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Effects of Commercial Gill Net Bycatch on the Black Crappie Fishery at Lake Dora, Florida

Permanent Link: http://ufdc.ufl.edu/UFE0021386/00001

Material Information

Title: Effects of Commercial Gill Net Bycatch on the Black Crappie Fishery at Lake Dora, Florida
Physical Description: 1 online resource (71 p.)
Language: english
Creator: Dotson, Jason Randall
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 2007

Subjects

Subjects / Keywords: bycatch, crappie, dora, exploitation, gillnet, model, mortality, spr, sra
Fisheries and Aquatic Sciences -- Dissertations, Academic -- UF
Genre: Fisheries and Aquatic Sciences thesis, M.S.
bibliography   ( marcgt )
theses   ( marcgt )
government publication (state, provincial, terriorial, dependent)   ( marcgt )
born-digital   ( sobekcm )
Electronic Thesis or Dissertation

Notes

Abstract: Commercial bycatch can potentially cause population-level effects and represents serious concerns for sustainability and efficiency of fisheries. A commercial gill net fishery for gizzard shad Dorosoma cepedianum took place during 2005 and 2006 at Lake Dora, Florida. The primary bycatch of the gill net fishery was reproductively mature black crappie Pomoxis nigromaculatus, which also support the primary sport fishery at the lake. I assessed total black crappie bycatch, mortality rates of black crappie entangled in gill nets, and quantified recreational fishing effort and harvest for 2005 and 2006, and estimated exploitation for the recreational and commercial (bycatch) fisheries in 2006. I utilized age-structured population dynamics modeling techniques to investigate potential population-level impacts of bycatch. Onboard observer data of commercial fishing activity showed that approximately 17,000 and 30,000 black crappie were captured in gill nets in 2005 and 2006, respectively. Estimates from a pen experiment revealed that about 30% and 47% of black crappie experienced 72-h mortality due to entanglement in gill nets in 2005 and 2006, respectively. Recreational exploitation (urec) was estimated to be 42% based on tag returns, and commercial exploitation (ucom) was estimated to be 16% based on the number of black crappie that died due to gill netting in 2006. Simulations were performed from a stock reduction analysis (SRA) population dynamics model for three exploitation scenarios to investigate the potential of recruitment overfishing and simulations were performed from a yield-per-recruit model with varying exploitation rates to investigate the potential for growth overfishing. Results suggested that the current level of recreational exploitation is operating near a target SPR goal of 0.3 to 0.35 and additional exploitation from the recreational or commercial fishery could risk recruitment overfishing. Given the current vulnerability to harvest schedule growth overfishing is not of concern, however a shift in vulnerability towards smaller fish could increase the risk of growth overfishing. The greatest risk for recruitment overfishing via bycatch occurs when recreational exploitation is also high (e.g., this work). My study revealed a trade off, where potential benefits of biomanipulation via gizzard shad harvesting must be weighed against bycatch impacts to recreational fisheries.
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 Jason Randall Dotson.
Thesis: Thesis (M.S.)--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: UFE0021386:00001

Permanent Link: http://ufdc.ufl.edu/UFE0021386/00001

Material Information

Title: Effects of Commercial Gill Net Bycatch on the Black Crappie Fishery at Lake Dora, Florida
Physical Description: 1 online resource (71 p.)
Language: english
Creator: Dotson, Jason Randall
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 2007

Subjects

Subjects / Keywords: bycatch, crappie, dora, exploitation, gillnet, model, mortality, spr, sra
Fisheries and Aquatic Sciences -- Dissertations, Academic -- UF
Genre: Fisheries and Aquatic Sciences thesis, M.S.
bibliography   ( marcgt )
theses   ( marcgt )
government publication (state, provincial, terriorial, dependent)   ( marcgt )
born-digital   ( sobekcm )
Electronic Thesis or Dissertation

Notes

Abstract: Commercial bycatch can potentially cause population-level effects and represents serious concerns for sustainability and efficiency of fisheries. A commercial gill net fishery for gizzard shad Dorosoma cepedianum took place during 2005 and 2006 at Lake Dora, Florida. The primary bycatch of the gill net fishery was reproductively mature black crappie Pomoxis nigromaculatus, which also support the primary sport fishery at the lake. I assessed total black crappie bycatch, mortality rates of black crappie entangled in gill nets, and quantified recreational fishing effort and harvest for 2005 and 2006, and estimated exploitation for the recreational and commercial (bycatch) fisheries in 2006. I utilized age-structured population dynamics modeling techniques to investigate potential population-level impacts of bycatch. Onboard observer data of commercial fishing activity showed that approximately 17,000 and 30,000 black crappie were captured in gill nets in 2005 and 2006, respectively. Estimates from a pen experiment revealed that about 30% and 47% of black crappie experienced 72-h mortality due to entanglement in gill nets in 2005 and 2006, respectively. Recreational exploitation (urec) was estimated to be 42% based on tag returns, and commercial exploitation (ucom) was estimated to be 16% based on the number of black crappie that died due to gill netting in 2006. Simulations were performed from a stock reduction analysis (SRA) population dynamics model for three exploitation scenarios to investigate the potential of recruitment overfishing and simulations were performed from a yield-per-recruit model with varying exploitation rates to investigate the potential for growth overfishing. Results suggested that the current level of recreational exploitation is operating near a target SPR goal of 0.3 to 0.35 and additional exploitation from the recreational or commercial fishery could risk recruitment overfishing. Given the current vulnerability to harvest schedule growth overfishing is not of concern, however a shift in vulnerability towards smaller fish could increase the risk of growth overfishing. The greatest risk for recruitment overfishing via bycatch occurs when recreational exploitation is also high (e.g., this work). My study revealed a trade off, where potential benefits of biomanipulation via gizzard shad harvesting must be weighed against bycatch impacts to recreational fisheries.
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 Jason Randall Dotson.
Thesis: Thesis (M.S.)--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: UFE0021386:00001


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EFFECTS OF COMMERCIAL GILL NET BYCATCH ON THE BLACK CRAPPIE FISHERY
AT LAKE DORA, FLORIDA























By

JASON RANDALL DOTSON


A THESIS PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF
FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF
MASTER OF SCIENCE

UNIVERSITY OF FLORIDA

2007





























2007 Jason Randall Dotson



























To my parents, Randy and Lynn Dotson; my grandmother, Jo Anne Dotson; and my sister,
Jennifer Dotson: thank you so much for all of your love, support, and guidance. I would not be
the person that I am today without you all.

To my late grandfather, Paul Dotson: you are dearly missed and I know that you are proud of my
accomplishments.









ACKNOWLEDGEMENTS

I would like to thank Dr. Mike Allen for giving me the opportunity to conduct this

research. I have the utmost respect and appreciation for Dr. Allen, who served as my major

advisor, committee chair, mentor, and friend. I would like to acknowledge Dr. Bill Pine, Bill

Johnson, and Marty Hale for serving as committee members, FWC personnel Bill Johnson,

Marty Hale, John Benton, and Brandon Thompson for their guidance and help in collecting data,

and the St. John's River Water Management District for their aid in collecting data.

I would like to thank the following people from the University of Florida who made

significant contributions to the field and lab portions of this study: C. Barrientos, G. Binion, M.

Catalano, D. Dutterer, K. Johnson, G. Kaufman, P. O'Rourke, and E. Thompson. Finally, I

would like to thank Matt Catalano for his advice, comments, and quantitative assistance.









TABLE OF CONTENTS

page

A CK N O W LED G EM EN TS .................................................................. ............................ 4

L IS T O F T A B L E S ................................................................................. 7

LIST OF FIGURES .................................. .. ..... ..... ................. .8

A B S T R A C T ................................ .................. .......................... ................ .. 9

CHAPTER

1 INTRODUCTION ............... ................. ........... ......................... .... 11

2 M E T H O D S .......................................................................................................1 5

S tu d y S ite ................... ...................1...................5..........
Com m ercial Fishing............ .... ................... ........... 15
T total B catch A ssessm ent.................................................................................... ........... 16
Bycatch M mortality .............................................. ....... ... ... 16
R recreational Fishing Effort and H arvest .................................................................... ...... 18
T ag g in g S tu d y .......................................................................... 19
A g e an d G row th ........................................................ ................. 2 0
A nalyses..................................... ...................................... ................. 2 1
T otal B ycatch A ssessm ent...................................................................... ...................2 1
B catch M ortality ....................... .............. .................. ..... ........... ... .... .. 22
Recreational Fishing Effort and Harvest................................ ................................. 23
T ag g in g S tu dy ......................................................................... 2 3
A ge and G row th ..............................................................................26
Population-Level Impacts of Exploitation.............................................. ...............26

3 R E SU L T S .............. ... ................................................................36

Com m ercial Fishing............ .... ................... ........... 36
T total B catch A ssessm ent............................................................................. ... ............ 36
Bycatch M mortality ............................................................... ... ..... ....37
Recreational Fishing Effort and H arvest ........................................ .......................... 37
T ag g in g S tu d y ......................................................................... .. 3 8
A ge and G row th ................................................ .....................................40
A ge-structured Population M odel Sim ulations ........................................... .....................41

4 D IS C U S S IO N ...................................... .................................. ................ 56

5 M A N A GEM EN T IM PLICA TION S ........................................................... .....................62

LIST OF REFERENCES ...................................................... 64









B IO G R A PH IC A L SK E T C H ............................................................................... .....................7 1









LIST OF TABLES


Table page

2-1 Mean water quality parameters for Lakes Dora (east and west) and Beauclair
(Florida LAK EW A TCH 2004) ......... .. ............. .................................................... 35

3-1 Summary of results from stratified sampling design in 2005 and 2006 .........................53

3-2 Summary of results from secondary mortality experiment for treatment fish in 2005
and 2006 and control fish in 2006........................................................... ............... 53

3-3 Estimates of recreational exploitation rate (irec) based on values of the number of
higher reward value (CH) and standard reward tag fish caught (Cs) ..............................54

3-4 Empirical estimates of vulnerable biomass (kg) and total exploitation (,total) in 2006
and model predicted values of vulnerable biomass and total exploitation in 2006 ..........55

3-5 Estimates of mean vulnerable biomass (kg), mean total harvest (numbers) and mean
weighted transitional SPR in the terminal year 2050 determined from Monte Carlo
sim ulations (1,000 iterations).................................................. ............................... 55









LIST OF FIGURES


Figure page

2-1 Map of Lakes Eustis, Dora, and Beauclair located in the Upper Ocklawaha River
B asin in Lake C county, Florida ........................................... ..... ........................... 34

3-1 Commercial fishing effort (number of boats fishing per day) and daily black crappie
bycatch (numbers) for the 2005 and 2006 commercial gill net seasons at Lake Dora......44

3-2 Total recreational fishing effort (hours), black crappie effort (hours), and harvest of
black crappie (numbers) during the three creel survey periods at Lake Dora ...................45

3-3 Relative length frequencies of black crappie measured from the recreational catch
(carcasses and creel) and commercial gill net bycatch on Lake Dora............................46

3-4 Age frequency of black crappie collected from the recreational catch (carcasses and
creel) and commercial gill net bycatch on Lake Dora .......... ....................................... 47

3-5 Von Bertalanffy growth curve fit to mean length-at-age values for black crappie
collected from the recreational fishery (carcasses and creel) at Lake Dora in 2006 .........48

3-6 Estimates of exploitation from 1961 to 2006 and estimates of historical total harvest
and vulnerable biomass from the SRA model ....................................... ............... 49

3-7 Maximum likelihood profile for recK values ranging from 5 to 20 and Bo values
ranging from 70,000 to 100,000 kg. ............................................................................50

3-8 Weighted transitional SPR estimated from SRA from 1961 to 2050 with Monte
Carlo simulations (100 iterations) under three exploitation scenarios...........................51

3-9 Results for YPR (kg) values at total exploitation rates from 0.2 to 1.0 from yield-per-
recruit m odel sim ulations......... ............................................ ...... ....... 52









Abstract of Thesis Presented to the Graduate School
of the University of Florida in Partial Fulfillment of the
Requirements for the Degree of Master of Science

EFFECTS OF COMMERCIAL GILL NET BYCATCH ON THE BLACK CRAPPIE FISHERY
AT LAKE DORA, FLORIDA

By

Jason Randall Dotson

August 2007

Chair: Micheal S. Allen
Major: Fisheries and Aquatic Sciences

Commercial bycatch can potentially cause population-level effects and represents serious

concerns for sustainability and efficiency of fisheries. A commercial gill net fishery for gizzard

shad Dorosoma cepedianum took place during 2005 and 2006 at Lake Dora, Florida. The

primary bycatch of the gill net fishery was reproductively mature black crappie Pomoxis

nigromaculatus, which also support the primary sport fishery at the lake. I assessed total black

crappie bycatch, mortality rates of black crappie entangled in gill nets, and quantified

recreational fishing effort and harvest for 2005 and 2006, and estimated exploitation for the

recreational and commercial (bycatch) fisheries in 2006. I utilized age-structured population

dynamics modeling techniques to investigate potential population-level impacts of bycatch.

Onboard observer data of commercial fishing activity showed that approximately 17,000 and

30,000 black crappie were captured in gill nets in 2005 and 2006, respectively. Estimates from a

pen experiment revealed that about 30% and 47% of black crappie experienced 72-h mortality

due to entanglement in gill nets in 2005 and 2006, respectively. Recreational exploitation (urec)

was estimated to be 42% based on tag returns, and commercial exploitation (ucom) was estimated

to be 16% based on the number of black crappie that died due to gill netting in 2006.









Simulations were performed from a stock reduction analysis (SRA) population dynamics model

for three exploitation scenarios to investigate the potential of recruitment overfishing and

simulations were performed from a yield-per-recruit model with varying exploitation rates to

investigate the potential for growth overfishing. Results suggested that the current level of

recreational exploitation is operating near a target SPR goal of 0.3 to 0.35 and additional

exploitation from the recreational or commercial fishery could risk recruitment overfishing.

Given the current vulnerability to harvest schedule growth overfishing is not of concern,

however a shift in vulnerability towards smaller fish could increase the risk of growth

overfishing. The greatest risk for recruitment overfishing via bycatch occurs when recreational

exploitation is also high (e.g., this work). My study revealed a trade off, where potential benefits

of biomanipulation via gizzard shad harvesting must be weighed against bycatch impacts to

recreational fisheries.









CHAPTER 1
INTRODUCTION

Bycatch, the incidental catch of non-target species with fishing gear, occurs in almost all

commercial fisheries, and has become a central resource management concern throughout the

world (Diamond et al. 2000; Crowder and Murawski 1998; Pikitch et al.1998). Many studies

have attempted to assess total bycatch in commercial fisheries (Hale et al. 1981; Hale et al. 1983;

Renfro et al. 1989; Hale et al. 1996; Clark and Hare 1998; Pikitch et al. 1998; Stein et al. 2004),

assess mortality of incidental bycatch (Hale et al. 1981; Hale et al. 1983; Clark and Hare 1998;

Belda and Sanchez 2001; Beerkircher et al. 2002; Stein et al. 2004), and ultimately address

population-level effects (Crouse et al. 1987; Mangel 1993; Crowder et al. 1994; Caswell et al.

1998; Diamond et al. 1999; Diamond et al. 2000; Tuck et al. 2001; Majluf et al. 2002). Prior to

1998, hypotheses about population-level impacts rarely had been tested (Crowder and Murawski

1998) and Diamond et al. (2000) noted that population-level effects of bycatch have been

difficult to quantify.

Observations made on board commercial fishing vessels have estimated the proportion of

total landings made up of bycatch and bycatch initial mortality rates (Hale et al. 1981; Hale et al.

1983; Hale et al. 1996; Clark and Hare 1998; Pikitch et al. 1998; Beerkircher et al. 2002; Stein et

al. 2004). Hale et al. (1983) observed pound net fishing operations in the St. Johns River,

Florida, and estimated game fish total bycatch and initial mortality with estimates of fishing

effort, area fished, and game fish catch rate. Pikitch et al. (1998) used on-board observer data to

estimate bycatch of Pacific halibut Hippoglossus stenolepis in Washington, Oregon, and

California bottom trawl fisheries to test differences in catch rates of trawl types and time of year.

Stein et al. (2004) tested for differences in total bycatch and mortality of Atlantic sturgeon

Acipenser oxyrinchus among three gear types (trawl and two gill nets). Beerkircher et al. (2002)









quantified shark bycatch by species and initial mortality rates in the Southeast United States

pelagic longline fishery with nine years of fisheries observer data. Onboard observations can

provide useful information for measuring the proportion of total landings made up of bycatch

and can provide estimates of initial mortality due to fishing.

Total bycatch mortality includes initial mortality occurring as part of the capture process

and secondary mortality, which occurs following release from fishing gear. Initial mortality is

most often calculated directly onboard as part of observer programs, whereas secondary

mortality is estimated via pen studies or tagging programs. Total bycatch mortality is difficult to

measure due to the long observation periods required after fish capture. Total mortality may

result from chronic effects such as injury or infection, or increased vulnerability to predation

(Crowder and Murawski 1998). Crowder and Murawski (1998) argued that secondary and total

mortality should be considered in bycatch management, and appropriate survival studies should

be conducted.

Total bycatch and bycatch mortality estimates provide useful information to aid in

optimizing gear choice, fishing areas, and fishing seasons, but these estimates alone do not

quantify population effects of bycatch. Catch of non-target species in fisheries can have

implications at the population level (Crowder and Murawski 1998), and there are concerns about

impacts to fish populations (Murray et al. 1992) and marine fauna such as sea turtles, seabirds,

sharks, and mammals (Lewison et al. 2004). Methods to determine the population impacts of

bycatch typically involve field estimates and population modeling. Age-and-stage-structured

modeling techniques have been applied successfully to examine bycatch population implications

for a variety of species including sea turtles (Crouse et al. 1987; Crowder et al. 1994), wandering

albatross Diomedea exulans (Tuck et al. 2001), humboldt penguins Spheniscus humboldti









(Majluf et al. 2002), right whale dolphins Lissodelphis borealis (Mangel 1993), and harbor

porpoises Phocoenaphocoena (Caswell et al. 1998). Diamond et al. (1999) explored the

population level effects of catch and bycatch on Atlantic croaker Micropogonias undulatus in the

Gulf of Mexico and the Atlantic Ocean.

Lake Dora was recently selected by Florida resource management agencies for

biomanipulation via intensive commercial fishing with gill nets. Gizzard shad Dorosoma

cepedianum are an omnivorous fish with the potential to influence lake nutrient cycling. Gizzard

shad can greatly reduce large crustacean zooplankton density (DeVries and Stein 1992; Stein et

al. 1995) and can also consume benthic detritus when zooplankton resources are low (Stein et al.

1995; Irwin et al. 2003). Density and biomass of gizzard shad increase with trophic state, and

gizzard shad often occupy the majority of total fish biomass in hypereutrophic systems

(Bachmann et al. 1996; Allen et al. 2000). Because gizzard shad have the potential to influence

zooplankton abundance and influence nutrient cycling between the sediment and the water

column (Schauss and Vanni 2000; Schaus et al. 2002; Gido 2003), gizzard shad at Lake Dora

were targeted for removal.

Gill nets are size selective and not species specific; thus, adult sport fish bycatch associated

with the commercial gill net fishery for gizzard shad at Lake Dora is of concern to state agency

scientists and anglers. Black crappie Pomoxis nigromaculatus comprise some of the most

popular sport fisheries throughout North America (Hooe 1991; Allen and Miranda 1998) and

represent the primary recreational fishery on Lake Dora, Florida (Benton 2005). Bycatch of

black crappie is of concern to lake managers because significant bycatch mortality could have

deleterious impacts on recreational fisheries. Thus, there is a need to evaluate whether bycatch

could influence black crappie fisheries, which would elucidate policy trade-offs between









potential benefits of gizzard shad removal and impacts of commercial gill net bycatch on

recreational fisheries.

