not exhibit a growth and survival advantage

at all lakes and I did not detect strong size-selective overwinter mortality.

My second obj ective evaluated the relative contributions of genetic and environmental

effects on spawning periodicity by rearing Florida largemouth bass M. s. floridanusd~~~~~ddddd~~~~ from Lake

Okeechobee in south Florida and intergrade largemouth bass M~ s.salmoides x floridanus~~~~dddd~~~~ddd (or









vice-versa) from Lake Seminole in north Florida in a similar environment in Gainesville, Florida.

Results showed that Florida fish began spawning earlier than intergrade fish in all ponds and

Florida fish had a longer spawning season than intergrade fish. Similarly, Florida fish at Lake

Okeechobee began spawning earlier and had a longer spawning season than intergrade fish at

Lake Seminole. Thus, environmental factors influenced spawning periodicity for both genetic

stocks, but spawning periodicity in ponds also reflected characteristics of their source

populations.

My last obj ective explored implications of hatching date-dependent growth and mortality

observations for age-0 largemouth bass to evaluate relative effects on recruitment to age-1.

Modeling results showed that hatching date-dependent mortality could influence the

contributions of differing hatching sub-cohorts to year class composition at age-1, but total age-1

and adult biomass was not largely affected. Thus, my models predicted large compensation

potential and strong regulation for largemouth bass recruitment.









CHAPTER I
GENERAL INTRODUCTION

Fisheries managers and ecologist struggle to understand fish recruitment because it is

variable and many factors influence survival of age-0 fishes (Post et al. 1998). Factors

influencing age-0 fish survival differ among latitudes owing to environmental influences (e.g.,

winter water temperatures) and can result in localized adaptations to adult spawning strategies

for maximizing offspring survival (Conover 1992). Thus, both genetic and environmental

components likely contribute to adult spawning timing, but their relative contributions and

implications for offspring survival are largely unknown. Identifying processes and factors that

regulate and control age-0 survival facilitates our ecological understanding of juvenile

recruitment processes, adult reproductive timing, and fisheries management across broad

latitudinal gradients. The recreational importance and broad distribution of largemouth bass

have resulted in extensive research and generalized hypotheses that identified important factors

(e.g., hatching date and growth rate) that can limit age-0 survival. Studies commonly concluded

that age-0 largemouth bass hatching early in the spawning season had early-life advantages (e.g.,

lower mortality) and were more likely to survive during winter relative to later-hatched members

of a year class (Miranda and Hubbard 1994a; Ludsin and DeVries 1997; Garvey et al. 1998),

however all of these studies were conducted at latitudes where growing season length and winter

water temperatures would likely strongly affect age-0 largemouth bass survival.

Florida winters are mild relative to latitudes where other age-0 largemouth bass

recruitments studies have been conducted (i.e., Alabama to Wisconsin). Mild winters could

strongly influence the onset and duration of spawning, growth, and the potential for overwinter

mortality, thus suggesting that age-0 recruitment processes may differ for Florida populations

relative to more northerly populations. I studied the 2003 and 2004 largemouth bass year classes










at six Florida lakes distributed across three regions (south, central, and north). I evaluated

hatching seasons, growth, mortality, and size-selective overwinter mortality across a range of

Florida latitudes and related my findings to popular largemouth bass recruitment hypotheses. I

reported an experiment to evaluate the contributions of genetic and environmental factors to

largemouth bass spawning season initiation and duration with implications for evolution of adult

spawning strategies across latitudes. I used a trophic-based ecosystem model to simulate how

hatching date-dependent survival would influence within year-class interactions and year class

strength, and related my findings to theory on the evolution of parental spawning strategies and

addressed implications for fisheries management.









CHAPTER 2
EXPLORING THE GENERALITY OF RECRUITMENT HYPOTHESES FOR
LARGEMOUTH BASS ALONG A LATITUDINAL GRADIENT OF FLORIDA LAKES

Aquatic ecologists and fisheries managers struggle to understand fish recruitment because

recruitment is inherently variable, and both density dependent and independent factors influence

pre-recruitment survival (Post et al. 1998). Environmental (e.g., temperature) and biological

(e.g., energetic and predator-prey relationships) factors interact to influence recruitment

processes (e.g., hatching dates, growth, and mortality), but the relative strengths of those

recruitment processes can vary greatly with latitude (Conover 1992; Garvey et al. 2002a).

Establishing latitudinal patterns in hatching, growth, and survival of juvenile fish is a goal of

ecologists (Garvey et al. 2002a) and facilitates understanding of species-specific variation in life-

history traits across large spatial gradients (Conover 1992).

Early-life survival in teleost fishes has been closely associated with body size (Miller et

al. 1988), which is largely influenced by hatching date and somatic growth rate (Conover 1992;

Houde 1997). Spawning initiation generally occurs earlier in the year at lower latitudes than at

higher latitudes for broadly distributed species due to influences of temperature (Lam 1983;

Conover 1992). Spawning strategies often reflect adaptive characteristics, such that spawning

season durations are protracted at lower latitudes relative to higher latitudes because of

differences in growing season duration and the strength of over-winter size-dependent mortality

(Conover 1992). When spawning distributions are protracted, variation in conditions for early-

versus late-hatched sub-cohorts (i.e., fish hatched in a specific period within the total hatching

distribution) can be large relative to systems that exhibit contracted spawning distributions,

potentially magnifying differences in growth and survival among sub-cohorts within a year class

(Phillips et al. 1995; Cargnelli and Gross 1996; Narimatsu and Munehara 1999). Thus, temporal









spawning patterns may result in differing strengths of factors regulating recruitment processes

among populations.

Early-hatching often leads to large juvenile size by the end of the first growing season

and higher survival relative to later-hatched members of a year class (e.g., Trebitz 1991,

Cargnelli and Gross 1996), but biotic interactions (e.g., competition) also can influence this

relationship (Olson 1996; Post 2003). Early hatching and increased size have been shown to be

beneficial by reducing vulnerability to predators (Christensen 1996; Hambright et al. 1991),

providing foraging advantages (Ludsin and DeVries 1997; Mittelbach and Persson 1998), and by

improving fish condition for overwinter survival (Shuter and Post 1990; Ludsin and DeVries

1997) relative to smaller members of a year class. Post (2003) suggested that early-hatching

provides opportunities that can result in earlier age-at-maturity, and thus, increased lifetime

fitness. Therefore, early hatching has often been considered advantageous in fish populations

structured by size-dependent mortality.

Life-history strategies across species distributions result from responses to both

environmental and genetic influences (Stearns 1976). Because temperature, and thus latitude,

can influence spawning success, fish populations are expected to exhibit adaptations to local

conditions that maximize progeny survival (Steamns 1976; Conover 1990). Without knowledge

of the relative strengths of processes influencing survival among populations, broad scale

ecological patterns of recruitment are difficult to establish. Broad recruitment hypotheses have

been proposed for largemouth bass; however, Parkos and Wahl (2002) reported that most

knowledge concerning recruitment processes for this species was largely derived from studies

conducted from only a portion of their distribution. For example, Garvey et al. (1998)

hypothesized that the strength of overwinter mortality should increase with decreasing latitude









because warmer winter climates facilitate increased predation during winter. Garvey et al.

(1998) developed their hypothesis from populations extending from Wisconsin to central

Alabama, but information was not available from the southern extent of the largemouth bass' s

native range. To evaluate the generality of recruitment hypotheses across latitudinal gradients,

recruitment processes need to be evaluated outside the areas used to develop the hypotheses.

Florida's climate ranges from subtropical to temperate, thus potentially providing a

prolonged breeding season and minimal winter effects relative to other temperate North

American latitudes. My obj ective was to evaluate the generality of recruitment hypotheses,

developed at more northerly latitudes, to juvenile largemouth bass survival in Florida. I

compared hatching distributions, growth, and survival of age-0 largemouth bass across a

latitudinal gradient of Florida lakes (i.e., at the southern extent of their native distribution) to

determine if survival patterns conform to current hypotheses regarding pre-recruitment

largemouth bass survival. I tested two hypotheses: 1) that early hatching would result in a

growth and survival advantage through the summer (e.g., Phillips et al. 1995; Ludsin and

DeVries 1997; Pine et al. 2000), and 2) that size-selective overwinter mortality would strongly

influence survival to age-1 (e.g., Miranda and Hubbard 1994; Ludsin and Devries 1997; Garvey

et al. 1998).

Methods

I divided Florida into three study regions (i.e., south, central, and north; per Crawford et

al. 2002) and selected two lakes from each region for my study. Study lakes in each region

included: 1) north region: Seminole and Talquin Reservoirs, 2) central region: Lakes Harris and

Monroe, and 3) south region: Lakes Okeechobee and Istokpoga (Table 2-1; Figure 2-1). Due to

Lake Okeechobee's large size, my sampling area (> 25 km2) was located in the northwest region









of the lake. Seminole Reservoir is not entirely within Florida; thus, I sampled only the west

littoral zone along the Chattahoochee Basin from the Alabama state line to Jim Woodruff Lock

and Dam (i.e., Florida waters). Sample sites at other lakes were distributed throughout the entire

littoral area. In general, lakes had vast, highly vegetated littoral zones composed of submergent

(e.g., Hydrilla verticullata), emergent (e.g., pickerelweed Pontederia cordata) and floating-

leaved (e.g., fragrant water lily Nymphaea odorata) plants. Vegetation types and abundances

were generally similar across lakes, except for Lake Talquin, where vegetation abundance was

lower and less diverse compared to the other lakes (M. Rogers, personal observation). I sampled

the 2003 and 2004 year classes of largemouth bass from shortly after hatching through the

following spring (i.e., about age-1) at each lake.

Early Age and Growth

I used block nets to sample age-0 largemouth bass during spring and summer at each

lake. Due to the potential for earlier-hatching in my south region relative to my central and north

regions, Lakes Okeechobee and Istokpoga were sampled in early spring (i.e., February or March)

and all lakes were sampled during spring (i.e., late April/May) and summer (i.e., late June/July)

in each year. A 100-m block net (3.2-mm knotless nylon mesh) was deployed in a 10m x 10m

square (total area = 0.01 ha) and liquid rotenone (Prenfish@ 5% active ingredient) was applied at

3 mg/L. Twelve block nets were set at each lake during each sampling event. Samples were

collected in shallow (< 2m) littoral zones, and sample sites were selected to be representative of

available habitat types (e.g., vegetation type). At each net, all fish were collected by 3-4 wading

investigators until fish did not continue to surface, and the net was then moved to another

location and set again. My strategy underestimated fish density due to incomplete recovery, but

allowed for higher samples sizes per habitat type and lake (Timmons et al. 1978).









All collected fish were placed on ice and returned to the laboratory. Age-0 largemouth

bass were measured to nearest 1 mm total length (TL) and weighed to the nearest 0.01 g. Age-0

largemouth bass from each sampling event were placed in 1 cm length groups and sagittal

otoliths were removed from a subsample of fish so that the length distribution of aged fish

closely reflected the length frequency of the entire fish sample collected in that period (Ludsin

and DeVries 1997; Pine and Allen 2001). Otolith preparation and age estimation followed the

procedures of Miller and Storck (1982) and Ludsin and DeVries (1997). A sample of 30 known-

age largemouth bass from the Richloam Fish Hatchery, Florida, was collected during May 2003

and was used to validate age accuracy. All otoliths were read twice by two independent readers.

Between-reader ages differing by less than 3.5 d were used to produce an average age (Maceina

et al. 1995).

Hatch date for each fish was estimated by subtracting the number of rings counted on the

otolith from the day of year when collection took place. Total length at age data were modeled

using both a linear model and an exponential model for each lake, and error variance was

compared using a variance ratio test (Zar 1999). The linear and exponential models were fit to

TL at age using data from fish between 30-75 d old to ensure that the age and subsequent growth

comparisons were similar across hatching cohorts and study lakes. Mean daily growth rate

(DGR, mm/d) for each fish was estimated as:

DGR = (TL 6)/age (2-1)

where TLe is the total length at capture, age is the number of days from swim-up (i.e., the ring

count), and 6 mm is subtracted to correct for total length at swimup (Goodgame and Miranda

1993; Ludsin and DeVries 1997). I used swimup dates as an indicator of hatch dates and an

index of adult spawning activities.









I compared mean DGR among hatching periods and lakes. Fish were grouped into early,

middle, and late hatch periods by partitioning the entire hatching date distribution for each lake

and year into 33 percentile groups. Estimated hatch dates from each sample were pooled to

construct the entire hatching distribution for each lake and year because I wanted to incorporate

early hatch dates that may not have been detectable in later samples due to mortality (see Isely et

al. 1987). Hatching periods that delineated sub-cohorts were lake and year specific to evaluate

the general prediction that early-hatching, within any given system, would result in a growth and

survival advantage relative to later-hatching. Mean DGR was compared among sub-cohorts (i.e.,

early, middle, late) and lakes using a two-way analysis of variance (ANOVA) for each year with

hatching period and lake as factors. If lakes within regions did not exhibit differences in mean

DGR among sub-cohorts, I grouped lakes into regions (north, central, south) and used a two-way

ANOVA with regions and hatching periods as factors. Least-squares means with Tukey's

modification were used to separate DGR means if differences were significant in the ANOVA.

Water temperature was measured at each lake throughout the study. Temperature loggers

manufactured by Onset 9 were placed at two stations within the sample areas at each lake

between 0.5 and 1 m water depth prior to initiating data collection and they recorded data during

the entire study period. Temperature loggers were programmed to record water temperature at

six-hour intervals, thus allowing us to relate age-0 largemouth bass hatching frequency

distributions and growth rates to water temperatures for each lake, region, and year.

Early survival of age-0 largemouth bass was assessed using changes in abundance-at-age

from sequential block net sampling events at each lake. Fish were grouped in 7-day cohorts for

each lake and sampling event. Survival of each cohort was estimated as:

Si= Nit/Ni,t-1 (2-2)









where Si is the survival rate of cohort i, Ni, t is the mean density of fish from cohort i at time t,

and Ni t-1 is the mean density of cohort i in the preceding sampling event. Survival was

estimated between the early spring and spring sampling periods at south lakes and the spring to

summer sampling periods at all lakes. Only cohorts that were fully recruited (i.e., > 15 mm TL)

to the block nets in each sampling event were used for estimating survival. Seven-day cohorts

were grouped into early, middle, and late-hatched sub-cohorts based on dates delineated by the

total hatching distribution as described above. I tested for differences in mean survival among

sub-cohorts for the early-spring to spring periods in the south lakes combined using a one-way

ANOVA. Low sample sizes (the number of cohorts per lake) precluded lake-specific survival

assessment for spring to summer sampling periods. Thus, I grouped lakes by region and tested

whether mean survival differed among sub-cohorts and regions using a two-way ANOVA.

Least-squares means was used to separate means if the overall ANOVA had significant effects (a

= 0.05).

Size-Dependent Over-Winter Survival

Electrofishing was used to sample fish in fall (October) and spring (March; ~age-1) of

each year. Twenty-minute electrofishing transects were conducted in the same habitats where

block-netting took place using pulsed DC current, and sampling was continued at each lake until

at least 100 juvenile largemouth bass were captured. Otoliths were removed from juvenile

largemouth bass collected by electrofishing and examined to ensure lack of an annulus. Size

structure of age-0 largemouth bass in each sampling event was used to assess relative overwinter

size-specific survival in each lake and year. Differences in size structure between sampling

periods were determined using Kolmogorov-Smirnov tests (Zar 1999).












Early Age and Growth

Age-0 largemouth bass hatching seasons varied widely by region in Florida, but were

generally similar for both lakes in each region. Spawning periodicity (i.e., the distribution of

hatching events during the spawning season) was variable within lakes and resulted in differing

hatch dates that corresponded to early-, middle-, and late-hatched sub-cohorts between years

(Table 2-2). Largemouth bass from south lakes hatched earlier than central and north lakes fish

during both years. Hatching initiation began in early December at south lakes (Figures 2-2, 2-3)

and not until early March at north lakes (Figures 2-2, 2-3). In central lakes, age-0 largemouth

bass initiated hatching in mid to late February, except at Lake Monroe some fish hatched in

January 2004 (Figures 2-2, 2-3). I detected different hatching initiation dates for the south

region lakes during 2003. During 2003, hatching began in February at Lake Istokpoga, but in

early December at Lake Okeechobee (Figures 2-2, 2-3). Hatching distributions were multimodal

in many cases, indicating that spawning activity peaked at several times through the spawning

seasons.

Total hatching distributions and water temperatures during spawning also varied among

regions, with more protracted hatching distributions in the south relative to north lakes.

Hatching durations ranged from 61 d at Lake Harris in 2003 to 160 d at Lake Istokpoga in 2004

(Table 2-2). Hatching durations were longest at south lakes, shorter at central and north lakes,

and least variable at north lakes relative to the other lakes (Table 2-2). Water temperatures at

hatching initiation were as low as 14.7 C at Lake Talquin and as high as 22.0 C at Lake

Istokpoga during the study. Hatching occurred at temperatures up to 29.6 C at Lake Istokpoga


Results









during 2004. Water temperatures at median hatching dates ranged from 19.0 C at Lake

Istokpoga during 2003 to 25.7 C at Lake Istokpoga during 2004 (Table 2-2).

Linear models described relationships between mean TL and age (days) as well as

exponential models (all Variance Ratio Tests with P > 0.12), and thus, I used linear models to

describe growth rates. Mean DGR of fish varied among lakes and sub-cohorts, but differences

were not always consistent for lakes within regions. The lake~hatching period interaction was

significant in 2003 (both P < 0.001) indicating that mean DGR varied with both factors, but

differences were not consistent across lakes or hatching periods. For example, all sub-cohorts

from Lake Istokpoga exhibited relatively rapid growth in 2003 (all hatching periods > 0.60

mm/d), whereas Lake Okeechobee growth rates were low for the early-hatched sub-cohort (0.43

mm/d) and were more rapid for mid- and late-hatched sub-cohorts (0.57 and 0.59 mm/d,

respectively; Table 2-3). In 2004, early-hatched sub-cohorts at both south lakes grew slowly

(mean DGR 0.40-0.43), whereas middle- and late-hatched sub-cohorts grew faster (mean DGR

0.51-0.59; Table 2-3). The 2003 year class exhibited rapid growth at north lakes with mean

DGR > 0.68 mm/d for all sub-cohorts. The 2004 year class had moderate to rapid growth at the

north lakes (mean DGR range = 0.59 0.82; Table 2-3). Central lakes had moderate growth

rates (range 0.53-0.73 mm/d) that did not vary among sub-cohorts during either year. Thus, for

three of four lake-year combinations at south lakes, I found that early-hatched sub-cohorts had

slow growth relative to later-hatched sub-cohorts and other study lakes in Florida.

Similar to growth-rate data, survival of age-0 largemouth bass varied among sub-cohorts

and regions and was generally lowest for early-hatched sub-cohorts at south lakes. In 2003,

survival of fish between March and May at south lakes was lower for early (mean S=0.06) than

for middle-hatched (mean S=0.38, P < 0.02; Table 2-4) sub-cohorts. Late-hatched sub-cohorts









had not recruited to the block net for comparison of survival between March and May. Survival

between the May and July sampling periods for the 2003 year class ranged from 0.16 to 0.29

across re ions and sub-cohorts, but mean survival did not differ for either variable (oth P > 0.2;

Table 2-4). In 2004, both early and middle-hatched sub-cohorts at south lakes had low survival

(0. 14 early, 0. 11 middle) between February and late April, and these values did not differ (P >

0.90). Late-hatched sub-cohorts again had not recruited to the gear for this comparison.

Survival between April/May and June/July for the 2004 year class differed by region (P = 0.02),

with the hatching period effect marginally significant (P = 0.07) and the interaction effect not

significant (P = 0.8). Survival from May to July was higher at north than south lakes (S = 0.32 in

north lakes, 0.12 in south lakes, P < 0.01), and central lakes had intermediate values (S = 0.19;

Table 2-4). The hatching period effect showed that early-hatched sub-cohorts across all regions

had marginally lower survival (S=0.12) than middle and late hatched sub-cohorts (both least

squares mean S=0.25, both P < 0.2 in least squares means comparisons; Table 2-4). Thus, the

severity of mortality varied among regions and hatching periods with early-hatched sub-cohorts

having low survival prior to summer tall S < 0.14) at south lakes in both years.

Water temperatures after hatching influenced age-0 largemouth bass growth. The mean

DGR for a given hatching period was positively related to the average water temperature during

the 40 d period following the median hatch date for that hatching period across all lakes and

years (r = 0.60, P < 0.001) (Figure 2-4). Early-hatched sub-cohorts at south lakes endured

average temperatures of approximately 18 C for the first 40 days after hatching, except for the

early-hatched sub-cohort at Lake Istokpoga in 2003. Later-hatched sub-cohorts at south lakes

and all sub-cohorts at middle and north lakes experienced average temperatures 20 C for the









first 40 days after hatching and all mean DGR for these sub-cohorts were greater than 0.53

mm/d.

Size-Dependent Over-Winter Survival

I found little evidence of size-selective over-winter mortality because length frequency

distributions from electrofishing in the fall (age-0) and following spring (age-1) were similar for

most lakes in both years (Figures 2-5, 2-6). Only the 2003 length frequency distributions from

Lakes Harris and Talquin (Figure 2-5) differed between October and March electrofishing

samples (both P = 0.02). At Lake Harris, the relative number of small Eish declined over winter,

but the minimum size between fall and spring did not change and the maximum size increased,

thus suggesting that growth occurred for the larger fish. At Lake Talquin, an apparent mode of

Eish at 9-10 cm during fall increased greatly to 10-13 cm the following spring and both the

minimum and maximum sizes increased between fall and spring. Small samples sizes at Lake

Monroe during March 2005 (2004 year class, ~ age-1) prevented evaluations of length

frequencies from fall to spring, which was likely due to hurricane effects that increased mortality

and/or greatly decreased sampling catchability because of extremely high water levels.

Maximum size increased from fall to winter at all lakes and years, suggesting that growth

occurred over-winter, except at Lake Seminole in 2003 where neither minimum size nor

maximum size changed over-winter. If over-winter mortality were highly size-selective for

Florida lakes, I expected to see significant changes in the shape of the length frequency

distributions between fall and spring, which rarely occurred in either year. Although I was

unable to assess the overall strength of overwinter mortality in Florida, my results suggested that

mortality during this period was not strongly size-dependent.









Discussion

I found evidence that limited the application of the current conceptual framework

regarding hatch date and severity of overwinter mortality for age-0 largemouth bass recruitment.

Current hypotheses would have predicted highest survival for early-hatched sub-cohorts relative

to later hatched sub-cohorts in all lakes and years (Trebitz 1991, Miranda and Hubbard 1994;

Ludsin and Devries 1997; Garvey et al. 1998; Pine et al. 2000). I observed very slow growth for

three of four early-hatched sub-cohorts at my lowest latitude lakes and never observed survival

advantages for early-hatched sub-cohorts through their first summer at any Florida latitude I

evaluated relative to later-hatched sub-cohorts. I also found no evidence that size-selective

overwinter mortality would restructure largemouth bass year classes for Florida lakes by limiting

survival of smaller fish. Garvey et al. (1998) reviewed 15 studies that investigated overwinter

survival of age-0 largemouth bass from Wisconsin to Alabama and predicted reduced over-

winter survival for small fish in southern systems with warm winter temperatures and active

predators. The addition of my results to those reviewed by Garvey et al. (1998) suggested that

size-selective overwinter mortality likely exhibits a parabolic pattern in North America, with

highest overwinter mortality at intermediate latitudes of the largemouth bass's distribution (i.e.,

Missouri Alabama; see Garvey et al. 1998).

The growth and survival differences from hatching through the first summer likely

influenced the potential for hatching sub-cohorts to contribute to the year class. Early-hatched

sub-cohorts of the 2003 and 2004 year classes at south lakes exhibited high mortality, and thus,

likely contributed less to the age-1 largemouth bass year classes than later-hatched sub-cohorts.

I suspected that adult largemouth bass likely initiated spawning at a similar time at both south

lakes in 2003, as seen in 2004, and that a mortality event prevented detection of some early-









hatched fish at Lake Istokpoga in 2003. Evidence includes the truncated hatching distribution at

this system in 2003 (Figure 2-2) and an independent radio telemetry study at Lake Istokpoga

during the 2003 spawning season that indicated movement of adult largemouth bass into

spawning habitats during months when no hatching was detected (i.e., Dec Jan) (unpublished

data, J. Furse, Florida Fish and Wildlife Conservation Commission). Low survival for early-

hatched fish also has been reported for other species, including American Shad Alosa

sapidissima (Crecco and Savoy 1985), bloater Coregonus hoyi (Rice et al. 1987), striped bass

M~orone saxtilis (Rutherford and Houde 1995), and Korean sandeel Hypoptychus dybowskii

(Narimatsu and Munehara 1999), owing to environmental limitations (e.g., storm events,

temperature). Reduced growth of early-hatched sub-cohorts at south lakes potentially prolonged

the period of vulnerability to gape-limited predators and reduced predator avoidance abilities

(e.g., swimming speed), and thus led to increased mortality (Houde 1987; Miller et al. 1988).

Bestgen et al. (2006) reported more rapid growth yet higher mortality for early-hatched Colorado

pikeminnows relative to later-hatched fish because of temporal patterns in predator abundances

in juvenile habitats. Survival and growth disadvantages for early-hatched sub-cohorts at north

and central lakes were not as strong as disadvantages detected at south Florida lakes. However,

the potential contribution of early-hatched sub-cohorts to year-classes at all lakes in 2004 was

likely decreased due to low survival through their first summer relative to later-hatched sub-

cohorts.

I was surprised to find that growth rates appeared to be limited by low water temperatures

at sub-tropical south Florida lakes. However, adults began spawning in early December in my

south region leaving nearly the entire "winter" for cold fronts to influence their progeny.

Although early-hatched sub-cohorts at south Florida lakes suffered high mortality, early-hatched









Eish that survived to July were larger than fish from the other hatching sub-cohorts. Furthermore,

early-hatched fish had more fish prey in diets than smaller, later hatched fish in July (Rogers and

Allen 2005). Thus, some of the proposed advantages to early hatching were still evident despite

poor survival for early-hatched members of the year class. Similar results were reported for age-

0 largemouth bass in Alabama ponds where early-hatched fish exhibited slower growth than

later-hatched fish, but they were larger and expressed increased piscivory relative to smaller,

later-hatched fish (Ludsin and DeVries 1997). Other early-hatched centrarchids (i.e., bluegill

Lepomis macrochirus and pumpkinseed Lepomis gibbosus) also have exhibited reduced growth

rates owing to cool water temperatures after hatching that resulted in low survival relative to

later-hatched fish born at warmer water temperatures (Garvey et al. 2002b).

Strong size-selective overwinter mortality has been more commonly reported for

southern systems (Boxrucker 1982; Miranda and Hubbard 1994; Ludsin and DeVries 1997, but

see Jackson and Noble 2000 and Peer et al. 2006) than at northern and central latitudes (Kohler

et al. 1993), however results have varied owing to study system-specific characteristics (e.g.,

predator presence; Garvey et al. 1998). Starvation and predation are the common mechanisms

attributed to size-selective over-winter mortality. Small fish have lower lipid reserves and higher

mass-specific metabolism relative to larger fish, thus starvation is typically higher for small

juveniles when prey resources and low winter temperatures limit feeding (Oliver et al. 1979;

Henderson et al. 1988; Miranda and Hubbard 1994; Ludsin and DeVries 1997, but see Wright et

al. 1999). Predation also has been suggested as a major mechanism resulting in overwinter

mortality when winter water temperatures reduce activities of juvenile largemouth bass (i.e., <

6 C; Garvey et al. 1998, Fullerton et al. 2000), but remain warm enough for predators to remain

active and preferentially prey on small age-0 fish (Garvey et al. 1998). Although winter water









temperatures were conducive to predation at all Florida study lakes in both years, they also

remained above 6 C, and thus, age-0 fish activity was not likely limited. In Florida lakes, size-

selective overwinter mortality may not have resulted in a survival bottleneck because most age-0

largemouth bass surpassed 10 cm TL by fall and overwinter growth occurred at almost all lakes,

whereas Ludsin and DeVries (1997) reported little overwinter growth in central Alabama ponds

and reported significantly higher overwinter mortality for fish < 100 mm than for larger fish.

Size-selective predation during winter could have been minimized in my study because lakes

generally had highly vegetated littoral zones that potentially provided refuge and localized food

resources as suggested by Miranda and Hubbard (1994) and Garvey et al. (1998). Starvation was

not apparent in my study because total lipid concentrations did not differ from fall to spring for

any size class at my study lakes (Rogers and Allen 2005) and suggested that juvenile largemouth

bass in Florida were not reliant on energy reserves for overwinter survival. Similarly, Peer et al.

(2006) reported over-winter growth and no evidence of size-selective over-winter mortality for

age-0 largemouth bass in southern Alabama (i.e., similar latitudes to my north Florida lakes).

Thus, conventional hypotheses that predict strong effects of size-selective over-winter mortality

apparently do not apply to systems with available predator refuges and over-winter growth, as

suggested by Garvey et al. (1998).

The populations I studied were primarily Florida largemouth bass at south and central

regions and naturally introgressed largemouth bass (crosses between Florida largemouth bass

and northern largemouth bass M~ s. salmoides) at my north region (Table 2-1; B. L. Barthel,

unpublished data). Genetic differences among my study populations could have influenced my

results because these differing genetic strains have been reported to respond differentially to

environmental conditions. For example, Cichra et al. (1982) reported lower tolerances to cold









shock for Florida largemouth bass than northern largemouth bass and Isely et al. (1987) reported

faster growth for northern and intergrade largemouth bass relative to Florida largemouth bass

that were stocked in Illinois ponds. Philipp et al. (1985) hypothesized that Florida and northern

largemouth bass spawning seasonality have differentially evolved owing to environmental

conditions during spawning seasons, and Rogers et al. (2006) reported that genetic composition

contributed to adult largemouth bass spawning times in Florida. Thus, genetic differences

among my study populations could have influenced my results. Interestingly, my results

indicated that despite a shorter growing season at north lakes relative to other regions, maximum

lengths at age-1 were similar across all lakes. These results are suggestive of counter-gradient

growth given known genetic differences among these populations; but, growth observations from

field studies result from complex ecological interactions (Garvey et al. 2003) and carefully

controlled (e.g., without latitudinal mortality influences as in this study) studies would be

required to further clarify the potential effects of genetics to my results. Kassler et al. (2002)

suggested elevating the M. s. floridanusd~~~~~ddddd~~~~ subspecies to the species level (i.e., Florida bass

M\~icropterus florid anus)dd~~dd~~ based on meristics and allozyme and mitochondrial DNA analyses,

however the populations I studied currently remain recognized as varying genetic strains of the

same species. The influence of genetics to my results cannot be discerned from this study, but

future research could reveal the importance of genetic variation to latitudinal hypotheses for

largemouth bass recruitment as seen for other species (e.g., Conover 1990).

My results suggested that current latitudinal hypotheses for largemouth bass do not

always apply to populations at the southern extent of their natural distribution. Hatching

distributions appeared to reflect characteristics that would compensate for local environmental

conditions when growing season length did not appear to constrain survival. More protracted









distributions at south lakes, relative to north lakes, were likely maintained by effects of annual

variability in environmental conditions (e.g., water temperature) following spawning that results

in variable hatching-date dependent survival. Winter water temperatures during my study were

below the 10-yr average for the period of December to February (unpublished data, South

Florida Water Management District, West Palm Beach, Florida). Annual mean water

temperature for the December to February time period ranged from 15.60C to 19.6 C for 1996-

2005 with an overall mean of 17.9 C. The years for this study (2003 and 2004) averaged 16.8 C

and 15.6 C (the 10-year low), respectively. Thus, the years I sampled had relatively cool water

temperatures, and early hatching may provide a survival advantage during years with warmer

conditions. At central and north region lakes, water temperatures prevented spawning until later

in the year, which limited the duration that juveniles were vulnerable to influences of winter cold

fronts relative to south lakes. Garvey et al. (2002b) proposed that protracted spawning

distributions acted to maximize mean fitness for bluegill and pumpkinseed sunfish at Lake

Opinicon, Canada, where spring conditions can result in variable survival for early-hatched fish.

Similarly, early spawning of largemouth bass in Florida, relative to other latitudes, likely

provides foraging and predator avoidance advantages during favorable years, and protracted

spawning should provide some progeny survival during unfavorable years. Largemouth bass life

history strategies in Florida' s mild climate appeared to differ from mid-temperate latitudes,

where growing season length and winter conditions can create size-dependent survival

bottlenecks. Recruitment processes vary among latitudes (Garvey et al. 1998), systems at similar

latitudes (Garvey et al. 1998), and sites within a system (Peer et al. 2006) because of complex

ecological interactions (e.g., among physical, chemical, and biological factors). Thus, our









understanding of recruitment and ability to refine recruitment hypotheses requires evaluations at

multiple scales throughout a species' distribution.










Table 2-1. Physical and chemical characteristics of 6 Florida study lakes and genetic characteristics of their largemouth bass
populations
Average
Winter water Summer water Trophic TP TN Chl-a SD MD SA % FL
Region Lake Latitude temperature ( C) temperature ( C) states (mg.L ') (mg.L ') (mg.L ') (m) (m) (m ) LMB alleles
North Seminoleb 30 12.5 28.5 Eutrophic 35 590 9 1.9 4.5 13,158 59
Talquinb 30 12.2 28.6 Eutrophic 54 670 29 3.3 3.0 3,560 64
Central Harrise 28 16.3 30.0 Eutrophic 28 1550 37 0.6 4.0 5,580 99
Monroed 28 16.1 29.6 Eutrophic 100 2200 39 0.7 2.3 3,308 100
South Istokpogae 27 18.4 29.7 Eutrophic 210 700 10 0.9 1.8 11,207 100
Okeechobeec 27 18.6 29.8 Eutrophic 92 1480 30 0.5 2.7 173,000 100
winter = Dec Feb, summer = Jun Aug, TP = total phosphorus, TN = total nitrogen, Chl-a = chlorophyll a, SD = secchi depth, MD =
mean depth, SA surface area, FL LMB Florida largemouth bass M~icropterus salmoides florid~~dd~~dd anusd a estimated according to
criteria of Forsberg and Ryding (1980). bFlorida Lakewatch (2000). cBachmann et al. (1996). dSeminole County Watershed Atlas
(2001). Genetics results are from diagnostic allozyme analyses conducted by the Illinois Natural History Survey (B. Barthel, personal
communication).














Year Early sub-cohort Middle sub-cohort Late sub-cohort Median Water temperature
Class Regi on Lake hatch range hatch range hatch range hac ate atm mdan hatch
2003 North Seminole 06 Mar. 03 Apr. 04 Apr. 23 Apr. 24 Apr. 04 Jun. 17 Apr. 22.4


VUVIV I Y .----- .


Table 2-2. Dates corresponding to sub-cohort hatching periods, median hatch dates, and water temperatures (oC) at corresponding
median hatch dates for the 2003 and 2004 largemouth bass year classes at 6 Florida study lakes.


05 Mar. 14 Apr.
17 Feb. 06 Mar.
01 Mar. 22 Mar.
06 Feb. 03 Mar.
07 Dec. 24 Jan.
03 Mar. 16 Mar.
09 Mar. 23 Mar.
14 Feb. 02 Mar.
11 Jan. 02 Mar.
15 Dec. 10 Feb.


20.7
24.1
23.0
25.7
19.9
20.9
19.9
19.6
21.5
19.0


15 Apr. 24 Apr.
07 Mar. 25 Mar.
23 Mar. 17 Apr.
04 Mar. 05 Apr.
25 Jan. 28 Feb.
17 Mar. 20 Apr.
24 Mar. 20 Apr.
03 Mar. 23 Mar.
03 Mar. 23 Mar.
11 Feb. 06 Apr.


25 Apr. 02 Jun.
26 Mar. 18 Apr.
18 Apr. 06 May
06 Apr. 05 May
29 Feb. 24 Apr.
21 Apr. 05 Jun.
21 Apr. 25 May
24 Mar. 14 May
24 Mar. 06 May
07 Apr. 23 May


19 Apr.
12 Mar.
14 Apr.
23 Mar.
30 Jan.
31 Mar.
02 Apr.
12 Mar.
07 Mar.
01 Mar.


Talquin
Central Harris
Monroe
South Istokpoga
Okeechobee
2004 North Seminole
Talquin
Central Harris
Monroe
South Istokpoga
Okeechobee P


02 Mar. 19.6


3 1 Dec 13 Jan 14 Jan 13 y










Table 2-3. Mean daily growth rate (mm/d, Mean DGR), and standard deviation (SD) for age-0
largemouth bass collected in block nets during spring and summer of each year.
Year class Region Lake Hatching period Mean DGR SD
2003 North Seminole Early 0.68 0. 11
Seminole Middle 0.72 0. 14
Seminole Late 0.72 0. 11
Talquin Early 0.76 0. 12
Talquin Middle 0.78 0.12
Talquin Late 0.77 0. 15
Central Harris Early 0.57 0. 13
Harris Middle 0.55 0. 11
Harris Late 0.61 0. 14
Monroe Early 0.72 0. 11
Monroe Middle 0.72 0. 10
Monroe Late 0.73 0.09
South Istokpoga Early 0.69 0.08
Istokpoga Middle 0.60 0.13
Istokpoga Late 0.61 0. 11
Okeechobee Early 0.43 0.07
Okeechobee Middle 0.57 0. 10
Okeechobee Late 0.59 0. 10
2004 North Seminole Early 0.59 0. 13
Seminole Middle 0.62 0. 17
Seminole Late 0.82 0.21
Talquin Early 0.73 0. 16
Talquin Middle 0.64 0.12
Talquin Late 0.65 0. 13
Central Harris Early 0.53 0.07
Harris Middle 0.53 0.09
Harris Late 0.56 0.07
Monroe Early 0.60 0. 10
Monroe Middle 0.53 0. 11
Monroe Late 0.69 0.06
South Istokpoga Early 0.43 0. 11
Istokpoga Middle 0.52 0.09
Istokpoga Late 0.57 0.08
Okeechobee Early 0.40 0.09
Okeechobee Middle 0.51 0.09
Okeechobee Late 0.59 0. 10












Table 2-4. Analysis of variance results for survival comparisons among hatching periods and
regions of Florida.
Least squares means
Year class Time period Comparison F statistic P value Hatching sub-cohort Survival
2003 Mar May Hatch period 7.2100.02 Early 0.06
Middle 0.38
May -Jul Region 0.45 ,4, 0.85 North 0.19
Central 0.22
South 0.24
Hatch period 16 0.20 Early 0.16
Middle 0.29
Late 0.20
2004 Feb Apr Hatch period 0.00 1.9 0.96 Early 0.14
Middle 0.11
May -Jun/Jul Region 41 0.02 North 0.32
Central 0.19
South 0.12
Hatch period .8 0.07 Early 0.12
Middle 0.25
Late 0.25
Column two defines the period that survival was estimated for, column three gives variables
tested in the model for that time period, column four gives the F value and test degrees of
freedom, column five reports the signifance level for the variable, and columns six and seven
give least squares means estimates for the individual groups within each factor. March to May
and February to April survival could only be estimated for our south region.





























31H10~


3FN1 0 |

-Lake Monroe
2P "o' Lake Harrs



Lake Istokpoga
27'N O'

26~N N Lake Okeechobee




S50 100 200 300 400Clorss "~~E
BTYO' 96%YO' 95WO' 84WHO' 83rWO' 8200D B14Y0" 80%O'

Lonagitude


Figure 2-1. Selected north region (Lakes Seminole and Talquin), central region (Lakes Harris
and Monroe), and south region (Lakes Istokpoga and Okeechobee) study lakes for
comparing hatching distributions, growth, and mortality of age-0 largemouth bass
across Florida' s latitudinal gradient.












35 Lake Seminole Lake Talquin 135
30N=114 N=118
330
25 -
20 l 25

10







S35 Lake Harris Lake Mo~nroe 35 -
a,30 N =s 96 N =102
30
U"25
S20 ^.25
LL
15 0

5 O
a 0| ] 1 f 10




35 Lake Istokpaga L ake Olkeechobee 135
30 N =153 N = 115
30
25
20 / 125
15 1 B I --\ (12
10


Dec Jan Feb Marr Apr May Jun Dec Jan Feb Mar Apr May Jun

Hatch Date


Figure 2-2. Relative frequency distributions (bars, y axes) of age-0 largemouth bass hatching at
north lakes (top panels), central lakes (middle panels), and south lakes (bottom panels)
in 2003. Hatch dates (x axes) were determined using daily rings on otoliths
(N=numnber of fish aged). Temperature is indicated on the z axis and by the solid line.












353 Lake Seminole
30N=119


-Lake Talquin
N=124





-


35

30

25

20

15

10





35

30 3


r*+
20 C

15

10 O




35

30





15

10


25
20
15
10


0


Lake Harris
N =114


35 Lake Istopoga
30 N =122
25


10 -
15


Dec Jan Feb Mar Apr May Jun


Dec Jan Feb Mar Apr May Jun


Hatch Date


Figure 2-3. Relative frequency distributions (bars, y axes) of age-0 largemouth bass hatching at
north lakes (top panels), central lakes (middle panels), and south lakes (bottom panels)
in 2004. Hatch dates (x axes) were determined using daily rings on otoliths
(N=number of fish aged). Temperature is indicated on the z axis and by the solid line.





















































Figure 2-4. Relationship between mean daily growth rates and average temperatures from the
40-day period following the median hatch date for early, middle, and sub-cohorts,
from 6 Florida lakes during 2003 and 2004. Data points (n = 3) closest to the origin
result from slow-growing early-hatched sub-cohorts at Lake Okeechobee during 2003
and 2004, and at Lake Istokpoga during 2004.


0.8



S0.7 -



0.-







0.4 -



0 3


.


* o v


t
II


O
Q

0"


16 18 20 22 24 26 28 30

Mean Temperature ("C) After Hatch




SEarly Hatched
O Middle Hatched
r Late Hatched



























Lake Harris


Lake Mlonroe













Lake Ta quin


100 -


Lake Istokpoga


Lake Okeechobee


Lake Seminole


10 15


5 10 15 20


20 25 30


25 30


Size Group (cm, TL.)

mmFall (Octorber)
-~ Spring (M/arch, age-1)


Figure 2-5. Length frequency distributions for 2003 fall and spring (~age-1) samples of age-0
largemouth bass collected by electrofishing at north (Lakes Seminole and Talquin),
central (Lakes Harris and Monroe), and south (Lakes Istokpoga and Okeechobee)
Florida lakes.





Lake: Harris


Lake: Istokpoga


Larke Okeechobee


Lake Monroe


Lake: Seminole


Larke Tatlquin


5 10 15 20 25 30 5 10 15 20 25 30

Size Grolup (crn, TI.)

SFall (October)
Spring (March, age-1)


Figure 2-6. Length frequency distributions for 2004 fall and spring (~age-1) samples of age-0
largemouth bass collected by electrofishing at north (Lakes Seminole and Talquin),
central (Lakes Harris and Monroe), and south (Lakes Istokpoga and Okeechobee)
Florida lakes.









CHAPTER 3
SEPARATING GENETIC AND ENVIRONMENTAL INFLUENCES ON TEMPORAL
SPAWNING DISTRIBUTIONS OF LARGEMOUTH BASS (M~icropterus salmoides)

Genetic and environmental factors influence fish spawning periodicity (i.e., the

distribution of spawning events during the breeding season), but their relative contributions are

often difficult to discern. Many studies (e.g., Ludsin and DeVries 1997; Garvey et al. 1998)

have illustrated the importance of hatching date to growth and survival of age-0 fishes, but few

have evaluated the factors influencing spawning periodicity (i.e., duration and frequency of

spawning events through the season). Spawning initiation (i.e., the onset of the breeding season)

is regulated by environmental factors such as temperature and photoperiod (Kramer and Smith

1960; Lam 1983), and thus, spawning seasons occur later in the year at high latitudes relative to

low latitudes (Conover 1992). Spawning season duration is often inversely related to latitude, in

part, because adults cease spawning when offspring no longer have a chance for over-winter

survival (Johannes 1978; Munro et al. 1990; Conover 1992). Spawning periodicity has been

related to multiple environmental factors such as water temperature (Conover 1992), photoperiod

(Heidinger 1975), changes in water levels (Ozen and Noble 2002), and food availability during

gonadal development (Koslowski 1992). For example, Baltic cod (Gadus morhua) spawning

was delayed during years of cooler spring water temperatures (Wieland et al. 2000). Largemouth

bass began spawning in a Puerto Rico reservoir, which was thermally stable (24-30 oC) through

the year, when photoperiod began to increase during winter (Ozen and Noble 2005). Spawning

duration was also related to water level fluctuations in Puerto Rico reservoirs (Ozen and Noble

2002).

Genetic composition of a stock also influences spawning periodicity. Reproductive

processes in fish are regulated, in part, by endogenous hormone cues (Patifio 1997; Van Der









Kraak et al. 1998), which are regulated by genes (e.g., Denslow et al. 2001). Genotypic effects

have led to synchronous spawning of predators in relation to prey abundance, thus resulting in

high availability of food resources for newly hatched larvae, assuming increased foraging

opportunities at hatching leads to increased survival for offspring (Hj ort 1914; Cushing 1975).

Atlantic herring (Chipea harengus harengus) exhibit genotypic influences to spawning

periodicity across their broad latitudinal range because hatching within specific larval retention

areas is related to increased local food availability (i.e., plankton blooms) for larvae in that

specific locale (Cushing 1975; Sinclair and Tremblay 1984). Similarly, genetics can influence

spawning periodicity because evolution of multiple spawning or a prolonged spawning season

duration may prevent loss of an individual's annual reproductive output due to environmental

conditions (Conover 1992; Fox and Crivelli 1998). However, the relative contributions of

genetic and environmental influences are poorly understood, and variable spawning initiation

and periodicity are often attributed to phenotypic plasticity (Baylis et al. 1993; Conover and

Schultz 1997). Contributions of genotypic variability to phenotypic patterns have largely been

ignored (Conover and Schultz 1997).

Largemouth bass provide an excellent species for evaluating genetic and environmental

influences on spawning periodicity because they have a wide native geographic distribution with

a natural genetic gradient, as indicated by latitudinal lines in allele frequencies at several loci

(Philipp et al. 1983). Ecologically, juvenile largemouth bass suffer from differing mortality

factors across latitudes (Garvey et al. 1998), and spawning periodicity strongly influences

juvenile largemouth bass survival and recruitment (Ludsin and DeVries 1997; Pine et al. 2000).

Thus, differing selection pressures may exist along the latitudinal distribution of largemouth bass

that would facilitate localized adaptations for spawning periodicity. Comparisons between










genetically verified fish indicated that NLMB spawned earlier than FLMB and ILMB when

stocked together in Illinois ponds and a Texas reservoir (Isely et al. 1987; Maceina et al. 1988),

but those studies occurred outside the native range of FLMB. No studies have separated

environmental influences on spawning periodicity from genetic influences by comparing

populations with known genetic contrasts while monitoring environmental conditions.

Genetic differences across the distribution of largemouth bass have been recognized for

decades. Northern and Florida largemouth bass have been recognized as distinct subspecies for

more than 50 years (Bailey and Hubbs 1949). Northern largemouth bass are endemic to the

northern United States, FLMB naturally occur in south Florida, and intergrades (ILMB) occur in

north Florida, several southeastern states (e.g., Georgia, Alabama, Mississippi, South Carolina,

North Carolina, Virginia and Maryland), and other areas where introductions have occurred.

Kassler et al. (2002) recommended elevating the status of FLMB from a subspecies to species

status (i.e., Florida bass, M. floridanus)~~dddd~~~ddd~~~ based on discriminate function analysis of meristic

characters, allozyme analysis, and mitochondrial DNA (mtDNA) data. Physiological attributes

(e.g., temperature tolerances) and relative survival differences have also been reported for

translocated fish in several performance evaluations (e.g., Cichra et al. 1982, Philipp and Whitt

1991). However, phenotypic variability in morphometric and life history traits of broadly

distributed species is not uncommon (Schultz et al. 1996). At the time of my study, the

taxonomic nomenclature accepted by the American Fisheries Society remains at the subspecies

level .

I compared temporal hatching distributions between a population of FLMB from Lake

Okeechobee in south Florida and an ILMB population from Lake Seminole at the Florida-

Georgia border. Lake Okeechobee represents a pure population of FLMB, and Lake Seminole is









an intergrade population (ILMB, Philipp et al. 1983). I used estimated hatch dates, from sagittal

otoliths as indices of spawning periodicity assuming that hatching occurred two days after

fertilization. Spawning periodicity was compared between brood sources for fish reared in

environmentally similar experimental ponds at an intermediate latitude. I also assessed whether

the trends found in experimental ponds corresponded to the spawning periodicity for the two

natural populations at their source lakes. My study design allowed us to maintain similar

environmental conditions during brood fish sexual maturation at the intermediate latitude and

evaluate influences of genetic factors to spawning periodicity. If genetic composition affected

spawning periodicity, I expected spawning of translocated fish to reflect the periodicity of their

source populations. In contrast, I surmised that if environmental factors more strongly

influenced spawning periodicity, then translocated fish that spawned in ponds would have

similar distributions, and pond distributions would differ from both source lake populations.

Methods

Pond Methods

Brood largemouth bass were captured by electrofishing at Lake Okeechobee, Florida

(latitude: 27oN 7') and Lake Seminole, Florida (latitude: 30oN 44 ') during September 2003

(Figure 3-1). Using broodfish from Lakes Okeechobee and Seminole allowed us to nearly

encompass the maximum latitudinal distance in Florida, and therefore, nearly the maximum

environmental gradient (i.e., temperature and photoperiod) acting as selective pressures on

spawning periodicity. Philipp et al. (1983) observed clinal variation in allele frequencies at

several loci in largemouth bass that had been collected from Lake Seminole in north Florida

down to Lake Okeechobee. Philipp et al. (1983) failed to detect NLMB alleles at Lake

Okeechobee and estimated a sub specific NLMB:FLMB genomic presence of 49:51, respectively,









at Lake Seminole based on electrophoresis of two diagnostic enzyme loci. Recent analyses at the

same loci also detected no northern alleles at Lake Okeechobee, and indicated that largemouth

bass at Lake Seminole were highly introgressed and had likely been introgressed for an extended

time period (B.L. Barthel, Illinois Natural History Survey, 1816 South Oak Street, Champaign,

Illinois, 61820, unpublished data). Analyses of mtDNA and allozyme data have resulted in

Lakes Seminole and Okeechobee being grouped into separate largemouth bass genetic

conservation management units within Florida (B.L. Barthel, unpublished data).

Broodstock from source populations were size selected within 300-430 mm total length

(TL) so that fish were of a reproductively mature size (Chew 1974) and to avoid influences of

brood fish size on spawning periodicity (Miranda and Muncy 1987; Goodgame and Miranda

1993). Adult fish were transported to Gainesville, Florida (latitude: 290N 43') using an aerated

2x3 meter fish transport tank within 24 h of capture (Figure 3-1).

I stocked six experimental ponds in Gainesville, Florida with brood fish. Ponds

approximately measured 25 m x 5 m with an average maximum depth of 1 m, and were parallel

to each other with a 3 m levee separating each pond. One week prior to stocking, the ponds were

treated with rotenone (5% liquid rotenone; >3 mg*L )~, drained to ensure no fish remained, and

then refilled. Each pond was randomly assigned 10 11 brood stock from a single lake (N = 3

replicates per brood source) assuming a similar sex ratio for each group (Chew 1974). Brood

fish were fed 90-110 mm (TL) golden shiners (Notemogonus chrysoleuca~s) at 3.5% of

largemouth bass biomass per day (Miranda and Hubbard 1994) until spawning behavior was

observed in spring. Aquatic vegetation in ponds was maintained at a minimum using manual

removal, but removals were ceased when largemouth bass spawning bed construction was first

observed to prevent disturbance of spawning activity. Pond water levels were maintained at









bank-full to avoid influences of water level on timing of bass spawning (Sammons et al. 1999;

Ozen and Noble 2002). Water temperature was measured four times daily in each pond, at 1 m

depth, using remote temperature recorders (Onset Incorporated). Age-0 fish were collected

during April and May using dipnets and electrofishing. Rotenone was also used during the May

sample to maximize the likelihood that all sizes and ages of age-0 largemouth bass were

collected from each pond. The experiment was terminated in May 2004 to reduce potential

effects of cannibalism and high water temperatures effects on age-0 largemouth bass, and to

avoid increased maintenance due to rapid evaporation.

Field Methods

Hatching dates at source lakes were estimated using age-0 largemouth bass captured at

Lake Okeechobee during February, April, and June, 2004 and at Lake Seminole during May and

July, 2004. The earlier trip at Lake Okeechobee was conducted because of the potential for early

hatching at low latitudes (Gran 1995). Age-0 largemouth bass were collected using 10 m x 10 m

blocknets and applying rotenone at 3 mg*L Twelve block nets were set at each lake during

each sampling event and fish were collected using dip nets by wading investigators.

Laboratory Analyses

A subsample of age-0 largemouth bass from experimental ponds and source lakes were

size selected for age estimation so that the age sample mirrored the length-frequency of the fish

collected at each waterbody (Pine et al. 2000). Selected age-0 largemouth bass were measured

(TL; mm) and weighed (wet weight; 0.001 g), and their sagittal otoliths were removed. Sagittal

otoliths were prepared using the methods of Miller and Storck (1982). Each otolith was read by

two independent readers and ages were averaged when they agreed within three days between

readers. If agreement was not met, the otolith was re-read by both readers and discarded if










agreement was not met (N = 0 for pond fish). Some early hatched age-0 fish from source

populations were too old (>150 days) for reliable age estimation in July, but I assumed their

hatch dates were represented in samples collected earlier in the year (i.e., February or April-

May). Median hatch date in ponds was compared between stocking sources using a non-

parametric median test (Zar 1999). Mean hatch date, mean water temperature at first and median

hatch date, and mean hatching duration in ponds were compared between broodstock sources

using one-way analysis of variance (ANOVA). Quantitative comparisons between pond and

source lake spawning distributions were not performed due to differences in parental size

distributions. However, source lake spawning patterns were used to evaluate whether spawning

periodicity observed in ponds was similar to source populations in their native environment.

Results

Several largemouth bass nests (N > 3 per pond), with guarding males, were observed in

each pond, and age-0 bass were captured in all six experimental ponds. Female largemouth bass

may use multiple nests and deposit multiple egg clutches during a spawning season (Heidenger

1975), thus I assumed that bass progeny in my experimental ponds represented offspring from

several families. About twenty age-0 bass were selected for age estimation from each pond

during each sample. Age estimates were only made for 30 age-0 bass from pond a (Lake

Okeechobee broodstock) because of a low sample size (N 25) and small total length

distribution during April. The low sample size in April was likely due to a high mortality event

because all Eish collected were less than 25 mm, except one individual was 32 days older and

much larger than any other fish in that sample.

In ponds, FLMB had initial hatching dates beginning as early as 26 January and as late as

12 February (Table 3-1; Figure 3-2). In contrast, the range of initial hatch dates was 22 February









to 7 March for ILMB (Table 3-1; Figure 3-2). On average, median hatch date in ponds was 11

days earlier for FLMB than ILMB (X2 = 3 1.22, df = 1, P < 0.001), and mean hatch date in ponds

was five days earlier for FLMB (F = 5.10, P = 0.025) (Table 3-1). Florida largemouth bass

began spawning at cooler water temperatures (12.3 15.1 oC) than ILMB (15.7- 20.6 oC) in

experimental ponds (F = 7.82, df = 4, P = 0.049), but water temperatures at median hatch date

did not differ between brood types (F = 0.010, df = 4, P = 0.771) (Table 3-1). Hatching duration

in experimental ponds ranged 24 -72 days for FLMB and 10-12 days for ILMB (Table 3-1).

Florida largemouth bass hatching duration was marginally different than ILMB hatching

duration (F = 5.40, df = 4, P = 0.08), but low statistical power (N = 3 per treatment) reduced my

ability to detect a difference (Peterman 1990). Florida largemouth bass hatching occurred as late

as 7 April, whereas the last ILMB hatch occurred on 18 March in experimental ponds. Florida

largemouth bass began spawning earlier and also had a longer spawning season duration than

ILMB in experimental ponds.

My experimental pond results were corroborated by data from Lakes Okeechobee and

Seminole. Age-0 bass at Lake Okeechobee began hatching as early as 12 December, whereas

the earliest fish collected from Lake Seminole hatched on March 1 (Figure 3-3). The median

hatch date at Lake Okeechobee occurred 29 days earlier than the median hatch date at Lake

Seminole. Unlike my pond results, water temperatures at first hatch were similar between source

lakes (Lake Okeechobee = 17.9 oC and Lake Seminole = 16.3 oC), and water temperatures at

median hatch date were similar at Lake Okeechobee and Lake Seminole (19.6 oC and 20.9 oC,

respectively), as seen in my pond study. Hatching duration results also supported experimental

pond results because Lake Okeechobee hatching duration (146 d) was substantially longer than

the Lake Seminole hatching duration (97 d) (Figure3-3). In summary, FLMB had earlier










spawning and longer spawning season duration than ILMB in both research ponds and at their

respective source lakes.

My results indicated both environmental and genetic effects on spawning periodicity of

largemouth bass. Translocation illustrated environmental effects on hatching periodicity because

rearing FLMB in research ponds at a higher latitude led to later hatching than at Lake

Okeechobee. Similarly, rearing ILMB in research ponds at a lower latitude led to earlier

hatching in ponds than at Lake Seminole (Figure 3-4). Genetic effects on hatching periodicity

were also evident because translocated fish reflected characteristics of their brood source

populations. For example, FLMB hatched earlier and had longer hatching distributions than

ILMB in both the pond experiment and at brood source lakes (Figure 3-4).

Relative differences in spawning times between brood sources in ponds were detected

despite the low number of families and adult sizes represented by my pond brood fish relative to

source lake populations. Although my intent was to compare relative differences between brood

sources in ponds, source lake hatching patterns mirrored my pond results providing further

support for a genetic contribution to spawning periodicity. This corroboration occurred even

though my brood fish samples were not representative of the entire spawning population from

the source lakes (i.e., lower range in brood fish size in ponds compared to lakes).

Discussion

Environmental and genetic factors influenced spawning timing and periodicity of

translocated largemouth bass. Environmental and genetic effects to breeding periodicity have

rarely been investigated, but have been shown in some cases for terrestrial (e.g., Japanese

macaques M~acacafuscata; Fooden and Aimi 2003) and aquatic species (e.g., Atlantic salmon,

Salmo salar; Donaghy and Verspoor 1997). For example, Atlantic salmon exhibited a reversal in









hatching order between two populations when reared in a hatchery versus their native rivers

(Donaghy and Verspoor 1997). Donaghy and Verspoor (1997) attributed the reversal in hatching

order to a genotype-environmental interaction; although they could not explain the mechanism

leading to the reversal they suggested that local genetic adaptations to water temperatures were

responsible.

Environmental influences were evident by a temporal shift in the onset of spawning for

translocated broodfish. In my research ponds, FL1VB began spawning later than their source

population did at Lake Okeechobee, which is located much further south. In contrast, IL1VB in

research ponds began spawning before their source population at Lake Seminole, which is

further north. Water temperatures were the most plausible explanation for observed temporal

shifts because temperatures at median hatch date were similar between FL1VB and IL1VB in

experimental ponds and Hield collections, but similar patterns in ponds and source lakes

suggested a genetic component to spawning periodicity.

Genetic factors played a role in spawning timing because FLMB from Lake Okeechobee

spawned earlier in the research ponds than 11VIB from Lake Seminole, even though

temperatures, photoperiod, and water levels were similar in all ponds during brood fish sexual

maturation and spawning. Adaptations for reproductive strategies that maximize individual

fitness via offspring survival and reproductive success should occur within an environment given

a heritable component and selection pressure on phenotypic variability (Endler 1986). Einum

and Fleming (2000) documented "critical episode of selection" following the emergence of

Atlantic salmon fry, which resulted in a phenotypic shift towards earlier emergence. A heritable

component to breeding date has been established for some salmonids (Siitonen and Gall 1989;

Gharrett and Smoker 1993), thus Einum and Fleming (2000) concluded that local adaptations for









breeding dates are possible and may explain the variability in breeding dates within and among

Atlantic salmon populations. The evidence of a genetic component to breeding times in my

study and other studies is not surprising given that local populations of fishes, with restricted

gene flow, have an underappreciated capacity to adapt to local selection (Conover and Schultz

1997).

In my study, FLMB exhibited protracted spawning periods in both ponds and lakes

relative to ILMB. Protracted spawning distributions increase the likelihood that individuals with

differing hatching dates will experience differing environmental conditions (Narimatsu and

Munehara 1999). Mild winter water temperatures that typically occur in peninsular Florida

likely prevent exposure of early-hatched fish (e.g., hatch in December) to very cold temperatures

(<12 oC) that would limit survival as per Philipp et al. (1985). Atypical winter cold fronts can

reduce growth or survival of early-hatched largemouth bass at Lake Okeechobee, thus

reproductive success may vary among years for early versus late hatched fish. Garvey et al.

(2002) found a similar pattern for bluegill at Lake Opinicon, Ontario, and hypothesized that

protracted spawning distributions maximized lifetime fitness in variable environments where

temperature regulates juvenile survival. Conversely, early spawning (e.g., December) of

largemouth bass at Lake Seminole would likely result in very limited offspring survival. Lake

Seminole fish began spawning at suitable temperatures in March, and spawned over a relatively

shorter period compared to Lake Okeechobee fish. A contracted spawning distribution at Lake

Seminole maximizes the growing season for most age-0 bass (e.g., Conover 1992) at the more

northern latitude. Contracted spawning seasons for ILMB at Lake Seminole could be the result

of stabilizing selection, where progeny from both early and late hatching times are at a survival

disadvantage, which ultimately led to individuals adapted to spawning within a shorter time










period (Schultz 1993). Protracted spawning distributions of Lake Okeechobee fish and

contracted spawning distributions of Lake Seminole fish appear better suited for the

environments found at each source lake and inherent environmental influences on juvenile

survival .

Philipp et al. (1985) found a lower a-threshold temperature (i.e., the theoretical lower

limit to embryonic development) for FLMB than ILMB. Philipp et al. (1985) hypothesized that

NLMB evolved strategies that delay spawning to prevent exposure of embryos to lethally cold

temperatures, whereas FLMB evolved to allow spawning at lower and higher temperatures

relative to NLMB. My results support this hypothesis, and I concluded that largemouth bass

spawning seasons are locally adapted to environmental conditions.

Natural selection may also lead to spawning periodicity that is synchronized with prey

species abundance to maximize food availability for progeny (Sinclair and Tremblay 1984). The

relationship between reproductive timing and food supply has been described by the

"match/mismatch hypothesis," which asserts that temperate fishes spawn at a fixed time

corresponding to peaks in plankton production, and offspring success or survival depends on

how well their production matches with food production (Cushing 1975, 1990). At Lake

Okeechobee, prey fish likely spawn earlier than at Lake Seminole because of earlier spring

warming. Earlier hatching of fish at Lake Okeechobee, relative to hatching times at more

northern latitudes, may lead to increased survival due to a size advantage relative to prey fish,

which is a prerequisite for piscivory (Mittelbach and Persson 1998). Potential prey fish at low

latitudes commonly have extended spawning seasons (Conover 1992) (e.g., mummichog,

Funduhus heteroclitus; Conover 1990) relative to spawning seasons at more northern latitudes.









Thus, differences in spawning periodicity I observed may have resulted from selection for

optimal environmental conditions, food availability, or a combination of these factors.

Previous studies comparing spawning timing of largemouth bass indicated that northern

and ILMB hatched earlier and at cooler water temperatures than FLMB (Isely et al. 1987;

Maceina et al. 1988), which is contrary to my findings. These previous comparisons were

conducted in Illinois and Texas, respectively, which are outside the native range of FLMB and

have much cooler winter water temperatures than FLMB experience in their native range. My

pond study was conducted in a transition zone where both pure FLMB and ILMB populations

naturally occur (Philipp et al. 1983), so brood fish were reared in temperatures that did not vary

as widely from local conditions compared to Isely et al. (1987) and Maceina et al. (1988). Isely

et al. (1987) and Maceina et al. (1988) also used sympatric populations of NLMB and FLMB

potentially allowing for confounding effects of hormonal cues and/or reproductive behaviors,

which may have influenced spawning times. I used separate ponds for each genetic source to

prevent interbreeding and behavioral influences among brood source types. Broodstock lengths

may have also contributed to differing results among studies because larger largemouth bass

have been shown to spawn earlier than smaller individuals (Goodgame and Miranda 1993), and a

large range in length distribution of spawning bass likely leads to extended spawning activities

(Miranda and Muncy 1987). In my study, I used similar-sized brood stock from both sources to

minimize potential size effects on spawning in ponds. Adult size distributions in ponds did not

reflect adult size distributions at source lakes, thus I focused my comparisons of spawning

distributions between ponds and used lake spawning periodicities to evaluate the relative

differences. Adult size structures did not drastically differ between source lakes (M.W. Rogers,

unpublished data), thus fish size effects on spawning periodicity were probably similar for both









source populations. I standardized brood fish size in ponds and spawning periodicity trends were

similar to lake populations for each source, providing further evidence of a genetic component to

spawning periodicity.

Comparisons of spawning periodicity using otoliths only reveal data for survivors and not

the true distribution if age or size-selective mortality occurs (Miller and Storck 1984; Isely et al.

1987). My analyses only allowed for comparisons of surviving offspring among brood sources,

which is the main concern for management and conservation purposes, but fish that hatched and

incurred high short-term mortality had lower detection probability in my study. Sampling timing

is important to my results because differing mortality among ponds could have biased spawning

periodicity results, especially if sensitivities to mortality factors differed by source. For example,

FLMB are less tolerant of cold temperatures than ILMB (Williamson and Carmichael 1990;

Philipp and Whitt 1991). I found only one individual in one of the Lake Okeechobee

broodstock experimental ponds that was hatched in January, suggesting a high mortality event.

My median hatch date results for Lake Okeechobee experimental fish would not have differed

without capturing the early-hatched individual, but the spawning duration for that pond would

have been shorter. My age-0 fish from both ponds and lakes were in the range of 13-73 days and

18-136 days, respectively, which provided a valid assessment of relative spawning times

between sources. Interpretation of my results should consider potential biases of sampling

timing on combined spawning distributions. Fish hatched prior to April collections were

potentially available for collection during both samples, which would shift median estimates to

earlier in the year. In contrast, early-hatched fish also endured mortality factors for a longer time

period relative to later hatched fish, which could potentially shift my estimated spawning

distributions towards later in the year. Lastly, termination of my experiment in mid-May could









have led to an under-representation of Eish that would have hatched later, however I detected no

hatching during the 30-day period prior to ending the experiment suggesting that my data

represent the entire spawning distributions. In summary, comparisons of my results with future

studies should consider the time of collection for age-0 fish and potential influences on the

apparent spawning distributions.

Environmental experiences of brood fish prior to relocation may persist and confound

apparent genetic effects in common environment studies (Conover and Schultz 1997). Earlier

spawning of FLMB could be partially due to environmental influences prior to relocation if

FLMB were further in their annual reproduction cycle and gamete development was more

advanced than ILMB when they were translocated. I stocked broodfish into experimental ponds

in mid-September when such effects should have been minimized. Gross et al. (2002) reported

that plasma sex steroid concentrations were low for male and female FLMB in September for

fish reared at Gainesville, Florida in their study. Increased gonadosomatic index (GSI) of FLMB

reared in Gainesville, Florida began in November and peaked in February-March, which was

strongly correlated with gonadal maturation (Gross et al. 2002). I translocated brood Hish at least

three months before spawning occurred at either lake. A future study utilizing progeny of

translocated Hish would further reveal genetic influences on spawning periodicity. Transplanting

studies are useful for evaluating the genetic basis of phenotypic variation in spawning

periodicity, but the genetic component I identified suggests further need to test and develop

hypotheses to determine natural selection processes responsible for observed differences

(Conover and Schultz 1997).

An important consideration when interpreting my study results is that my fish were only

from two lakes, thus I did not have a random sample of FLMB or ILMB genotypes. Recent









genetic studies concluded that my source populations were from differing genetic conservation

management units (B.L. Barthel, unpublished data), however I did not include brood fish from a

range of lakes for each genetic conservation unit.

My study has implications to management decisions regarding fish stocking programs.

Outbreeding depression effects (e.g., lower recruitment, adult abundance, and fish size structure)

of stocking FLMB with native LMB populations have not been reported throughout a widely

distributed range of public water bodies stocked in the United States. However, Gharett et al.

(1999) reported outbreeding depression in the F2 generation of pink salmon (Oncorhynchus

gorbuscha) that were hybrids of stocks with distinctly different breeding seasons, and warned

that deleterious effects of outbreeding depression may take decades to detect. In a series of

common garden experiments, Philipp et al. (2002) concluded that hybridization of largemouth

bass from widely separate geographic locations (e.g., Florida, Illinois, Texas, and Wisconsin)

with native Illinois fish led to a more than a 50 percent reduction in reproductive fitness relative

to the original, local stock. My study did not address individual or population level effects of

mixing ILMB and FLMB, but genetic factors played a role in spawning timing and periodicity of

translocated largemouth bass. Observed spawning periodicity appeared to be better suited for,

and a local adaptation to, the environments found at each source lake. Genetic variation among

local populations is likely prevalent (Conover and Schultz 1997), therefore I recommended that

agencies take a conservative approach in stocking programs to avoid potential outbreeding

depression. I also recommended that agencies develop long term studies that evaluate effects of

mixing stocks with phenotypic differences in life history strategies.










Table 3-1. Earliest, median, and latest hatch dates of Florida (Lake Okeechobee fish) and intergrade (Lake Seminole fish) largemouth
bass (Micropterus salmoides) translocated to experimental ponds at Gainesville, Florida in 2004 and corresponding water
temperatures
Water temperature (oC
Pond Source N Earliest Median Latest Hatch range (d) Earliest hatch Median hatch Latest hatch
pond a Okeechobee 30 26 Jan 02 Apr 07 Apr 72 12.4 18.2 18.6
pond b Okeechobee 40 01 Feb 29 Feb 09 Mar 37 12.3 12.3 18.4
pond c Okeechobee 41 12 Feb 21 Feb 07 Mar 24 15.1 15.4 22.6
pond d Seminole 40 07 Mar 12 Mar 18 Mar 11 17.7 14.2 17.7
pond e Seminole 40 22 Feb 27 Feb 05 Mar 12 15.7 14.0 19.6
pond f Seminole 40 06 Mar 11 Mar 16 Mar 10 20.6 16.0 19.4
(N = number aged, Earliest = earliest estimated hatch date, Median = median estimated hatch date, Latest = last estimated hatch date,
Earliest hatch water temperature = mean water temperature for earliest hatch date, Median hatch water temperature = mean water
temperature for median hatch date, Latest hatch water temperature = mean water temperature for latest hatch date).





















































87% O' 869 O' 85% iO' 849 O' 83% 0' 82W0W O 81C% O' 80CWO~'


Figure 3-1. Locations and latitudes for Lake Seminole, Lake Okeechobee and Gainesville,
Florida, USA.













57













10 (a) (d) -2
N = 30 -t N = 40 22
80-
20


40 -16

I 14
0 10



100 26
(b) (e) m
N =40 N = 40 -24 cb







80

6D-I~~~~~~ / v 20
60- 18

40 Is 1

e 14
12
0~ 10


Jan. Fe. M r p.Jn. Fb a. Ar
Hatc date

Fiur 32.Fie-aycoor prcn hachn dit ibto (y-xis for ag- areothbs
hace nrsac od a ansilFoiaacosdts(-xs n



coum ( -1 f) broo sorc wa Laeemnoe
















S30


-25


-20


S15







o



3




- 5






- 20


1


Jan. Feb.


Mar. Apr. May


Hatch date


Figure 3-3. Five-day cohort percent hatching distribution (y-axis) for age-0 largemouth bass
collected at (a )Lakes Okeechobee (N = 159) and (b) Seminole (N = 121) across dates
(x-axis) and corresponding mean water temperatures (z-axis). Sampling occurred at
Lake Okeechobee on 15 16 February, 22 April, and 21 22 June, 2004. Sampling
occurred at Lake Seminole on 12 13 May, and 13 14 July, 2004.





Percent frequency of combined hatch dates


Lazker Ok eecho~bee


Lazker Ok eechorbee Poends






La~ke Seminole Ponds


Dec.


Jan.


Feb.


May


June


Lake Seminole


- 1 25%


m > 50%


Figure 3-4. Semi-monthly hatching distributions for age-0 largemouth bass reared in research ponds in Gainesville, Florida and in
source populations at Lake Seminole and Lake Okeechobee in 2004. Figure lines are weighted to represent percent
frequency.









CHAPTER 4
SIMULATED INFLUENCES OF HATCH-DATE SPECIFIC SURVIVAL ON
RECRUITMENT OF LARGEMOUTH BASS

Density-dependent processes can dampen or magnify juvenile mortality, but the resulting

pattern is relatively stable average recruit abundances across a broad range of spawner

abundances for many fish stocks (Walters and Korman 1999). Density-dependent mortality is

believed to result in compensatory juvenile survival at low egg production and regulation of

juvenile survival at high egg production, thus causing the observed stability in stock-recruitment

relationships for many species (Walters and Martell 2004). Regulation can be influenced by

multiple biotic (e.g., predation and starvation) and abiotic (e.g., temperature and water clarity)

factors, which have greatest effects during early life stages (i.e., "crucial period," Shepherd and

Cushing 1990) and interact to affect survival. Although density-dependent mortality in juvenile

fishes has received much attention, few cases exist where mechanisms leading to regulatory

processes have been identified (Shepherd and Cushing 1990) or how those mechanisms may act

within year classes to influence recruitment.

The relative effect of mechanisms influencing survival, and thus, resulting in

compensation and regulation, have been shown to vary with hatching dates such that members of

a year class born at different times may suffer from differing mortality forces. For example,

Bestgen et al. (2007) reported that early hatched Colorado pikeminnow Ptychocheilus lucius

have higher mortality during early life than later hatched members of a year class due to

temporal habitat overlap with their predators. In contrast, early hatching may result in increased

survival by enhanced foraging or reduced predation mortality to gape-limited predators (e.g.,

Ludsin and DeVries 1997). Hatch-date dependent survival has often been identified for both

marine and freshwater fishes, but effects of hatching-date dependent survival on total year class









abundance and composition (i.e., contributions of differing hatching sub-cohorts) are difficult to

investigate.

My Hield study (Chapter 2) showed that survival and growth of age-0 largemouth bass

were hatch date specific. Here, I explored the long term implications of observed sub-cohort-

specific growth and mortality on total recruitment to age-1 and adult biomass. I also assessed

how those metrics would change if mortality observations had persistently differed for a given

sub-cohort or among sub-cohorts. Thus, I evaluated the potential for compensation and

regulation at varying levels of sub-cohort specific mortality relative to my Hield observations. I

used trophic-based ecosystem models to evaluate relationships between sub-cohort mortality and

recruitment to the adult population. Models were evaluated for largemouth bass and represented

a south Florida population and a north Florida population to incorporate among system variation

in juvenile largemouth bass population characteristics and community composition.

Methods

I used Ecopath with Ecosim (EWE; www.ecopath.org) ecological modeling software to

evaluate influences of hatching sub-cohort-specific survival on year class structure and biomass.

Two EWE models were developed to explore how results may vary among populations (i.e.,

between a north Florida system and south Florida system). Models differed via observed

differences in hatching distributions due to latitudinal (e.g., temperature) and source population

(e.g., differing genetic composition) influences and community composition (e.g., prey fish

abundance). A mass-balance food web model was developed (Ecopath process) for each

population and simulations were performed to predict effects of differential sub-cohort survival

on age-1 biomass, adult biomass, and year class composition at equilibrium (Ecosim process).









Ecopath Models

An Ecopath model is a static mass-balance representation of production and losses among

living components (i.e., functional groups) of an ecosystem. Balance occurs when production is

equal to predation mortality, non-predation mortality, and fishing harvests (i.e., in the absence of

immigration or emigration) (Pauly et al. 2000) for each prey functional group (i) and predator

functional group (f=1 to n predator groups) according to the Ecopath master equations:


B, F B) EE,=~K B D, + (4-1)
I=1

and

Q. = q + R, + U, (4-2)

where :

where B, and B, are biomasses of i and j, (P/BXi is the production/biomass ratio for i and should

be entered as the total instantaneous mortality rate (Z,) for vertebrate groups or turnover rate for

invertebrates and primary producers, EE, is the fraction of (P/B), specified in the model, (Q/B), is

the total food consumption per unit biomass of j, DC,, is the proportion of prey group i to

predator group j' s total diet, Y, is harvest of group i, R, is respiration of group i, and U, is the

unassimilated portion of group i's consumption according to the equations (Christensen et al.

2005):

Mlz, + Y
ecotrophic efficiency = EEI = 2()(4-3)


total predation mortality = M2(2) = BI B DC, for j = 1 to n predators (4-4)

other mortality = M,(1,) = ((1 -EE), where (1 = Y: + B, M2(2) + $ (1- EEI ) (4-5)


harvest = Y = [(~ B, for wv = 1 to n fisheries F, is fleet specific mortality on i (4-6)










(Figure 4-1). Input rates (e.g., P B) are entered using annual estimates. Components of my

ecosystems consisted of functional groups with similar foraging life histories (Table 4-1).

Linkages among functional groups were input by a diet composition matrix for each model that

described the percent weight of each prey functional group to each predator functional group's

diet (i.e., DC,,).

Each of my models (i.e., one for north Florida and one for south Florida lakes) was

comprised of over 20 functional groups, but at least 12 of these represented multiple (i.e., four to

five) age stanzas for early, middle, and late-hatched largemouth bass hatching sub-cohorts

(Tables 4-2, 4-3; see Chapter 2 for hatching dates corresponding to sub-cohorts). The age

stanzas for each sub-cohort were used to track fish through their ontogeny, so that different

information (e.g., P/B, diet composition, etc.) could be specified for each life stage and sub-

cohort. The stanza structure also allowed specification of sub-cohort-specific age at a given time

period due to differences in hatching dates among sub-cohorts. For example, the "summer to

fall" age stanza would have relatively older fish for the early hatched sub-cohort compared to the

late hatched sub-cohort in each model. Ecopath required several inputs for each functional group

that allowed the model to balance population additions and losses. For each functional group,

the model required three of the four following inputs: B (kg/ha), P/B (year- ), Q/B (year- ), and

EE. Ecopath creates a series of linear models (Equation 4-1) (i.e., one for each functional group)

and simultaneously solves the equations for the parameter not input by the user (i.e., either B,

P B, Q B, or EE) (Christensen et al. 2005). Individual fisheries and functional-group specific

exploitation rates were also model inputs as described below.

Input data for my Ecopath models were obtained from my field data and published

literature (see Tables 4-2 and 4-3 for specific sources). Field data were collected at two south









Florida lakes (i.e., Lakes Istokpoga and Okeechobee) and two north Florida lakes (i.e., Lakes

Seminole and Talquin) during 2003 and 2004. Biomass for each functional group was estimated

from average summer (i.e., June/July) block-net catches for each region (see Chapter 2 for block-

netting methods). Production/biomass for juvenile largemouth bass stanzas were estimated from

hatching sub-cohort specific survival rates in consecutive block-netting samples. Other

functional group P/B and all Q/B estimates were derived from www.Hishbase.org and published

literature (Tables 4-2 and 4-3). A weighted average (weighted by species abundance) was used

for each non-largemouth bass fish functional group's B, P/B, and Q/B inputs. Juvenile

largemouth bass diet matrices were obtained from Hield data (see Chapter 2), whereas other

functional group diet matrices were derived from www.fishbase.org and published literature (see

Appendices 1 and 2). Diet contents were specified for each sub-cohort through their first

summer. I could not estimate ages (i.e., specify sub-cohorts) for age-0 largemouth bass after

summer, and thus, I assumed that all sub-cohort diet matrices and survival were the same for a

given age following their first summer through their adult stages (see Appendices 1 and 2).

For fishes exhibiting negative exponential mortality through time and length at age

following the von Bertalanffy growth function, P/B ratios are equivalent to total instantaneous

mortality (Z) (Allen 1971) such that:

P/B = Z = F + M22 + Mo0 (4-7)

(Christensen et al. 2005). Four fisheries were established in each of my Ecopath models. A

recreational fishery exploited each adult largemouth bass functional group at 20%, assuming that

fishing mortality in these ecosystems was similar to fishing rates from other Florida and

southeast black bass fisheries (Renfro et al. 1999; O'Bara et al. 2001; Allen et al. In Press). An

individual fishery was created to target each largemouth bass hatching sub-cohort soon after









hatching, which allowed me to vary mortality in simulations. Thus, I used the Hishing mortality

function to modify total mortality of each juvenile largemouth bass hatching sub-cohort in

Ecosim (see below).

Ecosim Simulations

Ecosim provides temporally dynamic simulations of functional group biomass changes

via system perturbations relative to Ecopath's baseline (balanced) conditions. Ecosim is flexible

in how system perturbations can be modeled, but most common applications involve simulations

by varying fishing mortality rates relative to baseline conditions. Ecosim estimates biomass

changes for functional groups using differential equations similar to those in Ecopath.

Abundance changes for age-specified multi-stanzas are modeled using Deriso-Schnute delay-

difference models (Deriso 1980; Schnute 1987; Walters et al. 2000). Following a simulated

system perturbation in Ecosim, functional group consumption rates and predation rates on those

functional groups are moderated by prey behaviors that limit predation exposure (Walters et al.

1997; Christensen et al. 2005). Ecosim models have been described as "hungry predator

models" (Plaganyi and Butterworth 2004) where predators compete for vulnerable prey and

predator-prey interactions are estimated with foraging arena assumptions (Walters et al. 2000).

Vulnerable (V,) and non-vulnerable (B,-y,) prey biomasses are modeled with differential

equations as:


dV



= -v B,- F v ; F (4-9)


where v,, is a flow rate for prey i from invulnerable to vulnerable, v ', is a flow rate for prey i

from vulnerable to invulnerable, a,,VB, is total consumption rate Q,, of prey i by predator j, B, is









abundance of predator functional group j, and a, is an effective search rate for prey i by predator

j (Figure 4-2). Solving the vulnerability equations (Equations 4-8 and 4-9) when changes to v,

are zero (i.e., assuming that the distribution of prey i between vulnerable and invulnerable states

reaches equilibrium faster than changes in total biomass; Walters and Martell 2004) and

substitution results in the equation used by Ecosim to describe trophic flows of prey group i to

predator j :

Q,; = a v BB,/ +v', + a B (4-10)

(Christensen et al. 2005). Equation 4-10 results in ratio dependent predation (Walters and

Martell 2004) and has been extended in recent EWE software to incorporate other components of

predator foraging such as: prey and predator feeding times, handling times, mediation forcing

effects, and long-term or seasonal forcing effects (see Christensen et al. 2005). Thus,

consumption of predator j on prey i is a function of search and predator and prey biomasses, but

is constrained by vulnerabilities following foraging arena theory (Walters and Martell 2004).

Modeling functional groups without foraging time adjustments or switching power results in

changes to predator diets according to foraging arena theory relative to encounter rates (see

Walters and Martell 2004). Thus, predator diet compositions change proportionally with

changes in prey functional group abundances following the assumption that a reduction in a prey

biomass reduces intraspecific competition among remaining prey and reduces prey risk to

predator functional groups (i.e., lowering encounter rates), but this relationship is mediated by

vulnerabilities.

Vulnerabilities are required inputs of Ecosim and represent the maximum predation

mortality a predator can exert on a prey functional group relative to baseline (i.e., Ecopath)

predation mortality due to mediation via vulnerability flow rates. Low vulnerabilities (e.g., one)









for a prey functional group represent slow flows from the invulnerable to the vulnerable state and

make prey availability to predators largely independent of predator biomasses. High

vulnerabilities (e.g., 100) represent fast flows from the invulnerable to vulnerable state and result

in large changes to predation mortality for a prey functional group following predator biomass

increases (Christensen et al. 2005). Vulnerabilities for the earliest age-stanzas of all largemouth

bass sub-cohorts were low (i.e., close to one) to emulate factors resulting in very low

vulnerability to predators shortly after hatching (e.g., a spatial refuge, schooling, or parental nest

guarding) and were allowed to exhibit risk-sensitive foraging behaviors (i.e., allowed to vary

foraging times), thus resulting in Beverton-Holt shaped stock-recruitment relationships

(Christensen et al. 2005). For the other age stanzas, I allowed Ecosim to estimate the

vulnerability for the most abundant LMB hatching sub-cohort from each age stanza (i.e., time

period) and used a scaling factor to estimate vulnerabilities for other LMB hatching sub-cohorts

within that age/time stanza. Sub-cohort vulnerabilities for each stanza were scaled such that the

vulnerability value for a sub-cohort times their base biomass was equal across all sub-cohorts,

thus resulting in increased vulnerabilities for sub-cohorts with lower biomass (as suggested by

Carl Walters, personal communication). Scaling vulnerabilities relative to baseline biomasses

allowed for increased predator consumption as biomasses increased for initially less abundant

largemouth bass sub-cohorts. In general, all functional group vulnerabilities were entered such

that 1< v < 6. Estimated subchort vulnerabilities were higher for older age stanzas than at their

initial life stages. These groups were not allowed to change foraging times relative to baseline

conditions. The result of these specifications was compensatory growth with biomass changes as

predicted by foraging arena theory (Walters and Martell 2004).









Simulations increased and decreased hatching sub-cohort specific mortality by + 50%

relative to baseline conditions. Simulated mortality changes were incorporated by changing sub-

cohort specific fishing mortality (F in Equation 4-7), which resulted in changes to Z for a sub-

cohort. Mortality was applied until the system re-equilibrated. Changes to mortality were

applied to the first age stanza for each sub-cohort to represent an early-life mortality source,

where that early mortality could have represented multiple factors commonly reported to result

in early juvenile mortality (e.g., predation, Bestgen et al. 2006; or environmental factors,

Steinhart et al. 2005). Importantly, the simulated changes in mortality were applied to juvenile

biomasses estimated from block-net samples (all > 15 mm total length, TL), and thus, the 50%

mortality was additional to mortality acting on these hatching sub-cohorts from their hatching

date to 15 mm TL. I used hatching sub-cohort specific biomass estimates at age-1 and adult

stages as evaluation metrics for the relative effects of hatching-date dependent mortality on year

class abundance and composition.

Results

Ecopath Results

Ecopath models did not initially balance because EE estimates exceeded one for some

functional groups, thus indicating that losses were greater for those groups than production using

my initial inputs. Model balances were achieved following suggestions by Christensen et al.