The objectives of this study were to (1) estimate total black crappie bycatch in commercial

gill nets, (2) estimate bycatch mortality (initial and secondary) from commercial gill nets on

black crappie, (3) assess recreational fishing effort and harvest of black crappie, and (4) address

population-level effects that bycatch could have on the black crappie fishery at Lake Dora. I

assessed the population-level impacts of black crappie bycatch from the gizzard shad gill net

fishery at Lake Dora, Florida by investigating the potential for recruitment overfishing via a

stock reduction analysis (SRA) model and evaluating the potential for growth overfishing with a

yield-per-recruit model. Growth overfishing occurs when fish are being harvested at an average

size that is less than the size that produces maximum yield per recruit, and usually results from

excessive effort and a selectivity schedule where small fish are vulnerable to harvest and not

allowed to reach their maximum growth potential. Recruitment overfishing occurs when fishing

mortality rates are so high that the adult population does not have the reproductive capacity to

replace itself. Recruitment overfishing is less common but is of serious concern because it can

lead to stock depletion and collapse. If selectivity schedules are skewed towards larger fish that

have passed the age at sexual maturity recruitment overfishing may occur where growth

overfishing is not a concern.









CHAPTER 2
METHODS

Study Site

Lakes Dora and Beauclair are part of the Upper Ocklawaha River Basin, located

approximately 30 miles northwest of Orlando in central Florida (Magley 2003, Figure 2-1).

Lakes Dora (1,774 ha) and Beauclair have a combined surface area of 2,211 ha and they are

connected by a short open canal, commercial fishing for gizzard shad was permitted on both

water bodies, and both systems have similar trophic status and fish communities (Table 2-1). I

considered Lakes Dora and Beauclair as one water body for the purposes of this study and will

refer to the system collectively as Lake Dora. Surface outflow from Lake Dora is through the

Dora Canal into Lake Eustis (3,139 ha) (Figure 2-1).

Commercial Fishing

Permits were issued by the Florida Fish and Wildlife Conservation Commission (FWC) for

28 commercial fishers to remove gizzard shad from Lake Dora in 2005 and 2006. The fishery

was regulated in an effort to minimize bycatch mortality as much as possible with the following

restrictions. A maximum of two gill nets, not to total more than 1,097 meters could be used

simultaneously by each boat, and gill net specifications were a minimum stretch mesh size of

10.2 cm. The maximum allowable length of one net was 549 meters, and nets were allowed 2

hours maximum soak time. There was no restriction on the maximum number of nets fished

daily, as long as all other guidelines were followed. Floating and sinking gill nets were used.

Commercial fishing was allowed only during daylight hours in open water areas at least 90

meters from shore during open seasons. Commercial fishers harvested gizzard shad, Florida gar

Lepisosteus platyrhincus, longnose gar Lepisosteus osseus, blue tilapia Oreochromis aurea, and









the nonnative sailfin catfish Liposarcus multiradiatus. All other fish species caught in gill nets

were required to be returned to the water immediately after removal from the nets.

Total Bycatch Assessment

Gill net operations during the gizzard shad removal were monitored by St. Johns River

Water Management District (SJRWMD) observers. Monitoring was conducted at least twice per

week during the commercial seasons and consisted of random observations of gill net fishing

operations. Observers reported catch numbers, species composition, mesh size, net type (floating

or sinking), and net length. An observation day consisted of at least six gill net sets. If there was

no commercial gill net activity or weather prohibited observations, an attempt was made to

average 12 gill net set observations per week and two sampling days per week over a one-month

period. Subsamples of crappie bycatch were measured for total length (TL) weekly until a

maximum of 100 fish per species was recorded each month. The first four weeks of fishing in

2006 required increased monitoring as follows; observations were conducted at least three days

per week, at least 18 gill net sets were observed per week, and all black crappie encountered

were measured until a maximum of 200 were recorded. The SJRWMD was required to follow

these methods set forth in the sampling permit for the shad removal project issued by FWC.

Bycatch Mortality

To evaluate bycatch mortality of black crappie I collected fish from commercial fishing

vessels as gill nets were being retrieved in both years. After black crappie were removed from

gill nets by commercial fishers, I transferred the fish to a research vessel where they were

measured to the nearest mm TL and placed in a 190 liter cooler with aerators used to maintain

dissolved oxygen levels over 5 mg/L. Dissolved oxygen levels were recorded in the cooler to

assure that they exceeded 5 mg/L at all times. Any initial mortality of fish from gill nets was

recorded. I considered a fish to be alive when the net was pulled if there was opercular









movement (Kwak and Henry 1995). I recorded gill net mesh size and style (sinking or floating)

for each sample fish were collected from.

I estimated secondary mortality of black crappie entangled in gill nets. Secondary

mortality has been effectively measured for largemouth bass in live-release tournaments

(Schramm et al. 1987; Kwak and Henry 1995; Weathers and Newman 1997; Neal and Lopez-

Clayton 2001; Edwards et al. 2004) using pens to hold fish that were captured during hook-and-

line tournaments. Holding time ranged from two to 21 days (Schramm et al. 1987; Kwak and

Henry 1995; Weathers and Newman 1997; Neal and Lopez-Clayton 2001; Edwards et al. 2004),

and Edwards et al. (2004) considered the three-day observation period adequate compared to

other studies. Secondary mortality was measured using replicates of fish held in pens for 72

hours.

After fish were collected from the commercial fishers, they were transported to holding

pens placed in the lake. The pens used were large hoop nets measuring 4.57 meters long, 1.22

meter diameter, and 50.8 mm stretch mesh nylon. A total of four hoop nets were used, and the

nets were placed in three meters of water on a hard sand substrate bottom and marked with

University of Florida research buoys. All net replicates were performed in the same area of Lake

Dora during both commercial seasons. A minimum of 10 and maximum of 20 fish were placed

in each pen. If a minimum of 10 fish could not be collected within 30 minutes of net pull time

with the fishers, any fish that had been collected were transported to the pens to avoid further

stress. All fish exhibiting opercular movement were placed in the pens for measures of

secondary mortality. After the 72 hour treatment all fish were released, and any dead fish were

measured to the nearest mm TL. Consistent with Hale et al. (1981) and Hale et al. (1983), we

considered a fish to be dead if it was unable to swim away after 72 hours.









Pollock and Pine (2007) recognized the need for control replications in assessing delayed

mortality for catch and release studies. It is not possible to obtain an unbiased estimate of fish

captured in gill nets alone unless one assumes that there is no handling mortality (Pollock and

Pine 2007). This is most likely not a reasonable assumption, hence control fish are necessary to

account for handing mortality. Control fish were collected via electrofishing and hoop net gear

during the 2006 season. Replicates of control fish placed in pens were used to account for

potential mortality effects from transporting and holding fish. The same methods were applied

during replications of control fish as described for treatment replications.

Water temperature and dissolved oxygen are critical factors influencing secondary

mortality of fishes (Schramm et al. 1987; Gallinat et al. 1997; Weathers and Newman 1997;

Wilde et al. 2000; Edwards et al. 2004). A temperature logger was placed at our pen holding site

to record temperature every four hours during the course of the experiment. Dissolved oxygen

(mg/L) was also measured each time a pen was set and retrieved, and in 2006 a dissolved oxygen

logger was placed at my pen holding site to record dissolved oxygen levels every four hours

during the course of the experiment to measure oxygen levels throughout the 72-hour treatment

period.

Recreational Fishing Effort and Harvest

Roving creel surveys were conducted by the FWC on Lake Dora from November 2004 to

June 2005, November 2005 to May 2006, and November 2006 to March 2007, respectively

(three fishing seasons) to measure angling effort, harvest, and catch rates. Each survey was

conducted on ten randomly selected days (six weekdays and four weekend days) for each 28-day

period (Benton 2005). Using a randomly selected time, lake section, and direction of travel on

each sample day, a clerk completed a survey of the entire lake by taking an instantaneous count

of all anglers actively fishing on the lake to determine fishing effort (man-hour) (Benton 2005).









The clerk also interviewed anglers about their target species (if any species were specified by the

angler), the number of each species caught, and how much time was spent fishing to determine

fishing success (fish/hour) (Benton 2005). Catch from the angler interviews was extrapolated to

angler effort estimates from the instantaneous counts to estimate total harvest at each lake in both

years (Malvestuto et al. 1978; Malvestuto 1996; Benton 2005). Measurements of TL were

recorded for a subsample of the black crappie catches during the three survey periods.

Tagging Study

A tagging study was conducted in 2006 for a direct estimate of exploitation from the

recreational fishery (jrec). Lake Dora was divided into four areas and an approximately equal

number of fish were tagged in each area. Area one encompassed Lake Beauclair, and areas two

through four encompassed Lake Dora; the three areas of Lake Dora were the east lobe (2),

middle lobe (3), and west lobe (4) (Figure 2-1). Fish were collected for tagging with a boat

electrofisher, hoop nets, and an otter trawl. All fish captured were measured to the nearest mm

TL, and fish 230 mm TL and greater were tagged and released into approximately the same area

they were captured. Although there was no minimum size limit in place, I assumed that all fish

230 mm TL and greater had recruited to the fishery based on creel survey data.

All black crappie were tagged with dart tags with a yellow streamer containing information

specifying the tag specific identification number, monetary reward value, and return address.

Tags were inserted into the body of the fish below the dorsal fin rays using a hollow needle.

When injected the streamer of each tag extended in a posterior direction at a 450 angle to the

body. All black crappie were tagged from November 2005 to January 2006 to obtain an estimate

of exploitation for the 2006 fishing season. All fish were single tagged with either a standard tag

($5) or a higher value reward tag ($50). The tagging reward study allowed for estimates of

reporting rates (described below).









Age and Growth

Age and growth of black crappie at Lake Dora was estimated using fish collected from

the recreational fishery from January through March 2005 to 2007, which is when black crappie

angling effort peaks (Benton 2005; FWC 2005). Lake Dora has numerous fish camps where

anglers clean harvested fish daily and these camps were the source of fish for age samples.

Collecting recreationally harvested fish is an efficient way to gather age information and has

been utilized for many marine species (Potts et al. 1998; Potts and Manooch 1999; Patterson et

al. 2001; Fischer et al. 2004; Fischer et al. 2005), although like all sampling gears is subject to

size and age selectivity.

Coolers with ice were placed at fish cleaning stations for three camps. Information signs

were also posted at the fish cleaning stations explaining the purpose of the project. Some anglers

may fish multiple lakes on a given day and thus, I asked anglers not to donate black crappie if

they had fished more than one lake in an effort to assure all black crappie ages represented the

correct population. Coolers were left for two to three days before retrieval. All black crappie

collected from recreational anglers were brought back to the lab where they were measured to

the nearest mm TL and sagittal otoliths were removed from ten randomly selected fish for each

centimeter group. Because fish larger than 330 mm TL were rare, all black crappie greater than

this size were aged.

Ages of fish collected from the recreational fishery were determined by counting annuli

on whole otoliths with the aid of a dissecting microscope. The use of otoliths to determine ages

of black crappie has been verified (Hammers and Miranda 1991; Ross et al. 2005). Two

independent readers aged each fish. Schramm and Doerzbacher (1982) found that black crappie

have relatively thin otoliths that had clearly visible bands present in patterns expected for annual

marks. Older fish (fish showing four or more opaque bands) have thicker otoliths, and therefore









are more likely to have bands masked in whole view (Schramm and Doerzbacher 1982). Thus,

any otoliths showing four or more opaque bands, and any otolith disagreements from whole view

readings were sectioned for verification of aging accuracy. One otolith was sectioned

transversally using a South Bay Technology, Inc. low speed diamond wheel saw. Two

transverse sections, 0.5 mm wide, were cut from each otolith and mounted on a labeled glass

slide using ThermoShandon Synthetic Mountant for reading. Two independent readers used a

dissecting microscope to read the sections. A third independent reader reexamined all

disagreements and the majority reading was recorded as number of annuli. Not all black crappie

form new opaque bands on their otoliths at the same time during spring, although opaque bands

on otoliths from all age classes should be formed by June ist in Florida (Schramm and

Doerzbacher 1982). I used an arbitrary birth date of June 1st, so that all fish collected prior to

June 1st were assigned ages corresponding to the number of annuli observed plus one.

Analyses

Total Bycatch Assessment

I obtained estimates of total black crappie bycatch from the commercial fishery using a

stratified sampling design (see Krebs 1999). Onboard observer data were stratified into three

time strata (A, B, and C) for both commercial fishing seasons. The strata represented periods of

high, moderate, and low fishing effort, and were grouped such that the variance of bycatch

observed was homogeneous within and heterogeneous among strata. The total bycatch estimate

and variance on this total were determined using the equations for a stratified design from

Pollock et al. (1994):

XST = NXsT (2-1)


VAR(XS) = N,2 x VAR(XST) (2-2)









where,


XsT = total bycatch estimate,

N= number of total possible fishing days in a season,

Xs = stratified bycatch mean per fishing day.

h = stratum number (A, B, C)

and,

Nh = total possible fishing days in stratum

Bycatch Mortality

I measured the mortality rate for each pen replication in each year as the number of dead

black crappie observed per pen divided by the total number of black crappie held in each pen. I

then estimated the annual mean bycatch mortality rate as the average mortality rate across all

replications for each year, with uncertainty expressed as the standard error around the yearly

means. Mean and variance were also estimated for control replications.

I used the annual mean bycatch mortality rate multiplied by our estimate of total bycatch

for black crappie in each year to achieve total commercial fishing mortality of black crappie by

year given by the equation:

GD = GC x GM (2-3)

where,

GD = estimated total number of black crappie that died from gill net mortality,

GC= estimated total number of black crappie caught by gill nets,

and,

GM= total gill net mortality rate.









Recreational Fishing Effort and Harvest

All data were entered and analyzed in a creel survey analysis program developed by

FWC (version 2, Conner and Sheaffer 2000) and were stored in a Microsoft Access database on

an FWC regional server (Benton 2005). Data was lost overboard from one 28-day period in

2006. We approximated the missing time period in 2006 using the percentage of effort for that

period during 2005, assuming that the percentage of effort during that period in 2005 would

serve as the best model to reconstruct the missing data in 2006.

Tagging Study

Tag returns were adjusted for tag-related mortality, tag loss, and non-reporting prior to

estimating exploitation. I assumed 5 10% tagging mortality and tag loss for all black crappie

tagged. Reporting rates of higher value reward tags ($50) in 2006 were estimated based on a

linear-logistic model created by Nichols et al. (1991):

(-0 0045+0 0283(H))
H = + e(o 0045+0 0283(H)) (2-4)

where,

H = the dollar value of higher value reward tags,

and,

AH = the reporting rate of tags from higher reward value fish.

The reward values (H) were converted from 2006 standards to the 1988 monetary equivalents

based on the Consumer Price Index. The 1988 monetary equivalents used in equation 2-4 were

$30.29 for $50 rewards (U.S. Department of Labor 2006). Reporting rate estimates calculated

from equation 2-4 were most precise at higher reward values (Nichols et al. 1991) and thus, I

used equation 2-4 to estimate reporting rates of high-reward tag fish and then estimated the

reporting rate of standard tags based on the assumption that all tagged fish had an equal









probability of recapture regardless of reward value. Alternate methods for estimating reporting

rate, such as those presented in Taylor et al. (2006) assume 100% reporting rate of higher value

tags in order to estimate the reporting rate of standard tags. I felt that a $50 tag value was not

sufficient to make the assumption that all higher value reward tags were returned.

I estimated the total number of high value reward tag fish caught in 2006 using the

equation:

RH
CH H (2-5)


where,



CH = estimated number of higher value reward tag fish caught,

and,


RH = total number of tags returned in 2006 from fish tagged with a higher reward value.

I assumed that standard tags and higher reward value tags had an equal probability of capture by

anglers and estimated the total number of standard tag fish caught in 2006 using the ratio:

C C
-H CS (2-6)
T T
H S

where,

S = the dollar value of a fish tagged with a standard tag,

Cs = estimated number of standard tag fish caught,

Ts = original number of fish tagged with standard reward tags,

and,

TH = original number of fish tagged with higher value reward tags.









I estimated the reporting rates of standard reward tags ($5) in 2006 using the equation

R
As = (2-7)
Cs

Reporting rate estimates for high-value reward tags were varied to evaluate how uncertainty in

XH would influence the exploitation rate.

Estimates of exploitation for the recreation fishery (UREc) were estimated using the

equation:


REC (C CH) (2-8)
(Ts xl-(7TM+TL))+(TH x-(7TM+ TL))

where TM= tagging mortality and TL = tag loss.

The instantaneous rate of fishing mortality for the recreational fishery (Free) was estimated using

the equation:

FRE = -LN(1 -~C ) (2-9)

Estimates of exploitation for the commercial fishery (,coM) could not be obtained directly

from tagging data because a reliable reporting rate could not be calculated. There was evidence

that vulnerability with fish size to gill nets was similar to recreational angling, but commercial

fishers had an incentive not to return tags. Thus, I was unable to use Nichol's equation to

estimate commercial reporting rate. To estimate commercial exploitation I first estimated the

vulnerable black crappie population size with the equation:


N=CREC (2-10)
PIREC

where,

S= the number of vulnerable black crappie in the population,

and,









CREC = recreational catch from creel survey data.

I estimated the exploitation rate from the commercial fishery (.iCOM) as:

GD
/'com (2-11)


The instantaneous fishing mortality for the commercial fishery (Fcom) was estimated as:

Fcom = -LN(1- /coM) (2-12)

I simulated changes in FcoM by changing the gill net mortality rate (GM), which changed

the number of black crappie that died from gill nets (GD). The instantaneous fishing mortality

for the commercial and recreational fisheries were estimated with varying levels of reporting

rates, tag loss, tagging mortality, recreational catch, and total gillnet bycatch mortality to

evaluate uncertainty in F values for a range of input parameters.

Age and Growth

Data collected from the recreational fishery (carcasses and creel) was used to estimate

growth rates for black crappie. I created an age-length key from a subsample of black crappie

aged from recreationally harvested carcasses and assigned an age to each individual from the

entire sample of carcasses and the recreational creel measurements in order to obtain age and

size structure of the population. Mean-length-at-age (MLA) and its associated variance (c2)

were found by equations for fixed-length subsamples presented by DeVries and Frie (1996). I

used the Von Bertalanffy growth model (Ricker 1975) to describe growth rates. Von Bertalanffy

parameter estimates (Loo, k, and tO) were obtained using Procedure NLIN (SAS 9.1).

Population-Level Impacts of Exploitation

I used Microsoft Excel to construct a stock reduction analysis (SRA) with stochastic

recruitment (see Walters et al. 2006) in order to evaluate the potential of recruitment overfishing

occurring at varying exploitation rates. The basic idea of an SRA is to construct an age-









structured population dynamics model that consists of leading parameters (e.g., Bo and recK in

this study) that describe the underlying production and carrying capacity and subtract known

removals from the population over time (Walters et al. 2006). When leading parameter estimates

produce a stock size that is too low to have sustained historical catches, the model predicts that

the population should have disappeared prior to today (Walters et al. 2006). When leading

parameters estimates produce a stock size that is too high, it predicts too little fishing impact and

a current population size that is much too large to fit recent estimates (Walters et al. 2006).

The SRA I created reconstructed the historic stock size of black crappie in order to match

model predicted estimates of exploitation and vulnerable biomass in 2006 to empirical estimates

of exploitation and vulnerable biomass in 2006, given estimates of the leading parameters Bo

and recK. Typically the leading parameter Bo is a measure of vulnerable biomass in the

unfished condition. However, in this study Bo represents an estimate of vulnerable biomass far

enough back in time to achieve a stable age distribution in the simulated population prior to this

study (2005). The leading parameter recK is the Goodyear recruitment compensation ratio

(Goodyear 1980) and is a measure of the juvenile survival at extremely low stock size relative to

juvenile survival in the unfished condition. The parameter recK examines relationships between

maximum recruitment at low stock size and the density dependence of recruitment at high stock

size or the unfished condition (Goodwin et al. 2006). The two leading parameters are correlated

in the sense that a lower Bo and higher recK can produce the same stock size as a higher Bo and

lower recK. SRA models often have an exorbitant amount of combinations of Bo and recK that

can explain the same stock size. The best combination of recK and Bo chosen must be supported

statistically and biologically so that the parameter estimates are logical.









My empirical estimate of vulnerable biomass in 2006 in the fished condition was estimated

as the vulnerable biomass per acre times the surface area (acres) of Lakes Dora and Beauclair

combined. Vulnerable biomass per acre was estimated as the vulnerable number of black


crappie per acre (-- ) times the average weight of a vulnerable black crappie, where the
acres

average weight of a vulnerable black crappie was estimated using a standard weight equation for

black crappie (Anderson and Neumann 1996) with an average length of vulnerable black crappie

harvested in 2006 (given from carcass and creel measurements). My empirical estimate of

exploitation in 2006 was estimated for the recreational and commercial fisheries using equations

2-8 and 2-11, respectively.