(2005) and Guenette et al. (2001), rather than using the automated mass-balance routine

(Kavanaugh et al. 2004). In general, I modified input values (i.e., B, P B, Q B, or DCij) for fish

functional groups using diagnostics (e.g, POe and M,2) recommended by Christensen et al.

(2005), C. Walters (personal communication), and personal knowledge of field data. I used

Ecopath's sensitivity analyses routine to evaluate how changes to my input parameter values for









the balanced models would affect basic parameters estimated by Ecopath. Ecopath's sensitivity

routine varies each input parameter from -50% to +50%, in 10% steps, and reports a percent

change in estimated parameters relative to estimates using the original parameter value

(Christensen et al. 2005). In general, biomass and production inputs had greater effects than

consumption inputs on estimates of EE, such that a + 30% change in input values could result in

up to a 43% change in EE estimates for that functional group (see Appendices 3 and 4).

Underestimating B and P/B input values resulted in stronger effects on Ecopath estimates for that

functional group than overestimating those values within the range of variation I evaluated.

Varying Q/B values only had strong effects (i.e., 20-30% change) for EE estimates of other

predators, sunfish, and insect prey functional groups. Sensitivity of Ecopath estimates to other

fish functional group inputs including adult largemouth bass, when varied + 30%, were less than

10%. Sensitivity analyses suggested that inputs for my lowest trophic levels could have large

effects on Ecopath's estimates for those trophic levels, but those inputs had very little effect on

estimates of upper trophic level biomasses (generally less than 0.02 kg/ha) with large changes in

lower trophic level inputs (i.e., + 100%). Thus, input values of a functional group had more

effect on Ecopath's estimates for that functional group than estimates for other functional groups,

and input values for top predators had more influence on Ecopath estimates than input values for

lower trophic levels.

Ecopath uses a modification of Pianka' s (1973) niche overlap index to describe

similarities in prey use between predator functional groups (Christensen et al. 2005) via the diet

matrix input from field data. Ecopath estimates of prey niche overlap indicated high similarities

in prey types among L1VB hatching sub-cohorts in spring and summer, but niche overlap values

were not always intuitive based on hatching sequence. For example, prey niche overlap









estimates for the north lakes model indicated that the early hatched sub-cohort had lower prey

niche overlap with the middle hatched sub-cohort in July (estimate = 0.57) than with the late

hatched sub-cohort (estimate = 0.84, Table 4-4). At south lakes, early and middle hatched sub-

cohorts had high prey niche overlap in May (estimate = 0.82, Table 4-4). There were no

estimates for the late-hatched sub-cohort' s diet overlap with other hatching sub-cohorts in May

because these fish were just beginning to enter the population at this time. In July, the early

hatched sub-cohort had similar diet overlap with both late and middle-hatched sub-cohorts

(estimates = 0.78 and 0.79, respectively) and middle and late-hatched sub-cohorts had very high

prey niche overlap (estimate = 0.97, Table 4-4). Thus, prey niche overlap was not always

consistent with my expectations of finding highest diet similarities between closest age sub-

cohorts.

Ecosim Results

Hatching-date specific mortality influenced contributions of hatching sub-cohorts to the

year class, but the strength of influence varied among simulations. At north lakes, increased

survival of the early-hatched sub-cohort resulted in similar biomass changes in age-1 abundance

of both middle and late-hatched sub-cohorts (< 1% difference between them). However,

decreased survival of the early-hatched sub-cohort resulted in stronger age-1 biomass changes

for the middle-hatched than the late-hatched sub-cohort relative to baseline conditions (25%

increase versus 17% increase, respectively) (Figure 4-3). Variable survival of the middle-

hatched sub-cohort, which had the highest baseline biomass at age-1, had similar influences on

age-1 biomass of early and late hatched sub-cohorts (I 3% difference between early and late-

hatched sub-cohorts) (Figure 4-3). Similarly, variable survival of the late-hatched sub-cohort

had similar influences on early and middle-hatched sub-cohort biomasses at age-1 (5 3%









difference between early and middle-hatched sub-cohorts) (Figure 4-3). Patterns between

hatching date-specifie sub-cohort survival and biomass were similar for age-1 and adult

abundance at north lakes (left versus right panels on Figure 4-3), except increased early-hatched

sub-cohort survival had similar effects on middle and late-hatched sub-cohort adult biomass. At

south lakes, variable mortality applied to individual hatching sub-cohorts resulted in < 15%

changes in age-1 biomass for other sub-cohorts (Figure 4-4). The late-hatched sub-cohorts

revealed the most sensitivity to mortality of other hatching sub-cohorts, because their age-1

biomass changed the most (up to 14 %) as result of changes in the early-hatched sub-cohort' s

survival, whereas the middle-hatched and early hatched sub-cohorts age-1 biomasses never

changed more than 1 1% (Figure 4-4). Effects of hatch date specific sub-cohort mortality at south

lakes were more pronounced for adult biomasses than for age-1 biomasses (Figure 4-4). Thus,

hatching date specific sub-cohort mortality influenced contributions of other sub-cohorts to the

year class, and those influences varied in magnitude among simulations.

Among-lake differences appeared to influence effects of variable sub-cohort specific

mortality simulations. In almost all cases, effects of simulated mortality of a given hatching sub-

cohort had greater influences on other hatching sub-cohorts for the north Florida model

compared to the south Florida model, and these results applied to biomass estimates at both age-

1 and adult stages. Exceptions occurred when the early-hatched sub-cohort mortality was

reduced which resulted in similar changes in age-1 biomass (14 %) for late-hatched sub-cohorts

in both regions and slightly higher late-hatched sub-cohort adult biomass for the south Florida

model (21% versus 17% change; Figures 4-3, 4-4). Thus, models predicted that hatching sub-

cohort specific survival influenced year class biomass and composition, but patterns should be

expected to vary among systems.









Discussion

Ecosystem models predicted that persistent changes in mortality of hatching sub-cohorts

could affect equilibrium year class composition and revealed high compensation in juvenile

survival under differing mortality treatments. The models also showed strong regulation effects

on total age-1 biomass with changes in sub-cohort mortality, via predation and cannibalism.

Model predictions indicated that effects of sub-cohort survival will likely vary among systems

due to differences in population and community characteristics. Results of my models were

somewhat expected based on ecological theory, but suggested that these types of models can be

useful for exploring population dynamics and recruitment questions within a large ecosystem

context.

Processes that regulate juvenile fish survival have received much attention, and it is now

recognized that survival to age-1 results from a series of interdependent events during larval and

juvenile stages (Ludsin and Devries 1997). The severity of mortality along this series of life-

stages can vary among hatching sub-cohorts and result in disproportionate contributions of

specific hatching sub-cohorts to the year class relative to their proportion of total fry production

(Cargnelli and Gross 1996). Given the identification of hatching date specific mortality,

remarkably little work has addressed how hatching date-dependent mortality may influence

dynamics within cohorts. My simulations showed weak effects of sub-cohort mortality on

overall biomass at age 1 and adult biomass, because 50% changes in survival of a specific sub-

cohort did not lead to large overall changes in other sub-cohort biomasses. However, biomasses

of other sub-cohorts did exhibit compensation where total age-1 and adult biomass did not

decline substantially as a result of higher mortality of a specific sub-cohort. My simulations

suggested weaker linkages among sub-cohorts than expected based on hatching-date sequence.









For example, early and middle-hatched sub-cohorts responded similarly to simulated changes in

late-hatched sub-cohort survival, whereas I expected sub-cohorts hatched consecutively to

interact more strongly.

The level of diet niche overlap among predators does not provide a reliable index of

competition (e.g., Olson et al. 1995), however it does provide information on use of resource

types among consumers (Abrams 1980). Ecopath estimated high prey niche overlap for all

juvenile largemouth bass sub-cohorts in spring and summer except for middle versus early-

hatched sub-cohorts from the north region. Prey resource use for gape-limited juvenile fishes is

often limited by body size, because larger offspring that were hatched earlier and/or had faster

growth can use a larger range of prey species than smaller offspring that were later-hatched

and/or slower growing (Mittelbach and Persson 1998). In my models, the large overlap in prey

resource use was potentially due to the high similarity in TL among sub-cohorts and large TL

ranges for each sub-cohort. For example, the early-hatched sub-cohort at north lakes in July

were 46-128mm TL (median TL = 92 mm), whereas the late-hatched sub-cohort were 28-100

mm TL (median TL = 58 mm). Thus, high prey overlap would be expected based on gape

limitation considerations, but length similarity did not explain the lower diet overlap estimated

for middle and early-hatched sub-cohorts at north lakes in July. Differences in growth rates

among hatching sub-cohorts (see Chapter 2) likely led to similar length ranges and high diet

overlap among hatching sub-cohorts. Although individual variability in the timing of

ontogenetic diet shifts of similar sized/age fish influences the relationship between largemouth

bass size/age and prey use (Post 2003; Olson 1996), within functional group diet variability was

incorporated into the diet matrices of my Ecopath models based on observed sub-cohort specific

diet compositions. My inability to assign age-specifie diet components for sub-cohorts in fall









and spring precluded diet overlap estimates for these stanzas, but the similarities in TL among

the sub-cohorts during this period suggested that all sub-cohorts could have potentially used

similar prey resources (i.e., similar gape limitations across all sub-cohorts). Diet overlap

estimates indicated that largemouth bass hatching sub-cohorts used many of the same prey

resources through summer, suggesting that variable survival of individual hatching sub-cohorts

could have strong effects on recruitment if prey abundances limited survival during early life.

However, high diet overlap did not strongly influence predicted biomass at age-1 and adult

populations in my Ecosim simulations, indicating that prey densities from my field data did not

infer strong prey limitations.

Complex interactions among predators and juvenile largemouth bass functional groups

largely regulated proportional contributions of hatching sub-cohorts to the year class. Predicted

increases in sub-cohort biomasses via lower mortality resulted in increased numbers of adult bass

acting as predators in the system, and thus, biomass reductions for other hatching sub-cohorts.

Importantly, functional groups were modeled such that foraging times did not vary with prey or

predator abundances, except for the youngest largemouth bass age stanzas which were assumed

to restrict feeding times rather than maximize growth when food was abundant as has been

shown for Atlantic salmon Salmo salar (Orpwood et al. 2006). Increased mortality for

individual hatching sub-cohorts decreased biomasses at all life stages for that sub-cohort,

increased prey fish functional group biomasses, and therefore increased biomasses of other

hatching sub-cohorts. Following induced mortality for a given sub-cohort, biomass increases for

other hatching sub-cohorts were regulated by predators and suggested a "competitive juvenile

bottleneck" (Werner and Hall 1979; Bystroim et al. 1998) between "other predators" and

largemouth bass functional groups. Decreased survival of a given hatching sub-cohort resulted









in decreased predation on "other predators" because there were fewer largemouth bass adults

acting as predators at equilibrium, and thus, increased biomass for "other predators" and

increased predation on other LMB hatching sub-cohorts. In contrast, the opposite phenomenon

occurred under simulated increased survival of juvenile largemouth bass hatching sub-cohorts.

Thus, the model suggested cultivation of largemouth bass juveniles through adult influences on

"other predators", however the model did not exhibit depensation because largemouth bass

hatching sub-cohort biomasses always rebounded to baseline levels if induced mortality was

returned to base Ecopath levels. Depensation may not have been evident in my models because

enough predators always existed in the systems to maintain restricted habitat use/foraging

activities of competitors (see Walters and Kitchell 2001). Evaluation of the hypothesized

"competitive juvenile bottleneck" between juvenile largemouth bass functional groups and

"other predators" would require further data that allowed more complex stage-structuring in the

model, however this type of relationship has commonly been found in freshwater ecosystems

(e.g., between bluegill and largemouth bass; Olson 1996, Aday et al. 2005).

My simulation results are dependent on Ecopath and Ecosim assumptions (e.g., foraging

arena theory), model constraints, and data from regions with differing largemouth bass genetics

(see Chapter 3). Ecopath and Ecosim have many years (> 20 and > 10, respectively) of

modification, improvement, and review; however model estimates, their errors, and their

application require scrutiny (see Plaganyi and Butterworth 2004). Essington (2007) used

simulations to show that the precision of Ecopath estimates for B and EE were equivalent to the

precision of the input data and he concluded that "bad data led to bad predictions." I collected

all the fish functional group biomass data for my models and attempted to obtain other inputs

from the same or similar systems, but inputs derived from other models and published literature









certainly contributed to Ecopath estimates in ways that may not mirror the populations I

simulated. Essington (2007) also reported that Ecopath inputs were more sensitive to B and P/B

inputs than to diet composition data. Uncertainties regarding EWE parameter inputs and

estimates are similar to those reported for other commonly used ecosystem and bioenergetics-

based models (e.g., Ney 1990, Plaganyi 2007). Model estimates were also dependent on

specified stock-recruitment relationships that were assumed to be the same for all largemouth

bass sub-cohorts. Identifying stock and recruitment relationships is one of the most difficult

problems in biological assessment (Hilborn and Walters 1992), thus there is undetermined

uncertainty in these in my models despite the tremendous body of largemouth bass literature.

However, my stock-recruitment relationships did conform to Beverton-Holt functions, which are

common across a wide range of fish species and populations (Walters and Martell 2004).

Furthermore, I could not account for parental effects on juveniles (e.g., parental size and juvenile

performance; Miranda and Muncy 1987; Baylis et al. 1993; Wright and Gibb 2005; and

Jorgensen et al. 2005). Thus, outcomes of these types of models should be treated as hypotheses

that direct future research and data collection.

One of the most fundamental bases for Ecosim involves assumptions for how prey fishes

compete and adjust their behaviors with changes in opportunities (i.e., prey supply) and risks

(i.e., predation). The vulnerabilities schedule for functional groups as predators on their prey is

one of the most important parameters in Ecosim and one of the hardest to know with reliability

(Plaganyi 2004). In my models, increasing vulnerability values changed the magnitude of

biomass responses, however the overall trends of my results remained the same. Default

vulnerability values in Ecosim (i.e., v = 2) represent mixed bottom-up and top-down trophic

flows, however using this default value can cause misleading results (Shannon et al. 2000).









Although it is imperative to increase vulnerability values for functional groups that were likely

sampled at levels much lower than Bmax (e.g., highly exploited marine stocks; Pauly 1995), there

is no reason to suspect that these values should have been largely changed for functional groups

in my models. Recent trends indicated relatively low fishing mortality rates for largemouth bass

(Quinn 1996; Allen et al. In Press), and thus, the low vulnerabilities in my models infer that large

increases in largemouth bass abundance are not expected. Vulnerabilities I used resulted in

similar fish functional group biomasses to those reported for southeastern reservoirs (e.g.,

Jenkins 1975).

Implications

Much research has indicated that juvenile fish survival is strongly density-dependent as a

result of regulating processes such as predation, starvation, and cannibalism (Shepherd and

Cushing 1990). Several authors have shown that the importance of the processes resulting in

strong density dependence is rarely specified (Walters and Juanes 1993 and references therein),

and Shepherd and Cushing (1990) suggested that a weak regulatory process could result in

regulation at high stock sizes and when Hishing mortality is low (as is likely for largemouth bass).

My simulations suggested that hatching specific sub-cohort mortality could have large influences

on relative contributions of individual hatching sub-cohorts to a year class, however total age-1

biomass was relatively stable across all simulations. Simulations that induced and reduced

mortality of individual largemouth bass hatching sub-cohorts had small effects on age-1 total

biomass (maximum biomass increase = 5.5% and maximum biomass decrease = 7.0%) relative

to Ecopath baseline estimates, thus suggesting high compensation following increased sub-

cohort mortality and strong regulation following decreased sub-cohort mortality. Predation was

the most important regulating process acting on recruitment. Although responses to variable









hatching sub-cohort mortality were stronger for the north Florida population than for the south

Florida population, predation still regulated total recruitment to age-1 for both spawning

strategies. Walters and Juanes (1993) proposed that mortality should result in selection for a

balance between growth and survival of juvenile fishes due to shorter foraging times and smaller

foraging volumes in the presence of high predator abundance, and thus, increased competition

and exaggerated density-dependent effects on growth rates. Sub-cohort-specific survival could

largely influence predation risks and feeding activities to result in strong competition, which has

implications for energy allocations that could affect life-history metrics such as age at maturity,

overwinter condition, and lifetime fitness.

My results also have implications for fisheries management. Other authors have

proposed that fishing regulations should consider the influence of removing spawning adults

during periods of assumed high juvenile survival (e.g., Trebitz 1991) or when progeny from any

spawning period may have survival advantages depending on inter-annual environmental

variability (e.g.,. Garvey et al. 2002). Similarly, previous studies have suggested the potential

for reduced progeny survival following the removal of nest guarding adults for black bass

M~icropterus spp. (e.g., Philipp et al. 1997, Suski et al. 2003). My results suggested that indirect

effects of fishing on juvenile survival would not likely have overwhelming effects on year-class

strength because ecological interactions were predicted to regulate total biomass and that

survival of other sub-cohorts would be expected to compensate if fishing greatly reduced

survival of one portion of a year class. My results also indicated only small increases in total

largemouth bass biomasses with large increases in hatching sub-cohort survival, which may

extend to stocking practices assuming that stocking induces similar dynamics as increased sub-

cohort survival in my simulations. Walters and Juanes (1993) presented similar reasoning for










failures in northwest Pacific salmonid stockings. Potential trade-offs between parental spawning

times and inter-sub-cohort interactions affecting juvenile survival necessitate further

investigation for understanding recruitment regulation, population level characteristics, and

fisheries management.
















































































mm TL = total length in millimeters, x indicates that species was collected in that region and is

represented in the model


Table 4-1. Species composition of non-largemouth bass fish groups.


Functional Group Name
other predators










killifish-topminnows









sunfish













generalists minnows








benthic fish


Species common name
Atlantic needlefish
Black acara
Black crappie (~ 200 mm TL)
Bowfin
Chain pickerel
Florida gar
Longnose gar
White catfish (~ 250 mm TL)
Yellow bullhead ( 250 mm TL)
Bluefin killifish
Eastern starhead topminnow
Golden topminnow
Least killifish
Lined topminnow
Mosquitofish
Sailfin molly
Seminole killifish
Banded pygmy sunfish
Black crappie
Blue gill
Bluespotted sunfish
Dollar sunfish
Everglades pygmy sunfish
Okefenokee pygmy sunfish
Redbreast sunfish
Redear sunfish
Spotted sunfish
Warmouth
Brook silverside
Coastal shiner
Flagfish
Golden shiner
Inland silverside
Pugnose minnow
Taillight shiner
Blue tilapia
Browna bull head
Channel catfish
Clowna goby
Gizzard shad
Lake chubsucker
Pirate perch
Plated catfish
Suckermouth catfish
Swamp darter
Tadpole madtom
Threadfin shad
White catfish
Yellow bullhead


Species taxonomic name
Strongylura marina
Cichlasonia bintaculatum
Pontoxis nigrontaculatus
A4nia calva
Esox niger
Lepisosteus plalvrhincus
Lepisosteus osseus
A4neiurus catus
A4neiurus natabis
Lucania goodei
Fundulus escanibiae
Fundulus chrusotus
Heterandria formosa
Fundulus lineolatus
Ganibusia holbrooki
Poecilia latipinna
Fundulus sentinolis
Elassonia zonatunt
Pontoxis nigrontaculatus
Lepontis niacrochirus
Enneacanthus glorious
Lepontis nmarginatus
Elassonia evergladei
Elassonia okefenokee
Lepontis auritus
Lepontis nmicrolophus
Lepontis punctatus
Lepontis gulosus
Labidesthes sicculus
Notropis petersoni
Jordanella floridae
Notenligonus crvsoleucas
Ml/enidia beryllina
Opsopoeodus entiliae
Notropis niaculatus
Tilapia aurea
A4neiurus nebulosus
Ictalurus punctatus
Ml/icrogobius gulosus
Dorosonia cepedianunt
Erinivzon sucetta
.4phredoderus savanus
Hoplosternunt littorale
Hypostonius plecostonius
Etheostoniafusifornme
Noturus gyrinus
Dorosonia petenense
A4neiurus catus
A4neiurus natabis


south model north model
x x

x
x x
x x
x x

x x









Table 4-2. Ecopath inputs for a north Florida eutrophic lake based on data from Lakes Seminole
and Talquin collected in 2003 and 2004.

Group Biomass P/B Q/B
number Functional group (age) (kg/ha) (yr ) (yr ) EE
1 Other predators 2.56a 0.400 3.200
2 LMB late-hatched (to summer) 0. 19b 8.51a 41.04b
3 LMB late-hatched (to fall) 1.46a 4. 16a 13.70b
4 LMB late-hatched (age-1) 1.78b 2.00' 6.51b
5 LMB late-hatched (adult) 5.29b 0.71e 3.34"
6 LMB middle-hatched (to summer) 0. 19b 8.77a 41.26b
7 LMB middle-hatched (to fall) 1.37a 4.16d 13.70b
8 LMB middle-hatched (age-1) 1.67b 2.00' 6.51b
9 LMB middle-hatched (adult) 4.96b 0.71e 3.34"
10 LMB early-hatched (to summer) 0. 15b 7.48a 40. 18b
11 LMB early-hatched (to fall) 1.29a 4.16d 13.70b
12 LMB early-hatched (age-1) 1.58b 2.00' 6.51b
13 LMB early-hatched (adult) 4.67b 0.71e 3.34"
14 killifish / topminnows 3.49a 2. 82" 44.000
15 sunfish 53.50a 1.30" 19.380
16 generalists/minnows 9.25a 1.600 27.80"
17 benthic fish 37.00a 1.390 18.680
18 crustaceans 26.00b* 13.90b* 22.00'
19 insects 30.20f 38.00~h 0.70h
20 zooplankton 15.00' 35.00' 0.80'
21 macrophytes 61824.00a 2.60g
22 phytoplankton 35.00 0.75'
23 detritus 100.00'
P = production, B = biomass, Q = consumption, EE = ecotrophic efficiency. measured in this
study. estimated by Ecopath, b*estimated by Ecopath based on inputs from Schramm et al.
(1983) and Bull et al. (1991). "derived from www.fishbase.org. derived from Wicker and
Johnson (1987). eAllen et al. (2002). fLobinske et al. (2002). within range reported in
Westlake (1982). hPoepperl (2003). within range reported from published Ecopath models.
iDeAngelis et al. (1993)









Table 4-3. Ecopath inputs for a north Florida eutrophic lake based on data from Lakes Istokpoga
and Okeechobee collected in 2003 and 2004.

Group Biomass P/B Q/B
number Functional group (age) (kg/ha) (vr ) (yr ) EE
1 Other predators 8.27a 0.22" 3.50"
2 LMB late-hatched (to spring) 0.03b 0.00a 48.99b
3 LMB late-hatched (to summer) 0.45b 6.31a 20.73b
4 LMB late-hatched (to fall) 0.54a 4. 16d 11.00b
5 LMB late-hatched (age-1) 0.98b 2.00' 6.35b
6 LMB late-hatched (adult) 2.90b 0.71e 3.26,
7 LMB middle (to spring) 0. 11b 5.82a 50.07b
8 LMB middle-hatched (to summer) 0.75b 5.94a 22.01b
9 LMB middle-hatched (to fall) 2. 12a 4. 16d 11.00b
10 LMB middle-hatched (age-1) 2. 17b 2.001 5.99b
11 LMB middle-hatched (adult) 7.04b 0.71e 3.17,
12 LMB early-hatched (to spring) .16b 12.72a 52.35b
13 LMB early-hatched (to summer) 0.48b 6.72a 21.48b
14 LMB early-hatched (to fall) 1.06a 4. 16d 11.00b
15 LMB early-hatched (age-1) 1.61b 2.00' 5.96b
16 LMB early-hatched (adult) 4.77b 0.71e 3.06"
17 killifish / topminnows 10.40" 2.32" 44.000
18 sunfish 73.15a 0.85" 17.17,
19 generalists/minnows 11.30" 2.00" 39.00"
20 benthic fish 12.00a 1.15" 22.30"
21 crustaceans 31.00b* 11.00b* 22.00'
22 insects 30.20' 38.00h 0.70h
23 zooplankton 15.00' 35.00' 0. 80'
24 macrophytes 91232.00a 2.60g
25 phytoplankton 35.00 0.75'
26 detritus 100.00'
P = production, B = biomass, Q = consumption, EE = ecotrophic efficiency. measured in this
study. estimated by Ecopath, b*estimated by Ecopath based on inputs from Schramm et al.
(1983) and Bull et al. (1991). derived from www.fishbase.org. derived from Wicker and
Johnson (1987). eAllen et al. (2002). fLobinske et al. (2002). within range reported in
Westlake (1982). hPoepperl (2003). within range reported from published Ecopath models.
iDeAngelis et al. (1993)









Table 4-4. Ecopath estimates of diet niche overlap among age-0 largemouth bass hatching sub-
cohorts

Model Age Late Middle
North In July Late 1.000
Middle 0.734 1.000
Early 0.838 0.574
South In May Late 1.000
Middle -1.000
Early -0.823
In July Late 1.000
Middle 0.965 1.000
Early 0.783 0.793











harvest Respiration
(V,) (R3)


Figure 4-1. Representation of ecosystem flows for components of an Ecopath model consisting
of three consumer groups and a detritus group such that predation on a group results in
production for their predators




I Vulnerable j ,VB
(VI)


Figure 4.2. Representation of vulnerable and invulnerable states of prey functional group
biomass and predator consumption.











a


-50% 0 % + 50%


% Simulated mortality


Early-hatched
Middle-hatched
I I Late-hatched

Figure 4-3. Percent biomass change at equilibrium relative to baseline Ecopath values for
simulations of variable hatch-date specific mortality for a north Florida lake. Left
column panels (a-c) represent changes in total biomass at age-1 for varying mortality for
early-hatched (a), middle-hatched (panel b), and late-hatched (panel c) largemouth bass.
Right column panels (d-f) represent estimated changes in total adult biomass for varying
mortality for early hatched (d), middle-hatched (panel e), and late-hatched (panel f)
largemouth bass.


-50% 0% +50%











a


-50% 0 % + 50%


% Simulated mortality


Early-hatched
Middle-hatched
I I Late-hatched

Figure 4-4. Percent biomass change at equilibrium relative to baseline Ecopath values for
simulations of variable hatch-date specific mortality for a south Florida lake. Left
column panels (a-c) represent changes in total biomass at age-1 for varying mortality for
early-hatched (a), middle-hatched (panel b), and late-hatched (panel c) largemouth bass.
Right column panels (d-f) represent estimated changes in total adult biomass for varying
mortality for early hatched (d), middle-hatched (panel e), and late-hatched (panel f)
largemouth bass.


-50% 0% +50%









CHAPTER 5
SYNTHESIS AND FUTURE RESEARCH

Age-0 Largemouth Bass Recruitment

The results presented in this study suggested that hatching date-specific sub-cohort

characteristics had large implications to the composition of recruits to age-1 for juvenile

largemouth bass in Florida lakes. Hatching date was important to growth and survival of age-0

largemouth bass, but the strength of importance differed among latitudes. I identified slower

growth for the early-hatched largemouth bass sub-cohorts at south Florida lakes relative to early-

hatched sub-cohorts at north Florida lakes. I also found no evidence of survival advantages for

early-hatched sub-cohorts relative to later-hatched sub-cohorts through the end of summer.

These findings contrasted a popular recruitment hypothesis for largemouth bass (i.e., that early-

hatching is always advantageous), but suggested support for Garvey et al.'s (2002) hypothesis

that protracted spawning may be advantageous to adult fitness where environments may be quite

variable over the spawning season. My findings also contrasted the hypothesis that size-selective

overwinter mortality would largely affect survival to age-1 at southern latitudes. A lack of

strong size-selective overwinter mortality was likely due to winter water temperatures that did

not limit age-0 largemouth bass activities, and thus, facilitated foraging and predator avoidance

relative to winter water temperatures at more northerly latitudes. My research was conducted at

a lower latitude than other largemouth bass recruitment work that has contributed to recruitment

hypotheses for this species and provided evidence that limited the application of those

hypotheses at the extreme southern range of largemouth bass distributions.

My results also provided insight and potential hypotheses regarding the evolution of

parental spawning strategies across Florida' s latitudinal gradient. Experimental results presented

in this dissertation showed that both environmental and genetic factors contributed to parental










spawning times. Environmental conditions were conducive to spawning from December to May

at south Florida lakes, whereas spawning at north Florida lakes was restrained to beginning in

March due to water temperatures. Spawning distributions differed between adult fish from north

and south Florida when they were translocated and spawned in similar environments, however

translocated spawning distributions exhibited temporal shifts relative to in-situ populations.

Thus, I concluded that both genetic and environmental factors contributed to population

spawning periodicity. Having found little evidence for size-selective overwinter mortality in

Florida, I sought other explanations for this observation. Simulation modeling in this

dissertation suggested that parental spawning strategies could affect interactions among hatching

sub-cohorts such that variable mortality of hatching sub-cohorts could differentially influence

other hatching sub-cohorts and that strength of interactions depended on hatching distributions.

Thus, simulations provided hypotheses regarding mechanisms that regulate juvenile survival and

may impose selection for adult spawning times across Florida's latitudinal gradient.

Fisheries Management

Fisheries managers are most interested in age-0 survivors and management activities that

may increase the number of juveniles surviving to enter a fishery. My study identified

differences in hatch-date specific survival among Florida latitudes during the period from

hatching through the first summer. These results appeared to be due to environmental

influences, and thus, suggested that annual conditions during spring may provide insight to

fisheries managers relative to the strength of the year class. My results also had implications for

stocking strategies in Florida lakes. I identified largemouth bass spawning seasons and size-

distributions across Florida' s latitudes, and this information can be used by state resource

managers to identify preferable timing and sizes at stocking. Fish stocking strategies such as









"matching the hatch" (i.e., stocking Eish at similar sizes to naturally spawned fish) or stocking

Eish at lengths corresponding to the right tail of the length-frequency of natural Eish, which has

been successful at Lake Talquin, Florida (C. Mesing, FWC, personal communication), both

require expected spawning dates for hatchery planning. Experimental results provided further

evidence that genetic stocks should not be translocated throughout Florida because of the

contribution of genetics to parental spawning times. Lastly, my simulations suggested that

variable mortality among individual hatching sub-cohorts should not have strong effects on total

age-1 biomass, however year class structure (i.e., percent contributions of individual hatching

sub-cohorts) could be strongly affected by variable hatching sub-cohort-specific mortality.

Future Research

Genetic Contributions

Largemouth bass populations used for this study encompassed a natural gradient of

genetic stocks. North Florida lake populations were intergrade largemouth, whereas central and

south Florida lake populations were dominantly Florida largemouth bass (Brandon Barthel,

personal communication). Thus, genetic differences may have influenced the growth and

survival results I found. Experimental work could discern the contribution of genetics to growth

and survival, which would help further identify factors that contribute to age-0 largemouth bass

recruitment processes across Florida' s latitudinal gradient. Further research could also

potentially identify physiological differences among genotypes that would facilitate energetic

analyses for these populations and life history theory.

Evolution of Spawning Strategies

Hatching-date dependent survival and influences on adult fitness have largely depended

on short-term studies that typically have only followed one or two year classes. Technological









advances in genetics techniques provide opportunities for long-term research evaluating

relationships between parental spawning, offspring survival, and fitness. For example,

microsatellite analyses could allow researchers to relate offspring characteristics and survival to

parental characteristics (e.g., parent's age or size, and parental spawning time) and parental

fitness. DeWoody et al. (1998) used microsatellites to identify paternity and maternity of

redbreast sunfish Lepomis auritus progeny and found differing levels of progeny success for

males with differing reproductive strategies. Genetic markers could also allow the evaluation of

offspring production by first-time versus repeat spawners for iteroparous species. Information

that could arise from genetics research may have important implications for evolutionary theory

and fisheries management (e.g., fishing regulations and stock assessments for commercially

fished species).

Latitudinal Patterns in Life History

Conducting work at the geographic extent of the largemouth bass' s range suggested that

conclusions regarding recruitment processes from more northerly latitudes were not always

applicable. Thus, there appears to be a need for further research of populations at the extremes

of their species range for understanding latitudinal patterns in factors affecting juvenile life

histories and evolution of adult spawning strategies. Evidence that late-hatched progeny likely

come from smaller adults (Miranda and Muncy 1988; Baylis et al. 1993) in black bass

populations has resulted in hypotheses that propose fitness costs to small parents that spawn late

at latitudes with severe size-selective overwinter mortality. However, the apparent lack of size-

selective overwinter mortality in Florida lakes suggests that fitness trade-offs associated with

spawning at small sizes may not be as large in Florida relative to more northerly latitudes.

However, high fitness costs could still be associated with spawning at small sizes in Florida if










progeny survival was low due to increased predation or intra-year class cannibalism relative to

survival for earlier-hatched fish. Future research in Florida, and at other latitudes, that can

investigate mechanisms regulating juvenile survival from specific parents across several

generations would greatly facilitate our understanding of latitudinal patterns in juvenile life

history and the evolution of parental spawning strategies.


































APPENDIX A
DIET COMPOSITION MATRICES FOR ECOPATH MODELS











Table A-1. Diet composition inputs for north region Ecopath model

Group
number Group name lb 2a 3a 4a 5" 6a 7a 80 90 10a 11a
1 Other predators 0.02 0.02
2 L1VB late-hatched (to summer) < 0.01
3 L1VB late-hatched (to fall) 0.05 < 0.01 < 0.01
4 L1VB late-hatched (age-1) 0.02
5 L1VB late-hatched (adult)
6 L1VB middle-hatched (to summer) < 0.01
7 L1VB middle-hatched (to fall) 0.05 < 0.01
8 L1VB middle-hatched (age-1) 0.02 < .01
9 L1VB middle-hatched (adult)
10 L1VB early-hatched (to summer) < 0.01
11 L1VB early-hatched (to fall) 0.04 < 0.01 < 0.01
12 L1VB early-hatched (age-1) 0.02
13 L1VB early-hatched (adult)
14 killifish /topminnows 0.09 0.10 <0.01 0.06 0.01 0.20 <0.01 0.06 0.01 0.19 <0.01
15 sunfish 0.26 0.20 0.37 0.10 0.68 0.46 0.37 0.10 0.68 0.19 0.37
e 16 generalists/minnows 0.08 0.05 0.12 0.06 0.06 0.13 0.06 0.06 0.04 0.13
A 17 benthic fish 0.04 0.20 0.27 0.64 0.03 0.27 0.64 0.03 0.38 0.27
18 crustaceans 0.13 0.21 0.21 0.07 0.18 0.23 0.21 0.07 0.18 0.10 0.21
19 insects 0.14 0.20 0.01 0.05 < 0.01 0.11 0.01 0.05 < 0.01 0.10 0.01
20 zooplankton 0.05 0.05 < 0.01 0.01 < 0.01 < 0.01 0.01 < 0.01 < 0.01
21 macrophytes
22 phytoplankton
23 detritus < 0.01 < 0.01 < 0.01 < 0.01 < 0.01 < 0.01 < 0.01
24 import
atis study. derived from data at www.fishbase.org. c~ammons and Maceina (2006). dDurant et al. (1979). derived from literature







































0.20
0.30
0.40
0.10


0.10
0.90


cSammons and Maceina (2006). dDurant et al.


(1979). derived from literature


Group


130 14d 15b 16b 1b e8 19e 20e
0.02


number
1


Bible A-1.


2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
this study.


Ta


Continued


Group name 12a
Other predators
L1VB late-hatched (to summer)
L1VB late-hatched (to fall)
L1VB late-hatched (age-1)
L1VB late-hatched (adult)
L1VB middle-hatched (to summer)
L1VB middle-hatched (to fall)
L1VB middle-hatched (age-1)
L1VB middle-hatched (adult)
L1VB early-hatched (to summer)
L1VB early-hatched (to fall)
L1VB early-hatched (age-1)
L1VB early-hatched (adult)
killifish / topminnows 0.06
sunfish 0.10
generalists/minnows 0.06
benthic fish 0.64
crustaceans 0.07
insects 0.05
zooplankton 0.01
macrophytes
phytoplankton
detritus < 0.01
import
derived from data at www.fishbase.org.


< 0.01





< 0.01



< 0.01



0.01
0.68
0.06
0.03
0.18
< 0.01











Table A-2. Diet composition inputs for south region Ecopath model

Group
number Group name lb 2a 3a 4a 5a 60 7a 8a 9a 10a 110
1 Other predators 0.02 0.02
2 LMB late-hatched (to spring)
3 LMB late-hatched (to summer) 0.02
4 LMB late-hatched (to fall) 0.02 < 0.01 < 0.01
5 LMB late-hatched (age-1) 0.01
6 LMB late-hatched (adult)
7 LMB middle (to spring) 0.01
8 LMB middle-hatched (to summer) 0.02
9 LMB middle-hatched (to fall) 0.02 < 0.01
10 LMB middle-hatched (age-1) 0.01 <0.01
11 LMB middle-hatched (adult)
12 LMB early-hatched (to spring) 0.01
13 LMB early-hatched (to summer) 0.02
14 LMB early-hatched (to fall) 0.02 < 0.01 < 0.01
15 LMB early-hatched (age-1) 0.01
16 LMB early-hatched (adult)
S17 killifish / topminnows 0.10 0.10 0.10 0.15 0.01 0.02 0.10 0.17 0.15 0.01
o\18 sunfish 0.20 0.20 0.40 0.68 0.19 0.40 0.68
19 generalists 0.15 0.15 0.06 0.03 0.09 0.15 0.06
20 benthic fish 0.09 0.20 0.03 0.03 0.05 0.20 0.03
21 crustaceans 0.16 0.20 0.30 0.10 0.18 0.43 0.30 0.24 0.10 0.18
22 insects 0.10 0.50 0.40 < 0.01 0.52 0.40 0.19 < 0.01
23 zooplankton 0.05 0.20 0.17 0.07
24 macrophytes
25 phytoplankton
26 detritus
27 import 1.00
this study. derived from data at www.fishbase.org. "Sammons and Maceina (2006). dDurant et al. (1979). derived from literature















Group
number Group name 12a 13a 14a 15a 16c 17d 18b 19b 20b 21e 22e 23e
1 Other predators 0.02


27 import
this study. bderived from data at www.fishbase.org. cSammons and Maceina (2006). dDurant et al. (1979). derived from literature


Table A-2. Continued


2 LMB late-hatched (to spring)
3 LMB late-hatched (to summer)
4 LMB late-hatched (to fall)
5 LMB late-hatched (age-1)
6 LMB late-hatched (adult)
7 LMB middle (to spring)
8 LMB middle-hatched (to summer)
9 LMB middle-hatched (to fall)
10 LMB middle-hatched (age-1)
11 LMB middle-hatched (adult)
12 LMB early-hatched (to spring)
13 LMB early-hatched (to summer)
14 LMB early-hatched (to fall)
15 LMB early-hatched (age-1)
16 LMB early-hatched (adult)
17 killifish/ topminnows
18 sunfish
19 generalists
20 benthic fish
21 crustaceans
22 insects
23 zooplankton
24 macrophytes
25 phytoplankton
26 detritus


=0.01






<0.01




<0.01


0.05 0.20 0.30 0.15 0.01
0.20 0.25 0.40 0.68
0.02 0.10 0.10 0.15 0.06
0.05 0.20 0.03
0.03 0.15 0.15 0.10 0.18 0.05 0.20
0.70 0.35 0.10 <0.01 0.70 0.60 0.30 0.60 0.20
0.20 0.06 0.25 0.2 0.60 0.20 0.20 0.30 0.10
0.40 0.90
0.10 0.10
0.1 0.2 0.70




































APPENDIX B
SENSITIVITY ANALYSIS RESULTS FOR ECOPATH MODELS


















Group parameter
1 Biom
1 Biom
1 Biom
1 Biom
1 Biom
1 Biom
1 Biom
1 Biom
1 Prod/biom
1 Cons/biom
1 Cons/biom
1 Cons/biom
1 Cons/biom
1 Cons/biom
1 Cons/biom
1 Cons/biom
2 Biom
2 Biom
2 Prod/biom
2 Cons/biom
3 Biom
3 Biom
3 Biom
3 Biom
3 Prod/biom
3 Cons/biom
3 Cons/biom
3 Cons/biom
4 Biom
4 Biom
4 Biom
4 Prod/biom
4 Cons/biom
4 Cons/biom
5 Biom
5 Biom
5 Biom
5 Biom
5 Biom
5 Biom
5 Biom


affected parameter
1 EE
3 EE
4 EE
7 EE
8 EE
11 EE
12 EE
14 EE
1 EE
3 EE
4 EE
7 EE
8 EE
11 EE
12 EE
14 EE
2 EE
14 EE
2 EE
14 EE
3 EE
15 EE
16 EE
17 EE
3 EE
15 EE
16 EE
17 EE
4 EE
14 EE
17 EE
4 EE
14 EE
17 EE
1 EE
3 EE
5 EE
7 EE
11 EE
15 EE
16 EE


-50 -40 -30 -20 -10
1.00 0.67 0.43 0.25 0.11
-0.30 -0.24 -0.18 -0.12 -0.06
-0.50 -0.40 -0.30 -0.20 -0.10
-0.30 -0.24 -0.18 -0.12 -0.06
-0.50 -0.40 -0.30 -0.20 -0.10
-0.29 -0.23 -0.17 -0.11 -0.06
-0.50 -0.40 -0.30 -0.20 -0.10
-0.05 -0.04 -0.03 -0.02 -0.01
1.00 0.67 0.43 0.25 0.11
-0.30 -0.24 -0.18 -0.12 -0.06
-0.50 -0.40 -0.30 -0.20 -0.10
-0.30 -0.24 -0.18 -0.12 -0.06
-0.50 -0.40 -0.30 -0.20 -0.10
-0.29 -0.23 -0.17 -0.11 -0.06
-0.50 -0.40 -0.30 -0.20 -0.10
-0.05 -0.04 -0.03 -0.02 -0.01
1.00 0.67 0.43 0.25 0.11
-0.06 -0.05 -0.04 -0.02 -0.01
1.00 0.67 0.43 0.25 0.11
-0.06 -0.05 -0.04 -0.02 -0.01
1.00 0.67 0.43 0.25 0.11
-0.06 -0.05 -0.03 -0.02 -0.01
-0.09 -0.07 -0.06 -0.04 -0.02
-0.06 -0.05 -0.04 -0.03 -0.01
1.00 0.67 0.43 0.25 0.11
-0.06 -0.05 -0.03 -0.02 -0.01
-0.09 -0.07 -0.06 -0.04 -0.02
-0.06 -0.05 -0.04 -0.03 -0.01
1.00 0.67 0.43 0.25 0.11
-0.05 -0.04 -0.03 -0.02 -0.01
-0.09 -0.07 -0.05 -0.04 -0.02
1.00 0.67 0.43 0.25 0.11
-0.05 -0.04 -0.03 -0.02 -0.01
-0.09 -0.07 -0.05 -0.04 -0.02
-0.18 -0.14 -0.11 -0.07 -0.04
-0.07 -0.06 -0.04 -0.03 -0.01
1.00 0.67 0.43 0.25 0.11
-0.07 -0.06 -0.04 -0.03 -0.01
-0.08 -0.06 -0.05 -0.03 -0.02
-0.09 -0.07 -0.05 -0.04 -0.02
-0.04 -0.03 -0.02 -0.02 -0.01