I solved for my leading parameters (Bo and recK) by fitting the model predicted values of

vulnerable biomass and exploitation in 2006 to empirical estimates in 2006 given by the log

likelihood of the lognormal distribution:

MLE = ln((ln(06preduot,) ln(06estu,,t, ))2 + (ln(06estVB) ln(06predVB))2) (2-13)

where,

MLE= the maximum likelihood estimate,

06predutota = 2006 model predicted estimate of total exploitation,

06estutota = 2006 empirical estimate of total exploitation,

06estVB = 2006 empirical estimate of vulnerable biomass (kg),

and,

06predVB = 2006 model predicted estimate of vulnerable biomass (kg).

I used Excel table function to construct a maximum likelihood profile for a range of Bo and

recK values that made sense biologically in order to determine combinations of parameter

estimates that were supported statistically. Considering a review of maximum reproductive rates









of fish at low population sizes by Myers et al. (1999), black crappie most likely have a recK

value between five and 20 based on fish species with similar life history characteristics.

Estimates of Bo were considered from 70,000 to 100,000 kg, which were supported by my

empirical estimates of adult fish density and fishing mortality rates.

When solving for leading parameter estimates, my model was very sensitive to starting

values because of the correlation between leading parameters and multiple possible

combinations. Thus, I was not able to solve for Bo and recK simultaneously. This phenomenon

is very common in SRA model fitting. Therefore, I fixed Bo and solved for recK, because Bo

exhibited much less variability than recK in the maximum likelihood profile and I had data for

black crappie at Lake Dora that supported my estimate. Once reasonable parameter estimates

were obtained the model was used to predict how the black crappie stock would respond in the

future under different scenarios of exploitation. The output metrics of interest were vulnerable

biomass (kg), total harvest (numbers) and weighted transitional spawning potential ratio (SPR).

The SRA required estimates of mean length at age, weight at age, fishing and natural

mortalities, fecundity, and a vulnerability to harvest schedule in order to function. Fishing

mortalities were separated into FREC and FCOM, as described above. Estimates of total length-at-

age were obtained from the Von Bertalanffy growth model and age specific weight was

calculated using a standard weight equation for black crappie (Anderson and Neumann 1996).

Equal vulnerability schedules were assumed for the recreational and commercial fisheries, based

on the length frequencies from the recreational and commercial fisheries. Vulnerabilities at age

were estimated using a cumulative normal distribution, which predicted expected catches at age

in a yield-per-recruit model simulation that approximated the observed age structure of the catch.

Fecundity was calculated as the weight at age minus weight at maturity (Wmat). Walters et al.









(2007) noted that fecundity is typically proportional to body weight above the weight at maturity.

Weight at maturity was assumed to be the weight predicted at age 2, given that black crappie

mature at approximately age 2 in this system (FWC 2005).

Survivorship at age in the unfished condition (SurvivorshipOa) was calculated as

survivorship in the previous year multiplied by survivorship in the absence of fishing (So). The

instantaneous rate of natural mortality (M) was assumed to be 0.4 for all simulations, which is

similar to values found in a review of black crappies (Pomoxis spp.) from Allen et al. (1998).

Survival from natural mortality was found by So = e-". Survivorship at age a in the fished

condition (SurvivorshipFa) was calculated as:

SurvivorshipF, = SurvivorshipFa 1 x So x (I Ptota x vul0 1) (2-14)

where survivorship at age one was assumed to be 1, the first age in the model.

Expected numbers were assumed to change over a ages and t years according to the

survival equation (Walters et al. 2006):

N, +,t+ = N, xSo x (1- vult xu totalt) (2-15)

I used an accounting scheme with 8 ages from 1961 2050 (N = 90). Expected numbers at age

in the initial year were calculated as:

N~,t = Ro x V survivorshipO (2-16)
a

where Ro is the recruitment abundance in the unfished condition estimated as:


Ro = (2-17)
Dvb0

The Botsford incidence function for vulnerable biomass per recruit in the unfished condition was

calculated as (Box 3.1, Walters and Martell 2004):









(2-18)


Vulnerable biomass was determined annually with the equation:

B = ZN,,,vula,wt0 (2-19)


The model required exploitation (pUtota, t) and recruitment time series for all years after

1961. For each year the total exploitation rate was estimated as:

HARyVOtO1
t, HARVt (2-20)
N, ,vul
a

Total harvest was estimated from historical creel data from 1977 to 1981 and from creel and

commercial landings data in 2005 and 2006. Logical estimates of total harvest were simulated

for the remaining years from 1961 to 2006. For future projections, estimates of exploitation

were assumed under different fishing scenarios and total harvest estimates were calculated as:

HARVot = Nx Pt,,t (2-21)

This allowed the model to explore a range of assumed exploitation rates in the future and

determine the expected vulnerable biomass, total harvest, and SPR given an exploitation rate.

Recruitment rates were predicted from estimates of annual egg production (Et) as:

Et = N,,, fec, (2-22)
a

using a Beverton and Holt stock-recruit relationship with recruitment variability of the form of

the relationship (Walters et al. 2006):

aE
N,t,, = -- x rand, (2-23)
1 + E,

where the alpha and beta Beverton and Holt parameters are described by the relationships:


(DVB' =Ywt,, l l1I-I-I1hilO










a = recK x (2-24)
Eo

recK 1
/= K 1 (2-25)
E0

Variability around recruitment at time t Randd,) was accounted for with a random number that

was determined with PopTools in Microsoft Excel by using a log normal distribution with a

mean of 1.0 and recruitment coefficient of variation of 0.4. Allen (1997) observed black crappie

recruitment coefficient of variation values ranging from 0.55 to 0.84 for 6 populations in

Southeast and Midwest reservoirs, but there is evidence that recruitment variation in this system

is considerably lower based on age-0 black crappie catch rates in bottom trawls (M. Hale, FWC,

unpublished data).

Recruitment variability was added to the model simulations for future projections once

estimates of the leading parameters were obtained via equation 2-13 in order to explore how

abundance, catch, and spawning potential ratio varied through time with different exploitation

rates. A weighted transitional SPR was used as a biological reference point to investigate the

potential for recruitment overfishing at various exploitation scenarios. A weighted transitional

SPR allows fishing mortality to vary by age and year and accounts for changes in the numbers at

age over years. The SPR was estimated with the equation:

SNat+1,2,3 89,fec
SPRt+1,2,3 89= ,t c (2-26)
a

I determined the uncertainty in my terminal year SPR (2050) by using Monte Carlo analysis with

1,000 iterations to determine a terminal year mean SPR and 95% confidence limits around the

mean. The same methods were applied to total harvest and vulnerable biomass estimates. I also

used Monte Carlo analysis with 100 iterations to determine mean annual SPR values and 95%









confidence intervals for the entire model time series to show how the SPR would be expected to

vary with variation in recruitment.

Future projections were simulated from 2007 through the terminal year 2050 under three

exploitation scenarios; (1) Ptotai = 0.42, (2) total = 0.51, and (3) totai = 0.60. Exploitation

scenario one was chosen because it was the empirical estimate of pree in 2006, scenario two was

chosen because it was the empirical estimate of totai in 2006 and scenario three was chosen as an

arbitrary increase in exploitation either due to recreational fishing, bycatch mortality, or both.

The model simulations examined the three different exploitation scenarios and the implications

they have on black crappie abundance, total harvest, and SPR if they were sustained through the

terminal year 2050.

In order to investigate the potential for growth overfishing, I constructed a yield-per-

recruit model in Excel. Yield-per-recruit (kg) was determined as:

YPR = VBF x Utotal (2-27)

where,

The Botsford incidence function for vulnerable biomass per recruit in the fished condition

((VBF) was calculated as (Box 3.1, Walters and Martell 2004):

'F VB = wta, vul0, survivorshipF~ (2-28)
a

To investigate if growth overfishing was a concern I used Excel table function to profile YPR

values at total exploitation (ptorat) scenarios ranging from 0.2 to 1.0.

















Lake Eustis


0 0.5 1 2 Kilometers
I I I I I I I I


Figure 2-1. Map of Lakes Eustis, Dora, and Beauclair located in the Upper Ocklawaha River
Basin in Lake County, Florida. Areas 1 4 represent designated capture and release
areas for tagging study.









Table 2-1. Mean water quality parameters for Lakes Dora (east and west) and Beauclair (Florida
LAKEWATCH 2004). Water quality parameters include total phosphorous (TP
([tg/L), total nitrogen TN (utg/L), chlorophyll CHL ([tg/L), secchi depths (meters), and
trophic state and reflect the annual average for 2004.


LAKE TP ([tg/L) TN ([tg/L) CHL ([tg/L) SECCHI (meters) Trophic State

Dora east 55 2941 102.7 0.4 hypereutrophic

Dora west 50 2889 99.4 0.43 hypereutrophic

Beauclair 81 2971 100.5 0.4 hypereutrophic

*Trophic state based on Forsburg and Ryding (1980).









CHAPTER 3
RESULTS

Commercial Fishing

Commercial fishing occurred from March 1 to April 22, 2005 and from January 3 to March

28, 2006. Fishing was not permitted until March 1, 2005 because pre-harvest data were being

collected for the gizzard shad population. Generally, there were two permitted fishermen per

fishing vessel; there was a maximum of 16 vessels and a minimum of 1 vessel per fishing day

during the 2005 and 2006 commercial fishing seasons. Total commercial effort was 258 boat

days in 2005 and 251 boat days in 2006 (Figure 3-1) with an average of six boats per fishing day

in 2005 and five boats per fishing day in 2006.

Total Bycatch Assessment

Black crappie bycatch was higher in 2006 than 2005 (Table 3-1). For 2005, there were a

total of 487 black crappie observed during gillnet operations, 294 in stratum A (March 1 to

March 14), 156 in stratum B (March 15 to Mar 31), and 37 in stratum C (April 1 to April 22).

The average total daily bycatch per stratum (xh) was 595, 488, and 26 for strata A, B, and C,

respectively. The total bycatch estimate (XsT) for 2005 was 17,199 black crappie and the 95%

confidence intervals were 8,777 to 25,622. For 2006, there were a total of 2,109 black crappie

observed during gillnet operations, 1,375 in stratum A (January 3 to January 31), 545 in stratum

B (February 1 to February 28), and 189 in stratum C (March 1 to March 28). The average total

daily bycatch per stratum was 979, 498, and 265 for strata A, B, and C, respectively. The total

bycatch estimate (Xks) for 2006 was 30,258 black crappie, and the 95% confidence intervals

were 19,048 to 41,469. Total daily bycatch of black crappie is reported in Figure 3-1 for days

with onboard observer data in 2005 and 2006.









Bycatch Mortality

I conducted 17 pen replications from March 1 to April 8 during the 2005 commercial gill

net season, and 23 pen replications from January 3 to March 15 during the 2006 season. Six

control replications were made with fish caught in hoop nets, and four pen replications were

made with fish caught with electrofishing gear in 2006 from January 13 to January 29. In 2005,

bycatch mortality rates ranged from 0 to 0.75 during the treatment period with a mean of 0.31

(GM2oos) and a standard error of 0.06. In 2006, bycatch mortality rates ranged from 0.05 to 1

during the treatment period with a mean of 0.47 (GM2oos) and a standard error of 0.07. In 2006,

control replications of fish collected with hoop nets (n = 6) ranged in mortality from 0 to 0.35

during the treatment period with a mean of 0.10 and a standard error of 0.05; control replications

of fish collected with electrofishing gear (n = 4) had zero mortality. Results are summarized in

Table 3-2. Estimates of bycatch mortality were not adjusted for pen related mortality due to low

mortality estimates from control replicates.

I combined the mortality estimation and total bycatch estimates to estimate the number of

black crappie deaths via bycatch each year. The estimated mean number of black crappie that

died from gill net mortality in 2005 (GD2oos) was 5,332 with a range from 2,194 to 9,480

considering the range in estimates of GM and GC. The mean number of bycatch deaths in 2006

(GD2oo6) was estimated at 14,221 with a range of 7,619 to 22,393 given the range in estimates in

GM and GC.

Recreational Fishing Effort and Harvest

Comparison of the existing creel survey data at the lake suggest that recreational fishing

effort and harvest have increased at Lake Dora. The annual fishing effort for black crappie at

Lake Dora historically (survey data from 1977 to 1981) ranged from 14,208 to 26,233 hours

constituting 25 to 39% of total angling effort (Benton 2005), and catch ranged from 16,603 to









41,745 black crappie per year (Benton 2005). The current surveys were only during the peak

fishing season from November 2004 to June 2005, November 2005 to May 2006, and November

2006 to March 2007. Directed black crappie effort ranged from approximately 27,000 to 29,000

hours and harvest ranged from about 32,000 to 39,000 from 2004/2005 through 2006/2007

(Figure 3-2). Black crappie angling effort accounted for 80 to 94% of the total fishing effort for

the three survey periods. No standard error could be calculated for the estimates from the

2005/2006 survey period because of missing data for one 28-day period that was estimated by

substituting the mean value of fishing effort from the same time period the previous year.

Tagging Study

Tagging was conducted from November 3, 2005 to January 13, 2006 during sixteen

sampling trips at Lakes Dora and Beauclair. A total of 514 black crappie were single-tagged

with standard and higher reward floy tags, 197 fish were captured with electrofishing gear

(38%), 214 fish were captured with hoop nets (42%), and the remaining 105 fish were captured

with an otter trawl (20%). Totals of 125, 118, 133, and 132 fish were tagged in areas 1 through

4, respectively (tagging location of six fish were not recorded). A total of 413 black crappie

were tagged with $5 standard reward tags and 101 black crappie were tagged with $50 higher

value reward tags.

A total of 69 tags were returned, 40 $5 tags (10% of available $5 reward tags 34 from

recreational anglers and six from commercial fishers) and 29 $50 tags (29% of available $50

reward tags 27 from recreational anglers and two from commercial fishers); recreational

anglers accounted for 88% of total tag returns (61 of 69 returns) and commercial fishermen only

accounted for 12% of total tag returns (8 of 69 returns). All tags were recaptured from

December 7, 2005 to April 7, 2006, and recapture location was obtained from 55 of the 69

returned tags. We received six returns from area 1 (11%), nine returns from area 2 (16%), nine









returns from area 3 (16%), 23 returns from area 4 (42%), and eight returns from outside our

study area in adjoining canals (15%). Although 15% percent of tag returns were from outside

the study area in adjoining canals, all canals had locks that prevented fish escapement from the

system.

Estimates of exploitation for the recreational fishery included adjustments for tag loss,

tagging mortality, and reporting rate. Tag loss and tagging mortality were simulated at values

from 5 to 10%. I assumed 5% tag loss and tagging mortality for the average estimate of

exploitation for model simulations; Miranda et al. (2003) estimated tag loss for black and white

black crappie to be 4.6% within 24 hours of tagging using t-bar tags, and there was a significant

effect of time on tag loss. Henry (2003) estimated tag loss for largemouth bass to be

approximately 5% using dart tags. I felt that 5% tag loss was a reasonable estimate, based on the

short amount of time between tagging and recaptures and results from other studies. Miranda et

al. (2003) estimated tagging mortality for black and white black crappie to be 11% (SE = 7.2%)

for fish captured with electrofishing gear and trap nets. Henry (2003) estimated tagging

mortality for largemouth bass to be 0% for fish collected with electrofishing gear and hook-and-

line. Results from control replications of black crappie greater than 230 mm TL captured with

hoop nets and electrofishing gear on Lake Dora (not tagged) had a mortality rate of 10% and 0%,

respectively, and control replicates of black crappie greater than 180 mm TL captured with an

otter trawl (pelvic fin clip) at Lake Jeffords, Florida had a mortality rate of 1% (G. Binion, UF,

unpublished data). I felt that 5% tagging mortality was a reasonable estimate, based on our

control replications of fish captured with hoop nets, an otter trawl, and electrofishing gear, and

results from similar studies. The expected reporting rate of tags from higher value reward tag









fish (AH) was 70% (H = $50) based on equation 2-4, and the expected reporting rate of standard

tags was 22% based on equation 2-7 (Table 3-3).

The recreational exploitation rate (pREc) was 42% (TM= 0.05, TL = 0.05, AH = 0.7), the

instantaneous rate of fishing mortality for the recreational fishery (Free) was 0.55. The

commercial exploitation rate (,coM) was 16%, and the instantaneous rate of fishing mortality for

the commercial fishery (Fcom) was 0.17. I simulated a range of higher value reward tag reporting

rates from 0.5 to 1.0 by intervals of 0.1 and tag loss/tagging mortality from 5 to 10% to analyze

the effects of reporting rate on exploitation (Table 3-3). Lower reporting rates and higher tag

loss/tagging mortality increase estimates of recreational exploitation and higher reporting rates

and lower tag loss/tagging mortality decrease estimates of recreational exploitation. I simulated

a range of the total number of black crappie that died from gill net mortality in 2006 (GD0oo6)

from 7,000 to 22,000, and the number of black crappie harvested in the recreational fishery in

2006 from 25,000 to 39,000 to evaluate effects on the instantaneous rate of fishing mortality for

the commercial fishery (Fom). As expected, Fom values were highest at low recreational catch

and high gillnet deaths, and lowest at high recreational catch and low gillnet deaths.

Age and Growth

A total of 882, 664, and 723 black crappie were collected and measured from the

recreational fishery (whole sample carcasses and creel) in 2005, 2006, and 2007, respectively.

Sub-samples of carcasses (N = 183, 158, and 153 in 2005, 2006, and 2007) ranging from

approximately 18 to 37 cm TL were analyzed to determine age annually. The size and age

frequencies from the recreational catch (whole sample) in 2005, 2006, and 2007 are reported in

Figures 3-3 and 3-4. Ages ranged from 2 to 8 years old for all three years. Mean length-at-age

and associated variance and growth for the whole sample for each year were determined. Mean

length-at-age and growth were similar for black crappie in all years. Results from 2006 were









used in model simulations and are reported in Figure 3-5. Ages were applied to 145 and 362

black crappie collected from the commercial gillnet fishery in 2005 and 2006, respectively. The

size and age frequencies from the commercial bycatch in 2005 and 2006 are shown in Figures 3-

4 and 3-5.

Age-structured Population Model Simulations

Estimates of historical harvest, vulnerable biomass, and exploitation from 1961 to 2006

are presented in Figure 3-6. Values of the total number of black crappie harvested from 1977 to

1981 were from historical creel data collected by FWC, values of harvest in 2005 and 2006 were

estimates of total harvest from the commercial (estimated from onboard observations) and

recreational fishery (estimated from creel survey) combined, and the remaining years were

logical estimates of total harvest based on limited creel survey data. The maximum likelihood

profile for Bo and recK is presented in Figure 3-7. I simulated a range of Bo values from 70,000

to 100,000 kg and a range of recK values from 5 to 20. Given the life history and known

population characteristics of black crappie in Lake Dora, the ranges of Bo and recK values that

were simulated include the most likely range of logical possibilities.

Based on the maximum likelihood profile a Bo estimate of 80,000 kg is supported

statistically and is biologically realistic given my estimates of stock size and exploitation. Thus,

I fixed Bo at 80,000 kg and used equation 2-13 to solve for a recK, resulting in an estimate of

15.2. The maximum likelihood estimate occurred at a Bo of 78,000 kg and a recK of 20;

however, I felt that the MLE was not the true best fit because it occurred at the maximum recK in

the likelihood profile. The model fit the recK value at the highest possible value it was restricted

to resulting in estimates that were not biologically reasonable. After model fitting with my best

parameter estimates, my predicted and empirical estimates of exploitation were 0.51 in 2006, and









the model predicted vulnerable biomass in 2006 approximated my empirical estimate (Table 3-

4).