10 20 30
-0.09 -0.17 -0.23
0.06 0.12 0.18
0.10 0.20 0.30
0.06 0.12 0.18
0.10 0.20 0.30
0.06 0.11 0.17
0.10 0.20 0.30
0.01 0.02 0.03
-0.09 -0.17 -0.23
0.06 0.12 0.18
0.10 0.20 0.30
0.06 0.12 0.18
0.10 0.20 0.30
0.06 0.11 0.17
0.10 0.20 0.30
0.01 0.02 0.03
-0.09 -0.17 -0.23
0.01 0.02 0.04
-0.09 -0.17 -0.23
0.01 0.02 0.04
-0.09 -0.17 -0.23
0.01 0.02 0.03
0.02 0.04 0.06
0.01 0.03 0.04
-0.09 -0.17 -0.23
0.01 0.02 0.03
0.02 0.04 0.06
0.01 0.03 0.04
-0.09 -0.17 -0.23
0.01 0.02 0.03
0.02 0.04 0.05
-0.09 -0.17 -0.23
0.01 0.02 0.03
0.02 0.04 0.05
0.04 0.07 0.11
0.01 0.03 0.04
-0.09 -0.17 -0.23
0.01 0.03 0.04
0.02 0.03 0.05
0.02 0.04 0.05
0.01 0.02 0.02


40
-0.29
0.24
0.40
0.24
0.40
0.23
0.40
0.04
-0.29
0.24
0.40
0.24
0.40
0.23
0.40
0.04
-0.29
0.05
-0.29
0.05
-0.29
0.05
0.07
0.05
-0.29
0.05
0.07
0.05
-0.29
0.04
0.07
-0.29
0.04
0.07
0.14
0.06
-0.29
0.06
0.06
0.07
0.03


50
-0.33
0.30
0.50
0.30
0.50
0.29
0.50
0.05
-0.33
0.30
0.50
0.30
0.50
0.29
0.50
0.05
-0.33
0.06
-0.33
0.06
-0.33
0.06
0.09
0.06
-0.33
0.06
0.09
0.06
-0.33
0.05
0.09
-0.33
0.05
0.09
0.18
0.07
-0.33
0.07
0.08
0.09
0.04


Table B-1. Sensitivity analysis for north region Ecopath model

Input Group Estimated Input parameter variation


0

















Group parameter
5 Prod/biom
5 Cons/biom
5 Cons/biom
5 Cons/biom
5 Cons/biom
5 Cons/biom
5 Cons/biom
6 Biom
6 Biom
6 Prod/biom
6 Cons/biom
7 Biom
7 Biom
7 Biom
7 Biom
7 Prod/biom
7 Cons/biom
7 Cons/biom
7 Cons/biom
8 Biom
8 Biom
8 Biom
8 Prod/biom
8 Cons/biom
8 Cons/biom
9 Biom
9 Biom
9 Biom
9 Biom
9 Biom
9 Biom
9 Biom
9 Prod/biom
9 Cons/biom
9 Cons/biom
9 Cons/biom
9 Cons/biom
9 Cons/biom
9 Cons/biom
10 Biom
10 Biom
10 Prod/biom
10 Cons/biom


affected parameter
5 EE
1 EE
3 EE
7 EE
11 EE
15 EE
16 EE
6 EE
14 EE
6 EE
14 EE
7 EE
15 EE
16 EE
17 EE
7 EE
15 EE
16 EE
17 EE
8 EE
14 EE
17 EE
8 EE
14 EE
17 EE
1 EE
3 EE
7 EE
9 EE
11 EE
15 EE
16 EE
9 EE
1 EE
3 EE
7 EE
11 EE
15 EE
16 EE
10 EE
14 EE
10 EE
14 EE


-50 -40 -30 -20 -10
1.00 0.67 0.43 0.25 0.11
-0.18 -0.14 -0.11 -0.07 -0.04
-0.07 -0.06 -0.04 -0.03 -0.01
-0.07 -0.06 -0.04 -0.03 -0.01
-0.08 -0.06 -0.05 -0.03 -0.02
-0.09 -0.07 -0.05 -0.04 -0.02
-0.04 -0.03 -0.02 -0.02 -0.01
1.00 0.67 0.43 0.25 0.11
-0.12 -0.09 -0.07 -0.05 -0.02
1.00 0.67 0.43 0.25 0.11
-0.12 -0.09 -0.07 -0.05 -0.02
1.00 0.67 0.43 0.25 0.11
-0.05 -0.04 -0.03 -0.02 -0.01
-0.09 -0.07 -0.05 -0.04 -0.02
-0.06 -0.05 -0.04 -0.02 -0.01
1.00 0.67 0.43 0.25 0.11
-0.05 -0.04 -0.03 -0.02 -0.01
-0.09 -0.07 -0.05 -0.04 -0.02
-0.06 -0.05 -0.04 -0.02 -0.01
1.00 0.67 0.43 0.25 0.11
-0.05 -0.04 -0.03 -0.02 -0.01
-0.08 -0.07 -0.05 -0.03 -0.02
1.00 0.67 0.43 0.25 0.11
-0.05 -0.04 -0.03 -0.02 -0.01
-0.08 -0.07 -0.05 -0.03 -0.02
-0.17 -0.13 -0.10 -0.07 -0.03
-0.07 -0.05 -0.04 -0.03 -0.01
-0.07 -0.05 -0.04 -0.03 -0.01
1.00 0.67 0.43 0.25 0.11
-0.07 -0.06 -0.04 -0.03 -0.01
-0.08 -0.07 -0.05 -0.03 -0.02
-0.04 -0.03 -0.02 -0.02 -0.01
1.00 0.67 0.43 0.25 0.11
-0.17 -0.13 -0.10 -0.07 -0.03
-0.07 -0.05 -0.04 -0.03 -0.01
-0.07 -0.05 -0.04 -0.03 -0.01
-0.07 -0.06 -0.04 -0.03 -0.01
-0.08 -0.07 -0.05 -0.03 -0.02
-0.04 -0.03 -0.02 -0.02 -0.01
1.00 0.67 0.43 0.25 0.11
-0.08 -0.07 -0.05 -0.03 -0.02
1.00 0.67 0.43 0.25 0.11
-0.08 -0.07 -0.05 -0.03 -0.02


10 20 30 40 50
-0.09 -0.17 -0.23 -0.29 -0.33
0.04 0.07 0.11 0.14 0.18
0.01 0.03 0.04 0.06 0.07
0.01 0.03 0.04 0.06 0.07
0.02 0.03 0.05 0.06 0.08
0.02 0.04 0.05 0.07 0.09
0.01 0.02 0.02 0.03 0.04
-0.09 -0.17 -0.23 -0.29 -0.33
0.02 0.05 0.07 0.09 0.12
-0.09 -0.17 -0.23 -0.29 -0.33
0.02 0.05 0.07 0.09 0.12
-0.09 -0.17 -0.23 -0.29 -0.33
0.01 0.02 0.03 0.04 0.05
0.02 0.04 0.05 0.07 0.09
0.01 0.02 0.04 0.05 0.06
-0.09 -0.17 -0.23 -0.29 -0.33
0.01 0.02 0.03 0.04 0.05
0.02 0.04 0.05 0.07 0.09
0.01 0.02 0.04 0.05 0.06
-0.09 -0.17 -0.23 -0.29 -0.33
0.01 0.02 0.03 0.04 0.05
0.02 0.03 0.05 0.07 0.08
-0.09 -0.17 -0.23 -0.29 -0.33
0.01 0.02 0.03 0.04 0.05
0.02 0.03 0.05 0.07 0.08
0.03 0.07 0.10 0.13 0.17
0.01 0.03 0.04 0.05 0.07
0.01 0.03 0.04 0.05 0.07
-0.09 -0.17 -0.23 -0.29 -0.33
0.01 0.03 0.04 0.06 0.07
0.02 0.03 0.05 0.07 0.08
0.01 0.02 0.02 0.03 0.04
-0.09 -0.17 -0.23 -0.29 -0.33
0.03 0.07 0.10 0.13 0.17
0.01 0.03 0.04 0.05 0.07
0.01 0.03 0.04 0.05 0.07
0.01 0.03 0.04 0.06 0.07
0.02 0.03 0.05 0.07 0.08
0.01 0.02 0.02 0.03 0.04
-0.09 -0.17 -0.23 -0.29 -0.33
0.02 0.03 0.05 0.07 0.08
-0.09 -0.17 -0.23 -0.29 -0.33
0.02 0.03 0.05 0.07 0.08


Table B-1. Continued

Input Group Estimated Input parameter variation


0
















Input parameter variation
-50 -40 -30 -20 -10 0 10 20 30 40 50
1.00 0.67 0.43 0.25 0.11 0.00 -0.09 -0.17 -0.23 -0.29 -0.33
-0.05 -0.04 -0.03 -0.02 -0.01 0.00 0.01 0.02 0.03 0.04 0.05
-0.08 -0.07 -0.05 -0.03 -0.02 0.00 0.02 0.03 0.05 0.07 0.08
-0.06 -0.05 -0.03 -0.02 -0.01 0.00 0.01 0.02 0.03 0.05 0.06
1.00 0.67 0.43 0.25 0.11 0.00 -0.09 -0.17 -0.23 -0.29 -0.33
-0.05 -0.04 -0.03 -0.02 -0.01 0.00 0.01 0.02 0.03 0.04 0.05
-0.08 -0.07 -0.05 -0.03 -0.02 0.00 0.02 0.03 0.05 0.07 0.08
-0.06 -0.05 -0.03 -0.02 -0.01 0.00 0.01 0.02 0.03 0.05 0.06
1.00 0.67 0.43 0.25 0.11 0.00 -0.09 -0.17 -0.23 -0.29 -0.33
-0.05 -0.04 -0.03 -0.02 -0.01 0.00 0.01 0.02 0.03 0.04 0.05
-0.08 -0.06 -0.05 -0.03 -0.02 0.00 0.02 0.03 0.05 0.06 0.08
1.00 0.67 0.43 0.25 0.11 0.00 -0.09 -0.17 -0.23 -0.29 -0.33
-0.05 -0.04 -0.03 -0.02 -0.01 0.00 0.01 0.02 0.03 0.04 0.05
-0.08 -0.06 -0.05 -0.03 -0.02 0.00 0.02 0.03 0.05 0.06 0.08
-0.16 -0.13 -0.09 -0.06 -0.03 0.00 0.03 0.06 0.09 0.13 0.16
-0.06 -0.05 -0.04 -0.03 -0.01 0.00 0.01 0.03 0.04 0.05 0.06
-0.06 -0.05 -0.04 -0.03 -0.01 0.00 0.01 0.03 0.04 0.05 0.06
-0.07 -0.05 -0.04 -0.03 -0.01 0.00 0.01 0.03 0.04 0.05 0.07
1.00 0.67 0.43 0.25 0.11 0.00 -0.09 -0.17 -0.23 -0.29 -0.33
-0.08 -0.06 -0.05 -0.03 -0.02 0.00 0.02 0.03 0.05 0.06 0.08
-0.04 -0.03 -0.02 -0.01 -0.01 0.00 0.01 0.01 0.02 0.03 0.04
1.00 0.67 0.43 0.25 0.11 0.00 -0.09 -0.17 -0.23 -0.29 -0.33
-0.16 -0.13 -0.09 -0.06 -0.03 0.00 0.03 0.06 0.09 0.13 0.16
-0.06 -0.05 -0.04 -0.03 -0.01 0.00 0.01 0.03 0.04 0.05 0.06
-0.06 -0.05 -0.04 -0.03 -0.01 0.00 0.01 0.03 0.04 0.05 0.06
-0.07 -0.05 -0.04 -0.03 -0.01 0.00 0.01 0.03 0.04 0.05 0.07
-0.08 -0.06 -0.05 -0.03 -0.02 0.00 0.02 0.03 0.05 0.06 0.08
-0.04 -0.03 -0.02 -0.01 -0.01 0.00 0.01 0.01 0.02 0.03 0.04
1.00 0.67 0.43 0.25 0.11 0.00 -0.09 -0.17 -0.23 -0.29 -0.33
-0.04 -0.04 -0.03 -0.02 -0.01 0.00 0.01 0.02 0.03 0.04 0.04
-0.04 -0.03 -0.02 -0.02 -0.01 0.00 0.01 0.02 0.02 0.03 0.04
-0.04 -0.03 -0.02 -0.02 -0.01 0.00 0.01 0.02 0.02 0.03 0.04
1.00 0.67 0.43 0.25 0.11 0.00 -0.09 -0.17 -0.23 -0.29 -0.33
-0.04 -0.04 -0.03 -0.02 -0.01 0.00 0.01 0.02 0.03 0.04 0.04
-0.04 -0.03 -0.02 -0.02 -0.01 0.00 0.01 0.02 0.02 0.03 0.04
-0.04 -0.03 -0.02 -0.02 -0.01 0.00 0.01 0.02 0.02 0.03 0.04
1.00 0.67 0.43 0.25 0.11 0.00 -0.09 -0.17 -0.23 -0.29 -0.33
-0.43 -0.34 -0.26 -0.17 -0.09 0.00 0.09 0.17 0.26 0.34 0.43
-0.25 -0.20 -0.15 -0.10 -0.05 0.00 0.05 0.10 0.15 0.20 0.25
-0.22 -0.18 -0.13 -0.09 -0.04 0.00 0.04 0.09 0.13 0.18 0.22
-0.22 -0.17 -0.13 -0.09 -0.04 0.00 0.04 0.09 0.13 0.17 0.22
1.00 0.67 0.43 0.25 0.11 0.00 -0.09 -0.17 -0.23 -0.29 -0.33
-0.43 -0.34 -0.26 -0.17 -0.09 0.00 0.09 0.17 0.26 0.34 0.43


Table B-1. Continued


Input
Group parameter
11 Biom
11 Biom
11 Biom
11 Biom
11 Prod/biom
11 Cons/biom
11 Cons/biom
11 Cons/biom
12 Biom
12 Biom
12 Biom
12 Prod/biom
12 Cons/biom
12 Cons/biom
13 Biom
13 Biom
13 Biom
13 Biom
13 Biom
S13 Biom
S13 Biom
13 Prod/biom
13 Cons/biom
13 Cons/biom
13 Cons/biom
13 Cons/biom
13 Cons/biom
13 Cons/biom
14 Biom
14 Biom
14 Biom
14 Biom
14 Prod/biom
14 Cons/biom
14 Cons/biom
14 Cons/biom
15 Biom
15 Biom
15 Biom
15 Biom
15 Biom
15 Prod/biom
15 Cons/biom


Group Estimated
affected parameter
11 EE
15 EE
16 EE
17 EE
11 EE
15 EE
16 EE
17 EE
12 EE
14 EE
17 EE
12 EE
14 EE
17 EE
1 EE
3 EE
7 EE
11 EE
13 EE
15 EE
16 EE
13 EE
1 EE
3 EE
7 EE
11 EE
15 EE
16 EE
14 EE
19 Biom
20 Biom
21 Cons/biom
14 EE
19 Biom
20 Biom
21 Cons/biom
15 EE
18 EE
19 Biom
20 Biom
21 Cons/biom
15 EE
18 EE
















Input
Group parameter
15 Cons biom
15 Cons biom
15 Cons biom
16 Biom
16 Biom
16 Prod biom
16 Cons biom
17 Biom
17 Biom
17 Biom
17 Biom
17 Prod biom
17 Cons biom
17 Cons biom
17 Cons biom
18 Biom
18 Biom
18 Prod biom
18 Cons biom
O 9 Po im
S19 Prod biom
19 Prod biom
19 Cronsbiom
19 Cons biom
19 EE /io
19 EE
19 EE

20 Prod biom
20 EE
21 Biom
21 Prod biom


Group Estimated
affected parameter
19 Biom
20 Biom
21 Cons biom
16 EE
20 Biom
16 EE
20 Biom
17 EE
19 Biom
20 Biom
21 Cons biom
17 EE
19 Biom
20 Biom
21 Cons biom
18 EE
21 Cons biom
18 EE
21 Cons biom
19 Biom
20 Biom
21 Cons biom
20 Biom
21 Cons biom
19 Biom
20 Biom
21 Cons biom
20 Biom
20 Biom
21 Cons biom
21 Cons biom


Input parameter variation


-50 -40 -30 -20 -10 0
-0.25 -0.20 -0.15 -0.10 -0.05 0.00
-0.22 -0.18 -0.13 -0.09 -0.04 0.00
-0.22 -0.17 -0.13 -0.09 -0.04 0.00
1.00 0.67 0.43 0.25 0.11 0.00
-0.09 -0.07 -0.06 -0.04 -0.02 0.00
1.00 0.67 0.43 0.25 0.11 0.00
-0.09 -0.07 -0.06 -0.04 -0.02 0.00
1.00 0.67 0.43 0.25 0.11 0.00
-0.17 -0.14 -0.10 -0.07 -0.03 0.00
-0.15 -0.12 -0.09 -0.06 -0.03 0.00
-0.14 -0.12 -0.09 -0.06 -0.03 0.00
1.00 0.67 0.43 0.25 0.11 0.00
-0.17 -0.14 -0.10 -0.07 -0.03 0.00
-0.15 -0.12 -0.09 -0.06 -0.03 0.00
-0.14 -0.12 -0.09 -0.06 -0.03 0.00
1.00 0.67 0.43 0.25 0.11 0.00
-0.07 -0.06 -0.04 -0.03 -0.02 0.00
1.00 0.67 0.43 0.25 0.11 0.00
-0.07 -0.06 -0.04 -0.03 -0.02 0.00
1.00 0.67 0.43 0.25 0.11 0.00
0.45 0.30 0.19 0.11 0.05 0.00
0.85 0.57 0.37 0.21 0.10 0.00
-0.23 -0.18 -0.14 -0.09 -0.05 0.00
-0.43 -0.34 -0.26 -0.17 -0.09 0.00
1.00 0.67 0.43 0.25 0.11 0.00
0.45 0.30 0.19 0.11 0.05 0.00
0.85 0.57 0.37 0.21 0.10 0.00
1.00 0.67 0.43 0.25 0.11 0.00
1.00 0.67 0.43 0.25 0.11 0.00
1.00 0.67 0.43 0.25 0.11 0.00
1.00 0.67 0.43 0.25 0.11 0.00


10 20 30 40 50
0.05 0.10 0.15 0.20 0.25
0.04 0.09 0.13 0.18 0.22
0.04 0.09 0.13 0.17 0.22
-0.09 -0.17 -0.23 -0.29 -0.33
0.02 0.04 0.06 0.07 0.09
-0.09 -0.17 -0.23 -0.29 -0.33
0.02 0.04 0.06 0.07 0.09
-0.09 -0.17 -0.23 -0.29 -0.33
0.03 0.07 0.10 0.14 0.17
0.03 0.06 0.09 0.12 0.15
0.03 0.06 0.09 0.12 0.14
-0.09 -0.17 -0.23 -0.29 -0.33
0.03 0.07 0.10 0.14 0.17
0.03 0.06 0.09 0.12 0.15
0.03 0.06 0.09 0.12 0.14
-0.09 -0.17 -0.23 -0.29 -0.33
0.02 0.03 0.04 0.06 0.07
-0.09 -0.17 -0.23 -0.29 -0.33
0.02 0.03 0.04 0.06 0.07
-0.09 -0.17 -0.23 -0.29 -0.33
-0.04 -0.08 -0.10 -0.13 -0.15
-0.08 -0.14 -0.20 -0.24 -0.28
0.05 0.09 0.14 0.18 0.23
0.09 0.17 0.26 0.34 0.43
-0.09 -0.17 -0.23 -0.29 -0.33
-0.04 -0.08 -0.10 -0.13 -0.15
-0.08 -0.14 -0.20 -0.24 -0.28
-0.09 -0.17 -0.23 -0.29 -0.33
-0.09 -0.17 -0.23 -0.29 -0.33
-0.09 -0.17 -0.23 -0.29 -0.33
-0.09 -0.17 -0.23 -0.29 -0.33


Table B-1. Continued















Input parameter variation


-40 -30 -20 -10
-D.07 -D.05 -0.03 -0.02
-0.28 -0.21 -0.14 -0.07
-1.40 -D.30 -0.20 -0.10
-0.19 -0.14 -0.10 -0.05
-1.40 -D.30 -0.20 -0.10
-0.28 -0.21 -0.14 -0.07
-1.40 -D.30 -0.20 -D.10
-D.06 -D.05 -0.03 -D.02
-1.40 -D.30 -0.20 -D.10
-0.28 -0.21 -0.14 -D.07
-1.40 -D.30 -0.20 -D.10
-D.06 -D.04 -0.03 -0.01
-D.04 -D.03 -0.02 -0.01
-D.11 -D.08 -0.05 -D.03
-D.09 -D.07 -0.05 -D.02
0.67 0.43 0.25 0.11
-D.07 -D.05 -0.03 -D.02
-0.28 -0.21 -0.14 -D.07
-1.40 -D.30 -0.20 -D.10
-0.19 -0.14 -0.10 -0.05
-1.40 -D.30 -0.20 -D.10
-0.28 -0.21 -0.14 -D.07
-0.40 -D.30 -D.20 -0.10
-0.06 -D.05 -D.03 -0.02
-0.40 -D.30 -D.20 -0.10
-0.28 -0.21 -D.14 -0.07
-0.40 -D.30 -D.20 -0.10
-0.06 -D.04 -D.03 -0.01
-0.04 -D.03 -D.02 -0.01
-1.11 -D.08 -D.05 -0.03
-0.09 -D.07 -D.05 -0.02
0.67 0.43 0.25 0.11
0.67 0.43 0.25 0.11
0.67 0.43 0.25 0.11
0.67 0.43 0.25 0.11
0.67 0.43 0.25 0.11
-D.04 -D.03 -0.02 -0.01
0.67 0.43 0.25 0.11
-0.04 -D.03 -D.02 -0.01


10
0.02
0.07
0.10
0.05
0.10
0.07
0.10
0.02
0.10
0.07
0.10
0.01
0.01
0.03
0.02
-0.09
0.02
0.07
0.10
0.05
0.10
0.07
0.10
0.02
0.10
0.07
0.10
0.01
0.01
0.03
0.02
-0.09
-0.09
-0.09
-0.09
-0.09
0.01
-0.09
0.01


20
0.03
0.14
0.20
0.10
0.20
0.14
0.20
0.03
0.20
0.14
0.20
0.03
0.02
0.05
0.05
-0.17
0.03
0.14
0.20
0.10
0.20
0.14
0.20
0.03
0.20
0.14
0.20
0.03
0.02
0.05
0.05
-D.17
-0.17
-D.17
-0.17
-D.17
0.02
-0.17
0.02


30
0.05
0.11
0.30
0.14
0.30
0.11
0.30
0.05
0.30
0.11
0.30
0.04
0.03
0.08
0.07
-0.23
0.05
0.11
0.30
0.14
0.30
0.11
0.30
0.05
0.30
0.11
0.30
0.04
0.03
0.08
0.07
-D.23
-0.23
-D.23
-0.23
-D.23
0.03
-0.23
0.03


40
0.07
0128
0.40
0.19
0.40
0128
0.40
0.06
0.40
0128
0.40
0.06
0.04
0.11
0.09
-0.29
0.07
0128
0.40
0.19
0.40
0128
0.40
0.06
0.40
0128
0.40
0.06
0.04
0.11
0.09
-1.29
-0.29
-1.29
-0.29
-1.29
0.04
-0.29
0.04


Table B-2. Sensitivity analysis for south region Ecopath model


Input Group Estimated
Group parameter affected parameter
1Bicmi 3 EE
1Bicmi 4 EE
1 Bicmi 5 EE
1Biom 7 EE
1 Bicmi 8 EE
1Bicmi 9 EE
1 Bicmi 10 EE
1Bicmi 12 EE
1 Bicmi 13 EE
1Bicmi 14 EE
1 Bicmi 15 EE
1Bicmi 17 EE
1 Bicmi 18 EE
1Bicmi 19 EE
1 Bicmi 20 EE
1Prod/biom 1 EE
1 Cons/biom 3 EE
1Cons/biom 4 EE
1 Cons/biom 5 EE
1Cons/biom 7 EE
1 Cons/biom 8 EE
1 Cons/biom 9 EE
1Cons/biom 10 EE
1 Cons/biom 12 EE
1Cons/biom 13 EE
1 Cons/biom 14 EE
1Cons/biom 15 EE
1 Cons/biom 17 EE
1Cons/biom 18 EE
1 Cons/biom 19 EE
1Cons/biom 20 EE
3 Bicmi 3 EE
3 Prod/biom 3 EE
4 Bicmi 4 EE
4 Prod/biom 4 EE
5 Bicmi 5 EE
5 Bicmi 20 EE
5 Prod/biom 5 EE
5 Cons/biom 20 EE













Table B-2. Continued

Input Group Estimated


Inout v~arameter variation


affected parameter -50
18 EE -0.05
6 EE 1.00
1EE -0.10
18 EE -0.05
7 EE 1.00
7 EE 1.00
8 EE 1.00
17 EE -0.04
8 EE 1.00
17 EE -0.04
9 EE 1.00
17 EE -0.10
18 EE -0.04
19 EE -0.07
20 EE -0.05
9 EE 1.00
17 EE -0.10
18 EE -0.04
19 EE -0.07
20 EE -0.05
10 EE 1.00
17 EE -0.05
18 EE -0.04
19 EE -0.06
20 EE -0.11
10 EE 1.00
17 EE -0.05
18 EE -0.04
19 EE -0.06
20 EE -0.11
1EE -0.24
4 EE -0.07


Group parameter
6 Biom
6 Prod/biom
6 Cons/biom
6 Cons/biom
7 Biom
7 Prod/biom
8 Biom
8 Biom
8 Prod/biom
8 Cons/biom
9 Biom
9 Biom
9 Biom
9 Biom
9 Biom
9 Prod/biom
9 Cons/biom
9 Cons/biom
9 Cons/biom
9 Cons/biom
10 Biom
10 Biom
10 Biom
10 Biom
10 Biom
10 Prod/biom
10 Cons/biom
10 Cons/biom
10 Cons/biom
10 Cons/biom
11 Biom
11 Biom


-40
-0.04
0.67
-0.08
-0.04
0.67
0.67
0.67
-0.03
0.67
-0.03
0.67
-0.08
-0.03
-0.05
-0.04
0.67
-0.08
-0.03
-0.05
-0.04
0.67
-0.04
-0.04
-0.05
-0.09
0.67
-0.04
-0.04
-0.05
-0.09
-0.19
-0.06


-30 -20 -10
-0.03 -0.02 -0.01
0.43 0.25 0.11
-0.06 -0.04 -0.02
-0.03 -0.02 -0.01
0.43 0.25 0.11
0.43 0.25 0.11
0.43 0.25 0.11
-0.02 -0.02 -0.01
0.43 0.25 0.11
-0.02 -0.02 -0.01
0.43 0.25 0.11
-0.06 -0.04 -0.02
-0.02 -0.02 -0.01
-0.04 -0.03 -0.01
-0.03 -0.02 -0.01
0.43 0.25 0.11
-0.06 -0.04 -0.02
-0.02 -0.02 -0.01
-0.04 -0.03 -0.01
-0.03 -0.02 -0.01
0.43 0.25 0.11
-0.03 -0.02 -0.01
-0.03 -0.02 -0.01
-0.04 -0.02 -0.01
-0.07 -0.05 -0.02
0.43 0.25 0.11
-0.03 -0.02 -0.01
-0.03 -0.02 -0.01
-0.04 -0.02 -0.01
-0.07 -0.05 -0.02
-0.14 -0.10 -0.05
-0.04 -0.03 -0.01


0 10 20
0.00 0.01 0.02
0.00 -0.09 -0.17
0.00 0.02 0.04
0.00 0.01 0.02
0.00 -0.09 -0.17
0.00 -0.09 -0.17
0.00 -0.09 -0.17
0.00 0.01 0.02
0.00 -0.09 -0.17
0.00 0.01 0.02
0.00 -0.09 -0.17
0.00 0.02 0.04
0.00 0.01 0.02
0.00 0.01 0.03
0.00 0.01 0.02
0.00 -0.09 -0.17
0.00 0.02 0.04
0.00 0.01 0.02
0.00 0.01 0.03
0.00 0.01 0.02
0.00 -0.09 -0.17
0.00 0.01 0.02
0.00 0.01 0.02
0.00 0.01 0.02
0.00 0.02 0.05
0.00 -0.09 -0.17
0.00 0.01 0.02
0.00 0.01 0.02
0.00 0.01 0.02
0.00 0.02 0.05
0.00 0.05 0.10
0.00 0.01 0.03


50
0.05
-0.33
0.10
0.05
-0.33
-0.33
-0.33
0.04
-0.33
0.04
-0.33
0.10
0.04
0.07
0.05
-0.33
0.10
0.04
0.07
0.05
-0.33
0.05
0.04
0.06
0.11
-0.33
0.05
0.04
0.06
0.11
0.24
0.07

















affected parameter -50 -40 -30 -20 -10
14 EE -0.07 -0.06 -0.04 -0.03 -0.01
18 EE -0.13 -0.10 -0.08 -0.05 -0.03
19 EE -0.04 -0.03 -0.02 -0.02 -0.01
11 EE 1.00 0.67 0.43 0.25 0.11
1EE -0.24 -0.19 -0.14 -0.10 -0.05
4 EE -0.07 -0.06 -0.04 -0.03 -0.01
9 EE -0.07 -0.06 -0.04 -0.03 -0.01
14 EE -0.07 -0.06 -0.04 -0.03 -0.01
18 EE -0.13 -0.10 -0.08 -0.05 -0.03
19 EE -0.04 -0.03 -0.02 -0.02 -0.01
12 EE 1.00 0.67 0.43 0.25 0.11
12 EE 1.00 0.67 0.43 0.25 0.11
13 EE 1.00 0.67 0.43 0.25 0.11
17 EE -0.05 -0.04 -0.03 -0.02 -0.01
13 EE 1.00 0.67 0.43 0.25 0.11
17 EE -0.05 -0.04 -0.03 -0.02 -0.01
14 EE 1.00 0.67 0.43 0.25 0.11
17 EE -0.08 -0.07 -0.05 -0.03 -0.02
19 EE -0.04 -0.03 -0.02 -0.01 -0.01
14 EE 1.00 0.67 0.43 0.25 0.11
17 EE -0.08 -0.07 -0.05 -0.03 -0.02
19 EE -0.04 -0.03 -0.02 -0.01 -0.01
15 EE 1.00 0.67 0.43 0.25 0.11
17 EE -0.03 -0.03 -0.02 -0.01 -0.01
19 EE -0.04 -0.04 -0.03 -0.02 -0.01
20 EE -0.08 -0.07 -0.05 -0.03 -0.02
15 EE 1.00 0.67 0.43 0.25 0.11
17 EE -0.03 -0.03 -0.02 -0.01 -0.01
19 EE -0.04 -0.04 -0.03 -0.02 -0.01
20 EE -0.08 -0.07 -0.05 -0.03 -0.02
1EE -0.16 -0.13 -0.09 -0.06 -0.03
4 EE -0.05 -0.04 -0.03 -0.02 -0.01
9 EE -0.05 -0.04 -0.03 -0.02 -0.01
14 EE -0.05 -0.04 -0.03 -0.02 -0.01
16 EE 1.00 0.67 0.43 0.25 0.11
18 EE -0.08 -0.07 -0.05 -0.03 -0.02
16 EE 1.00 0.67 0.43 0.25 0.11
1EE -0.16 -0.13 -0.09 -0.06 -0.03
4 EE -0.05 -0.04 -0.03 -0.02 -0.01
9 EE -0.05 -0.04 -0.03 -0.02 -0.01
14 EE -0.05 -0.04 -0.03 -0.02 -0.01
18 EE -0.08 -0.07 -0.05 -0.03 -0.02


0 10
0.00 0.01
0.00 0.03
0.00 0.01
0.00 -0.09
0.00 0.05
0.00 0.01
0.00 0.01
0.00 0.01
0.00 0.03
0.00 0.01
0.00 -0.09
0.00 -0.09
0.00 -0.09
0.00 0.01
0.00 -0.09
0.00 0.01
0.00 -0.09
0.00 0.02
0.00 0.01
0.00 -0.09
0.00 0.02
0.00 0.01
0.00 -0.09
0.00 0.01
0.00 0.01
0.00 0.02
0.00 -0.09
0.00 0.01
0.00 0.01
0.00 0.02
0.00 0.03
0.00 0.01
0.00 0.01
0.00 0.01
0.00 -0.09
0.00 0.02
0.00 -0.09
0.00 0.03
0.00 0.01
0.00 0.01
0.00 0.01
0.00 0.02


20
0.03
0.05
0.02
-0.17
0.10
0.03
0.03
0.03
0.05
0.02
-0.17
-0.17
-0.17
0.02
-0.17
0.02
-0.17
0.03
0.01
-0.17
0.03
0.01
-0.17
0.01
0.02
0.03
-0.17
0.01
0.02
0.03
0.06
0.02
0.02
0.02
-0.17
0.03
-0.17
0.06
0.02
0.02
0.02
0.03


40
0.06
0.10
0.03
-0.29
0.19
0.06
0.06
0.06
0.10
0.03
-0.29
-0.29
-0.29
0.04
-0.29
0.04
-0.29
0.07
0.03
-0.29
0.07
0.03
-0.29
0.03
0.04
0.07
-0.29
0.03
0.04
0.07
0.13
0.04
0.04
0.04
-0.29
0.07
-0.29
0.13
0.04
0.04
0.04
0.07


50
0.07
0.13
0.04
-0.33
0.24
0.07
0.07
0.07
0.13
0.04
-0.33
-0.33
-0.33
0.05
-0.33
0.05
-0.33
0.08
0.04
-0.33
0.08
0.04
-0.33
0.03
0.04
0.08
-0.33
0.03
0.04
0.08
0.16
0.05
0.05
0.05
-0.33
0.08
-0.33
0.16
0.05
0.05
0.05
0.08


Table B-2. Continued

Input Group Estimated


Input parameter variation


Group parameter
11 Biom
11 Biom
11 Biom
11 Prod biom
11 Cons biom
11 Cons biom
11 Cons biom
11 Cons biom
11 Cons biom
11 Cons biom
12 Biom
12 Prod biom
13 Biom
13 Biom
13 Prod biom
13 Cons biom
14 Biom
14 Biom
14 Biom
O 14 Prod biom
14 Cosbim
14 Cons biom
15 C sBiom
15 Biom
15 Biom
15 Biom

15 Prod biom
15 Cons biom
15 Cons biom
15 Cons biom
16 Biom
16 Biom
16 Biom
16 Biom
16 Biom
16 Biom
16 Prod biom
16 Cons biom
16 Cons biom
16 Cons biom
16 Cons biom
16 Cons biom














Table B-2. Continued

Input Group Estimated
Group parameter affected parameter
17 Biom 22 Biom
17 Biom 23 Biom
17 Biom 24 Cons/biom
17 Prod/biom 17 EE
17 Cons/biom 23 Biom
17 Cons/biom 24 Cons/biom
18 Biom 18 EE
18 Biom 21 EE
18 Biom 22 Biom
18 Biom 23 Biom
18 Biom 24 Cons/biom
18 Prod/biom 18 EE
18 Cons/biom 21 EE
18 Cons/biom 22 Biom
18 Cons/biom 23 Biom
18 Cons/biom 24 Cons/biom
19 Biom 19 EE
19 Biom 22 Biom
O 19 Biom 23 Biom
19 Biom 24 Cons/biom
19 Prod/biom 19 EE
19 Cons/biom 22 Biom
19 Cons/biom 23 Biom
19 Cons/biom 24 Cons/biom
20 Biom 20 EE
20 Biom 22 Biom
20 Biom 23 Biom
20 Biom 24 Cons/biom
20 Prod/biom 20 EE
20 Cons/biom 22 Biom
20 Cons/biom 23 Biom
20 Cons/biom 24 Cons/biom
21 Biom 21 EE
21 Biom 24 Cons/biom
21 Prod/biom 21 EE
21 Cons/biom 24 Cons/biom


Inout v~arameter variation


-50
-0.11
-0.10
-0.10
1.00
-0.10
-0.10
1.00
-0.41
-0.27
-0.22
-0.23
1.00
-0.41
-0.27
-0.22
-0.23
1.00
-0.05
-0.13
-0.04
1.00
-0.05
-0.13
-0.04
1.00
-0.06
-0.05
-0.05
1.00
-0.06
-0.05
-0.05
1.00
-0.08
1.00
-0.08


-40
-0.09
-0.08
-0.08
0.67
-0.08
-0.08
0.67
-0.33
-0.22
-0.17
-0.18
0.67
-0.33
-0.22
-0.17
-0.18
0.67
-0.04
-0.10
-0.03
0.67
-0.04
-0.10
-0.03
0.67
-0.05
-0.04
-0.04
0.67
-0.05
-0.04
-0.04
0.67
-0.06
0.67
-0.06


-30
-0.07
-0.06
-0.06
0.43
-0.06
-0.06
0.43
-0.25
-0.16
-0.13
-0.14
0.43
-0.25
-0.16
-0.13
-0.14
0.43
-0.03
-0.08
-0.02
0.43
-0.03
-0.08
-0.02
0.43
-0.03
-0.03
-0.03
0.43
-0.03
-0.03
-0.03
0.43
-0.05
0.43
-0.05


-20 -10
-0.05 -0.02
-0.04 -0.02
-0.04 -0.02
0.25 0.11
-0.04 -0.02
-0.04 -0.02
0.25 0.11
-0.16 -0.08
-0.11 -0.05
-0.09 -0.04
-0.09 -0.05
0.25 0.11
-0.16 -0.08
-0.11 -0.05
-0.09 -0.04
-0.09 -0.05
0.25 0.11
-0.02 -0.01
-0.05 -0.03
-0.02 -0.01
0.25 0.11
-0.02 -0.01
-0.05 -0.03
-0.02 -0.01
0.25 0.11
-0.02 -0.01
-0.02 -0.01
-0.02 -0.01
0.25 0.11
-0.02 -0.01
-0.02 -0.01
-0.02 -0.01
0.25 0.11
-0.03 -0.02
0.25 0.11
-0.03 -0.02


0 10 20
0.00 0.02 0.05
0.00 0.02 0.04
0.00 0.02 0.04
0.00 -0.09 -0.17
0.00 0.02 0.04
0.00 0.02 0.04
0.00 -0.09 -0.17
0.00 0.08 0.16
0.00 0.05 0.11
0.00 0.04 0.09
0.00 0.05 0.09
0.00 -0.09 -0.17
0.00 0.08 0.16
0.00 0.05 0.11
0.00 0.04 0.09
0.00 0.05 0.09
0.00 -0.09 -0.17
0.00 0.01 0.02
0.00 0.03 0.05
0.00 0.01 0.02
0.00 -0.09 -0.17
0.00 0.01 0.02
0.00 0.03 0.05
0.00 0.01 0.02
0.00 -0.09 -0.17
0.00 0.01 0.02
0.00 0.01 0.02
0.00 0.01 0.02
0.00 -0.09 -0.17
0.00 0.01 0.02
0.00 0.01 0.02
0.00 0.01 0.02
0.00 -0.09 -0.17
0.00 0.02 0.03
0.00 -0.09 -0.17
0.00 0.02 0.03


50
0.11
0.10
0.10
-0.33
0.10
0.10
-0.33
0.41
0.27
0.22
0.23
-0.33
0.41
0.27
0.22
0.23
-0.33
0.05
0.13
0.04
-0.33
0.05
0.13
0.04
-0.33
0.06
0.05
0.05
-0.33
0.06
0.05
0.05
-0.33
0.08
-0.33
0.08













Table B-2. Continued

Input Group Estimated Inpu
Group parameter affected parameter -50 -40 -30 -20 -10
22 Prod/biom 22 Biom 1.00 0.67 0.43 0.25 0.11
22 Prod/biom 23 Biom 0.42 0.28 0.18 0.11 0.05
22 Prod/biom 24 Cons/biom 0.85 0.57 0.36 0.21 0.09
22 Cons/biom 23 Biom -0.21 -0.17 -0.13 -0.08 -0.04
22 Cons/biom 24 Cons/biom -0.42 -0.34 -0.25 -0.17 -0.09
22 EE 22 Biom 1.00 0.67 0.43 0.25 0.11
22 EE 23 Biom 0.42 0.28 0.18 0.11 0.05
22 EE 24 Cons/biom 0.85 0.57 0.36 0.21 0.09
23 Prod/biom 23 Biom 1.00 0.67 0.43 0.25 0.11
23 EE 23 Biom 1.00 0.67 0.43 0.25 0.11
24 Biom 24 Cons/biom 1.00 0.67 0.43 0.25 0.11
24 Prod/biom 24 Cons/biom 1.00 0.67 0.43 0.25 0.11


t parameter variation
0 10 20 30 40 50
0.00 -0.09 -0.17 -0.23 -0.29 -0.33
0.00 -0.04 -0.07 -0.10 -0.12 -0.14
0.00 -0.08 -0.14 -0.20 -0.24 -0.28
0.00 0.04 0.08 0.13 0.17 0.21
0.00 0.09 0.17 0.25 0.34 0.42
0.00 -0.09 -0.17 -0.23 -0.29 -0.33
0.00 -0.04 -0.07 -0.10 -0.12 -0.14
0.00 -0.08 -0.14 -0.20 -0.24 -0.28
0.00 -0.09 -0.17 -0.23 -0.29 -0.33
0.00 -0.09 -0.17 -0.23 -0.29 -0.33
0.00 -0.09 -0.17 -0.23 -0.29 -0.33
0.00 -0.09 -0.17 -0.23 -0.29 -0.33










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BIOGRAPHICAL SKETCH

Mark Rogers was born to Wayne and Judy Rogers in fall, 1975, near Atlanta, GA. He

moved around the eastern US quite a bit while growing up, but finally settled in the piedmont of

North Carolina where he attended high school. This was an important move because it resulted

in Mark spending lots of time fishing on High Rock Lake and is likely the reason he is working

in fisheries. He decided to attend N.C. State University in fall, 1993, and majored in fisheries

and wildlife science. His advisor, Dr. Richard Noble, hired him as a technician and Mark was

sent me to Puerto Rico for a summer to work on tropical reservoirs. After college, he worked at

Virginia Tech on the king of the darters Percina rex under the supervision of Drs. Paul

Angermeier, Bill Ensign, and Brett Albanese. Thereafter, he taught high school science for two

years before deciding to attend a Master' s program. Mark received his Master's from the

University of Wisconsin-Stevens Point with Dr. Michael Hansen as his advisor. After

graduating in May, 2002, Mark moved to Gainesville, FL to work as a biological scientist for Dr.

Micheal Allen. He began his Ph.D. work in fall, 2003 and defended in fall, 2007. Mark feels

most fortunate to have lucked into the best three advisors he could have asked for during his

academic training (Drs. Noble, Hansen, and Allen). While working on his Ph.D., Mark married

Kristin (Henry) Rogers and they look forward to their future endeavors together.