Future simulated exploitation rates influenced the model predicted estimates of total

harvest, vulnerable biomass, and SPR (Table 3-5). Mean total harvest slightly increased as

exploitation increased in simulations; however, mean vulnerable biomass decreased with

increases in exploitation. The mean weighted transitional SPR in the terminal year decreased

from 0.32 (scenario one) to 0.19 (scenario three). The SPR target goal for most fish species is

approximately 0.3 to 0.4, used as a biological reference point where values below the target goal

increase the likelihood of recruitment overfishing (Goodyear 1993; Clark 2002). The terminal

year mean weighted transitional SPR was operating near the target goal of 0.3 to 0.35 at the

levels of exploitation found in 2006, and model simulations predicted that increased exploitation

may cause concern of recruitment overfishing. At the highest exploitation rate simulated, the

mean weighted transitional SPR was predicted to be well below the target goal (Table 3-5).

Results for the annual weighted transitional SPR values with recruitment variability (0.4) are

reported for the entire model time series from Monte Carlo analysis with 100 iterations to show

how recruitment variation would influence SPR values (Figure 3-8).

Results from yield-per-recruit model simulations are presented in Figure 3-9. The YPR

values exhibited an asymptotic relationship with exploitation, indicating that with the current

vulnerability schedules the black crappie fishery is not likely to exhibit growth overfishing. The

maximum YPR value was 0.13 occurring at a total exploitation rate of 1. Black crappie were not

fully vulnerable to either recreational fishing or commercial bycatch until age four, and they

become reproductively mature at age two, which allows enough reproduction to prevent growth









overfishing. However, at extremely high exploitation rates a shift in the size structure toward

smaller, younger fish would be anticipated.











3000


16 2006 daily bycatch 2500
14

12 -2000
10 -
-1500 ,
> 8 i5
6 6 1000

4)- 500 0
E 2 -
c o 11JJ I JI, o E

18 1000
16 I 2005 commercial effort
S2005 daily bycatch
14 -800
: 12 -
Z I 600 1
10
8
400
6

4 200
2-
0 0
Jan Feb Mar Apr May

Month

Figure 3-1. Commercial fishing effort (number of boats fishing per day) and daily black crappie
bycatch (numbers) for the 2005 and 2006 commercial gill net seasons at Lake Dora.
Daily bycatch estimates are shown for 2005 and 2006 for days where onboard
observation data was available.












50000 50000
crappie effort
crappie harvest
40000 total effort 40000

)s


0 3
o =









0 0
0 --- -- -- ----0
2004/2005 2005/2006 2006/2007

Survey Period

Figure 3-2. Total recreational fishing effort (hours), black crappie effort (hours), and harvest of
black crappie (numbers) during the three creel survey periods at Lake Dora. The
associated standard error is reported for the survey periods in 2004/2005 and
2006/2007 (no SE could be calculated in 2005/2006 due to missing data from one 28-
day time period).










0.20
0.18 Recreational Harvest
0.16
0.14 -
2006
0.12 2007
0.10 -
0.08
0.06
S0.04-
S0.02
0 0.00 ,. .
LL 0.20
0.8 Commercial Bycatch
S0.18 -
S0.16 2005
0.14 -2006
0.12
0.10
0.08
0.06
0.04 -
0.02
0.00 1



Length Group

Figure 3-3. Relative length frequencies of black crappie measured from the recreational catch
(carcasses and creel) and commercial gill net bycatch on Lake Dora. Measurements
of black crappie were sampled from the black crappie recreational catch on Lake
Dora in 2005 (N = 882), 2006 (N = 664), and 2007 (N = 723), and from commercial
gill net bycatch on Lake Dora in 2005 (N = 145) and 2006 (N = 362). Length group
on x-axis represents 10 mm size groups.














0.5 -- LUU J
2006
0.4 m 2007


0.3


0.2 -


0.1 -


z3 0.0

I("
LL
(D 0.6 Commercial Bycatch

0.5
l2005
2006
0.4


0.3


0.2


0.1 -


0.0 n
1 2 3 4 5 6 7 8 9

Age

Figure 3-4. Age frequency of black crappie collected from the recreational catch (carcasses and
creel) and commercial gill net bycatch on Lake Dora. Ages were determined from
the recreational catch in 2005 (N = 882), 2006 (N = 664), and 2007 (N = 723), and
from commercial gill net bycatch in 2005 (N = 145) and 2006 (N = 362).











360 -

340 -

320 -

300 -

280 -

260 -


MLA2006 = 349.886 x (1- e(-04112 (t+0.4897)))


I I I I I I I I
1 2 3 4 5 6 7 8


Age


Figure 3-5. Von Bertalanffy growth curve fit to mean length-at-age values for black crappie
collected from the recreational fishery (carcasses and creel) at Lake Dora in 2006.
Error bars represent one standard deviation around the mean length-at-age values.


- VB Growth Curve
Mean length-at-age


240

220

200
















0.5


0.4


0.3


0.2


0.1 -


0.0 -
1950

90000 -

80000


70000 -

60000 -

50000 -

40000 -

30000 -


20000

10000
1950


Exploitation rate


1960 1970 1980 1990 2000


--- Harvest estimates simulated
S-...- Vulnerable biomass
Harvest estimates from historic data
\ Harvest estimates from current data


1960


1970


1980

Year


1990 2000


2010


90000

80000

70000 :

60000 |
0
50000 5

40000 c

30000

20000

- 10000
2010


Figure 3-6. Estimates of exploitation from 1961 to 2006 and estimates of historical total harvest
and vulnerable biomass from the SRA model. Values of the total number of black
crappie harvested are simulated for years that harvest data is not available.


"\./


- /


I'j

I
1 V1
-- \ r


*

















1.0 ] 0.4


0.8 1.0

4AO
w 0.6

0
0
0 0.4

S20
S0.2 18
16

0.0 12 4o
95x1l 030x0 10
85xl 03 8
80x103 6
75x103
1O 70x1 03


Figure 3-7. Maximum likelihood profile for recK values ranging from 5 to 20 and Bo values
ranging from 70,000 to 100,000 kg.











































u=.60
0.8

0.6

0.4 A

0.2

0.0
1960 1970 1980 1990 2000 2010 2020 2030 2040 2050
Year

Figure 3-8. Weighted transitional SPR estimated from SRA from 1961 to 2050 with Monte
Carlo simulations (100 iterations) under three exploitation scenarios. The three
exploitation scenarios were .tota = 0.42, 0.51, and 0.6. Recruitment variability = 0.4
from 2007 to 2050.











0.14

0.12

0.10

0.08

a 0.06

0.04

0.02

0.00 -.
0.0 0.2 0.4 0.6 0.8 1.0

Total exploitation

Figure 3-9. Results for YPR (kg) values at total exploitation rates from 0.2 to 1.0 from yield-per-
recruit model simulations.









Table 3-1. Summary of results from stratified sampling design in 2005 and 2006. Results
include stratified bycatch mean per fishing day, total bycatch estimate, variances for
bycatch mean per fishing day and total bycatch estimate, 95% upper and lower
confidence intervals, and the number of degrees of freedom used.


Var(Xsr) VAR(Xsr) CIlow Xsr


CI high XST


2005 324.52

2006 630.38


17,199

30,258


4,011.76 11,269,026

12,357 28,469,427


8,777

19,048


25,622

41,469


5.50

17.85


Table 3-2. Summary of results from secondary mortality experiment for treatment fish in 2005
and 2006 and control fish in 2006. Year, treatment type, number of replicates, and
the mean mortality and associated standard error are shown.
Year Type Replicates Mean mortality Standard error


2005

2006

2006

2006


treatment

treatment

control (hoopnets)

control (electrofishing)


0.31

0.47

0.10


0.06

0.07

0.05

0


Year









Table 3-3. Estimates of recreational exploitation rate (Irec) based on values of the number of higher reward value (CH) and standard
reward tag fish caught (Cs). Tagging mortality (TM) and tag loss (TL) were simulated at 5% and 10%. The total number
of higher value tag fish caught (CH), and standard tag fish caught (Cs) were calculated based on differing values of higher
value reward tag reporting rate (XH) from 0.5 to 1.0.
4H CH RH RL CL TL TH XL lrec (5%TL-5%TM) jrec (10%TL-10%TM)


0.5 54 27 34 221 413

0.6 45 27 34 184 413

0.7 39 27 34 158 413

0.8 34 27 34 138 413

0.9 30 27 34 123 413

1.0 27 27 34 110 413


101 0.15

101 0.18

101 0.22

101 0.25

101 0.28

101 0.31


0.59

0.50

0.42

0.37

0.33

0.30


0.67

0.56

0.48

0.42

0.37

0.33









Table 3-4. Empirical estimates of vulnerable biomass (kg) and total exploitation (,Uotai) in 2006
and model predicted values of vulnerable biomass and total exploitation in 2006.
Empirical estimates in 2006 were calculated with an estimated total harvest of 54, 221
(recreational and commercial) and 2006 model predicted values of vulnerable
biomass and total exploitation were derived with leading parameter estimates of Bo=
80,000 kg and recK = 15.22.
Parameter 2006 empirical estimate 2006 model predicted value

vulnerable biomass (kg) 34,912 35,080

total exploitation (Utotal) 0.51 0.51

total harvest (numbers) 54,221


Table 3-5. Estimates of mean vulnerable biomass (kg), mean total harvest (numbers) and mean
weighted transitional SPR in the terminal year 2050 determined from Monte Carlo
simulations (1,000 iterations). Three exploitation scenarios totall = 0.42, Itota = 0.51,
totall = 0.60) are shown.
Exploitation scenario total Mean vulnerable biomass Mean total harvest Mean SPR

1 0.42 32,026 41,592 0.32

2 0.51 26,359 43,491 0.25

3 0.60 21,973 44,583 0.19









CHAPTER 4
DISCUSSION

Black crappie is the primary sport fish targeted by recreational anglers at Lake Dora, and

my results show that the population could be negatively impacted by increases in exploitation

resulting from either the recreational fishery or bycatch from the commercial gill net fishery for

gizzard shad. Currently, FWC has not defined a standard to measure impacts of bycatch and

determine levels of commercial exploitation that are acceptable. I used a biological reference

point (SPR) determined from an age-structured model to attempt to determine what levels of

total exploitation could be sustainable without risking recruitment overfishing. I also used

maximum yield per recruit to investigate the potential for growth overfishing to occur at varying

total exploitation rates. It is important to realize that negative impacts such as reduced catch or

decreased angler success may occur at fishing mortality rates below those which cause

recruitment overfishing and changes in the vulnerability to harvest schedule may influence the

potential for recruitment overfishing to occur at varying total exploitation rates.

Additionally, management decisions are still required to determine how much of the total

sustainable exploitation rate is allocated to the recreational fishery versus bycatch from the gill

net fishery. The total sustainable exploitation rate is approximately 0.42, which results in an

SPR near the target goal of 0.3 to 0.35. The estimated recreational exploitation rate in 2006 was

approximately the total sustainable exploitation rate, and increases due to recreational fishing

and/or commercial bycatch greatly increase the probability of recruitment overfishing. Total

exploitation in 2006 resulted in an estimated exploitation rate (0.51) that produces worrisome

SPR levels and is most likely not sustainable. The exploitation via bycatch of black crappie at

Lake Dora is a negative effect because it is not resulting from a directed fishery and all mortality

results in waste. The gill net fishery was regulated to minimize bycatch as much as possible, but









bycatch mortality occurred at rates that cause concern for recruitment overfishing. Resource

managers must evaluate policy trade-offs to consider the benefit of the gizzard shad removal and

the negative impacts of bycatch mortality.

Commercial fishing occurred on Lake Dora during 2005 and 2006 and total commercial

fishing effort was approximately equal during the two fishing seasons. However, the temporal

range of effort differed, which may have influenced total gill net bycatch mortality among the

commercial seasons. The commercial fishing season in 2005 began two months later than the

commercial season in 2006, which could have resulted in differences in catchability due to

differing vulnerability to capture in gill nets. This is plausible due to black crappie inshore

spawning movements occurring during the later months of the fishing seasons. Total bycatch

estimates in 2006 were nearly twice as high as total bycatch estimates in 2005. These results

suggest that bycatch could be reduced by timing the commercial fishing season to prevent fishing

during winter and early spring. Reducing total bycatch mortality is achieved by reducing the

amount of total bycatch or reducing mortality resulting from bycatch. Timing of season could

potentially reduce the amount of total bycatch without increasing mortality resulting from

bycatch. Bycatch mortality rates would not likely increase by timing of season because I found

no significant impact of water temperature or dissolved oxygen levels on bycatch mortality.

No initial mortality of bycatch was observed at Lake Dora during gill net operations, and

secondary mortality was the primary mortality source for black crappie caught in commercial gill

nets. This was likely due to the maximum soak time of two hours. Total mortality of black

crappie captured via gill nets at Lake Apopka, Florida was estimated from 1993 to 1997 and

results indicated that 87% survived the treatment (J. Crumpton, FWC, unpublished data).

Similarly, secondary mortality accounted for the majority of total mortality and only a small









percentage of total mortality observed was initial mortality, which generally occurred in nets

fished greater than two hours.

The potential for adverse population-level effects resulting from commercial bycatch is

greatest when recreational exploitation is already high. A previous evaluation found negligible

impacts from gill net bycatch for black crappie on Lake Apopka, Florida using a transitional SPR

constructed from an SRA (M. Allen, UF, unpublished data), due to low recreational exploitation

(-1 fish/acre/year). Conversely, commercial harvest of black crappie at Lake Okeechobee,

Florida coupled with recreational harvest increased exploitation to 65%, but the effects were

increased growth rates and the population did not show signs of overfishing (Schramm et al.

1985). However, the conclusions of this study were based on catches and angler success, and

they did not investigate the potential for recruitment overfishing.

Other studies have assessed population-level impacts of bycatch with modeling techniques.

Crouse et al. (1987) developed a stage-based matrix model that incorporated fecundity, survival,

and growth rates, and used yearly iterations to make population projections for loggerhead sea

turtles Caretta caretta. The model used seven life stages from eggs/hatchlings to mature

breeders and tested the sensitivity of bycatch mortality on population growth rates. They found

that reducing mortality in the large juvenile and adult life stages provided the best protection for

population viability. Diamond et al. (1999) explored the population level effects of catch and

bycatch on Atlantic croaker Micropogonias undulatus in the Gulf of Mexico and the Atlantic

Ocean. Catch of Atlantic croaker, including bycatch, had historically been at least three times

higher in the Gulf than the Atlantic; however, primarily juveniles are taken in the Gulf fisheries

whereas fisheries in the Atlantic have targeted adult fish. Long-term intensive fishing in the Gulf

caused severe declines in abundance of Atlantic croaker, but there was no change in size









distribution and age at maturity, and large fish remained common. In contrast, the Atlantic

fishery targeting adult fish has caused changes in age at maturity and size structure of that

population. Diamond et al. (2000) used stage-within-age based matrix models of Atlantic

croaker in the Gulf of Mexico and Atlantic to investigate population-level effects of shrimp trawl

bycatch. The Gulf model showed a rapidly declining population, and the Atlantic population

showed only a modest decline. Results indicated that both populations were more sensitive to

survival of adults than first-year survival, and reducing late juvenile and adult mortality could

reverse population declines. Results from these studies support my conclusion that population-

level impacts can occur, especially when targeted-fishery exploitation is also high.

Biological reference points such as spawning potential ratio are commonly used as critical

metrics to measure the potential of recruitment overfishing. Goodyear (1993) defines SPR as the

ratio of fished to unfished magnitude of P (reproductive potential of an average recruit) and is a

measure of the impact of fishing on the potential productivity of a stock. Critical levels had

typically been set in the range of 0.2 to 0.3, based primarily on work in the Northwest Atlantic

(Goodyear 1993). SPR target values of 0.35 to 0.4 have also been suggested (Clark 2002), but

the critical level for any particular species is influenced by the level of recruitment compensation

for fishing mortality (Goodyear 1993). The state of Florida has adopted a target SPR of 0.35 for

some heavily exploited marine species, including the spotted seatrout Cynoscion nebulosus,

which have shown worrisome levels of SPR values due to recreational exploitation (no

commercial exploitation and very limited bycatch) (Murphy et al. 1999).

Estimates of exploitation from tagging studies are always subject to uncertainty due to tag

loss, tagging mortality, and reporting rate. For my model simulations, I utilized the best estimate

of recreational exploitation (0.42) from tag returns corrected for tag loss of 5%, tagging mortality









of 5%, and reporting rate of higher value reward tags of 70%. My estimate of recreational

exploitation in 2006 (!Irec = 0.42) was comparable to estimates of exploitation for black crappie

in other southeastern systems. Larson et al. (1991) estimated exploitation rates ranging from 40

to 68% in three Georgia reservoirs, Allen and Miranda (1995) estimated a mean exploitation rate

of 42% for white and black crappie in 10 Southeast and Midwest lakes, and Allen et al. (1998)

found that exploitation averaged 48% for 18 lakes in the Southeast and Midwest. Black crappie

are one of the most heavily harvested and exploited freshwater fishes in the United States, and

strong size selectivity under heavy exploitation may affect black crappie population dynamics

(Miranda and Dorr 2000).

My exploitation estimate was critical for model simulations because the model was fit to

my 2006 empirical estimates of exploitation and vulnerable biomass. An unbiased estimate of

exploitation was additionally important to reduce parameter uncertainty, because there is also

structural uncertainty in the SRA. The SRA model reduces population size based on catches

alone, and does not account for other factors that may influence recruitment such as habitat

changes. This is of particular importance because if the gizzard shad removal is successful,

improved water clarity could result in increased aquatic macrophyte abundance thereby changing

the available habitat and factors that influence black crappie recruitment and growth.

All model simulations assumed vulnerability to harvest was equal for the recreational and

commercial fisheries. This is important because the vulnerability to harvest schedule directly

impacts estimates of exploitation. It is likely that vulnerability between commercial and

recreational fisheries were similar based on the size and age distributions of the harvest.

Although recreational anglers did tend to harvest some smaller black crappie that were not fully

vulnerable to the commercial fishery, Miranda and Dorr (2000) showed that recreational anglers









tend to select for fish over 250 mm TL. Additionally, much of the recreational angling effort

occurs in open water areas where gill nets are fished. The number of tag returns from the

commercial fishery was significantly lower possibly indicating a difference in vulnerability to

harvest, however commercial fishers had incentive to not return tags and no reliable reporting

rate could be obtained for the commercial fishery.

My future projections were conducted under the assumption that total exploitation

remained constant through the terminal year. This scenario is unlikely, because changes in

angler catch rates through fish reductions via recreational and/or commercial exploitation would

probably influence recreational fishing effort. Cox et al. (2003) found that angling effort

depends on the angler catch rate, and there is no reason to expect that the level of fishing effort

that produces the maximum total yield will also provide maximum total satisfaction to anglers.

Additionally, Walters and Martell (2004) state that most fisheries reach a bionomic equilibrium

where they become "self-regulating" in the sense that further stock decline past some

equilibrium caused by development of a fishery should trigger a reduction in fishing effort and

mortality allowing the stock to begin recovery. Thus, it is likely that recreational effort would

decline if total exploitation continued to increase and catch rates declined, due to decreased

angler satisfaction and shifts in fishing effort to other systems. Under this scenario of bionomic

equilibrium, commercial bycatch will probably not result in recruitment overfishing. However,

decreased angler satisfaction and fishing effort is still a negative impact resulting from increased

exploitation, which could occur due to bycatch mortality. Reduced recreational angler effort

caused by commercial bycatch mortality warrants future investigation because lower effort

would constitute "harm" to the recreational fishery.









CHAPTER 5
MANAGEMENT IMPLICATIONS

Impact on the black crappie fishery due to bycatch mortality may be acceptable if the

gizzard shad reduction is successful in improving water clarity and increasing aquatic

macrophyte abundance. A management decision must be made for the future of commercial

fishing with gill nets on Florida lakes that evaluates the trade-offs of the positive effects of

biomanipulation and possible negative effects of bycatch on recreational fisheries. Possible

management alternatives are to 1) discontinue the gill net fishery to eradicate bycatch and

optimize the black crappie recreational fishery, or 2) increase commercial effort and gizzard shad

exploitation to optimize the success of the biomanipulation. It is plausible that continuing the

program at the current level of commercial effort will most likely not optimize either

management objective.

Another alternative is to initiate an active adaptive management plan. Active management

of recreational fisheries implies that a complete management procedure is in place, with clear

goals or objectives for the fishery, management schemes to keep the total harvest or exploitation

rates within target limits, and methods to determine whether the goals or objectives have been

met (Walters 1986; Pereira and Hansen 2003). Little experience has been gained in actively

managing recreational fisheries due to the extensive and diverse array of recreational fisheries,

few recreational fisheries are of such singular importance that they demand the sociopolitical or

economic motives, and many passive management schemes are in place in response of the need

for management (Pereira and Hansen 2003). For successful active adaptive management in

recreational fisheries, agencies must commit to a clear goal or objective. In the case of the Lake

Dora commercial gill net fishery, possible objectives are 1) reducing the gizzard shad population

enough to change the trophic structure or 2) optimizing black crappie recreational angling









satisfaction. If the goal of the Lake Dora fishery is to reduce gizzard shad abundance to levels

that result in trophic structure alterations, then a long-term management plan should be

implemented that involves fishing the gizzard shad intensively, measuring the levels of gizzard

shad reduction, measuring levels of chlorophyll reduction, and measuring the black crappie

bycatch mortality and angling success. Another consideration in the evaluation of the policy

trade-off is the effect that a change in the trophic structure would have on the black crappie

population. A shift in the trophic structure may result in changes in water clarity, aquatic

macrophyte abundance, and fish productivity that could impact black crappie population

dynamics and angling success, which is not accounted for in SRA simulations.

Fisheries management inherently requires making decisions that involve trade-offs.

Management agencies often try to make decisions that optimize all alternatives, which can create

a situation where none of the management alternatives are optimized. Failure to admit the

severity of trade-off relationships can result in policy choices that are not beneficial for anyone

(Walters and Martell 2004). The trade-offs associated with the gizzard shad biomanipulation and

black crappie bycatch must be considered and clear management objectives defined. If

commercial fishing continues, methods must be set forth to measure the effectiveness of the

management objectives. My results show that the current size-selective removal of gizzard shad

at Lake Dora could cause negative impacts to the black crappie population, with the potential for

recruitment overfishing. Resource managers should consider these impacts and the trade-offs

they represent when considering commercial fishing operations.









LIST OF REFERENCES


Allen, M. S., and L. E. Miranda. 1995. An evaluation of the value of harvest restrictions in
managing black crappie fisheries. North American Journal of Fisheries Management
15:766-772.

Allen, M. S. 1997. Effects of variable recruitment on catch-curve analysis for black crappie
populations. North American Journal of Fisheries Management 17:202-205.

Allen, M. S., and L. E. Miranda. 1998. An age-structured model for erratic black crappie
fisheries. Ecological Modeling 107:289-303.

Allen, M. S., L. E. Miranda, and R. E. Brock. 1998. Implications of compensatory and additive
mortality to the management of selected sportfish populations. Lakes & Reservoirs:
Research and Management 3:67-69.

Allen, M. S., M. V. Hoyer, and D. E. Canfield, Jr. 2000. Factors related to gizzard shad and
threadfin shad occurrence and abundance in Florida lakes. Journal of Fish Biology
57:291-302.

Anderson, R. O., and R. M. Neumann. 1996. Length, weight, and associated structuralindices.
Pages 447-482 in B. R. Murphy and D. W. Willis, editors. Fisheries techniques, 2nd
edition. American Fisheries Society, Bethesda, Maryland.

Bachmann, R. W., B. L. Jones, D. D. Fox, M. V. Hoyer, L. A. Bull, and D. E. Canfield, Jr. 1996.
Relations between trophic state indicators and fish in Florida (U.S.A.) lakes. Canadian
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BIOGRAPHICAL SKETCH

Jason Randall Dotson was born February 11, 1980, in Manassas, Virginia, the son of Paul

Randall and Rae Lynn Dotson. He was raised with his younger sister Jennifer on Lake Jackson,

a private reservoir in Northern Virginia. Under the influence of his father Randy and grandfather

Paul, Jason acquired a love and appreciation for the outdoors at an early age. In 1998, Jason

began his collegiate studies at Virginia Tech, where he would earn a Bachelor of Science degree

in fisheries science in 2003. While at Virginia Tech, he worked on a variety of research projects

involving smallmouth bass, muskellunge, striped bass, and the federally endangered Roanoke

logperch. After graduation, Jason experienced a short-lived career as an insurance adjuster. In

August of 2004, he decided to pursue a career in fisheries science and served as a fisheries

technician for the University of Florida, where he worked on a variety of projects involving

gizzard shad, American shad, black crappie, spotted sunfish, and largemouth bass. In August of

2005, Jason began his own research as a graduate student in the Department of Fisheries and

Aquatic Sciences at the University of Florida. He graduated in August 2007 with a Master of

Science degree, and is currently working as a fisheries biologist for Florida Fish and Wildlife

Conservation Commission.





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EFFECTS OF COMMERCIAL GILL NET BY CATCH ON THE BLACK CRAPPIE FISHERY AT LAKE DORA, FLORIDA By JASON RANDALL DOTSON A THESIS PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE UNIVERSITY OF FLORIDA 2007 1

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2007 Jason Randall Dotson 2

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To my parents, Randy and Lynn Dotson; my gran dmother, Jo Anne Dotson; and my sister, Jennifer Dotson: thank you so much for all of your love, support, and guidance. I would not be the person that I am today without you all. To my late grandfather, Paul Dotson: you are dearly missed and I know that you are proud of my accomplishments. 3

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ACKNOWLEDGEMENTS I would like to thank Dr. Mike Allen for giving me the opportunity to conduct this research. I have the utmost respect and appr eciation for Dr. Allen, who served as my major advisor, committee chair, mentor, and friend. I would like to acknowledge Dr. Bill Pine, Bill Johnson, and Marty Hale for serving as committee members, FWC personnel Bill Johnson, Marty Hale, John Benton, and Brandon Thompson for their guidance and help in collecting data, and the St. Johns River Water Management Dist rict for their aid in collecting data. I would like to thank the fo llowing people from the University of Florida who made significant contributions to the fi eld and lab portions of this stu dy: C. Barrientos, G. Binion, M. Catalano, D. Dutterer, K. Johnson, G. Kaufman, P. ORourke, and E. Thompson. Finally, I would like to thank Matt Cata lano for his advice, comments, and quantitative assistance. 4

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TABLE OF CONTENTS page ACKNOWLEDGEMENTS.............................................................................................................4 LIST OF TABLES................................................................................................................. ..........7 LIST OF FIGURES.........................................................................................................................8 ABSTRACT.....................................................................................................................................9 CHAPTER 1 INTRODUCTION................................................................................................................. .11 2 METHODS...................................................................................................................... .......15 Study Site..................................................................................................................... ...........15 Commercial Fishing............................................................................................................. ...15 Total Bycatch Assessment......................................................................................................1 6 Bycatch Mortality.............................................................................................................. .....16 Recreational Fishing Effort and Harvest................................................................................18 Tagging Study.........................................................................................................................19 Age and Growth......................................................................................................................20 Analyses..................................................................................................................................21 Total Bycatch Assessment...............................................................................................21 Bycatch Mortality............................................................................................................22 Recreational Fishing Effort and Harvest.........................................................................23 Tagging Study.................................................................................................................23 Age and Growth..............................................................................................................26 Population-Level Impacts of Exploitation.......................................................................26 3 RESULTS...................................................................................................................... .........36 Commercial Fishing............................................................................................................. ...36 Total Bycatch Assessment......................................................................................................3 6 Bycatch Mortality.............................................................................................................. .....37 Recreational Fishing Effort and Harvest................................................................................37 Tagging Study.........................................................................................................................38 Age and Growth......................................................................................................................40 Age-structured Population Model Simulations......................................................................41 4 DISCUSSION................................................................................................................... ......56 5 MANAGEMENT IMPLICATIONS......................................................................................62 LIST OF REFERENCES...............................................................................................................64 5

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BIOGRAPHICAL SKETCH.........................................................................................................71 6

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LIST OF TABLES Table page 2-1 Mean water quality parameters for La kes Dora (east and west) and Beauclair (Florida LAKEWATCH 2004)..........................................................................................35 3-1 Summary of results from stratif ied sampling design in 2005 and 2006............................53 3-2 Summary of results from secondary mortality experiment for treatment fish in 2005 and 2006 and control fish in 2006......................................................................................53 3-3 Estimates of recreationa l exploitation rate (rec) based on values of the number of higher reward value (CH) and standard reward tag fish caught (CS).................................54 3-4 Empirical estimates of vulnerable biomass (kg) and to tal exploitation ( total) in 2006 and model predicted values of vulnerabl e biomass and total exploitation in 2006...........55 3-5 Estimates of mean vulnerable biomass ( kg), mean total harvest (numbers) and mean weighted transitional SPR in the terminal year 2050 determined from Monte Carlo simulations (1,000 iterations).............................................................................................55 7

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LIST OF FIGURES Figure page 2-1 Map of Lakes Eustis, Dora, and Beauclair located in the Upper Ocklawaha River Basin in Lake County, Florida...........................................................................................34 3-1 Commercial fishing effort (number of boats fishing per day) and daily black crappie bycatch (numbers) for the 2005 and 2006 commer cial gill net seasons at Lake Dora......44 3-2 Total recreational fishing effort (hours), black crappie effort ( hours), and harvest of black crappie (numbers) during the three creel survey periods at Lake Dora...................45 3-3 Relative length frequencies of black cra ppie measured from the recreational catch (carcasses and creel) and commercial gill net bycatch on Lake Dora...............................46 3-4 Age frequency of black crappie collected from the recreational catch (carcasses and creel) and commercial gill net bycatch on Lake Dora.......................................................47 3-5 Von Bertalanffy growth curve fit to m ean length-at-age values for black crappie collected from the recreationa l fishery (carcasses and creel) at Lake Dora in 2006.........48 3-6 Estimates of exploitation from 1961 to 2006 and estimates of historical total harvest and vulnerable biomass from the SRA model...................................................................49 3-7 Maximum likelihood profile for recK values ranging from 5 to 20 and Bo values ranging from 70,000 to 100,000 kg...................................................................................50 3-8 Weighted transitional SPR estimated from SRA from 1961 to 2050 with Monte Carlo simulations (100 iterations) un der three exploitation scenarios...............................51 3-9 Results for YPR (kg) values at total e xploitation rates from 0.2 to 1.0 from yield-perrecruit model simulations...................................................................................................52 8

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Abstract of Thesis Presen ted to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Master of Science EFFECTS OF COMMERCIAL GILL NET BY CATCH ON THE BLACK CRAPPIE FISHERY AT LAKE DORA, FLORIDA By Jason Randall Dotson August 2007 Chair: Micheal S. Allen Major: Fisheries a nd Aquatic Sciences Commercial bycatch can potenti ally cause population-level eff ects and represents serious concerns for sustainability and efficiency of fisher ies. A commercial gill net fishery for gizzard shad Dorosoma cepedianum took place during 2005 and 2006 at Lake Dora, Florida. The primary bycatch of the gill net fishery was reproductively mature black crappie Pomoxis nigromaculatus, which also support the primary sport fisher y at the lake. I assessed total black crappie bycatch, mortality rates of black cr appie entangled in gill nets, and quantified recreational fishing effort and harvest for 2005 and 2006, and estimated exploitation for the recreational and commercial (byc atch) fisheries in 2006. I utilized age-structured population dynamics modeling techniques to investigate potentia l population-level impacts of bycatch. Onboard observer data of commercial fishing activity showed that approximately 17,000 and 30,000 black crappie were captured in gill nets in 2005 and 2006, respectively. Estimates from a pen experiment revealed that about 30% and 47% of black crappie experienced 72-h mortality due to entanglement in gill nets in 2005 a nd 2006, respectively. Recreational exploitation ( urec) was estimated to be 42% based on tag re turns, and commercial exploitation ( ucom) was estimated to be 16% based on the number of black cra ppie that died due to gill netting in 2006. 9

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Simulations were performed from a stock redu ction analysis (SRA) population dynamics model for three exploitation scenarios to investigat e the potential of recr uitment overfishing and simulations were performed from a yield-per-recruit model with varying exploitation rates to investigate the potential for growth overfishing. Results suggested that the current level of recreational exploitation is ope rating near a target SPR goal of 0.3 to 0.35 and additional exploitation from the recreational or commercia l fishery could risk recruitment overfishing. Given the current vulnerability to harvest schedule growth overfishi ng is not of concern, however a shift in vulnerability towards smalle r fish could increase the risk of growth overfishing. The greatest risk fo r recruitment overfishing via bycat ch occurs when recreational exploitation is also high (e.g., this work). My study revealed a tr ade off, where potential benefits of biomanipulation via gizzard shad harvesting must be weighed against bycatch impacts to recreational fisheries. 10

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CHAPTER 1 INTRODUCTION Bycatch, the incidental catch of non-target species with fishin g gear, occurs in almost all commercial fisheries, and has become a central resource management concern throughout the world (Diamond et al. 2000; Crowder and Muraws ki 1998; Pikitch et al .1998). Many studies have attempted to assess total bycatch in commer cial fisheries (Hale et al. 1981; Hale et al. 1983; Renfro et al. 1989; Hale et al. 1996; Clark and Hare 1998; Pikitch et al. 1998; Stein et al. 2004), assess mortality of incidental bycatch (Hale et al. 1981; Hale et al. 1983; Clark and Hare 1998; Belda and Sanchez 2001; Beerkircher et al. 2002; Stein et al. 2004), and ultimately address population-level effects (Crouse et al. 1987; Mangel 1993; Crowde r et al. 1994; Caswell et al. 1998; Diamond et al. 1999; Diamond et al. 2000; Tuck et al. 2001; Majluf et al. 2002). Prior to 1998, hypotheses about population-level impacts rarely had been tested (Crowder and Murawski 1998) and Diamond et al. (2000) noted that popula tion-level effects of bycatch have been difficult to quantify. Observations made on board commercial fishi ng vessels have estimat ed the proportion of total landings made up of bycatch and bycatch initial mortality rates (Hale et al. 1981; Hale et al. 1983; Hale et al. 1996; Clark and Ha re 1998; Pikitch et al. 1998; Beer kircher et al. 2002; Stein et al. 2004). Hale et al. (1983) observed pound net fishing operations in the St. Johns River, Florida, and estimated game fish total bycatch and initial mortality with estimates of fishing effort, area fished, and game fish catch rate. Piki tch et al. (1998) used on-board observer data to estimate bycatch of Pacific halibut Hippoglossus stenolepis in Washington, Oregon, and California bottom trawl fisheries to test differences in catch rates of trawl t ypes and time of year. Stein et al. (2004) tested for di fferences in total bycatch and mortality of Atlantic sturgeon Acipenser oxyrinchus among three gear types (trawl and two gill nets). Beerkircher et al. (2002) 11

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quantified shark bycatch by species and initial mo rtality rates in the Southeast United States pelagic longline fishery with ni ne years of fisheries observer data. Onboard observations can provide useful information for measuring the proportion of total landing s made up of bycatch and can provide estimates of init ial mortality due to fishing. Total bycatch mortality includes initial mortality occurring as part of the capture process and secondary mortality, which occurs following rele ase from fishing gear. Initial mortality is most often calculated directly onboard as pa rt of observer programs, whereas secondary mortality is estimated via pen studies or tagging programs. Total bycatch mortality is difficult to measure due to the long observati on periods required after fish capture. Total mortality may result from chronic effects such as injury or infection, or increased vulnerability to predation (Crowder and Murawski 1998). Crowder and Mura wski (1998) argued that secondary and total mortality should be considered in bycatch mana gement, and appropriate survival studies should be conducted. Total bycatch and bycatch mortality estimates provide useful information to aid in optimizing gear choice, fishing areas, and fishing seasons, but these estimates alone do not quantify population effects of by catch. Catch of non-target sp ecies in fisheries can have implications at the population le vel (Crowder and Murawski 1998), and there are concerns about impacts to fish populations (Murra y et al. 1992) and marine fauna su ch as sea turtles, seabirds, sharks, and mammals (Lewison et al. 2004). Me thods to determine the population impacts of bycatch typically involve field estimates and population modeli ng. Age-and-stage-structured modeling techniques have been applied successf ully to examine bycatch population implications for a variety of species including sea turtles (Cro use et al. 1987; Crowder et al. 1994), wandering albatross Diomedea exulans (Tuck et al. 2001), humboldt penguins Spheniscus humboldti 12

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(Majluf et al. 2002), right whale dolphins Lissodelphis borealis (Mangel 1993), and harbor porpoises Phocoena phocoena (Caswell et al. 1998). Diam ond et al. (1999) explored the population level effects of catch and bycatch on Atlantic croaker Micropogonias undulatus in the Gulf of Mexico and the Atlantic Ocean. Lake Dora was recently selected by Fl orida resource management agencies for biomanipulation via intensive commercial fishin g with gill nets. Gizzard shad Dorosoma cepedianum are an omnivorous fish with the poten tial to influence lake nutrient cycling. Gizzard shad can greatly reduce large crustacean zooplan kton density (DeVries an d Stein 1992; Stein et al. 1995) and can also consume bent hic detritus when zooplankton resources are low (Stein et al. 1995; Irwin et al. 2003). Density and biomass of gizzard shad increase with trophic state, and gizzard shad often occupy the majority of total fish biomass in hypereutrophic systems (Bachmann et al. 1996; Allen et al 2000). Because gizzard shad ha ve the potential to influence zooplankton abundance and influence nutrient cy cling between the sediment and the water column (Schauss and Vanni 2000; Schaus et al. 2002; Gido 2003), gizzard shad at Lake Dora were targeted for removal. Gill nets are size selective and not species specific; thus, adult sport fish bycatch associated with the commercial gill net fishery for gizzard shad at Lake Dora is of concern to state agency scientists and anglers. Black crappie Pomoxis nigromaculatus comprise some of the most popular sport fisheries throughout North America (Hooe 1991; Allen and Miranda 1998) and represent the primary re creational fishery on Lake Dora, Florida (Benton 2005). Bycatch of black crappie is of concern to lake managers because significant bycatch mortality could have deleterious impacts on recreational fi sheries. Thus, there is a need to evaluate whether bycatch could influence black crappie fisheries, whic h would elucidate policy trade-offs between 13

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potential benefits of gizzar d shad removal and impacts of commercial gill net bycatch on recreational fisheries. The objectives of this study were to (1) estima te total black crappie bycatch in commercial gill nets, (2) estimate bycatch mortality (initia l and secondary) from commercial gill nets on black crappie, (3) assess recreational fishing effort and harvest of black crappie, and (4) address population-level effects that bycatch could have on the black cra ppie fishery at Lake Dora. I assessed the population-level impacts of black cr appie bycatch from the gizzard shad gill net fishery at Lake Dora, Florida by investigating the potential fo r recruitment overfishing via a stock reduction analysis (SRA) model and evalua ting the potential for growth overfishing with a yield-per-recruit model. Growth overfishing occurs when fish are being harvested at an average size that is less than the size that produces maximum yield per recruit, and usually results from excessive effort and a selectivity schedule wher e small fish are vulnerable to harvest and not allowed to reach their maximum growth potential Recruitment overfishing occurs when fishing mortality rates are so high that the adult popul ation does not have the reproductive capacity to replace itself. Recruitment overfishing is less common but is of serious concern because it can lead to stock depletion and collaps e. If selectivity schedules are skewed towards larger fish that have passed the age at sexual maturity recr uitment overfishing ma y occur where growth overfishing is not a concern. 14

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CHAPTER 2 METHODS Study Site Lakes Dora and Beauclair are part of th e Upper Ocklawaha River Basin, located approximately 30 miles northwest of Orlando in central Florida (Mag ley 2003, Figure 2-1). Lakes Dora (1,774 ha) and Beauclair have a comb ined surface area of 2,211 ha and they are connected by a short open canal, commercial fishing for gizzard shad was permitted on both water bodies, and both systems have similar trophic status and fish communities (Table 2-1). I considered Lakes Dora and Beauclair as one wa ter body for the purposes of this study and will refer to the system collectively as Lake Dora. Surface outflow from Lake Dora is through the Dora Canal into Lake Eustis (3,139 ha) (Figure 2-1). Commercial Fishing Permits were issued by the Florida Fish and Wildlife Conservation Commission (FWC) for 28 commercial fishers to remove gizzard shad from Lake Dora in 2005 and 2006. The fishery was regulated in an effort to minimize bycatch mortality as much as possible with the following restrictions. A maximum of two gill nets, not to total more than 1,097 meters could be used simultaneously by each boat, and gill net specifi cations were a minimum stretch mesh size of 10.2 cm. The maximum allowable length of one net was 549 meters, and nets were allowed 2 hours maximum soak time. There was no restri ction on the maximum number of nets fished daily, as long as all other guidelines were followed. Floating and sinking gill nets were used. Commercial fishing was allowe d only during daylight hours in open water areas at least 90 meters from shore during open seasons. Commerci al fishers harvested gizzard shad, Florida gar Lepisosteus platyrhincus longnose gar Lepisosteus osseus blue tilapia Oreochromis aurea, and 15