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1 RECRUITMENT DYNAMICS OF AGE-0 LARGEMOUTH BASS ALONG A LA TITUDINAL GRADIENT OF FLORIDA LAKES By MARK WAYNE ROGERS A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLOR IDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY UNIVERSITY OF FLORIDA 2007

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2 2007 Mark Wayne Rogers

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3 To Kristin and our future together

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4 ACKNOWLEDGMENTS My research was funded by a grant from th e Florida Fish and Wildlife Conservation Commission (FWC) and I was supported by a UF CALS Alumni Fellowship. Other financial support was provided by the AFS Southern Divi sion Reservoir Committee Robert M. Jenkins Scholarship, FL Chapter of AFS Roger Rottman Scholarship, and travel grants through: UF IFAS, the UF Graduate Student Association, FL Chapter of AFS, and AFS Education Section Skinner Award. I thank my advisor, Dr. Michael Allen, and other graduate committ ee members, Dr. Debra Murie, Dr. Tom Frazer, Dr. Craig Os enberg, and Dr. Ramon Littell for their service. I especially thank Dr. Allen for his high expectations and effo rts to do everything he could do to benefit my student experiences. My resear ch would not have been possible without the assistance of the following FWC cooperators: John Benton, Rich Cailte aux, Jim Estes, Don Fox Beacham Furse, Steve Gornak, Jay Holder, Bill Johnson, Earl Lundy, Charlie Mesing, Wes Porak, and Andy Strickland. University of Florida students and staff: C. Barrientos, Mo Bennett, G. Binion, T. Bonvechio, P. Cooney, J. Dotson, D. Dutterer, P. Hall, G. Kaufman, V. Maceina, V. Politano, and N. Trippel conducted field sampling, labor atory sample and data processing. I owe tremendous thanks to my academic mentors: Dr Micheal Allen, Dr. Michael Hansen, and Dr. Richard Noble and greatly appreci ate the willingness of Dr. Carl Walters to share his time to assist with my dissertation wor k. I also thank Dr. Chuck Cichra Dr. Karl Havens, Mark Hoyer, and Dr. Bill Pine for enhancing my education. Most importantly I thank Kristin and our fa mily for their love and support during this process. I appreciate Kate Lazar’s friendship to my wife and willingness to entertain Kristin on all the weekends and nights that I was working. I also thank Trinity Un ited Methodist Church for helping me keep my life prioritized.

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5 TABLE OF CONTENTS page LIST OF TABLES................................................................................................................. ..........6 LIST OF FIGURES................................................................................................................ .........7 ABSTRACT....................................................................................................................... ..............8 CHAPTER 1 GENERAL INTRODUCTION..............................................................................................10 2 EXPLORING THE GENERALITY OF RECRUITMENT HYPOTHESES FOR LARGEMOUTH BASS ALONG A LATIT UDINAL GRADIENT OF FLORIDA LAKES.......................................................................................................................... .........12 Methods........................................................................................................................ ..........14 Results........................................................................................................................ .............19 Discussion..................................................................................................................... ..........23 3 SEPARATING GENETIC AND ENVIRONMENTAL INFLUENCES ON TEMPORAL SPAWNING DISTRIBU TIONS OF LARGEMOUTH BASS (Micropterus salmoides)........................................................................................................ .40 Methods........................................................................................................................ ..........43 Results........................................................................................................................ .............46 Discussion..................................................................................................................... ..........48 4 SIMULATED INFLUENCES OF HATCHI NG DATE SPECIFIC SURVIVAL ON RECRUITMENT OF LARGEMOUTH BASS......................................................................61 Methods........................................................................................................................ ..........62 Results........................................................................................................................ .............69 Discussion..................................................................................................................... ..........73 5 SYNTHESIS AND FUTURE RESEARCH...........................................................................88 Age-0 Largemouth Bass Recruitment....................................................................................88 Fisheries Management........................................................................................................... .89 Future Research................................................................................................................ ......90 APPENDIX A DIET COMPOSITION MATRIC ES FOR ECOPATH MODELS........................................93 B SENSITIVITY ANALYSIS RESULTS FOR ECOPATH MODELS...................................98 REFERENCE LIST................................................................................................................. ....108 BIOGRAPHICAL SKETCH.......................................................................................................121

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6 LIST OF TABLES Table page 2-1 Physical and chemical characteri stics of 6 Florida study lakes and genetic characteristics of their largemouth bass populations............................................................30 2-2 Dates corresponding to hatching periods, median hatch dates, and water temperatures (C) at corresponding median hatch date s for the 2003 and 2004 largemouth bass year classes at 6 Florida study lakes............................................................................................31 2-3 Mean daily growth rate, and standa rd deviation (SD) for age-0 largemouth bass collected in block nets during spring and summer of each year..........................................32 2-4 Analysis of variance results for su rvival comparisons among hatching periods and regions of Florida............................................................................................................. ....33 3-1 Earliest, median, and latest hatc h dates of Florida (L ake Okeechobee fish) and intergrade (Lake Seminole fish) largemouth bass (Micropterus salmoides) translocated to experimental ponds at Gainesville, Florida in 2004 and corresponding water temperatures................................................................................................................... ......56 4-1 Species composition of non-largemouth bass fish groups....................................................81 4-2 Ecopath inputs for a north Florida eutr ophic lake based on data from Lakes Seminole and Talquin collected in 2003 and 2004..............................................................................82 4-3 Ecopath inputs for a south Florida eutr ophic lake based on data from Lakes Istokpoga and Okeechobee collected in 2003 and 2004.......................................................................83 4-4 Ecopath estimates of diet niche overlap among age-0 largemouth bass hatching subcohorts........................................................................................................................ ..........84 A-1 Diet composition inputs for north region Ecopath model...................................................94 A-2 Diet composition inputs for south region Ecopath model...................................................96 B-1 Sensitivity analysis for north region Ecopath model...........................................................99 B-2 Sensitivity analysis for south region Ecopath model.........................................................103

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7 LIST OF FIGURES Figure page 2-1 Selected north region, central region, and south region study lakes....................................34 2-2 Relative frequency distributions of ag e-0 largemouth bass hatching at north, central, and south study lakes in 2003..............................................................................................35 2-3 Relative frequency distributions of ag e-0 largemouth bass hatching at north, central, and south study lakes in 2004..............................................................................................36 2-4 Relationship between mean daily growth rates and average temperatures from the 40day period following the median hatch date for early, middle, and late hatching periods from 6 Florida lakes during 2003 and 2004............................................................37 2-5 Length frequency dist ributions for 2003 fall and spring (~age-1) samples of age-0 largemouth bass collected by electrofishing at north, central, and south study lakes..........38 2-6 Length frequency dist ributions for 2004 fall and spring (~age-1) samples of age-0 largemouth bass collected by electrofishing at north, central, and south study lakes..........39 3-1 Locations and latitudes for Lake Seminol e, Lake Okeechobee and Gainesville, Florida, USA............................................................................................................................ ..........57 3-2 Five-day cohort percent hatching dist ribution for age-0 largemouth bass hatched in research ponds at Gainesville, Florida.................................................................................58 3-3 Five-day cohort percent hatching dist ribution for age-0 largemouth bass collected at Lakes Okeechobee and Seminole ........................................................................................59 3-4 Semi-monthly hatching di stributions for age-0 largemouth bass reared in research ponds in Gainesville, Florida and in sour ce populations at Lake Seminole and Lake Okeechobee in 2004............................................................................................................. 60 4-1 Representation of ecosystem flows for components of an Ecopath model consisting of three consumer groups and a detritus group su ch that predation on a group results in production for their predators...............................................................................................85 4-2 Representation of vulnerable and invulne rable states of prey functional group biomass and predator consumption....................................................................................................85 4-3 Percent biomass change imposed by simu lating variable hatch-date specific mortality relative to baseline Ecopath va lues for a north Florida lake................................................86 4-4 Percent biomass change imposed by simu lating variable hatch-date specific mortality relative to baseline Ecopath va lues for a south Florida lake................................................87

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8 Abstract of Dissertation Pres ented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy RECRUITMENT DYNAMICS OF AGE-0 LARGEMOUTH BASS ALONG A LA TITUDINAL GRADIENT OF FLORIDA LAKES By Mark Wayne Rogers December 2007 Chair: Micheal Allen Major: Fisheries and Aquatic Sciences Juvenile fish life history varies across larg e spatial gradients b ecause of latitudinal influences on growing season length and winter severity. I evaluated recruitment processes (e.g., hatching date, growth, and mortal ity) affecting largemouth bass Micropterus salmoides recruitment to age-1 across a lati tudinal gradient of Florida la kes and related my findings to results for this species from more northerly la titudes. I sampled the 2003 and 2004 year classes at six Florida lakes that spanned latitudes from N27o0’ to N30o5’. My first objective tested whether 1) early-hatching provided a growth and survival ad vantage relative to later-hatching through their first summer, and 2) whether ov erwinter size-selectiv e mortality strongly influenced recruitment to age-1 across Florida’s latitudinal gradient. My results did not fully conform to common hypotheses because early-hatched sub-cohorts (i.e., fish hatched at dates in the left tail of the overall hatc hing distribution) did not exhibit a growth and survival advantage at all lakes and I did not detect strong size-selective overwinter mortality. My second objective evaluated the relative co ntributions of genetic and environmental effects on spawning periodicity by rearing Florida largemouth bass M. s. floridanus from Lake Okeechobee in south Florida a nd intergrade largemouth bass M. s.salmoides x floridanus (or

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9 vice-versa) from Lake Seminole in north Florida in a similar environment in Gainesville, Florida. Results showed that Florida fish began spawning earlier than intergrade fish in all ponds and Florida fish had a longer spawning season than intergrade fish. Si milarly, Florida fish at Lake Okeechobee began spawning earlier and had a longer spawning season than intergrade fish at Lake Seminole. Thus, environmental factors in fluenced spawning periodicity for both genetic stocks, but spawning periodicity in ponds also reflected char acteristics of their source populations. My last objective explored implications of hatching date-dependent growth and mortality observations for age-0 largemouth bass to evalua te relative effects on recruitment to age-1. Modeling results showed that hatching date -dependent mortality could influence the contributions of differing hatching sub-cohorts to year class com position at age-1, but total age-1 and adult biomass was not largely affected. Thus, my models predicted large compensation potential and strong regulation fo r largemouth bass recruitment.

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10 CHAPTER I GENERAL INTRODUCTION Fisheries managers and ecologist struggle to understand fish recruitment because it is variable and many factors influence survival of age-0 fishes (Post et al. 1998). Factors influencing age-0 fish survival differ among la titudes owing to environmental influences (e.g., winter water temperatures) and can result in loca lized adaptations to a dult spawning strategies for maximizing offspring survival (Conover 1992) Thus, both genetic and environmental components likely contribute to adult spawning timing, but their relativ e contributions and implications for offspring survival are largel y unknown. Identifying pro cesses and factors that regulate and control age-0 survival facilitate s our ecological unders tanding of juvenile recruitment processes, adult reproductive timing, and fisherie s management across broad latitudinal gradients. The recreational importa nce and broad distributi on of largemouth bass have resulted in extensive resear ch and generalized hypotheses that identified important factors (e.g., hatching date and growth rate) that can lim it age-0 survival. Studies commonly concluded that age-0 largemouth bass hatching early in the spawning season had early-life advantages (e.g., lower mortality) and were more likely to survive during winter relative to later-hatched members of a year class (Miranda and Hubbard 1994 a ; Ludsin and DeVries 1997; Garvey et al. 1998), however all of these studies were conducted at latitudes where growing season length and winter water temperatures would likely strongly affect age-0 largemouth bass survival. Florida winters are mild relative to latitudes where other age-0 largemouth bass recruitments studies have been conducted (i.e ., Alabama to Wisconsin). Mild winters could strongly influence the onset and duration of spawning, growth, a nd the potential for overwinter mortality, thus suggesting that age-0 recruitment processes may differ for Florida populations relative to more northerly popul ations. I studied the 2003 and 2004 largemouth bass year classes

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11 at six Florida lakes distributed across three re gions (south, central, a nd north). I evaluated hatching seasons, growth, mortalit y, and size-selective overwinter mortality across a range of Florida latitudes and related my findings to popular largemouth bass recruitment hypotheses. I reported an experiment to evalua te the contributions of genetic and environmental factors to largemouth bass spawning season initi ation and duration with implica tions for evolution of adult spawning strategies across latitude s. I used a trophic-based ec osystem model to simulate how hatching date-dependent survival would influence within year-class interactions and year class strength, and related my findings to theory on th e evolution of parental spawning strategies and addressed implications for fisheries management.

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12 CHAPTER 2 EXPLORING THE GENERALITY OF RECRUITMENT HYPOTHESES FOR LARGEMOUTH BASS ALONG A LATITUDI NAL GRADIENT OF FLORIDA LAKES Aquatic ecologists and fisheries managers str uggle to understand fish recruitment because recruitment is inherently variab le, and both density dependent a nd independent factors influence pre-recruitment survival (Post et al. 1998). Environmenta l (e.g., temperature) and biological (e.g., energetics and predator-pre y relationships) factors intera ct to influence recruitment processes (e.g., hatching dates, growth, and mo rtality), but the relativ e strengths of those recruitment processes can vary greatly w ith latitude (Conover 1992; Garvey et al. 2002 a ). Establishing latitudinal patterns in hatching, growth, and survival of juvenile fish is a goal of ecologists (Garvey et al. 2002 a ) and facilitates unders tanding of species-speci fic variation in lifehistory traits across large spatial gradients (Conover 1992). Early-life survival in teleost fishes has been closely associ ated with body size (Miller et al. 1988), which is largely influe nced by hatching date and somatic growth rate (Conover 1992; Houde 1997). Spawning initiation generally occurs ear lier in the year at lo wer latitudes than at higher latitudes for broadly dist ributed species due to influen ces of temperature (Lam 1983; Conover 1992). Spawning strategies often reflect adaptiv e characteristics, such that spawning season durations are protracted at lower lat itudes relative to higher latitudes because of differences in growing season duration and the st rength of over-winter size-dependent mortality (Conover 1992). When spawning dist ributions are protracted, varia tion in conditions for earlyversus late-hatched sub-cohorts (i.e., fish hatched in a specific period within the total hatching distribution) can be large relative to systems that exhibit contracted spawning distributions, potentially magnifying differences in growth and survival among s ub-cohorts within a year class (Phillips et al. 1995; Cargnelli and Gross 1996; Narimatsu and Munehara 1999). Thus, temporal

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13 spawning patterns may result in di ffering strengths of factors re gulating recruitment processes among populations. Early-hatching often leads to large juvenile size by the end of the first growing season and higher survival relative to later-hatched members of a year class (e.g., Trebitz 1991, Cargnelli and Gross 1996), but biotic interactions (e.g., competition) also can influence this relationship (Olson 1996; Post 2003). Early hatching and increased size have been shown to be beneficial by reducing vulnerability to pred ators (Christensen 1996; Hambright et al. 1991), providing foraging advantages (Ludsin and DeVr ies 1997; Mittelbach an d Persson 1998), and by improving fish condition for overwinter survival (Shuter and Post 1990; Ludsin and DeVries 1997) relative to smaller members of a year class. Post (2003) suggested that early-hatching provides opportunities that can re sult in earlier age-at-maturity, and thus, increased lifetime fitness. Therefore, early hatching has often b een considered advantageous in fish populations structured by size-dependent mortality. Life-history strategies acro ss species distributions resu lt from responses to both environmental and genetic influences (Stearns 197 6). Because temperature, and thus latitude, can influence spawning success, fi sh populations are expected to exhibit adaptations to local conditions that maximize progeny survival (S tearns 1976; Conover 1990). Without knowledge of the relative strength s of processes influencing surviv al among populations, broad scale ecological patterns of recruitmen t are difficult to establish. Broad recruitment hypotheses have been proposed for largemouth bass; however, Pa rkos and Wahl (2002) reported that most knowledge concerning recruitment processes for th is species was largely derived from studies conducted from only a portion of their distribut ion. For example, Garvey et al. (1998) hypothesized that the strength of overwinter mortality should increa se with decreasing latitude

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14 because warmer winter climates facilitate incr eased predation during winter. Garvey et al. (1998) developed their hypothesis from populati ons extending from Wisconsin to central Alabama, but information was not available from the southern extent of the largemouth bass’s native range. To evaluate the ge nerality of recruitment hypothese s across latitudinal gradients, recruitment processes need to be evaluated out side the areas used to develop the hypotheses. Florida’s climate ranges from subtropical to temperate, thus pot entially providing a prolonged breeding season and minimal winter effects relative to ot her temperate North American latitudes. My objective was to eval uate the generality of recruitment hypotheses, developed at more northerly latitudes, to juve nile largemouth bass surv ival in Florida. I compared hatching distributions growth, and survival of age-0 largemouth bass across a latitudinal gradient of Florida la kes (i.e., at the southern extent of their native distribution) to determine if survival patterns conform to current hypotheses regard ing pre-recruitment largemouth bass survival. I tested two hypothese s: 1) that early hatc hing would result in a growth and survival advantage through the summer (e.g., Phillips et al. 1995; Ludsin and DeVries 1997; Pine et al. 2000), and 2) that size-selective overw inter mortality would strongly influence survival to age-1 (e.g., Miranda and Hubbard 1994; Ludsin and Devries 1997; Garvey et al. 1998). Methods I divided Florida into three study regions (i.e., south, central and north; per Crawford et al. 2002) and selected two lakes from each re gion for my study. Study lakes in each region included: 1) north region: Semi nole and Talquin Reservoirs, 2) central region: Lakes Harris and Monroe, and 3) south region: La kes Okeechobee and Istokpoga (Table 2-1; Figure 2-1). Due to Lake Okeechobee’s large size, my sampling area (> 25 km2) was located in the northwest region

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15 of the lake. Seminole Reservoir is not entirely within Florida; thus, I sampled only the west littoral zone along the Chattahoo chee Basin from the Alabama st ate line to Jim Woodruff Lock and Dam (i.e., Florida waters). Sample sites at other lakes were distribut ed throughout the entire littoral area. In general, lakes had vast, highly vegetated littoral zones composed of submergent (e.g., Hydrilla verticullata ), emergent (e.g., pickerelweed Pontederia cordata ) and floatingleaved (e.g., fragrant water lily Nymphaea odorata ) plants. Vegetation types and abundances were generally similar across lakes, except for Lake Talquin, where vegetation abundance was lower and less diverse compared to the other lakes (M. Rogers, pe rsonal observation). I sampled the 2003 and 2004 year classes of largemouth bass from shortly after hatching through the following spring (i.e., about age-1) at each lake. Early Age and Growth I used block nets to sample age-0 largemouth bass during spring and summer at each lake. Due to the potential for earlier-hatching in my south region relative to my central and north regions, Lakes Okeechobee and Istokpoga were sample d in early spring (i.e., February or March) and all lakes were sampled during spring (i.e., late April/May) a nd summer (i.e., late June/July) in each year. A 100-m block net (3.2-mm knotle ss nylon mesh) was deployed in a 10m x 10m square (total area = 0.01 ha) and li quid rotenone (Prenfish 5% acti ve ingredient) was applied at 3 mg/L. Twelve block nets were set at each la ke during each sampling event. Samples were collected in shallow (< 2m) littoral zones, and sample sites were selected to be representative of available habitat types (e.g., vegeta tion type). At each net, all fi sh were collected by 3-4 wading investigators until fish did not continue to surface, and the net was then moved to another location and set again. My strategy underestimated fish density due to incomplete recovery, but allowed for higher samples sizes per habita t type and lake (Timm ons et al. 1978).

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16 All collected fish were placed on ice and retu rned to the laboratory. Age-0 largemouth bass were measured to nearest 1 mm total length (TL) and wei ghed to the nearest 0.01 g. Age-0 largemouth bass from each sampling event were placed in 1 cm length groups and sagittal otoliths were removed from a subsample of fish so that the length dist ribution of aged fish closely reflected the length frequency of the enti re fish sample collected in that period (Ludsin and DeVries 1997; Pine and Allen 2001). Otolith preparation a nd age estimation followed the procedures of Miller and Storck (1982) and L udsin and DeVries (1997). A sample of 30 knownage largemouth bass from the Richloam Fish Ha tchery, Florida, was collected during May 2003 and was used to validate age accuracy. All otolit hs were read twice by two independent readers. Between-reader ages differing by less than 3.5 d were used to produce an average age (Maceina et al. 1995). Hatch date for each fish was estimated by subt racting the number of rings counted on the otolith from the day of year when collection t ook place. Total length at age data were modeled using both a linear model and an exponential model for each lake, and error variance was compared using a variance ratio test (Zar 1999). The linear and exponentia l models were fit to TL at age using data from fish between 30-75 d old to ensure that the age and subsequent growth comparisons were similar across hatching cohorts and study lakes. Mean daily growth rate (DGR, mm/d) for each fish was estimated as: DGR = (TLc 6)/age (2-1) where TLc is the total length at capture, age is the number of days from swim-up (i.e., the ring count), and 6 mm is subtracted to correct for total length at swimup (Goodgame and Miranda 1993; Ludsin and DeVries 1997). I used swimup da tes as an indicator of hatch dates and an index of adult spawning activities.

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17 I compared mean DGR among hatching periods a nd lakes. Fish were grouped into early, middle, and late hatch periods by partitioning the en tire hatching date distribution for each lake and year into 33 percentile groups. Estimated hatch dates from each sample were pooled to construct the entire hatching dist ribution for each lake and year because I wanted to incorporate early hatch dates that may not have been detectable in later sample s due to mortality (see Isely et al. 1987). Hatching periods that delineated sub-c ohorts were lake and year specific to evaluate the general prediction that early-hatching, within any given syst em, would result in a growth and survival advantage relative to later-hatching. Mean DGR was compared among sub-cohorts (i.e., early, middle, late) and lakes using a two-way an alysis of variance (ANOVA) for each year with hatching period and lake as factors. If lakes within regions did not exhibit differences in mean DGR among sub-cohorts, I grouped lakes into regions (north, central, south) and used a two-way ANOVA with regions and hatching periods as factors. Least-squares means with Tukey’s modification were used to separate DGR means if differences were si gnificant in the ANOVA. Water temperature was measured at each la ke throughout the study. Temperature loggers manufactured by Onset were placed at two st ations within the sample areas at each lake between 0.5 and 1 m water depth prio r to initiating data collection and they recorded data during the entire study period. Temperat ure loggers were programmed to record water temperature at six-hour intervals, thus allo wing us to relate age-0 larg emouth bass hatching frequency distributions and growth rate s to water temperatures for each lake, region, and year. Early survival of age-0 largemouth bass was assessed using changes in abundance-at-age from sequential block net sampling events at each lake. Fish were grouped in 7-day cohorts for each lake and sampling event. Survival of each cohort was estimated as: Si = Ni, t/Ni, t-1 (2-2)

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18 where Si is the survival rate of cohort i, Ni, t is the mean density of fish from cohort i at time t, and Ni, t-1 is the mean density of cohort i in th e preceding sampling event. Survival was estimated between the early spring and spring samp ling periods at south la kes and the spring to summer sampling periods at all lakes. Only cohorts that were fully recrui ted (i.e., > 15 mm TL) to the block nets in each sampling event were us ed for estimating survival. Seven-day cohorts were grouped into early, middle, and late-hatched sub-cohorts based on dates delineated by the total hatching distribution as described above. I tested for differences in mean survival among sub-cohorts for the early-spring to spring periods in the south lakes combined using a one-way ANOVA. Low sample sizes (the number of cohor ts per lake) precluded lake-specific survival assessment for spring to summer sampling periods. Thus, I grouped lakes by region and tested whether mean survival differed among sub-co horts and regions using a two-way ANOVA. Least-squares means was used to separate mean s if the overall ANOVA had significant effects ( = 0.05). Size-Dependent Over-Winter Survival Electrofishing was used to sample fish in fall (October) and spri ng (March; ~age-1) of each year. Twenty-minute electro fishing transects were conducted in the same habitats where block-netting took place using pulsed DC current and sampling was continued at each lake until at least 100 juvenile largemouth bass were captured. Otoliths were removed from juvenile largemouth bass collected by electr ofishing and examined to ensure lack of an annulus. Size structure of age-0 largemouth bass in each sampli ng event was used to assess relative overwinter size-specific survival in each lake and year. Differences in size structure between sampling periods were determined using Kolmogorov-Smirnov tests (Zar 1999).

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19 Results Early Age and Growth Age-0 largemouth bass hatching seasons varied widely by region in Florida, but were generally similar for both lakes in each region. Spawning periodicity (i.e., the distribution of hatching events during the spawni ng season) was variable within lakes and resulted in differing hatch dates that corresponded to early-, middle, and late-hatched sub-cohorts between years (Table 2-2). Largemouth bass from south lakes hatched earlier than central and north lakes fish during both years. Hatching initia tion began in early December at south lakes (Figures 2-2, 2-3) and not until early March at north lakes (Figures 2-2, 2-3). In central lakes, age-0 largemouth bass initiated hatching in mid to late February, except at Lake Monroe some fish hatched in January 2004 (Figures 2-2, 2-3). I detected different hatching initiation dates for the south region lakes during 2003. During 2003, hatching began in February at Lake Istokpoga, but in early December at Lake Okeechobee (Figures 22, 2-3). Hatching distributions were multimodal in many cases, indicating that spawning activity peaked at se veral times through the spawning seasons. Total hatching distributions and water temp eratures during spawni ng also varied among regions, with more protracted ha tching distributions in the south relative to north lakes. Hatching durations ranged from 61 d at Lake Ha rris in 2003 to 160 d at Lake Istokpoga in 2004 (Table 2-2). Hatching durations were longest at south lakes, shorter at cent ral and north lakes, and least variable at north lakes relative to the other lakes (Table 2-2). Water temperatures at hatching initiation were as low as 14.7 C at Lake Talquin and as high as 22.0 C at Lake Istokpoga during the study. Hatching occurred at temperatures up to 29.6C at Lake Istokpoga

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20 during 2004. Water temperatures at median hatching dates ranged from 19.0 C at Lake Istokpoga during 2003 to 25.7 C at Lake Istokpoga during 2004 (Table 2-2). Linear models described relationships be tween mean TL and age (days) as well as exponential models (all Va riance Ratio Tests with P > 0.12), and thus, I used linear models to describe growth rates. Mean DGR of fish va ried among lakes and sub-cohorts, but differences were not always consistent for lakes within re gions. The lake*hatching period interaction was significant in 2003 (both P < 0.001) indicating that mean DGR varied with both factors, but differences were not consistent across lakes or hatching periods. For example, all sub-cohorts from Lake Istokpoga exhibited relatively ra pid growth in 2003 (all hatching periods 0.60 mm/d), whereas Lake Okeechobee growth rates we re low for the early-hatched sub-cohort (0.43 mm/d) and were more rapid for midand late-hatched sub-cohor ts (0.57 and 0.59 mm/d, respectively; Table 2-3). In 2004, early-hatched sub-cohorts at both sout h lakes grew slowly (mean DGR 0.40-0.43), whereas middleand late-hat ched sub-cohorts grew faster (mean DGR 0.51-0.59; Table 2-3). The 2003 year class exhibite d rapid growth at north lakes with mean DGR 0.68 mm/d for all sub-cohorts. The 2004 year class had moderate to rapid growth at the north lakes (mean DGR range = 0.59 – 0.82; Table 2-3). Central lakes had moderate growth rates (range 0.53-0.73 mm/d) that did not vary am ong sub-cohorts during either year. Thus, for three of four lake-year combina tions at south lakes, I found that early-hatched sub-cohorts had slow growth relative to later-hatched sub-c ohorts and other study lakes in Florida. Similar to growth-rate data, survival of age-0 largemouth bass varied among sub-cohorts and regions and was generally lowest for early-h atched sub-cohorts at south lakes. In 2003, survival of fish between March and May at south lakes was lower for early (mean S =0.06) than for middle-hatched (mean S =0.38, P < 0.02; Table 2-4) sub-cohorts. Late-hatched sub-cohorts

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21 had not recruited to the block net for comparison of survival between March and May. Survival between the May and July sampling periods fo r the 2003 year class ranged from 0.16 to 0.29 across regions and sub-cohorts, but mean surviv al did not differ for e ither variable (both P > 0.2; Table 2-4). In 2004, both early a nd middle-hatched sub-cohorts at south lakes had low survival (0.14 early, 0.11 middle) between Fe bruary and late April, and these values did not differ ( P > 0.90). Late-hatched sub-cohorts again had not re cruited to the gear fo r this comparison. Survival between April/May and June/July for the 2004 year class differed by region ( P = 0.02), with the hatching period eff ect marginally significant ( P = 0.07) and the inte raction effect not significant ( P = 0.8). Survival from May to July was higher at north than south lakes ( S = 0.32 in north lakes, 0.12 in south lakes, P < 0.01), and central lakes ha d intermediate values ( S = 0.19; Table 2-4). The hatching period effect showed that early-hatche d sub-cohorts across all regions had marginally lower survival ( S =0.12) than middle and late ha tched sub-cohorts (both least squares mean S =0.25, both P < 0.2 in least squares means comp arisons; Table 2-4). Thus, the severity of mortality varied among regions and hatching periods with early-hatched sub-cohorts having low survival prior to summer (all S < 0.14) at south lakes in both years. Water temperatures after hatching influenced age-0 largemouth bass growth. The mean DGR for a given hatching period was positively re lated to the average water temperature during the 40 d period following the median hatch date for that hatching peri od across all lakes and years (r = 0.60, P < 0.001) (Figure 2-4). Earlyhatched sub-cohorts at south lakes endured average temperatures of approximately 18C for the first 40 days after hatching, except for the early-hatched sub-cohort at Lake Istokpoga in 20 03. Later-hatched sub-cohorts at south lakes and all sub-cohorts at middle and north la kes experienced average temperatures > 20 C for the

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22 first 40 days after hatching and all mean DGR for these sub-cohorts were greater than 0.53 mm/d. Size-Dependent Over-Winter Survival I found little evidence of size-selective over-w inter mortality because length frequency distributions from electrofishing in the fall (age -0) and following spring (age-1) were similar for most lakes in both years (Figur es 2-5, 2-6). Only the 2003 lengt h frequency distributions from Lakes Harris and Talquin (Figure 2-5) diffe red between October a nd March electrofishing samples (both P = 0.02). At Lake Harris, the relative num ber of small fish declined over winter, but the minimum size between fall and spring di d not change and the maximum size increased, thus suggesting that growth occurred for the larger fish. At Lake Talqui n, an apparent mode of fish at 9-10 cm during fall in creased greatly to 10-13 cm the following spring and both the minimum and maximum sizes increased between fa ll and spring. Small sa mples sizes at Lake Monroe during March 2005 (2004 year class, ~ age-1) prevented evaluations of length frequencies from fall to spring, which was likely due to hurricane effects th at increased mortality and/or greatly decreased sampling catchability because of extremely high water levels. Maximum size increased from fall to winter at all lakes and years, suggesting that growth occurred over-winter, except at Lake Semi nole in 2003 where neither minimum size nor maximum size changed over-winter. If over-win ter mortality were highly size-selective for Florida lakes, I expected to see significant ch anges in the shape of the length frequency distributions between fall and spring, which ra rely occurred in either year. Although I was unable to assess the overall strength of overwinter mortality in Florida, my results suggested that mortality during this period was not strongly size-dependent.

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23 Discussion I found evidence that limited the applicati on of the current conceptual framework regarding hatch date and severity of overwinter mortality for age-0 largemouth bass recruitment. Current hypotheses would have predicted highest survival for early-hatched sub-cohorts relative to later hatched sub-cohorts in all lakes and years (Trebitz 1991, Miranda and Hubbard 1994; Ludsin and Devries 1997; Garvey et al. 1998; Pine et al. 2000). I observed very slow growth for three of four early-hatched sub-cohorts at my lowest latitude lakes and never observed survival advantages for early-hatched sub-cohorts through their first summer at a ny Florida latitude I evaluated relative to later-hat ched sub-cohorts. I also found no evidence that size-selective overwinter mortality would restructure largemouth bass year classes for Florida lakes by limiting survival of smaller fish. Garvey et al. (1998) reviewed 15 studies that investigated overwinter survival of age-0 largemouth bass from Wiscons in to Alabama and predicted reduced overwinter survival for small fish in southern sy stems with warm winter temperatures and active predators. The addition of my results to those reviewed by Garvey et al (1998) suggested that size-selective overwinter mortalit y likely exhibits a parabolic pattern in North America, with highest overwinter mortality at intermediate latitudes of the largemouth bass’s distribution (i.e., Missouri Alabama; see Garvey et al. 1998). The growth and survival differences from hatching through the first summer likely influenced the potential for hatc hing sub-cohorts to contribute to the year class. Early-hatched sub-cohorts of the 2003 and 2004 year classes at south lakes exhibi ted high mortality, and thus, likely contributed less to the age1 largemouth bass year classes th an later-hatched sub-cohorts. I suspected that adult largemouth bass likely initi ated spawning at a similar time at both south lakes in 2003, as seen in 2004, and that a mortal ity event prevented detection of some early-

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24 hatched fish at Lake Istokpoga in 2003. Evidence includes the truncated hatching distribution at this system in 2003 (Figure 2-2) and an inde pendent radio telemetry study at Lake Istokpoga during the 2003 spawning season that indicated movement of adult largemouth bass into spawning habitats during months when no hatching was detected (i.e., Dec – Jan) (unpublished data, J. Furse, Florida Fish and Wildlife Cons ervation Commission). Low survival for earlyhatched fish also has been reported for other species, including American Shad Alosa sapidissima (Crecco and Savoy 1985), bloater Coregonus hoyi (Rice et al. 1987), striped bass Morone saxtilis (Rutherford and Houde 1995), and Korean sandeel Hypoptychus dybowskii (Narimatsu and Munehara 1999), owing to envi ronmental limitations (e.g., storm events, temperature). Reduced growth of early-hatched sub-cohorts at south la kes potentially prolonged the period of vulnerability to gape-limited pred ators and reduced predat or avoidance abilities (e.g., swimming speed), and thus led to increased mortality (Houde 1987; Miller et al. 1988). Bestgen et al. (2006) reported mo re rapid growth yet higher mortality for early-hatched Colorado pikeminnows relative to later-hatched fish because of temporal patterns in predator abundances in juvenile habitats. Survival and growth disa dvantages for early-hatched sub-cohorts at north and central lakes were not as str ong as disadvantages detected at south Florida lakes. However, the potential contribution of early-h atched sub-cohorts to year-cla sses at all lakes in 2004 was likely decreased due to low survival through thei r first summer relative to later-hatched subcohorts. I was surprised to find that growth rates appe ared to be limited by low water temperatures at sub-tropical south Florida lakes. However, adults began spawning in early December in my south region leaving nearly the entire “winter” for cold fronts to influence their progeny. Although early-hatched sub-cohorts at south Florida lakes suffered high mortality, early-hatched

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25 fish that survived to July were larger than fi sh from the other hatching su b-cohorts. Furthermore, early-hatched fish had more fish prey in diets than smaller, later hatched fish in July (Rogers and Allen 2005). Thus, some of the proposed advantages to early hatching were still evident despite poor survival for early-hatched me mbers of the year class. Similar results were reported for age0 largemouth bass in Alabama ponds where early-hat ched fish exhibited slower growth than later-hatched fish, but they were larger and ex pressed increased piscivory relative to smaller, later-hatched fish (Ludsin and DeVries 1997). Other early-hatched centr archids (i.e., bluegill Lepomis macrochirus and pumpkinseed Lepomis gibbosus ) also have exhibited reduced growth rates owing to cool water temperatures after ha tching that resulted in low survival relative to later-hatched fish born at warmer wa ter temperatures (Garvey et al. 2002 b ). Strong size-selective overwinter mortality has been more commonly reported for southern systems (Boxrucker 1982; Miranda and Hubbard 1994; Ludsin and DeVries 1997, but see Jackson and Noble 2000 and Peer et al. 2006) th an at northern and central latitudes (Kohler et al. 1993), however results have varied owi ng to study system-specific characteristics (e.g., predator presence; Garvey et al. 1998). Star vation and predation are the common mechanisms attributed to size-selective over-w inter mortality. Small fish have lower lipid reserves and higher mass-specific metabolism relative to larger fish, thus starvation is typically higher for small juveniles when prey resources and low winter temperatures li mit feeding (Oliver et al. 1979; Henderson et al. 1988; Miranda an d Hubbard 1994; Ludsin and De Vries 1997, but see Wright et al. 1999). Predation also has been suggested as a major mechanism resulting in overwinter mortality when winter water te mperatures reduce activ ities of juvenile largemouth bass (i.e., < 6C; Garvey et al. 1998, Fullerton et al. 2000), but remain warm enough for predators to remain active and preferentially prey on small age-0 fish (Garvey et al. 1998). Although winter water

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26 temperatures were conducive to predation at all Florida study lakes in both years, they also remained above 6 C, and thus, age-0 fish activity was not likely limited. In Florida lakes, sizeselective overwinter mortality may not have result ed in a survival bottleneck because most age-0 largemouth bass surpassed 10 cm TL by fall and overw inter growth occurred at almost all lakes, whereas Ludsin and DeVries (1997) reported litt le overwinter growth in central Alabama ponds and reported significantly higher ove rwinter mortality for fish < 100 mm than for larger fish. Size-selective predation during winter could have been minimized in my study because lakes generally had highly vegetated li ttoral zones that potentially pr ovided refuge and localized food resources as suggested by Mira nda and Hubbard (1994) and Garvey et al. (1998). Starvation was not apparent in my study because total lipid conc entrations did not differ from fall to spring for any size class at my study lakes (Rogers and Alle n 2005) and suggested that juvenile largemouth bass in Florida were not reliant on energy reserves for overwinter su rvival. Similarly, Peer et al. (2006) reported over-winter grow th and no evidence of size-sele ctive over-winter mortality for age-0 largemouth bass in southern Alabama (i.e., similar latitudes to my north Florida lakes). Thus, conventional hypotheses that predict strong effects of si ze-selective over-winter mortality apparently do not apply to systems with availa ble predator refuges and over-winter growth, as suggested by Garvey et al. (1998). The populations I studied were primarily Fl orida largemouth bass at south and central regions and naturally introgressed largemouth bass (crosses between Florida largemouth bass and northern largemouth bass M. s. salmoides ) at my north region (Table 2-1; B. L. Barthel, unpublished data). Genetic differences among my study populations could have influenced my results because these differing genetic strains have been reported to respond differentially to environmental conditions. For example, Cichra et al. (1982) reported lowe r tolerances to cold

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27 shock for Florida largemouth bass than northern largemouth bass and Isely et al. (1987) reported faster growth for northern and intergrade larg emouth bass relative to Florida largemouth bass that were stocked in Illinois ponds. Philipp et al. (1985) hypothesized th at Florida and northern largemouth bass spawning seasonality have diffe rentially evolved owing to environmental conditions during spawning seasons, and Rogers et al. (2006) reported that genetic composition contributed to adult largemouth bass spawning ti mes in Florida. Thus, genetic differences among my study populations could have influenced my results. Interestingly, my results indicated that despite a shorter growing season at north lake s relative to other regions, maximum lengths at age-1 were similar across all lakes. These results are suggest ive of counter-gradient growth given known genetic differences among thes e populations; but, growth observations from field studies result from comp lex ecological interactions (Gar vey et al. 2003) and carefully controlled (e.g., without latitudina l mortality influences as in this study) studies would be required to further clarify the potential effects of genetics to my results Kassler et al. (2002) suggested elevating the M. s. floridanus subspecies to the specie s level (i.e., Florida bass Micropterus floridanus ) based on meristics and allozyme and mitochondrial DNA analyses, however the populations I studied currently remain recognized as varying genetic strains of the same species. The influence of genetics to my results cannot be discerned from this study, but future research could reveal the importance of genetic variation to latitudinal hypotheses for largemouth bass recruitment as seen for other species (e.g., Conover 1990). My results suggested that current latit udinal hypotheses for largemouth bass do not always apply to populations at the southern ex tent of their natural distribution. Hatching distributions appeared to reflect characteristics that would compensate for local environmental conditions when growing season le ngth did not appear to constrai n survival. More protracted

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28 distributions at south la kes, relative to north lakes, were likely maintained by effects of annual variability in environmental cond itions (e.g., water temperature) following spawning that results in variable hatching-date dependent survival. Winter water temperatur es during my study were below the 10-yr average for the period of December to February (unpublished data, South Florida Water Management Di strict, West Palm Beach, Fl orida). Annual mean water temperature for the December to Februa ry time period ranged from 15.6C to 19.6C for 19962005 with an overall mean of 17.9 C. The years for this study (2003 and 2004) averaged 16.8 C and 15.6 C (the 10-year low), respectively. Thus, th e years I sampled had relatively cool water temperatures, and early hatching may provide a survival advantage during years with warmer conditions. At central and north region lakes, wa ter temperatures prevented spawning until later in the year, which limited the duration that juveniles were vulnerable to influences of winter cold fronts relative to south lake s. Garvey et al. (2002 b ) proposed that protracted spawning distributions acted to maximize mean fitness for bluegill and pumpkins eed sunfish at Lake Opinicon, Canada, where spring conditions can result in variable survival for early-hatched fish. Similarly, early spawning of largemouth bass in Florida, relative to ot her latitudes, likely provides foraging and predator a voidance advantages during favor able years, and protracted spawning should provide some progeny survival duri ng unfavorable years. Largemouth bass life history strategies in Florida’s mild climate appeared to differ from mid-temperate latitudes, where growing season length and winter condit ions can create size-dependent survival bottlenecks. Recruitment processes vary among lati tudes (Garvey et al. 1998), systems at similar latitudes (Garvey et al. 1998), a nd sites within a system (Peer et al. 2006) because of complex ecological interactions (e.g., among physical, chem ical, and biological factors). Thus, our

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29 understanding of recruitment and ability to refi ne recruitment hypotheses requires evaluations at multiple scales throughout a species’ distribution.

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30Table 2-1. Physical and chemi cal characteristics of 6 Florid a study lakes and genetic characte ristics of their largemouth bass populations Average Winter waterSummer waterTrophic TPTNChl-aSDMDSA% FL Region LakeLatitude temperature (C)temperature (C) statea(mg L-1)(mg L-1)(mg L-1) (m)(m) (m2) LMB alleles North Seminoleb3012.528.5Eutrophic 3559091.94.513,15859 Tal q uinb3012.228.6Eutrophic 54670293.33.03,56064 Central Harrisc2816.330.0Eutrophic 281550370.64.05,58099 Monroed2816.129.6Eutrophic 1002200390.72.33,308100 South Istok p o g ac2718.429.7Eutrophic 210700100.91.811,207100 Okeechobeec2718.629.8Eutrophic 921480300.52.7173,000100 winter = Dec Feb, summer = Jun Aug, TP = total phosphorus, TN = total nitrogen, Chl-a = chlo rophyll a, SD = secchi depth, MD = mean depth, SA = surface area, FL LMB = Florida largemouth bass Micropterus salmoides floridanus a = estimated according to criteria of Forsberg and Ryding (1980). bFlorida Lakewatch (2000). cBachmann et al. (1996). dSeminole County Watershed Atlas (2001). Genetics results are from diagnostic allozyme analyses conducted by the Illinoi s Natural History Survey (B. Barthel, p ersonal communication).