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the nonnative sailfin catfish Liposarcus multiradiatus All other fish species caught in gill nets were required to be returned to the water immediately after remova l from the nets. Total Bycatch Assessment Gill net operations during the gizzard shad removal were monitored by St. Johns River Water Management District (SJRWMD) observers. Monitoring was conducte d at least twice per week during the commercial seasons and consis ted of random observations of gill net fishing operations. Observers reported ca tch numbers, species composition, mesh size, net type (floating or sinking), and net length. An observation day consisted of at least six gill net sets. If there was no commercial gill net activity or weather prohibited observations, an attempt was made to average 12 gill net set observations per week and two sampling days per week over a one-month period. Subsamples of crappie bycatch were measured for total length (TL) weekly until a maximum of 100 fish per species was recorded each month. The first four weeks of fishing in 2006 required increased monitoring as follows; observations were conducted at least three days per week, at least 18 gill net sets were observed per week, and all black crappie encountered were measured until a maximum of 200 were recorded. The SJRWMD was required to follow these methods set forth in the sampling permit for the shad removal pr oject issued by FWC. Bycatch Mortality To evaluate bycatch mortality of black crappi e I collected fish from commercial fishing vessels as gill nets were being retrieved in both years. After black crappie were removed from gill nets by commercial fishers, I transferred the fish to a research vessel where they were measured to the nearest mm TL and placed in a 190 liter cooler with aerators used to maintain dissolved oxygen levels over 5 mg/L. Dissolved oxygen levels were record ed in the cooler to assure that they exceeded 5 mg/L at all times. Any initial mortality of fish from gill nets was recorded. I considered a fish to be alive when the net was pulled if there was opercular 16

PAGE 17

movement (Kwak and Henry 1995). I recorded gill net mesh size and style (sinking or floating) for each sample fish were collected from. I estimated secondary mortality of black crappie entangled in gill nets. Secondary mortality has been effectively measured for largemouth bass in live-release tournaments (Schramm et al. 1987; Kwak and Henry 1995; W eathers and Newman 1997; Neal and LopezClayton 2001; Edwards et al. 2004 ) using pens to hold fish that were captured during hook-andline tournaments. Holding time ranged from two to 21 days (Schramm et al. 1987; Kwak and Henry 1995; Weathers and Newman 1997; Neal and Lopez-Clayton 2001; Edwards et al. 2004), and Edwards et al. (2004) considered the thr ee-day observation period adequate compared to other studies. Secondary mortalit y was measured using replicates of fish held in pens for 72 hours. After fish were collected from the commercial fishers, they were transported to holding pens placed in the lake. The pens used were large hoop nets measuring 4.57 meters long, 1.22 meter diameter, and 50.8 mm stretch mesh nylon. A total of four hoop nets were used, and the nets were placed in three meters of water on a hard sand substrate bottom and marked with University of Florida research buoys. All net repl icates were performed in the same area of Lake Dora during both commercial seasons. A minimu m of 10 and maximum of 20 fish were placed in each pen. If a minimum of 10 fish could not be collected within 30 minutes of net pull time with the fishers, any fish that had been collected were transported to th e pens to avoid further stress. All fish exhibiting ope rcular movement were placed in the pens for measures of secondary mortality. After the 72 hour treatment a ll fish were released, and any dead fish were measured to the nearest mm TL. Consistent with Hale et al. (1981) and Ha le et al. (1983), we considered a fish to be dead if it was unable to swim away after 72 hours. 17

PAGE 18

Pollock and Pine (2007) recognized the need fo r control replications in assessing delayed mortality for catch and release studi es. It is not possible to obtain an unbiased estimate of fish captured in gill nets alone unless one assumes that there is no handling mortality (Pollock and Pine 2007). This is most likely not a reasonable assumption, hence control fish are necessary to account for handing mortality. Control fish were collected via electrofishing and hoop net gear during the 2006 season. Replicates of control fi sh placed in pens were used to account for potential mortality effects from transporting a nd holding fish. The same methods were applied during replications of control fish as described for treatment replications. Water temperature and dissolved oxygen ar e critical factors in fluencing secondary mortality of fishes (Schramm et al. 1987; Gall inat et al. 1997; Weathers and Newman 1997; Wilde et al. 2000; Edwards et al. 2004). A temper ature logger was placed at our pen holding site to record temperature every f our hours during the course of the experiment. Dissolved oxygen (mg/L) was also measured each time a pen wa s set and retrieved, and in 2006 a dissolved oxygen logger was placed at my pen holding site to re cord dissolved oxygen levels every four hours during the course of the experiment to meas ure oxygen levels throughout the 72-hour treatment period. Recreational Fishing Effort and Harvest Roving creel surveys were conducted by the FWC on Lake Dora from November 2004 to June 2005, November 2005 to May 2006, and N ovember 2006 to March 2007, respectively (three fishing seasons) to measure angling effort harvest, and catch rate s. Each survey was conducted on ten randomly selected days (six weekdays and four weekend days) for each 28-day period (Benton 2005). Using a rando mly selected time, lake secti on, and direction of travel on each sample day, a clerk completed a survey of the entire lake by taking an instantaneous count of all anglers actively fishing on the lake to de termine fishing effort (man-hour) (Benton 2005). 18

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The clerk also interviewed angler s about their target species (if any species were specified by the angler), the number of each sp ecies caught, and how much time wa s spent fishing to determine fishing success (fish/hour) (Benton 2005). Catch fr om the angler interviews was extrapolated to angler effort estimates from the instantaneous count s to estimate total harvest at each lake in both years (Malvestuto et al. 1978; Malvestuto 1996; Benton 2005). Measurements of TL were recorded for a subsample of the black cra ppie catches during the three survey periods. Tagging Study A tagging study was conducted in 2006 for a di rect estimate of e xploitation from the recreational fishery ( rec). Lake Dora was divided into f our areas and an approximately equal number of fish were tagged in each area. Ar ea one encompassed Lake Beauclair, and areas two through four encompassed Lake Dora; the three areas of Lake Do ra were the east lobe (2), middle lobe (3), and west lobe (4) (Figure 2-1) Fish were collected for tagging with a boat electrofisher, hoop nets, and an ot ter trawl. All fish captured we re measured to the nearest mm TL, and fish 230 mm TL and greater were tagged and released into approximately the same area they were captured. Although there was no minimum size limit in place, I assumed that all fish 230 mm TL and greater had recruited to the fishery based on creel survey data. All black crappie were tagged w ith dart tags with a yellow st reamer containing information specifying the tag specific identification number, monetary reward value, and return address. Tags were inserted into the body of the fish be low the dorsal fin rays using a hollow needle. When injected the streamer of each tag extended in a posterior direction at a 45 angle to the body. All black crappie were tagged from Novemb er 2005 to January 2006 to obtain an estimate of exploitation for the 2006 fishing season. All fish were single tagged with either a standard tag ($5) or a higher value reward tag ($50). Th e tagging reward study allowed for estimates of reporting rates (described below). 19

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Age and Growth Age and growth of black crappie at Lake Dora was estimated using fish collected from the recreational fishery from January through March 2005 to 2007, which is when black crappie angling effort peaks (Benton 2005; FWC 2005). Lake Dora has numerous fish camps where anglers clean harvested fish daily and these camps were the source of fish for age samples. Collecting recreationally harvested fish is an efficient way to gather age information and has been utilized for many marine species (Potts et al. 1998; Potts and Ma nooch 1999; Patterson et al. 2001; Fischer et al. 2004; Fischer et al. 2005), a lthough like all sampling g ears is subject to size and age selectivity. Coolers with ice were placed at fish cleaning stations for three camps. Information signs were also posted at the fish cleaning stations ex plaining the purpose of the project. Some anglers may fish multiple lakes on a given day and thus, I asked anglers not to donate black crappie if they had fished more than one lake in an effort to assure all black crappie ages represented the correct population. Coolers were le ft for two to three days before retrieval. All black crappie collected from recreational angler s were brought back to the lab where they were measured to the nearest mm TL and sagittal ot oliths were removed from ten randomly selected fish for each centimeter group. Because fish larger than 330 mm TL were rare, all black crappie greater than this size were aged. Ages of fish collected from the recreational fishery were determined by counting annuli on whole otoliths with the aid of a dissecting microscope. The use of otoliths to determine ages of black crappie has been verified (Hamme rs and Miranda 1991; Ross et al. 2005). Two independent readers aged each fish. Schramm and Doerzbacher (1982) found that black crappie have relatively thin otoliths th at had clearly visible bands presen t in patterns expected for annual marks. Older fish (fish showing four or more opaque bands) have thicker otoliths, and therefore 20

PAGE 21

are more likely to have bands masked in whol e view (Schramm and Doerzbacher 1982). Thus, any otoliths showing four or more opaque bands, and any otolith disagreements from whole view readings were sectioned for verification of aging accuracy. One otolith was sectioned transversally using a South Bay Technology, Inc. low speed diamond wheel saw. Two transverse sections, 0.5 mm wide, were cut from each otolith and mounted on a labeled glass slide using ThermoShandon Synthetic Mountant for reading. Two independent readers used a dissecting microscope to read the sections. A third independent reader reexamined all disagreements and the majority reading was record ed as number of annuli. Not all black crappie form new opaque bands on their otoliths at th e same time during spring, although opaque bands on otoliths from all age classes should be formed by June 1st in Florida (Schramm and Doerzbacher 1982). I used an ar bitrary birth date of June 1st, so that all fish collected prior to June 1st were assigned ages corresponding to th e number of annuli observed plus one. Analyses Total Bycatch Assessment I obtained estimates of total black crappie bycatch from the commercial fishery using a stratified sampling design (see Krebs 1999). Onboard observer data were stratified into three time strata (A, B, and C) for both commercial fish ing seasons. The strata represented periods of high, moderate, and low fishing effort, and were grouped such that the variance of bycatch observed was homogeneous within and heterogeneous among strata. The total bycatch estimate and variance on this total were determined us ing the equations for a stratified design from Pollock et al. (1994): ST STXNX (2-1) )( ) (2 ST h STXVARNXVAR (2-2) 21

PAGE 22

where, STX = total bycatch estimate, N = number of total possible fishing days in a season, STX = stratified bycatch mean per fishing day. h = stratum number (A, B, C) and, Nh = total possible fish ing days in stratum Bycatch Mortality I measured the mortality rate for each pen repl ication in each year as the number of dead black crappie observed per pen divided by the total number of black crappie held in each pen. I then estimated the annual mean bycatch mortality rate as the average mortality rate across all replications for each year, with uncertainty e xpressed as the standard error around the yearly means. Mean and variance were also estimated for control replications. I used the annual mean bycatch mortality rate multiplied by our estimate of total bycatch for black crappie in each year to achieve total commercial fish ing mortality of black crappie by year given by the equation: GMGCGD (2-3) where, GD = estimated total number of black crappie that died from gill net mortality, GC = estimated total number of black crappie caught by gill nets, and, GM = total gill net mortality rate. 22

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Recreational Fishing Effort and Harvest All data were entered and analyzed in a creel survey analysis program developed by FWC (version 2, Conner and Sheaffer 2000) an d were stored in a Microsoft Access database on an FWC regional server (Benton 2005). Data was lost overboard from one 28-day period in 2006. We approximated the missing time period in 2006 using the percentage of effort for that period during 2005, assuming that the percentage of effort dur ing that period in 2005 would serve as the best model to recons truct the missing data in 2006. Tagging Study Tag returns were adjusted for tag-related mo rtality, tag loss, and non-reporting prior to estimating exploitation. I assumed 5 10% taggi ng mortality and tag loss for all black crappie tagged. Reporting rates of higher value reward tags ($50) in 2006 were estimated based on a linear-logistic model created by Nichols et al. (1991): ))(0283.00045.0( ))(0283.00045.0(1H H He e (2-4) where, H = the dollar value of higher value reward tags, and, H = the reporting rate of tags fr om higher reward value fish. The reward values ( H ) were converted from 2006 standard s to the 1988 monetary equivalents based on the Consumer Price Index. The 1988 mone tary equivalents used in equation 2-4 were $30.29 for $50 rewards (U.S. Department of Labor 2006). Reporting rate estimates calculated from equation 2-4 were most precise at higher reward values (Nichols et al. 1991) and thus, I used equation 2-4 to estimate reporting rates of high-reward tag fish and then estimated the reporting rate of standard tags based on the assumption that all tagge d fish had an equal 23

PAGE 24

probability of recapture regardless of reward value. Alternate methods for estimating reporting rate, such as those presented in Taylor et al. (2006) assume 100% reporting rate of higher value tags in order to estimate the reporting rate of standard tags. I felt that a $50 tag value was not sufficient to make the assumption that all higher value reward tags were returned. I estimated the total number of high value reward tag fish caught in 2006 using the equation: H H HR C (2-5) where, HC = estimated number of higher value reward tag fish caught, and, RH = total number of tags returned in 2006 from fish tagged with a higher reward value. I assumed that standard tags and higher reward value tags had an equal probability of capture by anglers and estimated the total number of standard tag fish caught in 2006 using the ratio: S S H HT C T C (2-6) where, S = the dollar value of a fish tagged with a standard tag, SC = estimated number of st andard tag fish caught, TS = original number of fish tagg ed with standard reward tags, and, TH = original number of fish tagged with higher value reward tags. 24

PAGE 25

I estimated the reporting rates of standard reward tags ($ 5) in 2006 using the equation S S SC R (2-7) Reporting rate estimates for high-va lue reward tags were varied to evaluate how uncertainty in H would influence the exploitation rate. Estimates of exploitation for the recreation fishery ( REC) were estimated using the equation: ))(1())(1( ) ( TLTMTTLTMT CCH S HS REC (2-8) where TM = tagging mortality and TL = tag loss. The instantaneous rate of fishing mortality for the recreational fishery ( Frec) was estimated using the equation: )1(REC RECLNF (2-9) Estimates of exploitation for the commercial fishery ( COM) could not be obtained directly from tagging data because a reliable reporting rate could not be calculated. There was evidence that vulnerability with fish si ze to gill nets was similar to recreational angling, but commercial fishers had an incentive not to return tags. Thus, I was unable to use Nichols equation to estimate commercial reporting rate. To estimate commercial exploitation I first estimated the vulnerable black crappie populat ion size with the equation: REC RECC N (2-10) where, N = the number of vulnerable blac k crappie in the population, and, 25

PAGE 26

CREC = recreational catch from creel survey data. I estimated the exploitation rate from the commercial fishery (COM) as: N GDcom (2-11) The instantaneous fishing mortality for the commercial fishery ( Fcom) was estimated as: )1(COM comLNF (2-12) I simulated changes in FCOM by changing the gill net mortality rate ( GM ), which changed the number of black crappie that died from gill nets ( GD). The instantaneous fishing mortality for the commercial and recreational fisheries we re estimated with varying levels of reporting rates, tag loss, tagging mortality, recreational catch, and total gillnet bycatch mortality to evaluate uncertainty in F values for a range of input parameters. Age and Growth Data collected from the recreational fishery (carcasses and creel) was used to estimate growth rates for black crappie. I created an age-length key from a subsample of black crappie aged from recreationally harves ted carcasses and assigned an ag e to each individual from the entire sample of carcasses and the recreational creel measurements in order to obtain age and size structure of the population. Mean-lengthat-age (MLA) and its associated variance ( 2) were found by equations for fixed-length subsampl es presented by DeVries and Frie (1996). I used the Von Bertalanffy growth model (Ricker 1975) to describe growth rates. Von Bertalanffy parameter estimates (L k, and t0) were obtained using Procedure NLIN (SAS 9.1). Population-Level Impacts of Exploitation I used Microsoft Excel to construct a stock reduction analysis (S RA) with stochastic recruitment (see Walters et al. 2006 ) in order to evaluate the pote ntial of recruitment overfishing occurring at varying exploitation rates. The basic idea of an SRA is to construct an age26

PAGE 27

structured population dynamics mo del that consists of leading pa rameters (e.g., Bo and recK in this study) that describe th e underlying production and carryi ng capacity and subtract known removals from the population over time (Walters et al. 2006). When leading parameter estimates produce a stock size that is too low to have sustai ned historical catches, the model predicts that the population should have disappeared prior to today (Walters et al. 2006). When leading parameters estimates produce a stock size that is too high, it predicts too little fishing impact and a current population size that is much too large to fit recent estimates (Walters et al. 2006). The SRA I created reconstructed the historic st ock size of black crappie in order to match model predicted estimates of exploitation and vuln erable biomass in 2006 to empirical estimates of exploitation and vulnerable bi omass in 2006, given estimates of the leading parameters Bo and recK. Typically the leading parameter Bo is a measure of vulnerable biomass in the unfished condition. However, in this study Bo re presents an estimate of vulnerable biomass far enough back in time to achieve a stable age distri bution in the simulated population prior to this study (2005). The leading parameter recK is the Goodyear recruitmen t compensation ratio (Goodyear 1980) and is a measure of the juvenile survival at extremely low stock size relative to juvenile survival in the unfishe d condition. The parameter recK examines relationships between maximum recruitment at low stock size and the density dependence of recruitment at high stock size or the unfished condition (Goodwin et al. 2006) The two leading parameters are correlated in the sense that a lower Bo a nd higher recK can produce the same stock size as a higher Bo and lower recK. SRA models often have an exorbitant amount of comb inations of Bo and recK that can explain the same stock size. The best combin ation of recK and Bo chosen must be supported statistically and biologically so that the parameter estimates are logical. 27

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My empirical estimate of vulnerable biomass in 2006 in the fished condition was estimated as the vulnerable biomass per acre times the surface area (acres) of Lakes Dora and Beauclair combined. Vulnerable biomass per acre was es timated as the vulnerable number of black crappie per acre ( acres N ) times the average weight of a vulnerable black crappie, where the average weight of a vulnerable bl ack crappie was estimated using a standard weight equation for black crappie (Anderson and Neumann 1996) with an average length of vuln erable black crappie harvested in 2006 (given from carcass and creel measurements). My empirical estimate of exploitation in 2006 was estimated for the recrea tional and commercial fisheries using equations 2-8 and 2-11, respectively. I solved for my leading parameters (Bo and r ecK) by fitting the model predicted values of vulnerable biomass and exploitation in 2006 to empirical estimates in 2006 given by the log likelihood of the lognormal distribution: )))06ln()06(ln())06ln() 06ln((ln(2 2predVB estVB estu predu MLEtotal total (2-13) where, MLE = the maximum likelihood estimate, 06predutotal = 2006 model predicted estimat e of total exploitation, 06estutotal = 2006 empirical estimate of total exploitation, 06estVB = 2006 empirical estimate of vulnerable biomass (kg), and, 06predVB = 2006 model predicted estimate of vulnerable biomass (kg). I used Excel table function to construct a ma ximum likelihood profile for a range of Bo and recK values that made sense biologically in order to determine combinations of parameter estimates that were supported statistically. C onsidering a review of maximum reproductive rates 28

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of fish at low population sizes by Myers et al. (1999), black cra ppie most likely have a recK value between five and 20 based on fish species with similar life history characteristics. Estimates of Bo were considered from 70,000 to 100,000 kg, which were supported by my empirical estimates of adult fish density and fishing mortality rates. When solving for leading parameter estimates, my model was very sensitive to starting values because of the correlation between leading parameters and multiple possible combinations. Thus, I was not able to solve fo r Bo and recK simultaneously. This phenomenon is very common in SRA model fitting. Therefore, I fixed Bo and solved for recK, because Bo exhibited much less variability than recK in the maximum like lihood profile and I had data for black crappie at Lake Dora that supported my estimate. Once reasonable parameter estimates were obtained the model was used to predict ho w the black crappie stock would respond in the future under different scenarios of exploitation. The output metrics of interest were vulnerable biomass (kg), total harvest (numbers) and weighted transitional spawning potential ratio (SPR). The SRA required estimates of mean length at age, weight at age, fishing and natural mortalities, fecundity, and a vulne rability to harvest schedule in order to function. Fishing mortalities were separated into FREC and FCOM, as described above. Esti mates of total length-atage were obtained from the Von Bertalanffy growth model and age specific weight was calculated using a standard we ight equation for black crappi e (Anderson and Neumann 1996). Equal vulnerability schedules were assumed for th e recreational and commercial fisheries, based on the length frequencies from the recreational and commercial fisheries. Vulnerabilities at age were estimated using a cumulative normal distri bution, which predicted e xpected catches at age in a yield-per-recruit model simulation that approxi mated the observed age structure of the catch. Fecundity was calculated as the weight at age minus weight at maturity ( Wmat). Walters et al. 29

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(2007) noted that fecundity is t ypically proportional to body weight above the weight at maturity. Weight at maturity was assumed to be the weig ht predicted at age 2, given that black crappie mature at approximately age 2 in this system (FWC 2005). Survivorship at age in the unfished condition (Survivorship0a) was calculated as survivorship in the previous year multiplied by survivorship in the absence of fishing ( S0). The instantaneous rate of natural mortality ( M ) was assumed to be 0.4 for all simulations, which is similar to values found in a review of black crappies ( Pomoxis spp .) from Allen et al. (1998). Survival from natural mortality was found by S0 = e-M. Survivorship at age a in the fished condition ( SurvivorshipFa) was calculated as: 1 011 a total a avul SipF Survivorsh ipF Survivorsh (2-14) where survivorship at age one was assume d to be 1, the first age in the model. Expected numbers were assumed to change over a ages and t years according to the survival equation (Walters et al. 2006): ttotalta ta tauvulSNN, 0,1,11 (2-15) I used an accounting scheme with 8 ages from 1961 2050 (N = 90). Expected numbers at age in the initial year were calculated as: a taipsurvivorsh RN 001, (2-16) where Ro is the recruitm ent abundance in the unfished condition estimated as: 0 0 0 vbB R (2-17) The Botsford incidence function for vulnerable biomass per recruit in the unfished condition was calculated as (Box 3.1, Walte rs and Martell 2004): 30

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a a aaipsurvivorsh vulwtVB 0 ,,0 (2-18) Vulnerable biomass was determined annually with the equation: a aata twtvulNB ,, (2-19) The model required exploitation ( total, t) and recruitment time series for all years after 1961. For each year the total exploitation rate was estimated as: a ata ttotal ttotalvulN HARV, (2-20) Total harvest was estimated from historical cr eel data from 1977 to 1981 and from creel and commercial landings data in 2005 and 2006. Logical estimates of total harvest were simulated for the remaining years from 1961 to 2006. For future projections, estimates of exploitation were assumed under different fishing scenarios a nd total harvest estimates were calculated as: ttotal totalN HARV, (2-21) This allowed the model to explore a range of assumed exploitation rates in the future and determine the expected vulnerable biomass, total harvest, and SPR given an exploitation rate. Recruitment rates were predicted from estimates of annual egg production (Et) as: a ata tfecNE ,, (2-22) using a Beverton and Holt stock-recr uit relationship with recruitmen t variability of the form of the relationship (Wa lters et al. 2006): t t t trand E E N 11,1 (2-23) where the alpha and beta Beverton and Holt pa rameters are described by the relationships: 31

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0 0E R recK (2-24) 01 E recK (2-25) Variability around recruitment at time t ( randt) was accounted for with a random number that was determined with PopTools in Microsoft Ex cel by using a log normal distribution with a mean of 1.0 and recruitment coefficient of vari ation of 0.4. Allen (1997) observed black crappie recruitment coefficient of variation values ranging from 0.55 to 0.84 for 6 populations in Southeast and Midwest reservoirs, but there is evidence that recruitment variation in this system is considerably lower based on age-0 black crappi e catch rates in bottom trawls (M. Hale, FWC, unpublished data). Recruitment variability was added to the mode l simulations for future projections once estimates of the leading parameters were obtai ned via equation 2-13 in order to explore how abundance, catch, and spawning pot ential ratio varied through tim e with different exploitation rates. A weighted transitional SPR was used as a biological reference poi nt to investigate the potential for recruitment overfishing at various ex ploitation scenarios. A weighted transitional SPR allows fishing mortality to vary by age and year and accounts for changes in the numbers at age over years. The SPR was estimated with the equation: a a ta a a ta tfecN fec N SPR ,1, 89...3,2,1, 89...3,2,1 (2-26) I determined the uncertainty in my terminal y ear SPR (2050) by using Mont e Carlo analysis with 1,000 iterations to determine a terminal year mean SPR and 95% confidence limits around the mean. The same methods were applied to total ha rvest and vulnerable biomass estimates. I also used Monte Carlo analysis with 100 iterations to determine m ean annual SPR values and 95% 32

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confidence intervals for the entire model time seri es to show how the SPR would be expected to vary with variation in recruitment. Future projections were simulated from 2007 through the terminal year 2050 under three exploitation scenarios; (1) total = 0.42, (2) total = 0.51, and (3) total = 0.60. Exploitation scenario one was chosen because it was the empirical estimate of rec in 2006, scenario two was chosen because it was the empirical estimate of total in 2006 and scenario three was chosen as an arbitrary increase in exploitation either due to recreational fish ing, bycatch mortality, or both. The model simulations examined the three different exploitation scenarios and the implications they have on black crappie abundance, total harv est, and SPR if they were sustained through the terminal year 2050. In order to investig ate the potential for growth over fishing, I constructed a yield-perrecruit model in Excel. Yield-per-recruit (kg) was determined as: total FuVBYPR (2-27) where, The Botsford incidence function for vulnerable biomass per recruit in the fished condition ( ) was calculated as (Box 3.1, Walters and Martell 2004): FVB a a aa FipF survivorsh vulwt VB (2-28) To investigate if growth overfishing was a c oncern I used Excel table function to profile YPR values at total exploitation (total) scenarios ranging from 0.2 to 1.0. 33

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Figure 2-1. Map of Lakes Eustis, Dora, and Be auclair located in the Upper Ocklawaha River Basin in Lake County, Florida. Areas 1 4 represent designated capture and release areas for tagging study. 34

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Table 2-1. Mean water quality parameters for Lakes Dora (east and west) and Beauclair (Florida LAKEWATCH 2004). Water quality parameters include total phosphorous (TP ( g/L), total nitrogen TN ( g/L), chlorophyll CHL ( g/L), secchi depths (meters), and trophic state and reflect the annual average for 2004. LAKE TP ( g/L) TN ( g/L) CHL ( g/L) SECCHI (meters) Trophic State Dora east 55 2941 102.7 0.4 hypereutrophic Dora west 50 2889 99.4 0.43 hypereutrophic Beauclair 81 2971 100.5 0.4 hypereutrophic *Trophic state based on Fo rsburg and Ryding (1980). 35

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CHAPTER 3 RESULTS Commercial Fishing Commercial fishing occurred fr om March 1 to April 22, 2005 and from January 3 to March 28, 2006. Fishing was not permitted until March 1, 2005 because pre-harvest data were being collected for the gizzard shad population. Gene rally, there were two permitted fishermen per fishing vessel; there was a maximum of 16 vesse ls and a minimum of 1 vessel per fishing day during the 2005 and 2006 commercial fishing seas ons. Total commercial effort was 258 boat days in 2005 and 251 boat days in 2006 (Figure 3-1) with an average of six boats per fishing day in 2005 and five boats per fishing day in 2006. Total Bycatch Assessment Black crappie bycatch was higher in 2006 than 2005 (Table 3-1). For 2005, there were a total of 487 black crappie observed during gilln et operations, 294 in stratum A (March 1 to March 14), 156 in stratum B (March 15 to Mar 31), and 37 in stratum C (April 1 to April 22). The average total daily bycatch per stratum ( hx ) was 595, 488, and 26 for strata A, B, and C, respectively. The total bycatch estimate ( ) for 2005 was 17,199 black crappie and the 95% confidence intervals were 8,777 to 25,622. For 2006, there were a total of 2,109 black crappie observed during gillnet operations, 1,375 in stratum A (January 3 to January 31), 545 in stratum B (February 1 to February 28), and 189 in stratu m C (March 1 to March 28). The average total daily bycatch per stratum was 979, 498, and 265 for stra ta A, B, and C, respectively. The total bycatch estimate ( ) for 2006 was 30,258 black crappie, a nd the 95% confidence intervals were 19,048 to 41,469. Total daily bycatch of black crappie is reported in Figure 3-1 for days with onboard observer data in 2005 and 2006. STX STX 36

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Bycatch Mortality I conducted 17 pen replications from March 1 to April 8 during the 2005 commercial gill net season, and 23 pen replications from January 3 to March 15 during the 2006 season. Six control replications were made with fish caugh t in hoop nets, and four pen replications were made with fish caught with electrofishing gear in 2006 from January 13 to January 29. In 2005, bycatch mortality rates ranged from 0 to 0.75 dur ing the treatment period with a mean of 0.31 ( GM2005) and a standard error of 0.06 In 2006, bycatch mortality rates ranged from 0.05 to 1 during the treatment peri od with a mean of 0.47 ( GM2006) and a standard error of 0.07. In 2006, control replications of fish co llected with hoop nets (n = 6) ranged in mortality from 0 to 0.35 during the treatment period with a mean of 0.10 and a standard erro r of 0.05; control replications of fish collected with electrofis hing gear (n = 4) had zero mortal ity. Results are summarized in Table 3-2. Estimates of bycatch mortality were not adjusted for pen related mortality due to low mortality estimates from control replicates. I combined the mortality estimation and total bycatch estimates to estimate the number of black crappie deaths via bycatch each year. Th e estimated mean number of black crappie that died from gill net mortality in 2005 ( GD2005) was 5,332 with a range from 2,194 to 9,480 considering the range in estimates of GM and GC The mean number of bycatch deaths in 2006 ( GD2006) was estimated at 14,221 with a range of 7,619 to 22,393 given the range in estimates in GM and GC Recreational Fishing Effort and Harvest Comparison of the existing creel survey data at the lake suggest that recreational fishing effort and harvest have increased at Lake Dora. The annual fishing effort for black crappie at Lake Dora historically (s urvey data from 1977 to 1981) ranged from 14,208 to 26,233 hours constituting 25 to 39% of tota l angling effort (Benton 2005), and catch ranged from 16,603 to 37

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41,745 black crappie per year (Ben ton 2005). The current surveys were only during the peak fishing season from November 2004 to June 2005, November 2005 to May 2006, and November 2006 to March 2007. Directed black crappie effo rt ranged from approximately 27,000 to 29,000 hours and harvest ranged from about 32,000 to 39,000 from 2004/2005 through 2006/2007 (Figure 3-2). Black crappie angling effort accounte d for 80 to 94% of the total fishing effort for the three survey periods. No standard error could be calculated for the estimates from the 2005/2006 survey period because of missing data for one 28-day period that was estimated by substituting the mean value of fishing effort from the same time period the previous year. Tagging Study Tagging was conducted from November 3, 2005 to January 13, 2006 during sixteen sampling trips at Lakes Dora and Beauclair. A total of 514 black crappie were single-tagged with standard and higher reward floy tags, 197 fish were captured with electrofishing gear (38%), 214 fish were captured with hoop nets (42%), and the remaining 105 fish were captured with an otter trawl (20%). Totals of 125, 118, 133, and 132 fish were tagged in areas 1 through 4, respectively (tagging location of six fish were not recorded). A total of 413 black crappie were tagged with $5 standard reward tags and 101 black crappie were tagged with $50 higher value reward tags. A total of 69 tags were returned, 40 $5 tags (10% of available $5 reward tags 34 from recreational anglers and six from commercial fishers) and 29 $50 tags (29% of available $50 reward tags 27 from recreational anglers a nd two from commercial fishers); recreational anglers accounted for 88% of total tag returns (6 1 of 69 returns) and commercial fishermen only accounted for 12% of total tag re turns (8 of 69 returns). All tags were recaptured from December 7, 2005 to April 7, 2006, and recapture location was obtained from 55 of the 69 returned tags. We received six returns from area 1 (11%), nine returns from area 2 (16%), nine 38

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returns from area 3 (16%), 23 re turns from area 4 (42%), and ei ght returns from outside our study area in adjoining canals (15%). Although 15% pe rcent of tag returns were from outside the study area in adjoining canals, all canals had locks that prevented fish escapement from the system. Estimates of exploitation for the recreationa l fishery included adjustments for tag loss, tagging mortality, and reporting rate. Tag loss an d tagging mortality were simulated at values from 5 to 10%. I assumed 5% tag loss and ta gging mortality for the average estimate of exploitation for model simulations ; Miranda et al. (2003) estimated tag loss for black and white black crappie to be 4.6% within 24 hours of ta gging using t-bar tags, and there was a significant effect of time on tag loss. Henry (2003) estimated tag loss for largemouth bass to be approximately 5% using dart tags I felt that 5% tag loss was a reasonable estimate, based on the short amount of time between tagging and recaptures and results from other studies. Miranda et al. (2003) estimated tagging mortality for black and white black crappie to be 11% (SE = 7.2%) for fish captured with electrofishing gear a nd trap nets. Henry (2003) estimated tagging mortality for largemouth bass to be 0% for fish collected with electr ofishing gear and hook-andline. Results from control re plications of black crappie gr eater than 230 mm TL captured with hoop nets and electrofishing gear on Lake Dora (not tagged) had a mortality rate of 10% and 0%, respectively, and contro l replicates of black crappie grea ter than 180 mm TL captured with an otter trawl (pelvic fin clip) at Lake Jeffords, Fl orida had a mortality rate of 1% (G. Binion, UF, unpublished data). I felt that 5% tagging mo rtality was a reasonable estimate, based on our control replications of fish captured with hoop ne ts, an otter trawl, and electrofishing gear, and results from similar studies. The expected repor ting rate of tags from higher value reward tag 39

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fish ( H) was 70% ( H = $50) based on equation 2-4, and the e xpected reporting rate of standard tags was 22% based on e quation 2-7 (Table 3-3). The recreational exploitation rate (REC) was 42% (TM = 0.05, TL = 0.05, H = 0.7), the instantaneous rate of fishing mort ality for the recreational fishery ( Frec) was 0.55. The commercial exploitation rate ( COM) was 16%, and the instantaneous rate of fishing mortality for the commercial fishery ( Fcom) was 0.17. I simulated a range of higher value reward tag reporting rates from 0.5 to 1.0 by intervals of 0.1 and tag loss/tagging mortality from 5 to 10% to analyze the effects of reporting rate on ex ploitation (Table 3-3). Lower reporting rates and higher tag loss/tagging mortality increase estimates of recreational exploitation and higher reporting rates and lower tag loss/tagging mortalit y decrease estimates of recreat ional exploitation. I simulated a range of the total number of black crappie that died from gill net mortality in 2006 (GD2006) from 7,000 to 22,000, and the number of black crappi e harvested in the re creational fishery in 2006 from 25,000 to 39,000 to evaluate effects on the instantaneous rate of fishing mortality for the commercial fishery ( Fcom). As expected, Fcom values were highest at low recreational catch and high gillnet deaths, and lowest at high recreational catch and low gillnet deaths. Age and Growth A total of 882, 664, and 723 black crappie we re collected and measured from the recreational fishery (whole sa mple carcasses and creel) in 2005, 2006, and 2007, respectively. Sub-samples of carcasses (N = 183, 158, and 153 in 2005, 2006, and 2007) ranging from approximately 18 to 37 cm TL were analyzed to determine age annually. The size and age frequencies from the recreational catch (whol e sample) in 2005, 2006, and 2007 are reported in Figures 3-3 and 3-4. Ages ranged from 2 to 8 year s old for all three years. Mean length-at-age and associated variance and growth for the whole sample for each year were determined. Mean length-at-age and growth were similar for black crappie in all years. Results from 2006 were 40

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used in model simulations and are reported in Figure 3-5. Ages were applied to 145 and 362 black crappie collected from the commercial gillnet fishery in 2005 and 2006, respectively. The size and age frequencies from the commercial by catch in 2005 and 2006 are shown in Figures 34 and 3-5. Age-structured Population Model Simulations Estimates of historical harvest, vulnerab le biomass, and exploitation from 1961 to 2006 are presented in Figure 3-6. Values of the tota l number of black crappie harvested from 1977 to 1981 were from historical creel data collected by FWC, values of harvest in 2005 and 2006 were estimates of total harvest from the commercial (estimated from onboard observations) and recreational fishery (estimated from creel su rvey) combined, and the remaining years were logical estimates of total harv est based on limited creel survey data. The maximum likelihood profile for Bo and recK is presented in Figure 3-7. I simulated a range of Bo values from 70,000 to 100,000 kg and a range of recK values from 5 to 20. Given the life history and known population characteristics of black crappie in Lake Dora, the ranges of Bo and recK values that were simulated include the most likel y range of logical possibilities. Based on the maximum likelihood profile a Bo estimate of 80,000 kg is supported statistically and is biologically realistic given my estimates of st ock size and exploitation. Thus, I fixed Bo at 80,000 kg and used equation 2-13 to so lve for a recK, resultin g in an estimate of 15.2. The maximum likelihood estimate occurred at a Bo of 78,000 kg and a recK of 20; however, I felt that the MLE was not the true best fit because it occurred at the maximum recK in the likelihood profile. The model fit the recK valu e at the highest possible value it was restricted to resulting in estimates that were not biological ly reasonable. After model fitting with my best parameter estimates, my predicte d and empirical estimates of exploitation were 0.51 in 2006, and 41

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the model predicted vulnerable biomass in 2006 approximated my empirical estimate (Table 34). Future simulated exploitation rates influenced the model predicted estimates of total harvest, vulnerable biomass, and SPR (Table 35). Mean total harvest slightly increased as exploitation increased in simulations; however mean vulnerable biomass decreased with increases in exploitation. The mean weighted transitional SPR in the terminal year decreased from 0.32 (scenario one) to 0.19 (scenario three). The SPR target goal for most fish species is approximately 0.3 to 0.4, used as a biological refe rence point where values below the target goal increase the likelihood of recr uitment overfishing (Goodyear 1993; Clark 2002). The terminal year mean weighted transitional SPR was operating near the target goal of 0.3 to 0.35 at the levels of exploitation found in 2006, and model si mulations predicted that increased exploitation may cause concern of recruitment overfishing. At the highest exploitation rate simulated, the mean weighted transitional SPR was predicted to be well below the target goal (Table 3-5). Results for the annual weighted transitional SPR values with recruitment variability (0.4) are reported for the entire model time series from Monte Carlo analysis with 100 iterations to show how recruitment variation would influence SPR values (Figure 3-8). Results from yield-per-recruit model simula tions are presented in Figure 3-9. The YPR values exhibited an asymptotic relationship w ith exploitation, indicating that with the current vulnerability schedules the black crappie fishery is not likely to exhibit growth overfishing. The maximum YPR value was 0.13 occurring at a total e xploitation rate of 1. Black crappie were not fully vulnerable to either recreational fishing or commercial bycatch until age four, and they become reproductively mature at age two, which allows enough reproducti on to prevent growth 42

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overfishing. However, at extremely high exploitation rates a shift in the size structure toward smaller, younger fish would be anticipated. 43

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Month Jan Feb Mar Apr May Number of boats fishing per day 0 2 4 6 8 10 12 14 16 18 Daily bycatch (numbers of crappie) 0 200 400 600 800 1000 2005 commercial effort 2005 daily bycatch 0 2 4 6 8 10 12 14 16 18 0 500 1000 1500 2000 2500 3000 2006 commercial effort 2006 daily bycatch Figure 3-1. Commercial fishing effort ( number of boats fishing per day) and daily black crappie bycatch (numbers) for the 2005 and 2006 commer cial gill net seasons at Lake Dora. Daily bycatch estimates are shown fo r 2005 and 2006 for days where onboard observation data was available. 44

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Survey Period 2004/20052005/20062006/2007 Effort (hours) 0 10000 20000 30000 40000 50000Harvest (numbers) 0 10000 20000 30000 40000 50000 crappie effort crappie harvest total effort Figure 3-2. Total recreational fishing effort (hours), bl ack crappie effort (hours), and harvest of black crappie (numbers) during the three cr eel survey periods at Lake Dora. The associated standard error is reported for the survey periods in 2004/2005 and 2006/2007 (no SE could be calculated in 2005/2006 due to missing data from one 28day time period). 45