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31 Table 2-2. Dates corresponding to sub-cohort hatching periods, median hatch dates, a nd water temperatures (C) at correspondin g median hatch dates for the 2003 and 2004 largemouth bass year classes at 6 Florida study lakes. YearEarly sub-cohortMiddle sub-cohortLa te sub-cohortMedianWater temperature Class Region Lakehatch rangehatch rang ehatch range hatch dateat median hatch 2003NorthSeminole06 Mar. 03 Apr.04 Apr. 23 Apr.24 Apr. 04 Jun.17 Apr.22.4 Talquin05 Mar. 14 Apr.15 Apr. 24 Apr.25 Apr. 02 Jun.19 Apr.20.7 CentralHarris17 Feb. 06 Mar.07 Mar. 25 Mar.26 Mar. 18 Apr.12 Mar.24.1 Monroe01 Mar. 22 Mar.23 Mar. 17 Apr.18 Apr. 06 May14 Apr.23.0 SouthIstokpoga06 Feb. 03 Mar.04 Mar. 05 Apr.06 Apr. 05 May23 Mar.25.7 Okeechobee07 Dec. 24 Jan. 25 Jan. 28 Feb.29 Feb. 24 Apr.30 Jan.19.9 2004NorthSeminole03 Mar. 16 Mar.17 Mar. 20 Apr.21 Apr. 05 Jun.31 Mar.20.9 Talquin09 Mar. 23 Mar.24 Mar. 20 Apr.21 Apr. 25 May02 Apr.19.9 CentralHarris14 Feb. 02 Mar. 03 Mar. 23 Mar.24 Mar. 14 May12 Mar.19.6 Monroe11 Jan. 02 Mar. 03 Mar. 23 Mar.24 Mar. 06 May07 Mar.21.5 SouthIstokpoga15 Dec. 10 Feb.11 Feb. 06 Apr.07 Apr. 23 May01 Mar.19.0 Okeechobee13 Dec. 13 Jan.14 Jan. 13 Apr.14 Apr. 07 May02 Mar.19.6

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32 Table 2-3. Mean daily growth rate (mm/d, Mean DGR), and sta ndard deviation (SD) for age-0 largemouth bass collected in block nets during spring and summer of each year. Year classRegionLakeHatching periodMean DGRSD 2003NorthSeminoleEarly0.680.11 SeminoleMiddle0.720.14 SeminoleLate0.720.11 TalquinEarly0.760.12 TalquinMiddle0.780.12 TalquinLate0.770.15 CentralHarrisEarly0.570.13 HarrisMiddle0.550.11 HarrisLate0.610.14 MonroeEarly0.720.11 MonroeMiddle0.720.10MonroeLate0.730.09 SouthIstokpogaEarly0.690.08 IstokpogaMiddle0.600.13 IstokpogaLate0.610.11 OkeechobeeEarly0.430.07 OkeechobeeMiddle0.570.10 OkeechobeeLate0.590.10 2004NorthSeminoleEarly0.590.13 SeminoleMiddle0.620.17 SeminoleLate0.820.21 TalquinEarly0.730.16 TalquinMiddle0.640.12 TalquinLate0.650.13 CentralHarrisEarly0.530.07 HarrisMiddle0.530.09 HarrisLate0.560.07 MonroeEarly0.600.10 MonroeMiddle0.530.11 MonroeLate0.690.06 SouthIstokpogaEarly0.430.11 IstokpogaMiddle0.520.09 IstokpogaLate0.570.08 OkeechobeeEarly0.400.09 OkeechobeeMiddle0.510.09 OkeechobeeLate0.590.10

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33 Table 2-4. Analysis of variance results for survival comparisons among hatching periods and regions of Florida. Least squares means Year classTime periodComparisonF statisticP valueHatching sub-cohortSurvival 2003Mar MayHatch period7.12 1,100.02Early0.06 Middle0.38 May JulRegion0.45 2,450.85North0.19 Central0.22 South0.24 Hatch period1.66 2,450.20Early0.16 Middle0.29 Late0.20 2004Feb AprHatch period0.00 1,90.96Early0.14 Middle0.11 May Jun/JulRegion4.41 2,480.02North0.32 Central0.19 South0.12 Hatch period2.81 2,480.07Early0.12 Middle0.25 Late0.25 Column two defines the period th at survival was estimated for, column three gives variables tested in the model for that time period, column four gives the F valu e and test degrees of freedom, column five reports the signifance leve l for the variable, and columns six and seven give least squares means estimates for the indivi dual groups within each factor. March to May and February to April survival could only be estimated for our south region.

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34 Figure 2-1. Selected north region (Lakes Seminol e and Talquin), central region (Lakes Harris and Monroe), and south region (Lakes Istokpoga and Okeechobee) study lakes for comparing hatching distributions, growth, and mortality of age-0 largemouth bass across Florida’s latitudinal gradient.

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35 Figure 2-2. Relative frequency di stributions (bars, y axes) of age-0 largemouth bass hatching at north lakes (top panels), central lakes (middl e panels), and south lakes (bottom panels) in 2003. Hatch dates (x axes) were dete rmined using daily rings on otoliths (N=number of fish aged). Temperature is indicated on the z axis and by the solid line.

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36 Figure 2-3. Relative frequency di stributions (bars, y axes) of age-0 largemouth bass hatching at north lakes (top panels), central lakes (middl e panels), and south lakes (bottom panels) in 2004. Hatch dates (x axes) were dete rmined using daily rings on otoliths (N=number of fish aged). Temperature is indicated on the z axis and by the solid line.

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37 Figure 2-4. Relationship between mean daily growth rates and average temperatures from the 40-day period following the median hatch date for early, middle, and sub-cohorts, from 6 Florida lakes during 2003 and 2004. Data points (n = 3) closest to the origin result from slow-growing early-hatched sub-cohorts at Lake Okeechobee during 2003 and 2004, and at Lake Istokpoga during 2004.

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38 Figure 2-5. Length freque ncy distributions for 2003 fall and spring (~age-1) samples of age-0 largemouth bass collected by electrofishing at north (Lakes Seminole and Talquin), central (Lakes Harris and M onroe), and south (Lakes Istokpoga and Okeechobee) Florida lakes.

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39 Figure 2-6. Length freque ncy distributions for 2004 fall and spring (~age-1) samples of age-0 largemouth bass collected by electrofishing at north (Lakes Seminole and Talquin), central (Lakes Harris and M onroe), and south (Lakes Istokpoga and Okeechobee) Florida lakes.

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40 CHAPTER 3 SEPARATING GENETIC AND ENVIRONMENTAL INFLUENCES ON TEMPORAL SPAWNING DISTRIBUTIONS OF LARGEMOUTH BASS ( Micropterus salmoides ) Genetic and environmental factors influen ce fish spawning periodicity (i.e., the distribution of spawning events during the breeding season), but their relative contributions are often difficult to discern. Many studies (e.g., Ludsin and DeVries 1997; Garvey et al. 1998) have illustrated the importance of hatching date to growth and survival of age-0 fishes, but few have evaluated the factors influencing spawni ng periodicity (i.e., dura tion and frequency of spawning events through the season ). Spawning initiation (i.e., th e onset of the breeding season) is regulated by environmental factors such as temperature and photoperiod (Kramer and Smith 1960; Lam 1983), and thus, spawning seasons occur la ter in the year at hi gh latitudes relative to low latitudes (Conover 1992). Spawning season durati on is often inversely re lated to latitude, in part, because adults cease spawning when offs pring no longer have a chance for over-winter survival (Johannes 1978; Munro et al. 1990; Co nover 1992). Spawning pe riodicity has been related to multiple environmental factors such as water temperature (Conover 1992), photoperiod (Heidinger 1975), changes in water levels (O zen and Noble 2002), and food availability during gonadal development (Koslowski 1992). For example, Baltic cod ( Gadus morhua ) spawning was delayed during years of cooler spring water temperatures (Wieland et al. 2000). Largemouth bass began spawning in a Puerto Rico rese rvoir, which was thermally stable (24-30 oC) through the year, when photoperiod began to increase during winter (Ozen a nd Noble 2005). Spawning duration was also related to water level fluctuati ons in Puerto Rico reservoirs (Ozen and Noble 2002). Genetic composition of a stock also influe nces spawning periodicity. Reproductive processes in fish are regulated, in part, by endogenous hormone cues (Patio 1997; Van Der

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41 Kraak et al. 1998), which are regulated by genes (e.g., Denslow et al. 2001). Genotypic effects have led to synchronous spawning of predators in relation to prey abundance, thus resulting in high availability of food resources for newl y hatched larvae, assuming increased foraging opportunities at hatching leads to increased survival for o ffspring (Hjort 1914; Cushing 1975). Atlantic herring ( Clupea harengus harengus ) exhibit genotypic influences to spawning periodicity across their broad lati tudinal range because hatching within specific larval retention areas is related to increased local food availabi lity (i.e., plankton blooms) for larvae in that specific locale (Cushing 1975; Sinc lair and Tremblay 1984). Sim ilarly, genetics can influence spawning periodicity because evolution of multip le spawning or a prolonged spawning season duration may prevent loss of an individual’s an nual reproductive output due to environmental conditions (Conover 1992; Fox and Crivelli 1998). However, the relative contributions of genetic and environmental infl uences are poorly understood, a nd variable spawning initiation and periodicity are often attri buted to phenotypic plasticity (B aylis et al. 1993; Conover and Schultz 1997). Contributions of genotypic variability to phenotypi c patterns have largely been ignored (Conover and Schultz 1997). Largemouth bass provide an excellent specie s for evaluating genetic and environmental influences on spawning periodicity because they have a wide native geographic distribution with a natural genetic gradient, as indicated by latitudinal clines in allele frequencies at several loci (Philipp et al. 1983). Ecologica lly, juvenile largemouth bass suffer from differing mortality factors across latitudes (Garvey et al. 1998), and spawning periodicity strongly influences juvenile largemouth bass survival and recruitment (Ludsin and De Vries 1997; Pine et al. 2000). Thus, differing selection pressures may exist alon g the latitudinal distri bution of largemouth bass that would facilitate localized adaptations for spawning peri odicity. Comparisons between

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42 genetically verified fish indi cated that NLMB spawned earlier than FLMB and ILMB when stocked together in Illinois ponds and a Texas re servoir (Isely et al. 1987 ; Maceina et al. 1988), but those studies occurred outsi de the native range of FLMB. No studies have separated environmental influences on spawning periodic ity from genetic influences by comparing populations with known genetic contrasts wh ile monitoring environmental conditions. Genetic differences across the distribution of largemouth bass have been recognized for decades. Northern and Florida largemouth bass have been recognized as distinct subspecies for more than 50 years (Bailey and Hubbs 1949). Northern largemouth bass are endemic to the northern United States, FLMB natura lly occur in south Florida, a nd intergrades (ILMB) occur in north Florida, several southeastern states (e .g., Georgia, Alabama, Mississippi, South Carolina, North Carolina, Virginia and Maryland), and ot her areas where introductions have occurred. Kassler et al. (2002) reco mmended elevating the status of FL MB from a subspecies to species status (i.e., Florida bass, M. floridanus ) based on discriminate function analysis of meristic characters, allozyme analysis, and mitochondria l DNA (mtDNA) data. Physiological attributes (e.g., temperature tolerances) and relative survival differences have also been reported for translocated fish in several performance eval uations (e.g., Cichra et al. 1982, Philipp and Whitt 1991). However, phenotypic variability in mor phometric and life histor y traits of broadly distributed species is not unc ommon (Schultz et al. 1996). At the time of my study, the taxonomic nomenclature accepted by the American Fisheries Society remains at the subspecies level. I compared temporal hatching distributions between a population of FLMB from Lake Okeechobee in south Florida and an ILMB popul ation from Lake Seminole at the FloridaGeorgia border. Lake Okeechobee represents a pure population of FLMB, and Lake Seminole is

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43 an intergrade population (ILMB, Philipp et al. 1983) I used estimated hatch dates, from sagittal otoliths as indices of spawni ng periodicity assuming that hatching occurred two days after fertilization. Spawning period icity was compared between brood sources for fish reared in environmentally similar experiment al ponds at an intermediate lat itude. I also assessed whether the trends found in experimental ponds correspon ded to the spawning pe riodicity for the two natural populations at their source lakes. My study design allowed us to maintain similar environmental conditions during brood fish sexual maturation at the intermediate latitude and evaluate influences of genetic factors to spawni ng periodicity. If gene tic composition affected spawning periodicity, I expected sp awning of translocated fish to reflect the periodicity of their source populations. In contrast, I surmised th at if environmental factors more strongly influenced spawning periodicity, then transloc ated fish that spawned in ponds would have similar distributions, and pond distributions w ould differ from both source lake populations. Methods Pond Methods Brood largemouth bass were captured by electrofishing at Lake Okeechobee, Florida (latitude: 27oN 7’) and Lake Seminole, Florida (latitude: 30oN 44 ’) during September 2003 (Figure 3-1). Using broodfish from Lakes Okeechobee and Seminole allowed us to nearly encompass the maximum latitudinal distance in Fl orida, and therefore, nearly the maximum environmental gradient (i.e., temperature a nd photoperiod) acting as selective pressures on spawning periodicity. Philipp et al. (1983) observed clinal varia tion in allele frequencies at several loci in largemouth bass that had been co llected from Lake Seminole in north Florida down to Lake Okeechobee. Philipp et al. (1983) failed to detect NLMB alleles at Lake Okeechobee and estimated a subspecific NLMB:F LMB genomic presence of 49:51, respectively,

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44 at Lake Seminole based on electrophoresis of two diagnostic enzyme loci. Recent analyses at the same loci also detected no northern alleles at Lake Okeechobee, and indicated that largemouth bass at Lake Seminole were highly introgressed and had likely been introgressed for an extended time period (B.L. Barthel, Illin ois Natural History Survey, 1816 South Oak Street, Champaign, Illinois, 61820, unpublished data). Analyses of mtDNA and allozyme data have resulted in Lakes Seminole and Okeechobee being grouped into separate largemouth bass genetic conservation management units within Fl orida (B.L. Barthel, unpublished data). Broodstock from source populations were si ze selected within 300-430 mm total length (TL) so that fish were of a reproductively mature size (Chew 1974) and to avoid influences of brood fish size on spawning periodicity (Mir anda and Muncy 1987; Goodgame and Miranda 1993). Adult fish were transported to Gainesville, Florida (latitude: 290N 43’) using an aerated 2x3 meter fish transport tank within 24 h of capture (Figure 3-1). I stocked six experimental ponds in Gain esville, Florida with brood fish. Ponds approximately measured 25 m x 5 m with an aver age maximum depth of 1 m, and were parallel to each other with a 3 m levee separating each pond. One week prior to stocking, the ponds were treated with rotenone (5 % liquid rotenone; >3 mg•L-1), drained to ensure no fish remained, and then refilled. Each pond was randomly assigned 10 11 brood stock from a single lake (N = 3 replicates per brood source) assuming a sim ilar sex ratio for each group (Chew 1974). Brood fish were fed 90-110 mm (TL) golden shiners ( Notemogonus chrysoleucas ) at 3.5% of largemouth bass biomass per day (Miranda a nd Hubbard 1994) until spawning behavior was observed in spring. Aquatic vegetation in ponds was maintained at a minimum using manual removal, but removals were ceased when larg emouth bass spawning bed construction was first observed to prevent disturbance of spawning activ ity. Pond water levels were maintained at

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45 bank-full to avoid influences of water level on timing of bass spawning (Sammons et al. 1999; Ozen and Noble 2002). Water temperature was m easured four times daily in each pond, at 1 m depth, using remote temperature re corders (Onset Incorporated). Age-0 fish were collected during April and May using dipnets and electrofis hing. Rotenone was also used during the May sample to maximize the likeli hood that all sizes and ages of age-0 largemouth bass were collected from each pond. The experiment was terminated in May 2004 to reduce potential effects of cannibalism and high water temperatur es effects on age-0 largemouth bass, and to avoid increased maintenance due to rapid evaporation. Field Methods Hatching dates at source lakes were estimat ed using age-0 largemouth bass captured at Lake Okeechobee during February, April, and J une, 2004 and at Lake Seminole during May and July, 2004. The earlier trip at Lake Okeechobee was conducted because of the potential for early hatching at low latitudes (Gran 1995). Age-0 la rgemouth bass were collected using 10 m x 10 m blocknets and applyi ng rotenone at 3 mg•L-1. Twelve block nets were set at each lake during each sampling event and fish were collected using dip nets by wading investigators. Laboratory Analyses A subsample of age-0 largemouth bass from e xperimental ponds and source lakes were size selected for age estimation so that the age sample mirrored the length-frequency of the fish collected at each waterbody (Pine et al. 2000). Selected age-0 largemouth bass were measured (TL; mm) and weighed (wet weight; 0.001 g), and thei r sagittal otoliths were removed. Sagittal otoliths were prepared using the methods of Mill er and Storck (1982). Each otolith was read by two independent readers and ages were averaged when they agreed within three days between readers. If agreement was not met, the otolith was re-read by both readers and discarded if

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46 agreement was not met (N = 0 for pond fish). Some early hatched age-0 fish from source populations were too old (>150 days) for reliable age estimation in July, but I assumed their hatch dates were represented in samples collected earlier in the year (i .e., February or AprilMay). Median hatch date in ponds was comp ared between stocking sources using a nonparametric median test (Zar 1999) Mean hatch date, mean water temperature at first and median hatch date, and mean hatching duration in ponds were compared between broodstock sources using one-way analysis of variance (ANOVA) Quantitative comparisons between pond and source lake spawning distributions were not performed due to differences in parental size distributions. However, source lake spawning patterns were used to evaluate whether spawning periodicity observed in ponds was similar to source populations in their native environment. Results Several largemouth bass nests (N > 3 per pond), with guarding males, were observed in each pond, and age-0 bass were captured in all six experimental ponds. Female largemouth bass may use multiple nests and deposit multiple eg g clutches during a spawning season (Heidenger 1975), thus I assumed that bass progeny in my experimental ponds represented offspring from several families. About twenty age-0 bass we re selected for age estimation from each pond during each sample. Age estimates we re only made for 30 age-0 bass from pond a (Lake Okeechobee broodstock) because of a low sample size (N 25) and small total length distribution during April. The lo w sample size in April was likely due to a high mortality event because all fish collected were less than 25 mm, except one individual was 32 days older and much larger than any other fish in that sample. In ponds, FLMB had initial hatching dates begi nning as early as 26 Janu ary and as late as 12 February (Table 3-1; Fi gure 3-2). In contrast, the range of initial hatch dates was 22 February

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47 to 7 March for ILMB (Table 3-1; Figure 3-2). On average, median hatch date in ponds was 11 days earlier for FLMB than ILMB ( 2 = 31.22, df = 1, P < 0.001), and mean hatch date in ponds was five days earlier for FLMB (F = 5.10, P = 0.025) (Table 3-1). Florida largemouth bass began spawning at cooler wa ter temperatures (12.3 – 15.1 oC) than ILMB (15.7– 20.6 oC) in experimental ponds (F = 7.82, df = 4, P = 0.049), but water temperatures at median hatch date did not differ between brood types (F = 0.010, df = 4, P = 0.771) (Table 3-1). Hatching duration in experimental ponds ranged 24 -72 days for FLMB and 10-12 days for ILMB (Table 3-1). Florida largemouth bass hatching duration was marginally different than ILMB hatching duration (F = 5.40, df = 4, P = 0.08), but low statisti cal power (N = 3 per treatment) reduced my ability to detect a difference (Peterman 1990). Florida largemouth bass hatching occurred as late as 7 April, whereas the last ILMB hatch occu rred on 18 March in experimental ponds. Florida largemouth bass began spawning earlier and also had a longer spawning season duration than ILMB in experimental ponds. My experimental pond results were corrobor ated by data from Lakes Okeechobee and Seminole. Age-0 bass at Lake Okeechobee began hatching as early as 12 December, whereas the earliest fish collected from Lake Seminol e hatched on March 1 (Figure 3-3). The median hatch date at Lake Okeechobee occurred 29 days earlier than the median hatch date at Lake Seminole. Unlike my pond results, water temperatures at first ha tch were similar between source lakes (Lake Okeechobee = 17.9 oC and Lake Seminole = 16.3 oC), and water temperatures at median hatch date were similar at Lake Okeechobee and Lake Seminole (19.6 oC and 20.9 oC, respectively), as seen in my pond study. Hatching duration results also supported experimental pond results because Lake Okeechobee hatching durat ion (146 d) was substantially longer than the Lake Seminole hatching duration (97 d) (F igure3-3). In summary, FLMB had earlier

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48 spawning and longer spawning season duration than ILMB in both research ponds and at their respective source lakes. My results indicated both environmental a nd genetic effects on sp awning periodicity of largemouth bass. Translocation illustrated envi ronmental effects on hatc hing periodicity because rearing FLMB in research ponds at a higher la titude led to later hatching than at Lake Okeechobee. Similarly, rearing ILMB in resear ch ponds at a lower lati tude led to earlier hatching in ponds than at Lake Seminole (Figure 3-4). Genetic effects on hatching periodicity were also evident because translocated fish reflected characteristics of their brood source populations. For example, FLMB hatched earlier and had longer hatching distributions than ILMB in both the pond experiment and at brood source lakes (Figure 3-4). Relative differences in spawning times betw een brood sources in ponds were detected despite the low number of families and adult si zes represented by my pond brood fish relative to source lake populations. Although my intent was to compare relative differences between brood sources in ponds, source lake hatching patte rns mirrored my pond results providing further support for a genetic contribution to spawning pe riodicity. This corroboration occurred even though my brood fish samples were not representa tive of the entire spawning population from the source lakes (i.e., lower ra nge in brood fish size in ponds compared to lakes). Discussion Environmental and genetic factors influe nced spawning timing and periodicity of translocated largemouth bass. Environmental an d genetic effects to breeding periodicity have rarely been investigated, but have been show n in some cases for terrestrial (e.g., Japanese macaques Macaca fuscata ; Fooden and Aimi 2003) and aquati c species (e.g., Atlantic salmon, Salmo salar ; Donaghy and Verspoor 1997). For example, A tlantic salmon exhibi ted a reversal in

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49 hatching order between two populations when reared in a hatchery versus their native rivers (Donaghy and Verspoor 1997). Donaghy and Verspoor ( 1997) attributed the reversal in hatching order to a genotype-environmental interaction; although they c ould not explain the mechanism leading to the reversal they suggested that local genetic adap tations to water temperatures were responsible. Environmental influences were evident by a te mporal shift in the onset of spawning for translocated broodfish. In my research ponds, FLMB began spawning later than their source population did at Lake Okeechobee, which is located much further south. In contrast, ILMB in research ponds began spawning before their s ource population at Lake Seminole, which is further north. Water temperatures were the mo st plausible explanati on for observed temporal shifts because temperatures at median hatch date were similar between FLMB and ILMB in experimental ponds and field co llections, but similar patterns in ponds and source lakes suggested a genetic component to spawning periodicity. Genetic factors played a role in spawning timing because FLMB from Lake Okeechobee spawned earlier in the resear ch ponds than ILMB from Lake Seminole, even though temperatures, photoperiod, and water levels were similar in all ponds during brood fish sexual maturation and spawning. Adaptations for repr oductive strategies that maximize individual fitness via offspring survival and reproductive su ccess should occur within an environment given a heritable component and selection pressure on phenotypic variability (Endler 1986). Einum and Fleming (2000) documented “critical episode of selection” following the emergence of Atlantic salmon fry, which resulted in a phenotypic shift towards ea rlier emergence. A heritable component to breeding date has been establis hed for some salmonids (Siitonen and Gall 1989; Gharrett and Smoker 1993), thus Einum and Flemi ng (2000) concluded that local adaptations for

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50 breeding dates are possible and may explain the variability in breeding dates within and among Atlantic salmon populations. The evidence of a genetic component to breeding times in my study and other studies is not surp rising given that local populations of fishes, with restricted gene flow, have an underappreciated capacity to adapt to local selec tion (Conover and Schultz 1997). In my study, FLMB exhibited protracted spawning periods in both ponds and lakes relative to ILMB. Protracted sp awning distributions increase the li kelihood that individuals with differing hatching dates will experience differing environmental conditions (Narimatsu and Munehara 1999). Mild winter wa ter temperatures that typically occur in peninsular Florida likely prevent exposure of early-hatched fish (e.g ., hatch in December) to very cold temperatures (<12 oC) that would limit survival as per Philipp et al. (1985). Atypical wi nter cold fronts can reduce growth or survival of early-hatched largemouth bass at Lake Okeechobee, thus reproductive success may vary among years for earl y versus late hatched fish. Garvey et al. (2002) found a similar pattern for bluegill at Lake Opinicon, Ontario, and hypothesized that protracted spawning distributions maximized lifetime fitness in variable environments where temperature regulates juvenile survival. Conversely, early spawning (e.g., December) of largemouth bass at Lake Seminole would likely resu lt in very limited offspr ing survival. Lake Seminole fish began spawning at suitable temper atures in March, and spawned over a relatively shorter period compared to Lake Okeechobee fish A contracted spawning distribution at Lake Seminole maximizes the growing season for mo st age-0 bass (e.g., Conover 1992) at the more northern latitude. Contracted sp awning seasons for ILMB at Lake Seminole could be the result of stabilizing selection, where progeny from both early and late hatching times are at a survival disadvantage, which ultimately led to individu als adapted to spawning within a shorter time

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51 period (Schultz 1993). Protracted spawning distributions of Lake Okeechobee fish and contracted spawning distributions of Lake Se minole fish appear better suited for the environments found at each source lake and i nherent environmental influences on juvenile survival. Philipp et al. (1985) found a lower -threshold temperature (i.e ., the theoretical lower limit to embryonic development) for FLMB than IL MB. Philipp et al. (1985) hypothesized that NLMB evolved strategies that de lay spawning to prevent exposure of embryos to lethally cold temperatures, whereas FLMB evolved to allo w spawning at lower and higher temperatures relative to NLMB. My results support this hypothesis, and I c oncluded that largemouth bass spawning seasons are locally adapte d to environmental conditions. Natural selection may also lead to spawning periodicity that is synchronized with prey species abundance to maximize food availability for progeny (Sinclair and Tremblay 1984). The relationship between reproduc tive timing and food supply has been described by the “match/mismatch hypothesis,” which asserts that temperate fishes spawn at a fixed time corresponding to peaks in plankton production, a nd offspring success or survival depends on how well their production ma tches with food production (C ushing 1975, 1990). At Lake Okeechobee, prey fish likely spaw n earlier than at Lake Semi nole because of earlier spring warming. Earlier hatching of fish at Lake Okeechobee, relative to hatching times at more northern latitudes, may lead to in creased survival due to a size a dvantage relative to prey fish, which is a prerequisite for pisc ivory (Mittelbach and Persson 1998). Potential prey fish at low latitudes commonly have extended spaw ning seasons (Conover 1992) (e.g., mummichog, Fundulus heteroclitus ; Conover 1990) relative to spawning seasons at more northern latitudes.

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52 Thus, differences in spawning periodicity I obs erved may have resulted from selection for optimal environmental conditions, food availabi lity, or a combination of these factors. Previous studies comparing spawning timing of largemouth bass indicated that northern and ILMB hatched earlier and at cooler water temperatures th an FLMB (Isely et al. 1987; Maceina et al. 1988), which is contrary to my findings. These previous comparisons were conducted in Illinois and Texas, respectively, which are outside the native range of FLMB and have much cooler winter water temperatures than FLMB experience in their native range. My pond study was conducted in a transition zone wh ere both pure FLMB and ILMB populations naturally occur (Philipp et al. 1983), so brood fish were reared in temperatures that did not vary as widely from local conditions co mpared to Isely et al. (1987) and Maceina et al. (1988). Isely et al. (1987) and Maceina et al (1988) also used sympatric populations of NLMB and FLMB potentially allowing for confoundi ng effects of hormonal cues a nd/or reproductive behaviors, which may have influenced spawning times. I used separate ponds for each genetic source to prevent interbreeding and behavi oral influences among brood source types. Broodstock lengths may have also contributed to differing result s among studies because larger largemouth bass have been shown to spawn earlier than sma ller individuals (Goodgame and Miranda 1993), and a large range in length distributi on of spawning bass likely leads to extended spawning activities (Miranda and Muncy 1987). In my study, I used similar-sized brood stock from both sources to minimize potential size effects on spawning in ponds Adult size distributions in ponds did not reflect adult size distributions at source lakes, thus I focused my comparisons of spawning distributions between ponds and used lake sp awning periodicities to evaluate the relative differences. Adult size structur es did not drastically differ betw een source lakes (M.W. Rogers, unpublished data), thus fish size effects on spaw ning periodicity were probably similar for both

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53 source populations. I standardized brood fish size in ponds and spawning periodicity trends were similar to lake populations for each source, provid ing further evidence of a genetic component to spawning periodicity. Comparisons of spawning periodic ity using otoliths only reveal data for survivors and not the true distribution if age or size-selective mort ality occurs (Miller and Storck 1984; Isely et al. 1987). My analyses only allowed for comparis ons of surviving offspring among brood sources, which is the main concern for management and conservation purposes, but fish that hatched and incurred high short-term mortality had lower de tection probability in my study. Sampling timing is important to my results because differing mortality among ponds could have biased spawning periodicity results, especially if sensitiv ities to mortality factors differed by source For example, FLMB are less tolerant of cold temperatures than ILMB (Williamson and Carmichael 1990; Philipp and Whitt 1991). I found only one in dividual in one of the Lake Okeechobee broodstock experimental ponds that was hatched in January, suggesting a high mortality event. My median hatch date results for Lake Okeechob ee experimental fish wo uld not have differed without capturing the early-hatch ed individual, but the spawni ng duration for that pond would have been shorter. My age-0 fish from both ponds and lakes were in the range of 13-73 days and 18-136 days, respectively, which provided a va lid assessment of re lative spawning times between sources. Interpretation of my results should consider potential biases of sampling timing on combined spawning distributions. Fish hatched prior to April collections were potentially available for collection during both sa mples, which would shift median estimates to earlier in the year. In contrast early-hatched fish also endured mortality factors for a longer time period relative to later hatched fish, which could potentially shift my estimated spawning distributions towards later in th e year. Lastly, termination of my experiment in mid-May could

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54 have led to an under-representation of fish that would have hatched later, however I detected no hatching during the 30-day period prior to endi ng the experiment suggesting that my data represent the entire spaw ning distributions. In summary, compar isons of my results with future studies should consider the time of collection for age-0 fish a nd potential influences on the apparent spawning distributions. Environmental experiences of brood fish pr ior to relocation may persist and confound apparent genetic effects in common environmen t studies (Conover and Schultz 1997). Earlier spawning of FLMB could be partially due to en vironmental influences prior to relocation if FLMB were further in their annual reproduction cycle and gamete development was more advanced than ILMB when they were translocated. I stocked broodfish into experimental ponds in mid-September when such effects should have been minimized. Gro ss et al. (2002) reported that plasma sex steroid concentrations were lo w for male and female FLMB in September for fish reared at Gainesville, Florida in their st udy. Increased gonadosomatic index (GSI) of FLMB reared in Gainesville, Florida began in Novemb er and peaked in February-March, which was strongly correlated with gonadal ma turation (Gross et al. 2002). I tr anslocated brood fish at least three months before spawning occurred at eith er lake. A future st udy utilizing progeny of translocated fish would further reveal genetic influences on spaw ning periodicity. Transplanting studies are useful for evaluating the geneti c basis of phenotypic va riation in spawning periodicity, but the genetic com ponent I identified suggests furthe r need to test and develop hypotheses to determine natural selection pro cesses responsible for observed differences (Conover and Schultz 1997). An important consideration when interpreting my study results is that my fish were only from two lakes, thus I did not have a random sample of FLMB or ILMB genotypes. Recent

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55 genetic studies concluded that my source populat ions were from differi ng genetic conservation management units (B.L. Barthel, unpublished data ), however I did not in clude brood fish from a range of lakes for each genetic conservation unit. My study has implications to management d ecisions regarding fish stocking programs. Outbreeding depression effects (e.g., lower recruitm ent, adult abundance, and fish size structure) of stocking FLMB with native LMB populations have not been reported throughout a widely distributed range of public water bodies stocked in the United States. However, Gharett et al. (1999) reported outbreeding depression in the F2 generation of pink salmon ( Oncorhynchus gorbuscha ) that were hybrids of stocks with distin ctly different breeding seasons, and warned that deleterious effects of outbr eeding depression may take decade s to detect. In a series of common garden experiments, Philipp et al. ( 2002) concluded that hybrid ization of largemouth bass from widely separate geographic locations (e.g., Florida, Illinois, Texas, and Wisconsin) with native Illinois fish led to a more than a 50 percent reduction in reproductive fitness relative to the original, local stock. My study did not address individual or popu lation level effects of mixing ILMB and FLMB, but genetic factors played a role in spawning timing and periodicity of translocated largemouth bass. Observed spawning periodicity appeared to be better suited for, and a local adaptation to, the environments found at each source lake. Genetic variation among local populations is likely prevalent (Conover a nd Schultz 1997), therefore I recommended that agencies take a conservative approach in st ocking programs to avoid potential outbreeding depression. I also recommended that agencies develop long term studi es that evaluate effects of mixing stocks with phenotypic differe nces in life history strategies.

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56Table 3-1. Earliest, median, a nd latest hatch dates of Florida (Lake Okeechob ee fish) and intergrade (Lake Seminole fish) larg emouth bass (Micropterus salmoides) translocated to experimental ponds at Gainesvill e, Florida in 2004 and corresponding water temperatures Water temperature (oC) PondSourceNEarliestMedianLatestHatch range (d)Earliest hatchMedian hatchLatest hatch pond aOkeechobee3026 Jan02 Apr07 Apr7212.418.218.6 pond bOkeechobee4001 Feb29 Feb09 Mar3712.312.318.4 pond cOkeechobee4112 Feb21 Feb07 Mar2415.115.422.6 pond dSeminole4007 Mar12 Mar18 Mar1117.714.217.7 pond eSeminole4022 Feb27 Feb05 Mar1215.714.019.6 pond fSeminole4006 Mar11 Mar16 Mar1020.616.019.4 (N = number aged, Earliest = earliest estimated hatch date, Median = median estimated hatch date, Latest = last estimated hatch date, Earliest hatch water temperature = mean water temperature for ear liest hatch date, Median hatch water temperature = mean water temperature for median hatch date, Late st hatch water temperature = mean wate r temperature for latest hatch date).

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57 Figure 3-1. Locations and lat itudes for Lake Seminole, Lake Okeechobee and Gainesville, Florida, USA.

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58 Figure 3-2. Five-day cohort pe rcent hatching distribution (y-axi s) for age-0 largemouth bass hatched in research ponds at Gainesv ille, Florida across dates (x-axis) and corresponding mean water temperatures (z-a xis). Sampling occurred on 15 April and 10 May, 2004. Left column (a – c) brood source was Lake Okeechobee and right column (d – f) brood source was Lake Seminole.

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59 Figure 3-3. Five-day cohort per cent hatching distribution (y-axi s) for age-0 largemouth bass collected at (a ) Lakes Okeechobee (N = 159) and (b) Seminole (N = 121) across dates (x-axis) and corresponding mean water temper atures (z-axis). Sampling occurred at Lake Okeechobee on 15 – 16 February, 22 April, and 21 – 22 June, 2004. Sampling occurred at Lake Seminole on 12 – 13 May, and 13 – 14 July, 2004.

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60 Figure 3-4. Semi-monthly hatching distributions for age-0 largemouth bass reared in research ponds in Ga inesville, Florida and in source populations at Lake Seminole and Lake Okeechobee in 2004. Figure lines are weighted to represent percent frequency.

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61 CHAPTER 4 SIMULATED INFLUENCES OF HATCH-DATE SPECIFIC SURVIVAL ON RECRUITMENT OF LARGEMOUTH BASS Density-dependent processes can dampen or magnify juvenile mortality, but the resulting pattern is relatively stable average recruit abunda nces across a broad range of spawner abundances for many fish stocks (Walters and Ko rman 1999). Density-dependent mortality is believed to result in compensatory juvenile su rvival at low egg produc tion and regulation of juvenile survival at high egg production, thus causing the observed stability in stock-recruitment relationships for many species (Walters and Ma rtell 2004). Regulation can be influenced by multiple biotic (e.g., predation and starvation) an d abiotic (e.g., temperature and water clarity) factors, which have greatest eff ects during early life stages (i.e., “crucial period,” Shepherd and Cushing 1990) and interact to affect survival. Although density-dependent mortality in juvenile fishes has received much attention, few cases exist where mechanisms leading to regulatory processes have been identified (Shepherd and Cushing 1990) or how those mechanisms may act within year classes to influence recruitment. The relative effect of mechanisms influe ncing survival, and thus, resulting in compensation and regulation, have been shown to va ry with hatching dates such that members of a year class born at different times may suffer from differing mo rtality forces. For example, Bestgen et al. (2007) re ported that early hatched Colorado pikeminnow Ptychocheilus lucius have higher mortality during earl y life than later hatched memb ers of a year class due to temporal habitat overlap with their predators. In contrast, early hatchi ng may result in increased survival by enhanced foraging or reduced pred ation mortality to gape-limited predators (e.g., Ludsin and DeVries 1997). Hatc h-date dependent survival has often been identified for both marine and freshwater fishes, but effects of hatc hing-date dependent surviv al on total year class

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62 abundance and composition (i.e., contributions of differing hatching sub-cohorts) are difficult to investigate. My field study (Chapter 2) showed that su rvival and growth of age-0 largemouth bass were hatch date specific. Here, I explored th e long term implications of observed sub-cohortspecific growth and mortality on total recruitment to age-1 and adult biomass. I also assessed how those metrics would change if mortality observations had persistently differed for a given sub-cohort or among sub-cohorts. Thus, I ev aluated the potential for compensation and regulation at varying levels of s ub-cohort specific mortality relative to my field observations. I used trophic-based ecosystem models to evaluate relationships between sub-cohort mortality and recruitment to the adu lt population. Models were evaluated for largemouth bass and represented a south Florida population and a north Florida po pulation to incorporate among system variation in juvenile largemouth bass population char acteristics and community composition. Methods I used Ecopath with Ecosim (EWE; www.ecopa th.org) ecological modeling software to evaluate influences of hatching su b-cohort-specific survival on year class structure and biomass. Two EWE models were developed to explore how results may vary among populations (i.e., between a north Florida system and south Flor ida system). Models differed via observed differences in hatching distribut ions due to latitudinal (e.g., te mperature) and source population (e.g., differing genetic composition) influences and community composition (e.g., prey fish abundance). A mass-balance food web model wa s developed (Ecopath process) for each population and simulations were performed to predic t effects of differentia l sub-cohort survival on age-1 biomass, adult biomass, and year cla ss composition at equilibrium (Ecosim process).

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63 Ecopath Models An Ecopath model is a static mass-balan ce representation of production and losses among living components (i.e., functional groups) of an ecosystem. Balance occurs when production is equal to predation mortality, non-predation mortalit y, and fishing harvests (i .e., in the absence of immigration or emigration) (Pauly et al. 2000) for each prey functional group ( i ) and predator functional group ( j =1 to n predator groups) according to the Ecopath master equations: i ij j n j j i i iY DC B Q B EE B P B 1 (4-1) and i i i iU R P Q (4-2) where: where Bi and Bj are biomasses of i and j ( P/B )i is the production/biomass ratio for i and should be entered as the total inst antaneous mortality rate ( Zi) for vertebrate groups or turnover rate for invertebrates and primary producers, EEi is the fraction of ( P/B )i specified in the model, (Q/B)j is the total food consumption per unit biomass of j DCij is the proportion of prey group i to predator group j ’s total diet, Yi is harvest of group i Ri is respiration of group i and Ui is the unassimilated portion of group i ’s consumption according to the equations (Christensen et al. 2005): ecotrophic efficiency = i i i iP Y M EE ) ( 2 (4-3) total predation mortality = n j ij j j iDC B Q B M1 ) ( 2for j = 1 to n predators (4-4) other mortality = ) 1 () ( 0EE P Mi i where i i i i i iEE P M B Y P 1) ( 2 (4-5) harvest = n w i i iB F Y1 for w = 1 to n fisheries Fi is fleet specific mortality on i (4-6)

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64 (Figure 4-1). Input rates (e.g., P/B) are entered using annual es timates. Components of my ecosystems consisted of functional groups with similar foraging life histories (Table 4-1). Linkages among functional groups were input by a diet composition matrix for each model that described the percent weight of each prey f unctional group to each predator functional group’s diet (i.e., DCij). Each of my models (i.e., one for north Florida and one for south Florida lakes) was comprised of over 20 functional groups, but at least 12 of these represented multiple (i.e., four to five) age stanzas for early, middle, and late-h atched largemouth bass hatching sub-cohorts (Tables 4-2, 4-3; see Chapter 2 for hatching dates corresponding to s ub-cohorts). The age stanzas for each sub-cohort were used to track fish through their ontoge ny, so that different information (e.g., P/B, diet composition, etc.) could be specified for each life stage and subcohort. The stanza structure also allowed specification of sub-cohor t-specific age at a given time period due to differences in ha tching dates among sub-cohorts. For example, the “summer to fall” age stanza would have relatively older fish for the early hatched subcohort compared to the late hatched sub-cohort in each model. Ecopath required several inputs for each functional group that allowed the model to balance population ad ditions and losses. Fo r each functional group, the model required three of the four following inputs: B (kg/ha), P/B (year-1), Q/B (year-1), and EE. Ecopath creates a series of linear models (Equation 4-1) (i.e., one for each functional group) and simultaneously solves the equations for the parameter not input by the user (i.e., either B, P/B, Q/B, or EE) (Christensen et al. 2005) Individual fisheries a nd functional-group specific exploitation rates were also mode l inputs as described below. Input data for my Ecopath models were obtained from my fiel d data and published literature (see Tables 42 and 4-3 for specific sources). Fiel d data were collected at two south

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65 Florida lakes (i.e., Lakes Istokpoga and Okeechobee) and two north Florida lakes (i.e., Lakes Seminole and Talquin) during 2003 and 2004. Bi omass for each functional group was estimated from average summer (i.e., June/July) block-ne t catches for each region (see Chapter 2 for blocknetting methods). Production/biomass for juveni le largemouth bass stanzas were estimated from hatching sub-cohort specific su rvival rates in c onsecutive block-netti ng samples. Other functional group P/B and all Q/B estimates were derived from www.fishbase.org and published literature (Tables 4-2 and 4-3). A weighted average (weighted by species abundance) was used for each non-largemouth bass fish functional group’s B, P/B, and Q/B inputs. Juvenile largemouth bass diet matrices were obtained fr om field data (see Chap ter 2), whereas other functional group diet matri ces were derived from www.fishbase.org and published literature (see Appendices 1 and 2). Diet c ontents were specified for each sub-cohort through their first summer. I could not estimate ages (i.e., speci fy sub-cohorts) for age-0 largemouth bass after summer, and thus, I assumed that all sub-cohort diet matrices a nd survival were the same for a given age following their first summer through their adult stages (see Appendices 1 and 2). For fishes exhibiting negativ e exponential mortality thr ough time and length at age following the von Bertalanffy growth function, P/B ratios are equivalent to total instantaneous mortality (Z) (Allen 1971) such that: P/B = Z = F + M2 + M0 (4-7) (Christensen et al. 2005). Four fisheries were established in each of my Ecopath models. A recreational fishery exploited e ach adult largemouth bass functiona l group at 20%, assuming that fishing mortality in these ecosystems was sim ilar to fishing rates from other Florida and southeast black bass fisher ies (Renfro et al. 1999; O’Bara et al 2001; Allen et al. In Press). An individual fishery was created to target each largemouth bass hatching sub-cohort soon after

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66 hatching, which allowed me to va ry mortality in simulations. T hus, I used the fishing mortality function to modify total mortality of each j uvenile largemouth bass hatching sub-cohort in Ecosim (see below). Ecosim Simulations Ecosim provides temporally dynamic simu lations of functional group biomass changes via system perturbations relative to Ecopath’s ba seline (balanced) conditions. Ecosim is flexible in how system perturbations can be modeled, but most common a pplications involve simulations by varying fishing mortality rates relative to ba seline conditions. Ecosim estimates biomass changes for functional groups using differentia l equations similar to those in Ecopath. Abundance changes for age-specified multi-stan zas are modeled using Deriso-Schnute delaydifference models (Deriso 1980; Schnute 1987; Wa lters et al. 2000). Following a simulated system perturbation in Ecosim, functional group consumption rates and predation rates on those functional groups are moderated by prey behaviors that limit pred ation exposure (Walters et al. 1997; Christensen et al. 2005). Ecosim models have been de scribed as “hungry predator models” (Plaganyi and Butterwor th 2004) where predat ors compete for vulnerable prey and predator-prey interactions are estimated with fo raging arena assumptions (Walters et al. 2000). Vulnerable (Vij) and non-vulnerable (Bi-Vij) prey biomasses are modeled with differential equations as: j ij ij ij ij ij i ij ijB V a V v V B v dt dV (4-8) ij ij ij i ij ij iV v V B v dt V B d' (4-9) where vij is a flow rate for prey i from invulnerable to vulnerable, v’ij is a flow rate for prey i from vulnerable to invulnerable, aijVijBj is total consumption rate Qij of prey i by predator j, Bj is

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67 abundance of predator functional group j, and aij is an effective search rate for prey i by predator j (Figure 4-2). Solving the vulnerability equati ons (Equations 4-8 and 4-9) when changes to vij are zero (i.e., assuming that the distribution of prey i between vulnerable and invulnerable states reaches equilibrium faster than changes in total biomass; Walters and Martell 2004) and substitution results in the equa tion used by Ecosim to describe trophic flows of prey group i to predator j: j ij ij ij j i ij ij ijB a v v B B v a Q '/ (4-10) (Christensen et al. 2005). E quation 4-10 results in ratio depe ndent predation (Walters and Martell 2004) and has been extend ed in recent EWE software to incorporate othe r components of predator foraging such as: prey and predator feeding times, ha ndling times, mediation forcing effects, and long-term or seasonal forcing e ffects (see Christensen et al. 2005). Thus, consumption of predator j on prey i is a function of search and pr edator and prey biomasses, but is constrained by vulnerabilitie s following foraging arena theory (Walters and Martell 2004). Modeling functional groups wit hout foraging time adjustments or switching power results in changes to predator diets according to foraging arena theory relative to encounter rates (see Walters and Martell 2004). Thus, predator di et compositions change proportionally with changes in prey functional group abundances follow ing the assumption that a reduction in a prey biomass reduces intraspecific competition among remaining prey and reduces prey risk to predator functional groups (i.e., lowering encounter rates), but this relationship is mediated by vulnerabilities. Vulnerabilities are required inputs of Ecosim and repres ent the maximum predation mortality a predator can exert on a prey functional gr oup relative to baseline (i.e., Ecopath) predation mortality due to mediation via vulnerab ility flow rates. Low vulnerabilities (e.g., one)

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68 for a prey functional group repres ent slow flows from the invulnera ble to the vulnerable state and make prey availability to predators largel y independent of predat or biomasses. High vulnerabilities (e.g., 100) represen t fast flows from the invulnerabl e to vulnerable state and result in large changes to predation mortality for a prey functional group following predator biomass increases (Christensen et al. 2005) Vulnerabilities for the earlie st age-stanzas of all largemouth bass sub-cohorts were low (i.e., close to one) to emulate factors resulting in very low vulnerability to predators shortly after hatching (e.g., a spatial refuge schooling, or parental nest guarding) and were allowed to exhibit risk-sens itive foraging behaviors (i.e., allowed to vary foraging times), thus resulting in Beverton-Holt shaped stoc k-recruitment relationships (Christensen et al. 2005). Fo r the other age stanzas, I allowed Ecosim to estimate the vulnerability for the most abundant LMB hatching sub-cohort from each age stanza (i.e., time period) and used a scaling fact or to estimate vulnerabilities fo r other LMB hatching sub-cohorts within that age/time stanza. Sub-cohort vulnerab ilities for each stanza were scaled such that the vulnerability value for a sub-cohort times their base biomass was equal across all sub-cohorts, thus resulting in increased vulnerabilities for sub-cohorts with lower biomass (as suggested by Carl Walters, personal communicatio n). Scaling vulnerabilities re lative to baseline biomasses allowed for increased predator consumption as biomasses increased for initially less abundant largemouth bass sub-cohorts. In general, all fu nctional group vulnerabilities were entered such that 1< v < 6. Estimated subchort vulnerabilities were higher for older age stanzas than at their initial life stages. These groups were not allowed to change foraging times relative to baseline conditions. The result of these specifications was compensatory growth with biomass changes as predicted by foraging arena theory (Walters and Martell 2004).

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69 Simulations increased and decreased hatchi ng sub-cohort specific mortality by 50% relative to baseline conditions. Simulated mort ality changes were inco rporated by changing subcohort specific fishing mortality (F in Equation 4-7), which resulted in changes to Z for a subcohort. Mortality was applied until the system re-equilibrated. Changes to mortality were applied to the first ag e stanza for each sub-cohort to repres ent an early-life mortality source, where that early mortality could have represente d multiple factors commonly reported to result in early juvenile mortality (e.g., predation, Be stgen et al. 2006; or environmental factors, Steinhart et al. 2005). Importantly the simulated changes in mortal ity were applied to juvenile biomasses estimated from block-net samples (a ll > 15 mm total length, TL), and thus, the 50% mortality was additional to mort ality acting on these hatching sub-cohorts from their hatching date to 15 mm TL. I used hatching sub-cohort specific biomass estimates at age-1 and adult stages as evaluation metrics for the relative effe cts of hatching-date dependent mortality on year class abundance and composition. Results Ecopath Results Ecopath models did not initially balance because EE estimates exceeded one for some functional groups, thus indicating th at losses were greater for t hose groups than production using my initial inputs. Model balances were achieve d following suggestions by Christensen et al. (2005) and Gunette et al. ( 2001), rather than using the au tomated mass-balance routine (Kavanaugh et al. 2004). In genera l, I modified input values (i.e., B, P/B, Q/B, or DCij) for fish functional groups usi ng diagnostics (e.g, P/Q and M2) recommended by Christensen et al. (2005), C. Walters (personal communication), a nd personal knowledge of field data. I used Ecopath’s sensitivity analyses routine to evaluate how changes to my input parameter values for

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70 the balanced models would affect basic paramete rs estimated by Ecopath. Ecopath’s sensitivity routine varies each input parameter from -50% to +50%, in 10% steps, and reports a percent change in estimated parameters relative to estimates using the original parameter value (Christensen et al. 2005). In general, biomass and production inputs had greater effects than consumption inputs on estimates of EE, such that a + 30% change in input values could result in up to a 43% change in EE estimates for that functional group (see A ppendices 3 and 4). Underestimating B and P/B input values resulted in stronger effects on Ecopa th estimates for that functional group than overestimating those values within the range of va riation I evaluated. Varying Q/B values only had strong eff ects (i.e., 20-30% change) for EE estimates of other predators, sunfish, and insect pr ey functional groups. Sensitivity of Ecopath estimates to other fish functional group inputs including adult largemouth bass, when varied + 30%, were less than 10%. Sensitivity analyses suggest ed that inputs for my lowest trophic levels could have large effects on Ecopath’s estimates for those trophic le vels, but those inputs had very little effect on estimates of upper trophic level bi omasses (generally less than 0. 02 kg/ha) with large changes in lower trophic level inputs (i.e., + 100%). Thus, input values of a functional group had more effect on Ecopath’s estimates for that functiona l group than estimates for other functional groups, and input values for top predator s had more influence on Ecopath es timates than input values for lower trophic levels. Ecopath uses a modification of Pianka’s (1973) niche overlap index to describe similarities in prey use between predator functional gr oups (Christensen et al. 2005) via the diet matrix input from field data. Ecopath estimates of prey niche overlap indi cated high similarities in prey types among LMB hatching sub-cohorts in spring and summe r, but niche overlap values were not always intuitive ba sed on hatching sequence. For example, prey niche overlap

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71 estimates for the north lakes model indicated th at the early hatched su b-cohort had lower prey niche overlap with the middle hatc hed sub-cohort in July (estimat e = 0.57) than with the late hatched sub-cohort (estimate = 0.84, Table 4-4). At south lakes, early and middle hatched subcohorts had high prey niche ove rlap in May (estimate = 0.82, Table 4-4). There were no estimates for the late-hatched sub-cohort’s diet overlap with other hatc hing sub-cohorts in May because these fish were just beginning to enter the population at this time. In July, the early hatched sub-cohort had similar diet overlap wi th both late and middl e-hatched sub-cohorts (estimates = 0.78 and 0.79, respectively) and mi ddle and late-hatched sub-cohorts had very high prey niche overlap (estimate = 0.97, Table 4-4). Thus, prey niche overlap was not always consistent with my expectations of finding highe st diet similarities between closest age subcohorts. Ecosim Results Hatching-date specific mortality influenced contributions of hatching sub-cohorts to the year class, but the strength of influence vari ed among simulations. At north lakes, increased survival of the early-hatched s ub-cohort resulted in similar bi omass changes in age-1 abundance of both middle and late-hatched sub-cohorts (< 1% difference between them). However, decreased survival of the early-h atched sub-cohort resulted in stronger age-1 biomass changes for the middle-hatched than the late-hatched sub-cohort relative to baseline conditions (25% increase versus 17% increase, respectively) (F igure 4-3). Variable survival of the middlehatched sub-cohort, which had the highest baseline biomass at age-1, had similar influences on age-1 biomass of early and late hatched sub-cohorts (< 3% difference between early and latehatched sub-cohorts) (Figure 4-3). Similarly, va riable survival of the late-hatched sub-cohort had similar influences on early and middl e-hatched sub-cohort biomasses at age-1 (< 3%

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72 difference between early and middle-hatched su b-cohorts) (Figure 4-3) Patterns between hatching date-specific sub-cohor t survival and biomass were similar for age-1 and adult abundance at north lakes (left ve rsus right panels on Figure 4-3), except incr eased early-hatched sub-cohort survival had similar effects on middle a nd late-hatched sub-cohor t adult biomass. At south lakes, variable mortality applied to individual hatching sub-cohorts resulted in < 15% changes in age-1 biomass for other sub-cohorts (Figure 4-4). The late -hatched sub-cohorts revealed the most sensitivity to mortality of other hatching sub-cohor ts, because their age-1 biomass changed the most (up to 14 %) as result of changes in the early-hatched sub-cohort’s survival, whereas the middle-hatched and early hatched sub-cohorts age-1 biomasses never changed more than 11% (Figure 44). Effects of hatch date speci fic sub-cohort mortality at south lakes were more pronounced for adult biomasses th an for age-1 biomasses (Figure 4-4). Thus, hatching date specific sub-cohort mortality influen ced contributions of other sub-cohorts to the year class, and those influences vari ed in magnitude among simulations. Among-lake differences appeared to influence effects of variable sub-cohort specific mortality simulations. In almost all cases, effect s of simulated mortality of a given hatching subcohort had greater influences on other hatchi ng sub-cohorts for the north Florida model compared to the south Florida model, and these results applied to biomass estimates at both age1 and adult stages. Exceptions occurred when the early-hatched sub-cohort mortality was reduced which resulted in similar changes in ag e-1 biomass (14 %) for late-hatched sub-cohorts in both regions and slightly higher late-hatched sub-cohort adult bioma ss for the south Florida model (21% versus 17% change; Figures 4-3, 44). Thus, models pred icted that hatching subcohort specific survival influenced year class biomass and composition, but patterns should be expected to vary among systems.

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73Discussion Ecosystem models predicted that persistent changes in mortality of hatching sub-cohorts could affect equilibrium year class composition and revealed high compensation in juvenile survival under differing mo rtality treatments. The models al so showed strong regulation effects on total age-1 biomass with changes in sub-cohor t mortality, via preda tion and cannibalism. Model predictions indicated that effects of s ub-cohort survival will likely vary among systems due to differences in population and community ch aracteristics. Results of my models were somewhat expected based on ecologi cal theory, but suggested that these types of models can be useful for exploring population dynamics and r ecruitment questions with in a large ecosystem context. Processes that regulate juvenile fish survival have received much attention, and it is now recognized that survival to age-1 results from a series of interdependent events during larval and juvenile stages (Ludsin and Devries 1997). The se verity of mortality alon g this series of lifestages can vary among hatching sub-cohorts and result in disproportiona te contributions of specific hatching sub-cohorts to th e year class relative to thei r proportion of total fry production (Cargnelli and Gross 1996). Given the identifica tion of hatching date specific mortality, remarkably little work has a ddressed how hatching date-depe ndent mortality may influence dynamics within cohorts. My simulations s howed weak effects of sub-cohort mortality on overall biomass at age 1 and adult biomass, beca use 50% changes in surviv al of a specific subcohort did not lead to large overall changes in ot her sub-cohort biomasses. However, biomasses of other sub-cohorts did exhi bit compensation where total ag e-1 and adult biomass did not decline substantially as a result of higher mort ality of a specific sub-cohort. My simulations suggested weaker linkages among su b-cohorts than expected base d on hatching-date sequence.

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74 For example, early and middle-hatched sub-cohort s responded similarly to simulated changes in late-hatched sub-cohort survival whereas I expected sub-coho rts hatched consecutively to interact more strongly. The level of diet niche over lap among predators does not pr ovide a reliable index of competition (e.g., Olson et al. 1995), however it does provide information on use of resource types among consumers (Abrams 1980). Ecopath estimated high prey niche overlap for all juvenile largemouth bass sub-c ohorts in spring and summer ex cept for middle versus earlyhatched sub-cohorts from the north region. Prey resource use for gape-limited juvenile fishes is often limited by body size, because larger offspring that were hatched earlier and/or had faster growth can use a larger range of prey species than smaller offspring that were later-hatched and/or slower growing (Mittelbach and Persson 1998). In my mode ls, the large overlap in prey resource use was potentially due to the high si milarity in TL among sub-cohorts and large TL ranges for each sub-cohort. For example, the ea rly-hatched sub-cohort at north lakes in July were 46-128mm TL (median TL = 92 mm), whereas the late-hatched sub-cohort were 28-100 mm TL (median TL = 58 mm). Thus, high prey overlap would be expected based on gape limitation considerations, but length similarity di d not explain the lower diet overlap estimated for middle and early-hatched sub-co horts at north lakes in July. Differences in growth rates among hatching sub-cohorts (see Chapter 2) likely led to similar length ranges and high diet overlap among hatching sub-c ohorts. Although individual vari ability in the timing of ontogenetic diet shifts of simila r sized/age fish influences th e relationship between largemouth bass size/age and prey use (Post 2003; Olson 19 96), within functional gr oup diet variability was incorporated into the di et matrices of my Ecopath models based on observed sub-cohort specific diet compositions. My inability to assign age-sp ecific diet components fo r sub-cohorts in fall

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75 and spring precluded diet overlap estimates for these stanzas, but the similarities in TL among the sub-cohorts during this period suggested that all sub-cohorts could have potentially used similar prey resources (i.e., similar gape limita tions across all sub-cohor ts). Diet overlap estimates indicated that largemouth bass hatching sub-cohorts used many of the same prey resources through summer, suggestin g that variable survival of individual hatching sub-cohorts could have strong effects on recruitment if prey abundances limited surviv al during early life. However, high diet overlap did not strongly in fluence predicted biomass at age-1 and adult populations in my Ecosim simulatio ns, indicating that prey densitie s from my field data did not infer strong prey limitations. Complex interactions among predators and juvenile largemouth bass functional groups largely regulated proportional cont ributions of hatching sub-cohorts to the year class. Predicted increases in sub-cohort biomasses via lower morta lity resulted in increased numbers of adult bass acting as predators in the syst em, and thus, biomass reductions for other hatching sub-cohorts. Importantly, functional groups were modeled such th at foraging times did not vary with prey or predator abundances, except for the youngest largemouth bass age stanzas which were assumed to restrict feeding times rath er than maximize growth when food was abundant as has been shown for Atlantic salmon Salmo salar (Orpwood et al. 2006). Increased mortality for individual hatching sub-cohorts decreased biomasses at all life stages for that sub-cohort, increased prey fish functional group biomasses, and therefore increased biomasses of other hatching sub-cohorts. Following i nduced mortality for a given subcohort, biomass increases for other hatching sub-cohorts were regulated by pr edators and suggested a “competitive juvenile bottleneck” (Werner and Hall 1979; Bystrm et al. 1998) between “other predators” and largemouth bass functional groups. Decreased survival of a given hatching sub-cohort resulted

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76 in decreased predation on “other predators” because there were fewer largemouth bass adults acting as predators at equilibrium, and thus, increased biomass for “other predators” and increased predation on other LM B hatching sub-cohorts. In c ontrast, the opposite phenomenon occurred under simulated increa sed survival of juvenile larg emouth bass hatching sub-cohorts. Thus, the model suggested cultiv ation of largemouth bass juven iles through adult influences on “other predators”, however the model did not exhibit depensation because largemouth bass hatching sub-cohort biomasses al ways rebounded to baseline levels if induced mortality was returned to base Ecopath levels. Depensation ma y not have been evident in my models because enough predators always existed in the systems to maintain restricted habitat use/foraging activities of competitors (see Walters and K itchell 2001). Evaluation of the hypothesized “competitive juvenile bottleneck” between juvenile largemouth bass functional groups and “other predators” would require further data that allowed more complex stage-structuring in the model, however this type of relationship has commonly been found in freshwater ecosystems (e.g., between bluegill and largemouth ba ss; Olson 1996, Aday et al. 2005). My simulation results are dependent on Ecopa th and Ecosim assumptions (e.g., foraging arena theory), model constraints, and data from regions with differing largemouth bass genetics (see Chapter 3). Ecopath and Ecosim have many years (> 20 and > 10, respectively) of modification, improvement, and review; however model estimates, their errors, and their application require scrutiny (s ee Plagnyi and Butterworth 2004). Essington (2007) used simulations to show that the precision of Ecopath estimates for B and EE were equivalent to the precision of the input data and he concluded that “bad data led to bad pr edictions.” I collected all the fish functional group biomass data for my models and attempted to obtain other inputs from the same or similar systems, but inputs de rived from other models and published literature

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77 certainly contributed to Ecopa th estimates in ways that may not mirror the populations I simulated. Essington (2007) al so reported that Ecopath inpu ts were more sensitive to B and P/B inputs than to diet composition data. Uncertainties regarding EWE parameter inputs and estimates are similar to those reported for other commonly used ecosystem and bioenergeticsbased models (e.g., Ney 1990, Plagnyi 2007). Model estimates were also dependent on specified stock-recruitment relati onships that were assumed to be the same for all largemouth bass sub-cohorts. Identifying stock and recruitmen t relationships is one of the most difficult problems in biological assessment (Hilborn and Walters 1992), thus there is undetermined uncertainty in these in my m odels despite the tremendous body of largemouth bass literature. However, my stock-recruitment relationships di d conform to Beverton-Holt functions, which are common across a wide range of fish species a nd populations (Walters and Martell 2004). Furthermore, I could not account fo r parental effects on juveniles (e .g., parental size and juvenile performance; Miranda and Muncy 1987; Bay lis et al. 1993; Wri ght and Gibb 2005; and Jorgensen et al. 2005). Thus, outcomes of these types of models should be treated as hypotheses that direct future research and data collection. One of the most fundamental bases for Ecosim involves assumptions for how prey fishes compete and adjust their behaviors with change s in opportunities (i.e., prey supply) and risks (i.e., predation). The vulnerabil ities schedule for functional groups as predators on their prey is one of the most important parameters in Ecosim and one of the hardest to know with reliability (Plagnyi 2004). In my models, increasing vulnerability values changed the magnitude of biomass responses, however the overall trends of my results remained the same. Default vulnerability values in Ecosim (i.e., v = 2) represent mixed bottom-up and top-down trophic flows, however using this default value can cause misleading results (Shannon et al. 2000).

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78 Although it is imperative to increas e vulnerability values for func tional groups that were likely sampled at levels much lower than Bmax (e.g., highly exploited mari ne stocks; Pauly 1995), there is no reason to suspect that thes e values should have been larg ely changed for functional groups in my models. Recent trends indicated relativel y low fishing mortality rates for largemouth bass (Quinn 1996; Allen et al. In Press) and thus, the low vulne rabilities in my mode ls infer that large increases in largemouth bass abunda nce are not expected. Vulnerab ilities I used resulted in similar fish functional group biomasses to thos e reported for southeaste rn reservoirs (e.g., Jenkins 1975). Implications Much research has indicated that juvenile fish survival is strongly density-dependent as a result of regulating processes such as predat ion, starvation, and ca nnibalism (Shepherd and Cushing 1990). Several authors have shown that the importance of the processes resulting in strong density dependence is rarely specified (W alters and Juanes 1993 and references therein), and Shepherd and Cushing (1990) suggested that a weak regulatory pro cess could result in regulation at high stock sizes and when fishing mort ality is low (as is likely for largemouth bass). My simulations suggested that hatching specific s ub-cohort mortality could have large influences on relative contributions of individual hatching sub-cohorts to a year class, however total age-1 biomass was relatively stable across all simula tions. Simulations that induced and reduced mortality of individual largem outh bass hatching sub-cohorts ha d small effects on age-1 total biomass (maximum biomass increase = 5.5% an d maximum biomass decrease = 7.0%) relative to Ecopath baseline estimates, thus sugge sting high compensation following increased subcohort mortality and strong regulation following d ecreased sub-cohort mortal ity. Predation was the most important regulating process acting on recruitment. Although re sponses to variable

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79 hatching sub-cohort mortality were stronger for the north Florid a population than for the south Florida population, predation still regulated total recruitm ent to age-1 for both spawning strategies. Walters and Juanes (1993) proposed that mortality should result in selection for a balance between growth and survival of juvenile fishes due to shorter foraging times and smaller foraging volumes in the presence of high predat or abundance, and thus, increased competition and exaggerated density-dependent effects on growth rates. S ub-cohort-specific survival could largely influence predation risks and feeding activ ities to result in strong competition, which has implications for energy allocations that could affect life-history me trics such as age at maturity, overwinter condition, and lifetime fitness. My results also have implications for fi sheries management. Other authors have proposed that fishing regulations should consider the influen ce of removing spawning adults during periods of assumed high juvenile survival (e.g., Trebitz 1991) or when progeny from any spawning period may have survival advantag es depending on inter-annual environmental variability (e.g.,. Garvey et al. 2002). Similarly, previous studies have suggested the potential for reduced progeny survival following the remo val of nest guarding adults for black bass Micropterus spp. (e.g., Philipp et al. 1997, Suski et al. 2003). My results suggest ed that indirect effects of fishing on juvenile su rvival would not likely have overwhelming effects on year-class strength because ecologi cal interactions were predicted to regulate total biomass and that survival of other sub-cohorts would be expect ed to compensate if fishing greatly reduced survival of one portion of a year class. My re sults also indicated only small increases in total largemouth bass biomasses with large increases in hatching sub-cohor t survival, which may extend to stocking practices assuming that stoc king induces similar dynamics as increased subcohort survival in my simulations. Walters a nd Juanes (1993) presen ted similar reasoning for

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80 failures in northwest Pacific salmonid stockings. Potential trade-offs between parental spawning times and inter-sub-cohort interactions affecting juvenile survival necessitate further investigation for understanding re cruitment regulation, population level characteristics, and fisheries management.

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81 Table 4-1. Species composition of non-largemouth bass fish groups. Functional Group NameSpecies common nameSpecies taxonomic namesouth modelnorth model other predatorsAtlantic needlefish Strongylura marina xx Black acara Cichlasoma bimaculatum x Black crappie (> 200 mm TL) Pomoxis nigromaculatus xx Bowfin Amia calva xx Chain pickerel Esox niger xx Florida gar Lepisosteus platyrhincus xx Longnose gar Lepisosteus osseus x White catfish (> 250 mm TL) Ameiurus catus xx Yellow bullhead (>250 mm TL) Ameiurus natalis x killifish-topminnowsBluefin killifish Lucania goodei xx Eastern starhead topminnow Fundulus escambiae x Golden topminnow Fundulus chrysotus xx Least killifish Heterandria formosa xx Lined topminnow Fundulus lineolatus x Mosquitofish Gambusia holbrooki xx Sailfin molly Poecilia latipinna x Seminole killifish Fundulus seminolis xx sunfishBanded pygmy sunfish Elassoma zonatum x Black crappie Pomoxis nigromaculatus xx Bluegill Lepomis macrochirus xx Bluespotted sunfish Enneacanthus gloriosus xx Dollar sunfish Lepomis marginatus xx Everglades pygmy sunfish Elassoma evergladei x Okefenokee pygmy sunfish Elassoma okefenokee x Redbreast sunfish Lepomis auritus x Redear sunfish Lepomis microlophus xx Spotted sunfish Lepomis punctatus xx Warmouth Lepomis gulosus xx generalists/minnowsBrook silverside Labidesthes sicculus xx Coastal shiner Notropis petersoni x Flagfish Jordanella floridae x Golden shiner Notemigonus crysoleucas xx Inland silverside Menidia beryllina xx Pugnose minnowOpsopoeodus emiliae xx Taillight shiner Notropis maculatus xx benthic fishBlue tilapia Tilapia aurea x Brown bullhead Ameiurus nebulosus xx Channel catfish Ictalurus punctatus xx Clown goby Microgobius gulosus x Gizzard shad Dorosoma cepedianum xx Lake chubsucker Erimyzon sucetta xx Pirate perch Aphredoderus sayanus x Plated catfish Hoplosternum littorale x Suckermouth catfish Hypostomus plecostomus x Swamp darter Etheostoma fusiforme xx Tadpole madtom Noturus gyrinus xx Threadfin shad Dorosoma petenense xx White catfish Ameiurus catus xx Yellow bullhead Ameiurus natalis x mm TL = total length in millimeters, x indicates that species was collected in that region and is represented in the model

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82 Table 4-2. Ecopath inputs for a north Florida eutrophic lake based on data from Lakes Seminole and Talquin collected in 2003 and 2004. Group BiomassP/BQ/B numberFunctional group (age)(kg/ha)(yr-1)(yr-1)EE 1Other predators2.56a0.40c3.20c2LMB late-hatched (to summer)0.19b8.51a41.04b3LMB late-hatched (to fall)1.46a4.16a13.70b4LMB late-hatched (age-1)1.78b2.00j6.51b5LMB late-hatched (adult)5.29b0.71e3.34c6LMB middle-hatched (to summer)0.19b8.77a41.26b7LMB middle-hatched (to fall)1.37a4.16d13.70b8LMB middle-hatched (age-1)1.67b2.00j6.51b9LMB middle-hatched (adult)4.96b0.71e3.34c10LMB early-hatched (to summer)0.15b7.48a40.18b11LMB early-hatched (to fall)1.29a4.16d13.70b12LMB early-hatched (age-1)1.58b2.00j6.51b13LMB early-hatched (adult)4.67b0.71e3.34c14killifish / topminnows3.49a2.82c44.00c15sunfish53.50a1.30c19.38c16generalists/minnows9.25a1.60c27.80c17benthic fish37.00a1.39c18.68c18crustaceans26.00b*13.90b*22.00i19insects30.20f38.00h0.70h20zooplankton15.00i35.00i0.80i21macrophytes61824.00a2.60g22phytoplankton35.00i-0.75i23detritus100.00i--P = production, B = biomass, Q = consum ption, EE = ecotrophic efficiency. ameasured in this study. bestimated by Ecopath. b*estimated by Ecopath based on inputs from Sc hramm et al. (1983) and Bull et al. (1991). cderived from www.fishbase.org dderived from Wicker and Johnson (1987). eAllen et al. (2002). fLobinske et al. (2002). gwithin range reported in Westlake (1982). hPoepperl (2003). iwithin range reported from published Ecopath models. jDeAngelis et al. (1993)

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83 Table 4-3. Ecopath inputs for a north Florida eutrophic lake based on data from Lakes Istokpoga and Okeechobee collected in 2003 and 2004. Group BiomassP/BQ/B numberFunctional group (age)(kg/ha) (yr1 )(yr1 ) EE 1Other predators 8.27a0.22c3.50c2LMB late-hatched (to spring) 0.03 b 0.00a48.99 b 3LMB late-hatched (to summer) 0.45 b 6.31a20.73 b 4LMB late-hatched (to fall) 0.54a4.16 d 11.00 b 5LMB late-hatched (age-1) 0.98 b 2.00 j 6.35 b 6LMB late-hatched (adult) 2.90 b 0.71e3.26c7LMB middle (to spring) 0.11 b 5.82a50.07 b 8LMB middle-hatched (to summer) 0.75 b 5.94a22.01 b 9LMB middle-hatched (to fall) 2.12a4.16 d 11.00 b 10LMB middle-hatched (age-1) 2.17 b 2.00 j 5.99 b 11LMB middle-hatched (adult) 7.04 b 0.71e3.17c12LMB early-hatched (to spring) .16 b 12.72a52.35 b 13LMB early-hatched (to summer) 0.48 b 6.72a21.48 b 14LMB early-hatched (to fall) 1.06a4.16 d 11.00 b 15LMB early-hatched (age-1) 1.61 b 2.00 j 5.96 b 16LMB early-hatched (adult) 4.77 b 0.71e3.06c17killifish / topminnows 10.40a2.32c44.00c18sunfish 73.15a0.85c17.17c19generalists/minnows 11.30a2.00c39.00c20benthic fish 12.00a1.15c22.30c21crustaceans 31.00 b* 11.00 b* 22.00 i 22insects 30.20 f 38.00 h 0.70 h 23zooplankton 15.00 i 35.00 i 0.80 i 24macrophytes 91232.00a2.60g25phytoplankton 35.00i0.75i26detritus 100.00i--P = production, B = biomass, Q = consum ption, EE = ecotrophic efficiency. ameasured in this study. bestimated by Ecopath. b*estimated by Ecopath based on inputs from Sc hramm et al. (1983) and Bull et al. (1991). cderived from www.fishbase.org dderived from Wicker and Johnson (1987). eAllen et al. (2002). fLobinske et al. (2002). gwithin range reported in Westlake (1982). hPoepperl (2003). iwithin range reported from published Ecopath models. jDeAngelis et al. (1993)

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84 Table 4-4. Ecopath estimates of diet niche overlap among age-0 largemouth bass hatching subcohorts ModelAgeLateMiddle NorthIn JulyLate1.000 Middle0.7341.000 Early0.8380.574 SouthIn MayLate1.000 Middle-1.000 Early-0.823 In JulyLate1.000 Middle0.9651.000 Early0.7830.793

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85 Figure 4-1. Representation of ecosystem flows for components of an Ecopath model consisting of three consumer groups and a detritus gr oup such that predation on a group results in production for their predators Figure 4.2. Representation of vulnerable and invulnerable st ates of prey functional group biomass and predator consumption.

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86 Figure 4-3. Percent biomass cha nge at equilibrium relative to baseline Ecopath values for simulations of variable hatch-date specific mortality for a north Florida lake. Left column panels (a-c) represent changes in tota l biomass at age-1 for varying mortality for early-hatched (a), middle-hatched (panel b), a nd late-hatched (panel c) largemouth bass. Right column panels (d-f) represent estimated changes in total adult biomass for varying mortality for early hatched (d), middle-hatche d (panel e), and late-hatched (panel f) largemouth bass.

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87 Figure 4-4. Percent biomass cha nge at equilibrium relative to baseline Ecopath values for simulations of variable hatch-date specific mortality for a south Florida lake. Left column panels (a-c) represent changes in tota l biomass at age-1 for varying mortality for early-hatched (a), middle-hatched (panel b), a nd late-hatched (panel c) largemouth bass. Right column panels (d-f) represent estimated changes in total adult biomass for varying mortality for early hatched (d), middle-hatche d (panel e), and late-hatched (panel f) largemouth bass.

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88 CHAPTER 5 SYNTHESIS AND FUTURE RESEARCH Age-0 Largemouth Bass Recruitment The results presented in this study suggest ed that hatching date -specific sub-cohort characteristics had large implications to the composition of recruits to age-1 for juvenile largemouth bass in Florida lakes. Hatching date was important to growth and survival of age-0 largemouth bass, but the strength of importance differed among latitudes. I identified slower growth for the early-hatched larg emouth bass sub-cohorts at south Florida lake s relative to earlyhatched sub-cohorts at north Florida lakes. I also found no evidence of survival advantages for early-hatched sub-cohorts relativ e to later-hatched sub-cohorts through the end of summer. These findings contrasted a popul ar recruitment hypothesis for la rgemouth bass (i.e., that earlyhatching is always advantageous), but suggested support for Garvey et al.’s (2002) hypothesis that protracted spawning may be advantageous to adult fitness where environments may be quite variable over the spawning season. My findings also contrasted th e hypothesis that size-selective overwinter mortality would largely affect survival to age-1 at southern latitudes. A lack of strong size-selective overwinter mo rtality was likely due to winter water temperatures that did not limit age-0 largemouth bass activities, and thus facilitated foraging a nd predator avoidance relative to winter water temperatures at more nor therly latitudes. My research was conducted at a lower latitude than other largemouth bass recrui tment work that has contributed to recruitment hypotheses for this species and provided eviden ce that limited the application of those hypotheses at the extreme southern ra nge of largemouth bass distributions. My results also provided in sight and potential hypotheses regarding the evolution of parental spawning strategies acro ss Florida’s latitudinal gradient. Experimental results presented in this dissertation showed that both environmental and genetic factors contributed to parental

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89 spawning times. Environmental conditions were conducive to spawning from December to May at south Florida lakes, whereas spawning at nort h Florida lakes was restrained to beginning in March due to water temperatures. Spawning distributions differed between adult fish from north and south Florida when they were translocated and spawned in simila r environments, however translocated spawning dist ributions exhibited temporal shifts relative to in-situ populations. Thus, I concluded that both genetic and e nvironmental factors c ontributed to population spawning periodicity. Having f ound little evidence for size-selective overwinter mortality in Florida, I sought other explan ations for this observation. Simulation modeling in this dissertation suggested that parent al spawning strategies could aff ect interactions among hatching sub-cohorts such that variable mortality of hatching sub-cohorts could differentially influence other hatching sub-cohorts and that strength of interactions de pended on hatching distributions. Thus, simulations provided hypothese s regarding mechanisms that regulate juve nile survival and may impose selection for adult spawning times across Florida’s latitudinal gradient. Fisheries Management Fisheries managers are most interested in age-0 survivors and mana gement activities that may increase the number of juveniles surviving to enter a fishery. My study identified differences in hatch-date specific survival among Florida latitude s during the period from hatching through the first summer. These resu lts appeared to be due to environmental influences, and thus, suggested that annual c onditions during spring ma y provide insight to fisheries managers relative to the strength of the year class. My results also had implications for stocking strategies in Florida lakes. I identified largemout h bass spawning seasons and sizedistributions across Florida’s la titudes, and this information can be used by state resource managers to identify preferable timing and sizes at stocking. Fish stocking strategies such as

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90 “matching the hatch” (i.e., stocki ng fish at similar sizes to natu rally spawned fish) or stocking fish at lengths corresponding to the right tail of the length-freque ncy of natural fish, which has been successful at Lake Talquin, Florida (C. Mesing, FWC, personal communication), both require expected spawning dates fo r hatchery planning. Experime ntal results provided further evidence that genetic stocks should not be tr anslocated throughout Florida because of the contribution of genetics to pare ntal spawning times. Lastly, my simulations suggested that variable mortality among individu al hatching sub-cohorts should not have strong effects on total age-1 biomass, however year class structure (i.e ., percent contributions of individual hatching sub-cohorts) could be strongly aff ected by variable hatching sub-cohor t-specific mortality. Future Research Genetic Contributions Largemouth bass populations used for this study encompassed a natural gradient of genetic stocks. North Florida la ke populations were intergrade largemouth, whereas central and south Florida lake populations were dominantly Florida la rgemouth bass (Brandon Barthel, personal communication). Thus, genetic differences may have influenced the growth and survival results I found. Experimental work coul d discern the contribution of genetics to growth and survival, which would help further identify f actors that contribute to age-0 largemouth bass recruitment processes across Florida’s latitudi nal gradient. Further research could also potentially identify physiological differences am ong genotypes that would facilitate energetic analyses for these populations and life history theory. Evolution of Spawning Strategies Hatching-date dependent survival and influen ces on adult fitness have largely depended on short-term studies that typical ly have only followed one or tw o year classes. Technological

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91 advances in genetics techni ques provide opportunities for long-term research evaluating relationships between parental spawning, offspring survival, and fitness. For example, microsatellite analyses could allo w researchers to relate offspring characteristics and survival to parental characteristics (e.g., parent’s age or size, and parental spaw ning time) and parental fitness. DeWoody et al. (1998) used microsatel lites to identify pate rnity and maternity of redbreast sunfish Lepomis auritus progeny and found differing levels of progeny success for males with differing reproductive strategies. Gene tic markers could also allow the evaluation of offspring production by first-time ve rsus repeat spawners for iter oparous species. Information that could arise from genetics research may have important implications fo r evolutionary theory and fisheries management (e.g., fishing regulations and stock assessments for commercially fished species). Latitudinal Patterns in Life History Conducting work at the geographic extent of the largemouth bass’s range suggested that conclusions regarding recruitmen t processes from more northerly latitudes were not always applicable. Thus, there appears to be a need for further research of populations at the extremes of their species range for understanding latitudina l patterns in factors affecting juvenile life histories and evolution of adult spawning strategies. Evidence th at late-hatched progeny likely come from smaller adults (Miranda and M uncy 1988; Baylis et al. 1993) in black bass populations has resulted in hypothese s that propose fitness costs to small parents that spawn late at latitudes with severe size-selective overwinter mortality. However, the apparent lack of sizeselective overwinter mortality in Florida lakes su ggests that fitness trade-offs associated with spawning at small sizes may not be as large in Florida relative to more northerly latitudes. However, high fitness costs could still be associated with spawning at small sizes in Florida if

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92 progeny survival was low due to increased predati on or intra-year class ca nnibalism relative to survival for earlier-hatched fish. Future research in Florida, and at ot her latitudes, that can investigate mechanisms regula ting juvenile survival from specific parents across several generations would greatly facili tate our understanding of latit udinal patterns in juvenile life history and the evolution of pa rental spawning strategies.

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93 APPENDIX A DIET COMPOSITION MATRICES FOR ECOPATH MODELS

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94Table A-1. Diet co mposition inputs for north region Ecopath model Group numberGroup name1b2a3a4a5c6a7a8a9c10a11a1Other predators0.020.02 2LMB late-hatched (to summer)< 0.01 3LMB late-hatched (to fall)0.05< 0.01< 0.01 4LMB late-hatched (age-1)0.02 5LMB late-hatched (adult) 6LMB middle-hatched (to summer)< 0.01 7LMB middle-hatched (to fall)0.05< 0.01 8LMB middle-hatched (age-1)0.02 < .01 9LMB middle-hatched (adult) 10LMB early-hatched (to summer)< 0.01 11LMB early-hatched (to fall)0.04< 0.01< 0.01 12LMB early-hatched (age-1)0.02 13LMB early-hatched (adult) 14killifish / topminnows0.090.10< 0.010.060.010.20< 0.010.060.010.19< 0.01 15sunfish0.260.200.370.100.680.460.370.100.680.190.37 16generalists/minnows0.080.050.120.060.060.130.060.060.040.13 17benthic fish0.040.200.270.640.030.270.640.030.380.27 18crustaceans0.130.210.210.070.180.230.210.070.180.100.21 19insects0.140.200.010.05< 0.010.110.010.05< 0.010.100.01 20zooplankton0.050.05< 0.010.01< 0.01< 0.010.01< 0.01< 0.01 21macrophytes 22phytoplankton 23detritus< 0.01< 0.01< 0.01< 0.01< 0.01< 0.01< 0.01 24import athis study. bderived from data at www.fishbase.org cSammons and Maceina (2006). dDurant et al. (1979). ederived from literature

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95Table A-1. Continued Group numberGroup name12a13c14d15b16b17b18e19e20e1Other predators0.02 2LMB late-hatched (to summer) 3LMB late-hatched (to fall)< 0.01 4LMB late-hatched (age-1) 5LMB late-hatched (adult) 6LMB middle-hatched (to summer) 7LMB middle-hatched (to fall) 8LMB middle-hatched (age-1)< 0.01 9LMB middle-hatched (adult) 10LMB early-hatched (to summer) 11LMB early-hatched (to fall)< 0.01 12LMB early-hatched (age-1) 13LMB early-hatched (adult) 14killifish / topminnows0.060.01 15sunfish0.100.68 16generalists/minnows0.060.06 17benthic fish0.640.03 18crustaceans0.070.180.050.20 19insects0.05< 0.010.700.600.300.600.20 20zooplankton0.010.250.200.600.200.200.300.10 21macrophytes 0.400.90 22phytoplankton 0.100.10 23detritus< 0.010.100.200.70 24import athis study. bderived from data at www.fishbase.org cSammons and Maceina (2006). dDurant et al. (1979). ederived from literature

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96Table A-2. Diet co mposition inputs for south region Ecopath model Group numberGroup name1b2a3a4a5a6c7a8a9a10a11c1Other predators 0.020.02 2LMB late-hatched (to spring) 3LMB late-hatched (to summer)0.02 4LMB late-hatched (to fall)0.02< 0.01< 0.01 5LMB late-hatched (age-1)0.01 6LMB late-hatched (adult) 7LMB middle (to spring)0.01 8LMB middle-hatched (to summer)0.02 9LMB middle-hatched (to fall)0.02< 0.01 10LMB middle-hatched (age-1)0.01 < 0.01 11LMB middle-hatched (adult) 12LMB early-hatched (to spring)0.01 13LMB early-hatched (to summer)0.02 14LMB early-hatched (to fall)0.02< 0.01< 0.01 15LMB early-hatched (age-1)0.01 16LMB early-hatched (adult) 17killifish / topminnows0.100.100.100.150.010.020.100.170.150.01 18sunfish0.200.200.400.680.190.400.68 19generalists0.150.150.060.030.090.150.06 20benthic fish0.090.200.030.030.050.200.03 21crustaceans0.160.200.300.100.180.430.300.240.100.18 22insects0.100.500.40< 0.010.520.400.19< 0.01 23zooplankton0.050.200.170.07 24macrophytes 25phytoplankton 26detritus 27import1.00 athis study. bderived from data at www.fishbase.org cSammons and Maceina (2006). dDurant et al. (1979). ederived from literature

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97Table A-2. Continued Group numberGroup name12a13a14a15a16c17d18b19b20b21e22e23e1Other predators0.02 2LMB late-hatched (to spring) 3LMB late-hatched (to summer) 4LMB late-hatched (to fall)< 0.01 5LMB late-hatched (age-1) 6LMB late-hatched (adult) 7LMB middle (to spring) 8LMB middle-hatched (to summer) 9LMB middle-hatched (to fall) 10LMB middle-hatched (age-1)< 0.01 11LMB middle-hatched (adult) 12LMB early-hatched (to spring) 13LMB early-hatched (to summer) 14LMB early-hatched (to fall)< 0.01 15LMB early-hatched (age-1) 16LMB early-hatched (adult) 17killifish / topminnows0.050.200.300.150.01 18sunfish0.200.250.400.68 19generalists0.020.100.100.150.06 20benthic fish0.050.200.03 21crustaceans0.030.150.150.100.180.050.20 22insects0.700.350.10< 0.010.700.600.300.600.20 23zooplankton0.200.060.250.20.600.200.200.300.10 24macrophytes 0.400.90 25phytoplankton 0.100.10 26detritus 0.10.20.70 27import athis study. bderived from data at www.fishbase.org cSammons and Maceina (2006). dDurant et al. (1979). ederived from literature

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98 APPENDIX B SENSITIVITY ANALYSIS RESULTS FOR ECOPATH MODELS

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99Table B-1. Sensitivity analysis for north region Ecopath model InputGroupEstimatedInput parameter variation Groupparameteraffectedparameter-50-40-30-20-1001020304050 1Biom1EE1.000.670.430.250.110. 00-0.09-0.17-0.23-0.29-0.33 1Biom3EE-0.30-0.24-0.18-0.12-0.060.000.060.120.180.240.30 1Biom4EE-0.50-0.40-0.30-0.20-0.100.000.100.200.300.400.50 1Biom7EE-0.30-0.24-0.18-0.12-0.060.000.060.120.180.240.30 1Biom8EE-0.50-0.40-0.30-0.20-0.100.000.100.200.300.400.50 1Biom11EE-0.29-0.23-0.17-0.11-0.060.000.060.110.170.230.29 1Biom12EE-0.50-0.40-0.30-0.20-0.100.000.100.200.300.400.50 1Biom14EE-0.05-0.04-0.03-0.02-0.010.000.010.020.030.040.05 1Prod/biom1EE1.000.670.430.250.1 10.00-0.09-0.17-0.23-0.29-0.33 1Cons/biom3EE-0.30-0.24-0.18-0.12-0.060.000.060.120.180.240.30 1Cons/biom4EE-0.50-0.40-0.30-0.20-0.100.000.100.200.300.400.50 1Cons/biom7EE-0.30-0.24-0.18-0.12-0.060.000.060.120.180.240.30 1Cons/biom8EE-0.50-0.40-0.30-0.20-0.100.000.100.200.300.400.50 1Cons/biom11EE-0.29-0.23-0.17-0.11-0.060.000.060.110.170.230.29 1Cons/biom12EE-0.50-0.40-0.30-0.20-0.100.000.100.200.300.400.50 1Cons/biom14EE-0.05-0.04-0.03-0.02-0.010.000.010.020.030.040.05 2Biom2EE1.000.670.430.250.110. 00-0.09-0.17-0.23-0.29-0.33 2Biom14EE-0.06-0.05-0.04-0.02-0.010.000.010.020.040.050.06 2Prod/biom2EE1.000.670.430.250.1 10.00-0.09-0.17-0.23-0.29-0.33 2Cons/biom14EE-0.06-0.05-0.04-0.02-0.010.000.010.020.040.050.06 3Biom3EE1.000.670.430.250.110. 00-0.09-0.17-0.23-0.29-0.33 3Biom15EE-0.06-0.05-0.03-0.02-0.010.000.010.020.030.050.06 3Biom16EE-0.09-0.07-0.06-0.04-0.020.000.020.040.060.070.09 3Biom17EE-0.06-0.05-0.04-0.03-0.010.000.010.030.040.050.06 3Prod/biom3EE1.000.670.430.250.1 10.00-0.09-0.17-0.23-0.29-0.33 3Cons/biom15EE-0.06-0.05-0.03-0.02-0.010.000.010.020.030.050.06 3Cons/biom16EE-0.09-0.07-0.06-0.04-0.020.000.020.040.060.070.09 3Cons/biom17EE-0.06-0.05-0.04-0.03-0.010.000.010.030.040.050.06 4Biom4EE1.000.670.430.250.110. 00-0.09-0.17-0.23-0.29-0.33 4Biom14EE-0.05-0.04-0.03-0.02-0.010.000.010.020.030.040.05 4Biom17EE-0.09-0.07-0.05-0.04-0.020.000.020.040.050.070.09 4Prod/biom4EE1.000.670.430.250.1 10.00-0.09-0.17-0.23-0.29-0.33 4Cons/biom14EE-0.05-0.04-0.03-0.02-0.010.000.010.020.030.040.05 4Cons/biom17EE-0.09-0.07-0.05-0.04-0.020.000.020.040.050.070.09 5Biom1EE-0.18-0.14-0.11-0.07-0.040.000.040.070.110.140.18 5Biom3EE-0.07-0.06-0.04-0.03-0.010.000.010.030.040.060.07 5Biom5EE1.000.670.430.250.110. 00-0.09-0.17-0.23-0.29-0.33 5Biom7EE-0.07-0.06-0.04-0.03-0.010.000.010.030.040.060.07 5Biom11EE-0.08-0.06-0.05-0.03-0.020.000.020.030.050.060.08 5Biom15EE-0.09-0.07-0.05-0.04-0.020.000.020.040.050.070.09 5Biom16EE-0.04-0.03-0.02-0.02-0.010.000.010.020.020.030.04

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100Table B-1. Continued InputGroupEstimatedInput parameter variation Groupparameteraffectedparameter-50-40-30-20-1001020304050 5Prod/biom5EE1.000.670.430.250.1 10.00-0.09-0.17-0.23-0.29-0.33 5Cons/biom1EE-0.18-0.14-0.11-0.07-0.040.000.040.070.110.140.18 5Cons/biom3EE-0.07-0.06-0.04-0.03-0.010.000.010.030.040.060.07 5Cons/biom7EE-0.07-0.06-0.04-0.03-0.010.000.010.030.040.060.07 5Cons/biom11EE-0.08-0.06-0.05-0.03-0.020.000.020.030.050.060.08 5Cons/biom15EE-0.09-0.07-0.05-0.04-0.020.000.020.040.050.070.09 5Cons/biom16EE-0.04-0.03-0.02-0.02-0.010.000.010.020.020.030.04 6Biom6EE1.000.670.430.250.110. 00-0.09-0.17-0.23-0.29-0.33 6Biom14EE-0.12-0.09-0.07-0.05-0.020.000.020.050.070.090.12 6Prod/biom6EE1.000.670.430.250.1 10.00-0.09-0.17-0.23-0.29-0.33 6Cons/biom14EE-0.12-0.09-0.07-0.05-0.020.000.020.050.070.090.12 7Biom7EE1.000.670.430.250.110. 00-0.09-0.17-0.23-0.29-0.33 7Biom15EE-0.05-0.04-0.03-0.02-0.010.000.010.020.030.040.05 7Biom16EE-0.09-0.07-0.05-0.04-0.020.000.020.040.050.070.09 7Biom17EE-0.06-0.05-0.04-0.02-0.010.000.010.020.040.050.06 7Prod/biom7EE1.000.670.430.250.1 10.00-0.09-0.17-0.23-0.29-0.33 7Cons/biom15EE-0.05-0.04-0.03-0.02-0.010.000.010.020.030.040.05 7Cons/biom16EE-0.09-0.07-0.05-0.04-0.020.000.020.040.050.070.09 7Cons/biom17EE-0.06-0.05-0.04-0.02-0.010.000.010.020.040.050.06 8Biom8EE1.000.670.430.250.110. 00-0.09-0.17-0.23-0.29-0.33 8Biom14EE-0.05-0.04-0.03-0.02-0.010.000.010.020.030.040.05 8Biom17EE-0.08-0.07-0.05-0.03-0.020.000.020.030.050.070.08 8Prod/biom8EE1.000.670.430.250.1 10.00-0.09-0.17-0.23-0.29-0.33 8Cons/biom14EE-0.05-0.04-0.03-0.02-0.010.000.010.020.030.040.05 8Cons/biom17EE-0.08-0.07-0.05-0.03-0.020.000.020.030.050.070.08 9Biom1EE-0.17-0.13-0.10-0.07-0.030.000.030.070.100.130.17 9Biom3EE-0.07-0.05-0.04-0.03-0.010.000.010.030.040.050.07 9Biom7EE-0.07-0.05-0.04-0.03-0.010.000.010.030.040.050.07 9Biom9EE1.000.670.430.250.110. 00-0.09-0.17-0.23-0.29-0.33 9Biom11EE-0.07-0.06-0.04-0.03-0.010.000.010.030.040.060.07 9Biom15EE-0.08-0.07-0.05-0.03-0.020.000.020.030.050.070.08 9Biom16EE-0.04-0.03-0.02-0.02-0.010.000.010.020.020.030.04 9Prod/biom9EE1.000.670.430.250.1 10.00-0.09-0.17-0.23-0.29-0.33 9Cons/biom1EE-0.17-0.13-0.10-0.07-0.030.000.030.070.100.130.17 9Cons/biom3EE-0.07-0.05-0.04-0.03-0.010.000.010.030.040.050.07 9Cons/biom7EE-0.07-0.05-0.04-0.03-0.010.000.010.030.040.050.07 9Cons/biom11EE-0.07-0.06-0.04-0.03-0.010.000.010.030.040.060.07 9Cons/biom15EE-0.08-0.07-0.05-0.03-0.020.000.020.030.050.070.08 9Cons/biom16EE-0.04-0.03-0.02-0.02-0.010.000.010.020.020.030.04 10Biom10EE1.000.670.430.250.11 0.00-0.09-0.17-0.23-0.29-0.33 10Biom14EE-0.08-0.07-0.05-0.03-0.020.000.020.030.050.070.08 10Prod/biom10EE1.000.670.430.250 .110.00-0.09-0.17-0.23-0.29-0.33 10Cons/biom14EE-0.08-0.07-0.05-0.03-0.020.000.020.030.050.070.08

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101Table B-1. Continued InputGroupEstimatedInput parameter variation Groupparameteraffectedparameter-50-40-30-20-1001020304050 11Biom11EE1.000.670.430.250.110.00-0.09-0.17-0.23-0.29-0.33 11Biom15EE-0.05-0.04-0.03-0.02-0.010.000.010.020.030.040.05 11Biom16EE-0.08-0.07-0.05-0.03-0.020.000.020.030.050.070.08 11Biom17EE-0.06-0.05-0.03-0.02-0.010.000.010.020.030.050.06 11Prod/biom11EE1.000.670.430.250.110.00-0.09-0.17-0.23-0.29-0.33 11Cons/biom15EE-0.05-0.04-0.03-0.02-0.010.000.010.020.030.040.05 11Cons/biom16EE-0.08-0.07-0.05-0.03-0.020.000.020.030.050.070.08 11Cons/biom17EE-0.06-0.05-0.03-0.02-0.010.000.010.020.030.050.06 12Biom12EE1.000.670.430.250.110.00-0.09-0.17-0.23-0.29-0.33 12Biom14EE-0.05-0.04-0.03-0.02-0.010.000.010.020.030.040.05 12Biom17EE-0.08-0.06-0.05-0.03-0.020.000.020.030.050.060.08 12Prod/biom12EE1.000.670.430.250.110.00-0.09-0.17-0.23-0.29-0.33 12Cons/biom14EE-0.05-0.04-0.03-0.02-0.010.000.010.020.030.040.05 12Cons/biom17EE-0.08-0.06-0.05-0.03-0.020.000.020.030.050.060.08 13Biom1EE-0.16-0.13-0.09-0.06-0.030.000.030.060.090.130.16 13Biom3EE-0.06-0.05-0.04-0.03-0.010.000.010.030.040.050.06 13Biom7EE-0.06-0.05-0.04-0.03-0.010.000.010.030.040.050.06 13Biom11EE-0.07-0.05-0.04-0.03-0.010.000.010.030.040.050.07 13Biom13EE1.000.670.430.250.110.00-0.09-0.17-0.23-0.29-0.33 13Biom15EE-0.08-0.06-0.05-0.03-0.020.000.020.030.050.060.08 13Biom16EE-0.04-0.03-0.02-0.01-0.010.000.010.010.020.030.04 13Prod/biom13EE1.000.670.430.250.110.00-0.09-0.17-0.23-0.29-0.33 13Cons/biom1EE-0.16-0.13-0.09-0.06-0.030.000.030.060.090.130.16 13Cons/biom3EE-0.06-0.05-0.04-0.03-0.010.000.010.030.040.050.06 13Cons/biom7EE-0.06-0.05-0.04-0.03-0.010.000.010.030.040.050.06 13Cons/biom11EE-0.07-0.05-0.04-0.03-0.010.000.010.030.040.050.07 13Cons/biom15EE-0.08-0.06-0.05-0.03-0.020.000.020.030.050.060.08 13Cons/biom16EE-0.04-0.03-0.02-0.01-0.010.000.010.010.020.030.04 14Biom14EE1.000.670.430.250.110.00-0.09-0.17-0.23-0.29-0.33 14Biom19Biom-0.04-0.04-0.03-0.02-0.010.000.010.020.030.040.04 14Biom20Biom-0.04-0.03-0.02-0.02-0.010.000.010.020.020.030.04 14Biom21Cons/biom-0.04-0.03-0.02-0.02-0.010.000.010.020.020.030.04 14Prod/biom14EE1.000.670.430.250.110.00-0.09-0.17-0.23-0.29-0.33 14Cons/biom19Biom-0.04-0.04-0.03-0.02-0.010.000.010.020.030.040.04 14Cons/biom20Biom-0.04-0.03-0.02-0.02-0.010.000.010.020.020.030.04 14Cons/biom21Cons/biom-0.04-0.03-0.02-0.02-0.010.000.010.020.020.030.04 15Biom15EE1.000.670.430.250.110.00-0.09-0.17-0.23-0.29-0.33 15Biom18EE-0.43-0.34-0.26-0.17-0.090.000.090.170.260.340.43 15Biom19Biom-0.25-0.20-0.15-0.10-0.050.000.050.100.150.200.25 15Biom20Biom-0.22-0.18-0.13-0.09-0.040.000.040.090.130.180.22 15Biom21Cons/biom-0.22-0.17-0.13-0.09-0.040.000.040.090.130.170.22 15Prod/biom15EE1.000.670.430.250.110.00-0.09-0.17-0.23-0.29-0.33 15Cons/biom18EE-0.43-0.34-0.26-0.17-0.090.000.090.170.260.340.43

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102Table B-1. Continued InputGroupEstimatedInput parameter variation Groupparameteraffectedparameter-50-40-30-20-1001020304050 15Cons/biom19Biom-0.25-0.20-0.15-0.10-0.050.000.050.100.150.200.25 15Cons/biom20Biom-0.22-0.18-0.13-0.09-0.040.000.040.090.130.180.22 15Cons/biom21Cons/biom-0.22-0.17-0.13-0.09-0.040.000.040.090.130.170.22 16Biom16EE1.000.670.430.250.110.00-0.09-0.17-0.23-0.29-0.33 16Biom20Biom-0.09-0.07-0.06-0.04-0.020.000.020.040.060.070.09 16Prod/biom16EE1.000.670.430.250.110.00-0.09-0.17-0.23-0.29-0.33 16Cons/biom20Biom-0.09-0.07-0.06-0.04-0.020.000.020.040.060.070.09 17Biom17EE1.000.670.430.250.110.00-0.09-0.17-0.23-0.29-0.33 17Biom19Biom-0.17-0.14-0.10-0.07-0.030.000.030.070.100.140.17 17Biom20Biom-0.15-0.12-0.09-0.06-0.030.000.030.060.090.120.15 17Biom21Cons/biom-0.14-0.12-0.09-0.06-0.030.000.030.060.090.120.14 17Prod/biom17EE1.000.670.430.250.110.00-0.09-0.17-0.23-0.29-0.33 17Cons/biom19Biom-0.17-0.14-0.10-0.07-0.030.000.030.070.100.140.17 17Cons/biom20Biom-0.15-0.12-0.09-0.06-0.030.000.030.060.090.120.15 17Cons/biom21Cons/biom-0.14-0.12-0.09-0.06-0.030.000.030.060.090.120.14 18Biom18EE1.000.670.430.250.110.00-0.09-0.17-0.23-0.29-0.33 18Biom21Cons/biom-0.07-0.06-0.04-0.03-0.020.000.020.030.040.060.07 18Prod/biom18EE1.000.670.430.250.110.00-0.09-0.17-0.23-0.29-0.33 18Cons/biom21Cons/biom-0.07-0.06-0.04-0.03-0.020.000.020.030.040.060.07 19Prod/biom19Biom1.000.670.430.250.110.00-0.09-0.17-0.23-0.29-0.33 19Prod/biom20Biom0.450.300.190.110.050.00-0.04-0.08-0.10-0.13-0.15 19Prod/biom21Cons/biom0.850.570.370.210.100.00-0.08-0.14-0.20-0.24-0.28 19Cons/biom20Biom-0.23-0.18-0.14-0.09-0.050.000.050.090.140.180.23 19Cons/biom21Cons/biom-0.43-0.34-0.26-0.17-0.090.000.090.170.260.340.43 19EE19Biom1.000.670.430.250.110.00-0.09-0.17-0.23-0.29-0.33 19EE20Biom0.450.300.190.110.050.00-0.04-0.08-0.10-0.13-0.15 19EE21Cons/biom0.850.570.370.210.100.00-0.08-0.14-0.20-0.24-0.28 20Prod/biom20Biom1.000.670.430.250.110.00-0.09-0.17-0.23-0.29-0.33 20EE20Biom1.000.670.430.250.110.00-0.09-0.17-0.23-0.29-0.33 21Biom21Cons/biom1.000.670.430.250.110.00-0.09-0.17-0.23-0.29-0.33 21Prod/biom21Cons/biom1.000.670.430.250.110.00-0.09-0.17-0.23-0.29-0.33

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103Table B-2. Sensitivity analysis for south region Ecopath model InputGroupEstimatedInput parameter variation Groupparameteraffectedparameter-50-40-30-20-1001020304050 1Biom3EE-0.09-0.07-0.05-0.03-0.020.000.020.030.050.070.09 1Biom4EE-0.36-0.28-0.21-0.14-0.070.000.070.140.210.280.36 1Biom5EE-0.50-0.40-0.30-0.20-0.100.000.100.200.300.400.50 1Biom7EE-0.24-0.19-0.14-0.10-0.050.000.050.100.140.190.24 1Biom8EE-0.50-0.40-0.30-0.20-0.100.000.100.200.300.400.50 1Biom9EE-0.36-0.28-0.21-0.14-0.070.000.070.140.210.280.36 1Biom10EE-0.50-0.40-0.30-0.20-0.100.000.100.200.300.400.50 1Biom12EE-0.08-0.06-0.05-0.03-0.020.000.020.030.050.060.08 1Biom13EE-0.50-0.40-0.30-0.20-0.100.000.100.200.300.400.50 1Biom14EE-0.36-0.28-0.21-0.14-0.070.000.070.140.210.280.36 1Biom15EE-0.50-0.40-0.30-0.20-0.100.000.100.200.300.400.50 1Biom17EE-0.07-0.06-0.04-0.03-0.010.000.010.030.040.060.07 1Biom18EE-0.05-0.04-0.03-0.02-0.010.000.010.020.030.040.05 1Biom19EE-0.13-0.11-0.08-0.05-0.030.000.030.050.080.110.13 1Biom20EE-0.11-0.09-0.07-0.05-0.020.000.020.050.070.090.11 1Prod/biom1EE1.000.670.430.250.110.00-0.09-0.17-0.23-0.29-0.33 1Cons/biom3EE-0.09-0.07-0.05-0.03-0.020.000.020.030.050.070.09 1Cons/biom4EE-0.36-0.28-0.21-0.14-0.070.000.070.140.210.280.36 1Cons/biom5EE-0.50-0.40-0.30-0.20-0.100.000.100.200.300.400.50 1Cons/biom7EE-0.24-0.19-0.14-0.10-0.050.000.050.100.140.190.24 1Cons/biom8EE-0.50-0.40-0.30-0.20-0.100.000.100.200.300.400.50 1Cons/biom9EE-0.36-0.28-0.21-0.14-0.070.000.070.140.210.280.36 1Cons/biom10EE-0.50-0.40-0.30-0.20-0.100.000.100.200.300.400.50 1Cons/biom12EE-0.08-0.06-0.05-0.03-0.020.000.020.030.050.060.08 1Cons/biom13EE-0.50-0.40-0.30-0.20-0.100.000.100.200.300.400.50 1Cons/biom14EE-0.36-0.28-0.21-0.14-0.070.000.070.140.210.280.36 1Cons/biom15EE-0.50-0.40-0.30-0.20-0.100.000.100.200.300.400.50 1Cons/biom17EE-0.07-0.06-0.04-0.03-0.010.000.010.030.040.060.07 1Cons/biom18EE-0.05-0.04-0.03-0.02-0.010.000.010.020.030.040.05 1Cons/biom19EE-0.13-0.11-0.08-0.05-0.030.000.030.050.080.110.13 1Cons/biom20EE-0.11-0.09-0.07-0.05-0.020.000.020.050.070.090.11 3Biom3EE1.000.670.430.250.110.00-0.09-0.17-0.23-0.29-0.33 3Prod/biom3EE1.000.670.430.250.110.00-0.09-0.17-0.23-0.29-0.33 4Biom4EE1.000.670.430.250.110.00-0.09-0.17-0.23-0.29-0.33 4Prod/biom4EE1.000.670.430.250.110.00-0.09-0.17-0.23-0.29-0.33 5Biom5EE1.000.670.430.250.110.00-0.09-0.17-0.23-0.29-0.33 5Biom20EE-0.05-0.04-0.03-0.02-0.010.000.010.020.030.040.05 5Prod/biom5EE1.000.670.430.250.110.00-0.09-0.17-0.23-0.29-0.33 5Cons/biom20EE-0.05-0.04-0.03-0.02-0.010.000.010.020.030.040.05

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104Table B-2. Continued InputGroupEstimatedInput parameter variation Groupparameteraffectedparameter-50-40-30-20-1001020304050 6Biom18EE-0.05-0.04-0.03-0.02-0.010.000.010.020.030.040.05 6Prod/biom6EE1.000.670.430.250.110.00-0.09-0.17-0.23-0.29-0.33 6Cons/biom1EE-0.10-0.08-0.06-0.04-0.020.000.020.040.060.080.10 6Cons/biom18EE-0.05-0.04-0.03-0.02-0.010.000.010.020.030.040.05 7Biom7EE1.000.670.430.250.110.00-0.09-0.17-0.23-0.29-0.33 7Prod/biom7EE1.000.670.430.250.110.00-0.09-0.17-0.23-0.29-0.33 8Biom8EE1.000.670.430.250.110.00-0.09-0.17-0.23-0.29-0.33 8Biom17EE-0.04-0.03-0.02-0.02-0.010.000.010.020.020.030.04 8Prod/biom8EE1.000.670.430.250.110.00-0.09-0.17-0.23-0.29-0.33 8Cons/biom17EE-0.04-0.03-0.02-0.02-0.010.000.010.020.020.030.04 9Biom9EE1.000.670.430.250.110.00-0.09-0.17-0.23-0.29-0.33 9Biom17EE-0.10-0.08-0.06-0.04-0.020.000.020.040.060.080.10 9Biom18EE-0.04-0.03-0.02-0.02-0.010.000.010.020.020.030.04 9Biom19EE-0.07-0.05-0.04-0.03-0.010.000.010.030.040.050.07 9Biom20EE-0.05-0.04-0.03-0.02-0.010.000.010.020.030.040.05 9Prod/biom9EE1.000.670.430.250.110.00-0.09-0.17-0.23-0.29-0.33 9Cons/biom17EE-0.10-0.08-0.06-0.04-0.020.000.020.040.060.080.10 9Cons/biom18EE-0.04-0.03-0.02-0.02-0.010.000.010.020.020.030.04 9Cons/biom19EE-0.07-0.05-0.04-0.03-0.010.000.010.030.040.050.07 9Cons/biom20EE-0.05-0.04-0.03-0.02-0.010.000.010.020.030.040.05 10Biom10EE1.000.670.430.250.110.00-0.09-0.17-0.23-0.29-0.33 10Biom17EE-0.05-0.04-0.03-0.02-0.010.000.010.020.030.040.05 10Biom18EE-0.04-0.04-0.03-0.02-0.010.000.010.020.030.040.04 10Biom19EE-0.06-0.05-0.04-0.02-0.010.000.010.020.040.050.06 10Biom20EE-0.11-0.09-0.07-0.05-0.020.000.020.050.070.090.11 10Prod/biom10EE1.000.670.430.250.110.00-0.09-0.17-0.23-0.29-0.33 10Cons/biom17EE-0.05-0.04-0.03-0.02-0.010.000.010.020.030.040.05 10Cons/biom18EE-0.04-0.04-0.03-0.02-0.010.000.010.020.030.040.04 10Cons/biom19EE-0.06-0.05-0.04-0.02-0.010.000.010.020.040.050.06 10Cons/biom20EE-0.11-0.09-0.07-0.05-0.020.000.020.050.070.090.11 11Biom1EE-0.24-0.19-0.14-0.10-0.050.000.050.100.140.190.24 11Biom4EE-0.07-0.06-0.04-0.03-0.010.000.010.030.040.060.07

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105Table B-2. Continued InputGroupEstimatedInput parameter variation Groupparameteraffectedparameter-50-40-30-20-1001020304050 11Biom14EE-0.07-0.06-0.04-0.03-0.010.000.010.030.040.060.07 11Biom18EE-0.13-0.10-0.08-0.05-0.030.000.030.050.080.100.13 11Biom19EE-0.04-0.03-0.02-0.02-0.010.000.010.020.020.030.04 11Prod/biom11EE1.000.670.430.250.110.00-0.09-0.17-0.23-0.29-0.33 11Cons/biom1EE-0.24-0.19-0.14-0.10-0.050.000.050.100.140.190.24 11Cons/biom4EE-0.07-0.06-0.04-0.03-0.010.000.010.030.040.060.07 11Cons/biom9EE-0.07-0.06-0.04-0.03-0.010.000.010.030.040.060.07 11Cons/biom14EE-0.07-0.06-0.04-0.03-0.010.000.010.030.040.060.07 11Cons/biom18EE-0.13-0.10-0.08-0.05-0.030.000.030.050.080.100.13 11Cons/biom19EE-0.04-0.03-0.02-0.02-0.010.000.010.020.020.030.04 12Biom12EE1.000.670.430.250.110.00-0.09-0.17-0.23-0.29-0.33 12Prod/biom12EE1.000.670.430.250.110.00-0.09-0.17-0.23-0.29-0.33 13Biom13EE1.000.670.430.250.110.00-0.09-0.17-0.23-0.29-0.33 13Biom17EE-0.05-0.04-0.03-0.02-0.010.000.010.020.030.040.05 13Prod/biom13EE1.000.670.430.250.110.00-0.09-0.17-0.23-0.29-0.33 13Cons/biom17EE-0.05-0.04-0.03-0.02-0.010.000.010.020.030.040.05 14Biom14EE1.000.670.430.250.110.00-0.09-0.17-0.23-0.29-0.33 14Biom17EE-0.08-0.07-0.05-0.03-0.020.000.020.030.050.070.08 14Biom19EE-0.04-0.03-0.02-0.01-0.010.000.010.010.020.030.04 14Prod/biom14EE1.000.670.430.250.110.00-0.09-0.17-0.23-0.29-0.33 14Cons/biom17EE-0.08-0.07-0.05-0.03-0.020.000.020.030.050.070.08 14Cons/biom19EE-0.04-0.03-0.02-0.01-0.010.000.010.010.020.030.04 15Biom15EE1.000.670.430.250.110.00-0.09-0.17-0.23-0.29-0.33 15Biom17EE-0.03-0.03-0.02-0.01-0.010.000.010.010.020.030.03 15Biom19EE-0.04-0.04-0.03-0.02-0.010.000.010.020.030.040.04 15Biom20EE-0.08-0.07-0.05-0.03-0.020.000.020.030.050.070.08 15Prod/biom15EE1.000.670.430.250.110.00-0.09-0.17-0.23-0.29-0.33 15Cons/biom17EE-0.03-0.03-0.02-0.01-0.010.000.010.010.020.030.03 15Cons/biom19EE-0.04-0.04-0.03-0.02-0.010.000.010.020.030.040.04 15Cons/biom20EE-0.08-0.07-0.05-0.03-0.020.000.020.030.050.070.08 16Biom1EE-0.16-0.13-0.09-0.06-0.030.000.030.060.090.130.16 16Biom4EE-0.05-0.04-0.03-0.02-0.010.000.010.020.030.040.05 16Biom9EE-0.05-0.04-0.03-0.02-0.010.000.010.020.030.040.05 16Biom14EE-0.05-0.04-0.03-0.02-0.010.000.010.020.030.040.05 16Biom16EE1.000.670.430.250.110.00-0.09-0.17-0.23-0.29-0.33 16Biom18EE-0.08-0.07-0.05-0.03-0.020.000.020.030.050.070.08 16Prod/biom16EE1.000.670.430.250.110.00-0.09-0.17-0.23-0.29-0.33 16Cons/biom1EE-0.16-0.13-0.09-0.06-0.030.000.030.060.090.130.16 16Cons/biom4EE-0.05-0.04-0.03-0.02-0.010.000.010.020.030.040.05 16Cons/biom9EE-0.05-0.04-0.03-0.02-0.010.000.010.020.030.040.05 16Cons/biom14EE-0.05-0.04-0.03-0.02-0.010.000.010.020.030.040.05 16Cons/biom18EE-0.08-0.07-0.05-0.03-0.020.000.020.030.050.070.08

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106Table B-2. Continued InputGroupEstimatedInput parameter variation Groupparameteraffectedparameter-50-40-30-20-1001020304050 17Biom22Biom-0.11-0.09-0.07-0.05-0.020.000.020.050.070.090.11 17Biom23Biom-0.10-0.08-0.06-0.04-0.020.000.020.040.060.080.10 17Biom24Cons/biom-0.10-0.08-0.06-0.04-0.020.000.020.040.060.080.10 17Prod/biom17EE1.000.670.430.250.110.00-0.09-0.17-0.23-0.29-0.33 17Cons/biom23Biom-0.10-0.08-0.06-0.04-0.020.000.020.040.060.080.10 17Cons/biom24Cons/biom-0.10-0.08-0.06-0.04-0.020.000.020.040.060.080.10 18Biom18EE1.000.670.430.250.110.00-0.09-0.17-0.23-0.29-0.33 18Biom21EE-0.41-0.33-0.25-0.16-0.080.000.080.160.250.330.41 18Biom22Biom-0.27-0.22-0.16-0.11-0.050.000.050.110.160.220.27 18Biom23Biom-0.22-0.17-0.13-0.09-0.040.000.040.090.130.170.22 18Biom24Cons/biom-0.23-0.18-0.14-0.09-0.050.000.050.090.140.180.23 18Prod/biom18EE1.000.670.430.250.110.00-0.09-0.17-0.23-0.29-0.33 18Cons/biom21EE-0.41-0.33-0.25-0.16-0.080.000.080.160.250.330.41 18Cons/biom22Biom-0.27-0.22-0.16-0.11-0.050.000.050.110.160.220.27 18Cons/biom23Biom-0.22-0.17-0.13-0.09-0.040.000.040.090.130.170.22 18Cons/biom24Cons/biom-0.23-0.18-0.14-0.09-0.050.000.050.090.140.180.23 19Biom19EE1.000.670.430.250.110.00-0.09-0.17-0.23-0.29-0.33 19Biom22Biom-0.05-0.04-0.03-0.02-0.010.000.010.020.030.040.05 19Biom23Biom-0.13-0.10-0.08-0.05-0.030.000.030.050.080.100.13 19Biom24Cons/biom-0.04-0.03-0.02-0.02-0.010.000.010.020.020.030.04 19Prod/biom19EE1.000.670.430.250.110.00-0.09-0.17-0.23-0.29-0.33 19Cons/biom22Biom-0.05-0.04-0.03-0.02-0.010.000.010.020.030.040.05 19Cons/biom23Biom-0.13-0.10-0.08-0.05-0.030.000.030.050.080.100.13 19Cons/biom24Cons/biom-0.04-0.03-0.02-0.02-0.010.000.010.020.020.030.04 20Biom20EE1.000.670.430.250.110.00-0.09-0.17-0.23-0.29-0.33 20Biom22Biom-0.06-0.05-0.03-0.02-0.010.000.010.020.030.050.06 20Biom23Biom-0.05-0.04-0.03-0.02-0.010.000.010.020.030.040.05 20Biom24Cons/biom-0.05-0.04-0.03-0.02-0.010.000.010.020.030.040.05 20Prod/biom20EE1.000.670.430.250.110.00-0.09-0.17-0.23-0.29-0.33 20Cons/biom22Biom-0.06-0.05-0.03-0.02-0.010.000.010.020.030.050.06 20Cons/biom23Biom-0.05-0.04-0.03-0.02-0.010.000.010.020.030.040.05 20Cons/biom24Cons/biom-0.05-0.04-0.03-0.02-0.010.000.010.020.030.040.05 21Biom21EE1.000.670.430.250.110.00-0.09-0.17-0.23-0.29-0.33 21Biom24Cons/biom-0.08-0.06-0.05-0.03-0.020.000.020.030.050.060.08 21Prod/biom21EE1.000.670.430.250.110.00-0.09-0.17-0.23-0.29-0.33 21Cons/biom24Cons/biom-0.08-0.06-0.05-0.03-0.020.000.020.030.050.060.08

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107Table B-2. Continued InputGroupEstimatedInput parameter variation Groupparameteraffectedparameter-50-40-30-20-1001020304050 22Prod/biom22Biom1.000.670.430.250.110.00-0.09-0.17-0.23-0.29-0.33 22Prod/biom23Biom0.420.280.180.110.050.00-0.04-0.07-0.10-0.12-0.14 22Prod/biom24Cons/biom0.850.570.360.210.090.00-0.08-0.14-0.20-0.24-0.28 22Cons/biom23Biom-0.21-0.17-0.13-0.08-0.040.000.040.080.130.170.21 22Cons/biom24Cons/biom-0.42-0.34-0.25-0.17-0.090.000.090.170.250.340.42 22EE22Biom1.000.670.430.250.110.00-0.09-0.17-0.23-0.29-0.33 22EE23Biom0.420.280.180.110.050.00-0.04-0.07-0.10-0.12-0.14 22EE24Cons/biom0.850.570.360.210.090.00-0.08-0.14-0.20-0.24-0.28 23Prod/biom23Biom1.000.670.430.250.110.00-0.09-0.17-0.23-0.29-0.33 23EE23Biom1.000.670.430.250.110.00-0.09-0.17-0.23-0.29-0.33 24Biom24Cons/biom1.000.670.430.250.110.00-0.09-0.17-0.23-0.29-0.33 24Prod/biom24Cons/biom1.000.670.430.250.110.00-0.09-0.17-0.23-0.29-0.33

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111 Garvey, J. E., R. A. Wright, and R. A. Stein. 1998. Overwinter growth and survival of age-0 largemouth bass (Micropterus salmoides): revisiting the role of body size. Canadian Journal of Fisheries and A quatic Sciences 55:2414-2424. Garvey, J. E., D. R. DeVries, R. A. Wright, a nd J. G. Miner. 2003. Energetic adaptations along a broad latitudinal gradient: implications for widely distributed assemblages. Bioscience 53:141-150. Garvey, J. E., R. A. Stein, R. A. Wright, and M. T. Bremigan. 2002a. Exploring ecological mechanisms underlying largemouth bass recrui tment along environmental gradients. In Black bass: ecology, conservation, and management. Pages 7-23 in D. P. Philipp and M. S. Ridgway, editors. Black Bass: Ecology, Conservation, and Management. American Fisheries Society Symposium 31, American Fisheries Society, Bethesda, Maryland. Garvey, J. E., T. P. Herra, and W. C. Leggett. 2002b. Protracted reproduc tion in sunfish: the temporal dimension in fish recruitment revi sited. Ecological Applications 12:194-205. Gharrett, A. J., and W. W. Smoker. 1993. Genetic components in life history traits contribute to population structure. Pages 197-202 in J. G. Cloud and G. H. Thorgaard, editors. Genetic Conservation of Salmonid Fishes Plenum Press, New York, N.Y. Gharrett, A.J., W. W. Smoker, R. R. Reisenbich ler, and S. G. Taylor. 1999. Outbreeding depression in hybrids between oddand even -broodyear pink salmon. Aquaculture 173:117-129. Goodgame, L. S., and L. E. Miranda. 1993. Early growth and survival of age-0 largemouth bass in relation to parental size and swim-up time. Transactions of the American Fisheries Society 122:131-138. Gran, J. E. 1995. Gonad development and spawning of largemouth bass in a tropical reservoir. M. Sc. Thesis, North Carolina St ate University, Raleigh, N.C. Gross, T.S., C. M. Wieser, M. S. Sepulveda, J. J. Wiebe, T. R. Schoeb, and N. D. Denslow. 2002. Characterization of annua l reproductive cycles for po nd-reared Florida largemouth bass Micropterus salmoi des floridanus. Pages 205-212 in D. P. Phillip and M. S. Ridgway, editors. Black Bass: Ecology, C onservation, and Management. American Fisheries Society Symposium 31, American Fisheries Society, Bethesda, Maryland. Gunette, S., V. Christensen, and D. Pauly. 2001. Fisheries impacts on north Atlantic ecosystems: models and analyses. Fisherie s Centre Research Reports 9(4), Fisheries Centre, University of British Columbia, Canada. Hambright, K. D., 1991. Experime ntal analysis of pr ey selection by largemouth bass: role of predator mouth width and prey body depth. Transactions of the American Fisheries Society 120:500-508.

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112 Heidinger, R. C. 1975. Life history and biology of the largemouth bass. Pages 11-20 in R. H. Stroud and H. Clepper, editors. Black ba ss biology and management. Sport Fishing Institute, Washington, D.C. Henderson, P. A., H. A. Holmes, and R. N. Bamber. 1988. Size-se lective overwintering mortality in the sand smelt, Atherina boyeri Risso, and its role in population regulation. Journal of Fish Biology 33:221-233. Hilborn, R. and C. J. Walters. 1992. Quan titative Fisheries Stock Assessment Choice, Dynamics and Uncertainty. Kluwer A cademic Publishers, Massachusetts. Hjort, J. 1914. Fluctuations in the great fisheries of the norther n Europe viewed in the light of biological research. Journal du Conseil pour l’Exploration de la Mer 20:1-228. Houde, E. D. 1987. Fish early life dynamics and recru itment variability. Pages 17-29 in R. D. Hoyt, editor. Proceedings of the 10th Annual Larval Fish C onference. American Fisheries Society, Symposium 2, Bethesda, Maryland. Houde, E. D. 1997. Patterns and consequences of selective processe s in Teleost early life histories. Pages 169-193 in R. C. Chambers and E. A. Trippe l, editors. Early life history and recruitment in fish populations Chapman and Hall, New York. Isely, J. J., R. L. Noble, J. B. Koppelman, a nd D. P. Philipp. 1987. Spawning period and firstyear growth of northern, Florid a, and intergrade stocks of largemouth bass. Transactions of the American Fisheries Society 116:757-762. Jackson, J. R., and R. L. Noble. 2000. Rela tionships between annual variations in reservoir conditions and age-0 largemouth bass year class strength. Transactions of the American Fisheries Society 129:699-715. Jenkins, R. M. 1975. Black bass crops and spec ies associations in reservoirs. Pages 114-124 in R. H. Stroud and H. Clepper, editors. Black Bass Biology an d Management. Sport Fishing Institute, Washington, D.C. Johannes, R. E. 1978. Reproductive strategies of coastal marine fish es in the tropics. Environmental Biology of Fishes 3:65-84. Jrgensen, C., B. Ernande, O. Fiksen, and U. Di eckmann. 2005. The logic of skipped spawning in fish. Canadian Journal of Fish eries and Aquatic Sciences 63:200-211. Kassler, T. W., and eight coauth ors. 2002. Molecular and mor phological analyses of the black basses: implications for taxonomy and conservation. Pages 291-322 in D. P. Philipp and M. S. Ridgway, editors. Black Bass: Ec ology, Conservation, and Management. American Fisheries Society Symposium 31, American Fisheries Society, Bethesda, Maryland.

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119 Walters, C. J., and F. Juanes. 1993. Recruitmen t limitation as a consequence of natural selection for use of restricted feeding habitats and predation risk taking by juvenile fishes. Canadian Journal of Fisheries and Aquatic Sciences 50:2058-2070. Walters, C., and J. F. Kitchell. 2001. Cultivatio n/depensation effects on juvenile survival and recruitment: implications for the theory of fishing. Canadian Journal of Fisheries and Aquatic Sciences 58:39-50. Walters, C., and J. Korman. 1999. Linking recr uitment to trophic f actors: revisiting the Beverton-Holt recruitment model from a life history and multispecies perspective. Reviews in Fish Biology and Fisheries 9:187-202. Walters, C. J., and S. J. D. Martell. 2004. Fisheries Ecology and Management. Princeton University Press, Princeton, N.J. Walters, C., V. Christensen, and D. Pauly. 1997. Structuring dynamic models of exploited ecosystems from trophic mass-balance assess ments. Reviews in Fish Biology and Fisheries 7:139-172. Walters, C., D. Pauly, V. Christensen, and J. F. Kitchell. 2000. Repr esenting density dependent consequences of life history strategies in aquatic ecosystems: Ecosim II. Ecosystems 3:70-83. Werner, E. E., and D. J. Hall. 1979. Foraging efficiency and habitat switching in competing sunfishes. Ecology 60:256-264. Westlake, D. F. 1982. The primary pr oductivity of water plants. Pages 165-180 in J. J. Symoens, S. S. Hooper, and P. Compere edito rs. Studies on aquati c vascular plants. Royal Botanical Society of Belgium, Brussels. Wicker, A. M., and W. E. Johnson. 1987. Re lationships among fat content, condition factor, and first year survival of Florida largemouth bass. Tran sactions of the American Fisheries Society 116:264-271. Wieland, K., A. Jarre-Teichmann, and K. Horbow a. 2000. Changes in the timing of spawning of Baltic cod: possible causes and implications for recruitment. ICES Journal Marine Science. 57:452-464. Williamson, J. H., and G. J. Carmichael. 1990. An aquacultural evaluation of Florida, northern, and hybrid largemouth bass, Micropterus salmoides. Aquaculture 85:247-257. Wright, P. J., and F. M. Gibb. 2005. Select ion for birth date in North Sea haddock and its relation to maternal age. Jour nal of Animal Ecology 74:303-312.

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120 Wright, R. A., J. E. Garvey, A. H. Fullerton, and R. A. Stein. 1999. Predicting how winter affects engergetics of age-0 la rgemouth bass: how do current models fare? Transactions of the American Fisheries Society 128:603-612. Zar, J. H. 1999, Bios tatistical analysis 4th Ed. Prentice-Hall, Upper Saddle River, N.J

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121 BIOGRAPHICAL SKETCH Mark Rogers was born to Wayne and Judy Rogers in fall, 1975, near Atlanta, GA. He moved around the eastern US quite a bit while growi ng up, but finally settled in the piedmont of North Carolina where he attended high school. Th is was an important move because it resulted in Mark spending lots of time fishing on High Rock Lake and is likely th e reason he is working in fisheries. He decided to at tend N.C. State University in fa ll, 1993, and majored in fisheries and wildlife science. His advi sor, Dr. Richard Noble, hired hi m as a technician and Mark was sent me to Puerto Rico for a summer to work on tropical reservoirs. Afte r college, he worked at Virginia Tech on the king of the darters Percina rex under the supervision of Drs. Paul Angermeier, Bill Ensign, and Brett Albanese. Thereafter, he taught high school science for two years before deciding to attend a Master’s program. Mark re ceived his Master’s from the University of Wisconsin-Stevens Point with Dr. Michael Hansen as his advisor. After graduating in May, 2002, Mark moved to Gainesville, FL to work as a biol ogical scientist for Dr. Micheal Allen. He began his Ph.D. work in fall, 2003 and defended in fall, 2007. Mark feels most fortunate to have lucked into the best th ree advisors he could ha ve asked for during his academic training (Drs. Noble, Hansen, and Alle n). While working on his Ph.D., Mark married Kristin (Henry) Rogers and they look forw ard to their future endeavors together.