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Commercial Bycatch Len g th Grou p 1 1 0 1 2 0 1 3 0 1 4 0 5 0 1 6 0 1 7 0 1 8 0 1 9 0 2 0 0 2 1 0 2 2 0 2 3 0 2 4 0 2 5 0 2 6 0 2 7 0 2 8 0 2 9 0 3 0 0 3 1 0 3 2 0 3 3 0 3 4 0 3 5 0 3 6 0 3 7 0 Percent Frequency 0.00 0.02 0.04 0.06 0.08 0.10 0.12 0.14 0.16 0.18 0.20 2005 2006 Recreational Harvest 0.00 0.02 0.04 0.06 0.08 0.10 0.12 0.14 0.16 0.18 0.20 2005 2006 2007 Figure 3-3. Relative length frequencies of black crappi e measured from the recreational catch (carcasses and creel) and co mmercial gill net bycatch on La ke Dora. Measurements of black crappie were sampled from the black crappie recreational catch on Lake Dora in 2005 (N = 882), 2006 (N = 664), a nd 2007 (N = 723), and from commercial gill net bycatch on Lake Dora in 2005 (N = 145) and 2006 (N = 362). Length group on x-axis represents 10 mm size groups. 46

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Commercial BycatchAge 123456789 Age Frequency 0.0 0.1 0.2 0.3 0.4 0.5 0.6 2005 2006 Recreational Harvest 0.0 0.1 0.2 0.3 0.4 0.5 0.6 2005 2006 2007 Figure 3-4. Age frequency of black crappie collected from the recreational catch (carcasses and creel) and commercial gill net bycatch on Lake Dora. Ages were determined from the recreational catch in 2005 (N = 882), 2006 (N = 664), and 2007 (N = 723), and from commercial gill net bycatch in 2005 (N = 145) and 2006 (N = 362). 47

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Age 123456789 Total length (mm) 200 220 240 260 280 300 320 340 360 VB Growth Curve Mean length-at-age ) 1( 886 349)) 4897 .0( 4112 .0( 2006 te MLA Figure 3-5. Von Bertalanffy growth curve fit to mean length-at-age values for black crappie collected from the recreationa l fishery (carcasses and creel) at Lake Dora in 2006. Error bars represent one standard deviat ion around the mean length-at-age values. 48

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Year 1950196019701980199020002010 Total harvest (numbers) 10000 20000 30000 40000 50000 60000 70000 80000 90000Vulnerable biomass (kg) 10000 20000 30000 40000 50000 60000 70000 80000 90000 Harvest estimates simulated Vulnerable biomass Harvest estimates from historic data Harvest estimates from current data 1950196019701980199020002010 Exploitation rate 0.0 0.1 0.2 0.3 0.4 0.5 0.6 Exploitation rate Figure 3-6. Estimates of exploitation from 1961 to 2006 a nd estimates of historical total harvest and vulnerable biomass from the SRA model Values of the total number of black crappie harvested are simulated for years that harvest data is not available. 49

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0.0 0.2 0.4 0.6 0.8 1.06 8 10 12 14 16 18 20 70x10375x10380x10385x10390x10395x103L i k e l i h o o d Es t i m a t er e c KB o 0.0 0.2 0.4 0.6 0.8 1.0 Figure 3-7. Maximum likelihood profile for recK values ranging from 5 to 20 and Bo values ranging from 70,000 to 100,000 kg. 50

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u=.42 0.0 0.2 0.4 0.6 0.8 1.0 u=.51 W eighted Transitional SP R 0.0 0.2 0.4 0.6 0.8 1.0 u=.60Year 1960197019801990200020102020203020402050 0.0 0.2 0.4 0.6 0.8 1.0 Figure 3-8. Weighted transitional SPR estimated from SRA from 1961 to 2050 with Monte Carlo simulations (100 iterations) under th ree exploitation scen arios. The three exploitation scenarios were total = 0.42, 0.51, and 0.6. Recru itment variability = 0.4 from 2007 to 2050. 51

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Total exploitation 0.00.20.40.60.81.0 YPR (kg) 0.00 0.02 0.04 0.06 0.08 0.10 0.12 0.14 Figure 3-9. Results for YPR (kg) values at total exploitation rate s from 0.2 to 1.0 from yield-perrecruit model simulations. 52

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53 Year Table 3-1. Summary of results from stratified sampling design in 2005 and 2006. Results include stratified bycatch mean per fishi ng day, total bycatch estimate, variances for bycatch mean per fishing day and total bycatch estimate, 95% upper and lower confidence intervals, and the number of degrees of freedom used. STXSTX )(STXVar) (STXVARSTX STX CI low CI high DF 2005 324.52 17,199 4,011.76 11,269,026 8,777 25,622 5.50 2006 630.38 30,258 12,357 28,469,427 19,048 41,469 17.85 Table 3-2. Summary of results from s econdary mortality experiment for treatment fish in 2005 and 2006 and control fish in 2006. Year, treat ment type, number of replicates, and the mean mortality and associated standard error are shown. Year Type Replicates Mean mortality Standard error 2005 treatm ent 17 0.31 0.06 2006 treatm ent 23 0.47 0.07 2006 control (hoopnets) 6 0.10 0.05 2006 control (electrofishing) 4 0 0

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54T Table 3-3 Estimates of recreationa l exploitation rate (rec) based on values of the number of higher reward value (CH) and standard reward tag fish caught (CS). Tagging mortality (TM) and tag loss (TL) were simulated at 5% and 10%. The total number of higher value tag fish caught (CH), and standard tag fish caught (CS) were calculated based on differing values of higher value reward tag reporting rate ( H) from 0.5 to 1.0. C C T H H RH RL L L H L rec (5%TL-5%TM) rec (10%TL-10%TM) 0.5 54 27 34 221 413 101 0.15 0.59 0.67 0.6 45 27 34 184 413 101 0.18 0.50 0.56 0.7 39 27 34 158 413 101 0.22 0.42 0.48 0.8 34 27 34 138 413 101 0.25 0.37 0.42 0.9 30 27 34 123 413 101 0.28 0.33 0.37 1.0 27 27 34 110 413 101 0.31 0.30 0.33

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Table 3-4. Empirical estimates of vulnera ble biomass (kg) and total exploitation ( total) in 2006 and model predicted values of vulnerabl e biomass and total exploiation in 2006. Empirical estimates in 2006 were calculated with an estimated total harvest of 54, 221 (recreational and commercial) and 2006 m odel predicted values of vulnerable biomass and total exploitation were derived with leading parameter estimates of Bo = 80,000 kg and recK = 15.22. Parameter 2006 empirical estimate 2006 model predicted value vulnerable biomass (kg) 34,912 35,080 total exploitation ( total) 0.51 0.51 total harvest (numbers) 54,221 Table 3-5. Estimates of mean vulnerable biomass (kg), mean total harvest (numbers) and mean weighted transitional SPR in the terminal year 2050 determined from Monte Carlo simulations (1,000 iterations). Three exploitation scenarios (total = 0.42, total = 0.51, total = 0.60) are shown. Exploitation scenario utotal Mean vulnerable biomass Mean total harvest Mean SPR 1 0.42 32,026 41,592 0.32 2 0.51 26,359 43,491 0.25 3 0.60 21,973 44,583 0.19 55

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CHAPTER 4 DISCUSSION Black crappie is the primary sport fish target ed by recreational angl ers at Lake Dora, and my results show that the population could be ne gatively impacted by incr eases in exploitation resulting from either the recreational fishery or bycatch from the commercial gill net fishery for gizzard shad. Currently, FWC has not defined a standard to measure impacts of bycatch and determine levels of commercial exploitation that are acceptable. I used a biological reference point (SPR) determined from an age-structured model to attempt to determine what levels of total exploitation could be sust ainable without risking recruitm ent overfishing. I also used maximum yield per recruit to inve stigate the potential for growth ove rfishing to occur at varying total exploitation rates. It is important to real ize that negative impacts such as reduced catch or decreased angler success may occur at fishing mortality rates below those which cause recruitment overfishing and changes in the vulner ability to harvest schedule may influence the potential for recruitment overfishing to occu r at varying total exploitation rates. Additionally, management decisions are still required to dete rmine how much of the total sustainable exploitation rate is allocated to the recreational fishery versus bycatch from the gill net fishery. The total sustainable exploitation rate is approx imately 0.42, which results in an SPR near the target goal of 0.3 to 0.35. The esti mated recreational exploitation rate in 2006 was approximately the total sustainable exploitation rate, and increases due to recreational fishing and/or commercial bycatch greatly increase the probability of recruitment overfishing. Total exploitation in 2006 resulted in an estimated exploitation rate (0.51) that produces worrisome SPR levels and is most likely not sustainable. The exploitation via bycat ch of black crappie at Lake Dora is a negative effect because it is not resulting from a directed fishery and all mortality results in waste. The gill net fishery was regulated to minimize bycatch as much as possible, but 56

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bycatch mortality occurred at rates that cause concern for re cruitment overfishing. Resource managers must evaluate policy trad e-offs to consider the benefit of the gizzard shad removal and the negative impacts of bycatch mortality. Commercial fishing occurred on Lake Dora during 2005 and 2006 and total commercial fishing effort was approximately equal during th e two fishing seasons. However, the temporal range of effort differed, which may have influe nced total gill net bycatch mortality among the commercial seasons. The commercial fishing se ason in 2005 began two months later than the commercial season in 2006, which could have resu lted in differences in catchability due to differing vulnerability to capture in gill nets. This is plausibl e due to black crappie inshore spawning movements occurring duri ng the later months of the fishing seasons. Total bycatch estimates in 2006 were nearly twice as high as total bycatch estimates in 2005. These results suggest that bycatch could be reduced by timing th e commercial fishing season to prevent fishing during winter and early spring. Reducing total bycatch mortality is achieved by reducing the amount of total bycatch or reduc ing mortality resulting from by catch. Timing of season could potentially reduce the amount of total bycatch without increasing mort ality resulting from bycatch. Bycatch mortality rates would not likely increase by timing of season because I found no significant impact of water temperature or dissolved oxygen levels on bycatch mortality. No initial mortality of bycatch was observed at Lake Dora during gill net operations, and secondary mortality was the primary mortality so urce for black crappie caught in commercial gill nets. This was likely due to the maximum soak time of two hours. To tal mortality of black crappie captured via gill nets at Lake Apopka, Florida was estimated from 1993 to 1997 and results indicated that 87% survived the trea tment (J. Crumpton, FWC, unpublished data). Similarly, secondary mortality accounted for the majority of total mortality and only a small 57

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percentage of total mortality observed was initial mortality, which generally occurred in nets fished greater than two hours. The potential for adverse populat ion-level effects resulting fr om commercial bycatch is greatest when recreational explo itation is already high. A previ ous evaluation found negligible impacts from gill net bycatch for black crappie on Lake Apopka, Florida using a transitional SPR constructed from an SRA (M. Allen, UF, unpublishe d data), due to low r ecreational exploitation (~1 fish/acre/year). Conversely, commercial ha rvest of black crappie at Lake Okeechobee, Florida coupled with recreational harvest increa sed exploitation to 65%, but the effects were increased growth rates and the population did not show signs of overfishing (Schramm et al. 1985). However, the conclusions of this study we re based on catches and angler success, and they did not investigate the poten tial for recruitment overfishing. Other studies have assessed popul ation-level impacts of bycatch with modeling techniques. Crouse et al. (1987) developed a st age-based matrix model that in corporated fecundity, survival, and growth rates, and used yearly iterations to make population projections for loggerhead sea turtles Caretta caretta The model used seven life stages from eggs/hatchlings to mature breeders and tested the sensitivity of bycatch mo rtality on population grow th rates. They found that reducing mortality in the la rge juvenile and adult life stages provided the best protection for population viability. Diamond et al. (1999) explor ed the population level effects of catch and bycatch on Atlantic croaker Micropogonias undulatus in the Gulf of Mexico and the Atlantic Ocean. Catch of Atlantic croaker, including bycatch, had historical ly been at least three times higher in the Gulf than the Atlantic; however, pr imarily juveniles are taken in the Gulf fisheries whereas fisheries in the Atlantic have targeted ad ult fish. Long-term intensive fishing in the Gulf caused severe declines in abundance of Atlan tic croaker, but there was no change in size 58

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distribution and age at maturit y, and large fish remained comm on. In contrast, the Atlantic fishery targeting adult fish has caused changes in age at maturity and size structure of that population. Diamond et al. (2000) used stage-within-age based matrix models of Atlantic croaker in the Gulf of Mexico a nd Atlantic to investigate populati on-level effects of shrimp trawl bycatch. The Gulf model showed a rapidly de clining population, and th e Atlantic population showed only a modest decline. Results indicated that both populations were more sensitive to survival of adults than first-year survival, a nd reducing late juvenile and adult mortality could reverse population declines. Results from thes e studies support my conclusion that populationlevel impacts can occur, especially when targeted-fishery expl oitation is also high. Biological reference points such as spawning pot ential ratio are commonly used as critical metrics to measure the potential of recruitment overfishing. Goody ear (1993) defines SPR as the ratio of fished to unfished magnitude of P (reproductive poten tial of an average recruit) and is a measure of the impact of fishing on the potential productivity of a stoc k. Critical levels had typically been set in the range of 0.2 to 0.3, based primarily on wo rk in the Northwest Atlantic (Goodyear 1993). SPR target values of 0.35 to 0.4 have also been suggested (Clark 2002), but the critical level for any particular species is influenced by the level of recruitment compensation for fishing mortality (Goodyear 1993). The state of Florida has adopted a target SPR of 0.35 for some heavily exploited marine speci es, including the spotted seatrout Cynoscion nebulosus, which have shown worrisome levels of SPR values due to recreat ional exploitation (no commercial exploitation and very lim ited bycatch) (Murphy et al. 1999). Estimates of exploitation from tagging studies are always subject to uncertainty due to tag loss, tagging mortality, and reporting rate. For my model simulations, I utilized the best estimate of recreational exploitation (0.42) from tag returns corrected for tag loss of 5%, tagging mortality 59

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of 5%, and reporting rate of hi gher value reward tags of 70%. My estimate of recreational exploitation in 2006 (rec = 0.42) was comparable to estimates of exploitation for black crappie in other southeastern systems. Larson et al. ( 1991) estimated exploitati on rates ranging from 40 to 68% in three Georgia reservoirs, Allen and Miranda (1995) esti mated a mean exploitation rate of 42% for white and black crappie in 10 South east and Midwest lakes, and Allen et al. (1998) found that exploitation averaged 48% for 18 lakes in the Southeast and Mi dwest. Black crappie are one of the most heavily harv ested and exploited freshwater fi shes in the United States, and strong size selectivity under heavy exploitation may affect black crappie population dynamics (Miranda and Dorr 2000). My exploitation estimate was critical for mode l simulations because the model was fit to my 2006 empirical estimates of exploitation and vu lnerable biomass. An unbiased estimate of exploitation was additionally im portant to reduce parameter uncer tainty, because there is also structural uncertainty in the SRA. The SRA model reduces population size based on catches alone, and does not account for other factors th at may influence recruitment such as habitat changes. This is of particular importance becau se if the gizzard shad removal is successful, improved water clarity could result in increase d aquatic macrophyte abunda nce thereby changing the available habitat and factors that influe nce black crappie recruitment and growth. All model simulations assumed vulnerability to harvest was equal for the recreational and commercial fisheries. This is important because the vulnerability to harv est schedule directly impacts estimates of exploitati on. It is likely that vulnerabi lity between commercial and recreational fisheries were sim ilar based on the size and age distributions of the harvest. Although recreational anglers did tend to harvest some smaller black crappie that were not fully vulnerable to the commercial fish ery, Miranda and Dorr (2000) show ed that recreational anglers 60

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tend to select for fish over 250 mm TL. Additio nally, much of the recreational angling effort occurs in open water areas where gill nets ar e fished. The number of tag returns from the commercial fishery was significantly lower possibly indicating a di fference in vulnerability to harvest, however commercial fishers had incentiv e to not return tags and no reliable reporting rate could be obtained for the commercial fishery. My future projections were conducted unde r the assumption that total exploitation remained constant through the terminal year. This scenario is unlikely, because changes in angler catch rates through fish reductions via recreational and/or commercial exploitation would probably influence recreational fi shing effort. Cox et al. (2003) found that angling effort depends on the angler catch rate, and there is no r eason to expect that the level of fishing effort that produces the maximum total yield will also pr ovide maximum total satisfaction to anglers. Additionally, Walters and Martell (2004) state that most fisheries reach a bionomic equilibrium where they become self-regulating in the sense that further stock decline past some equilibrium caused by development of a fishery s hould trigger a reduction in fishing effort and mortality allowing the stock to begin recovery. T hus, it is likely that recreational effort would decline if total exploitation continued to incr ease and catch rates declined, due to decreased angler satisfaction and shifts in fishing effort to other systems. Under this scenario of bionomic equilibrium, commercial bycatch will probably not result in recr uitment overfishing. However, decreased angler satisfaction and fishing effort is still a negative impact resulting from increased exploitation, which could occur due to bycatch mortality. Reduced recreational angler effort caused by commercial bycatch mortality warrants furture investigation because lower effort would constitute harm to the recreational fishery. 61

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CHAPTER 5 MANAGEMENT IMPLICATIONS Impact on the black crappie fishery due to bycatch mortality may be acceptable if the gizzard shad reduction is successful in im proving water clarity and increasing aquatic macrophyte abundance. A manageme nt decision must be made fo r the future of commercial fishing with gill nets on Florida lakes that evaluates the trade-offs of the positive effects of biomanipulation and possible negative effects of bycatch on recr eational fisheries. Possible management alternatives are to 1) discontinue the gill net fishery to eradicate bycatch and optimize the black crappie recreational fishery, or 2) increase commercial effort and gizzard shad exploitation to optimize the success of the biomani pulation. It is plausi ble that continuing the program at the current level of commercial effort will most likely not optimize either management objective. Another alternative is to init iate an active adaptive management plan. Active management of recreational fisheries implies that a complete management procedure is in place, with clear goals or objectives for the fishery, management sc hemes to keep the total harvest or exploitation rates within target limits, and methods to dete rmine whether the goals or objectives have been met (Walters 1986; Pereira and Hansen 2003). L ittle experience has been gained in actively managing recreational fisheries due to the extensive and diverse array of recreational fisheries, few recreational fisheries are of such singular im portance that they demand the sociopolitical or economic motives, and many passive management schemes are in place in response of the need for management (Pereira and Hansen 2003). For successful active adaptive management in recreational fisheries, agencies must commit to a clear goal or objective. In the case of the Lake Dora commercial gill net fisher y, possible objectives are 1) re ducing the gizzard shad population enough to change the trophic structure or 2) optimizing black crappi e recreational angling 62

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satisfaction. If the goal of the Lake Dora fisher y is to reduce gizzard shad abundance to levels that result in trophic structur e alterations, then a long-term management plan should be implemented that involves fishing the gizzard shad intensively, m easuring the levels of gizzard shad reduction, measuring levels of chlorophy ll reduction, and measuring the black crappie bycatch mortality and angling success. Another consideration in the evaluation of the policy trade-off is the effect that a change in the trophic structure would ha ve on the black crappie population. A shift in th e trophic structure may result in changes in water clarity, aquatic macrophyte abundance, and fish productivity th at could impact bl ack crappie population dynamics and angling success, which is not accounted for in SRA simulations. Fisheries management inherently requires making decisions that involve trade-offs. Management agencies often try to make decisions that optimize all alternatives, which can create a situation where none of the management altern atives are optimized. Failure to admit the severity of trade-off relationships can result in policy choices that are not beneficial for anyone (Walters and Martell 2004). The trade-offs associ ated with the gizzard shad biomanipulation and black crappie bycatch must be considered a nd clear management objectives defined. If commercial fishing continues, methods must be set forth to measure th e effectiveness of the management objectives. My results show that th e current size-selective removal of gizzard shad at Lake Dora could cause negative impacts to th e black crappie population, with the potential for recruitment overfishing. Resource managers should consider these impact s and the trade-offs they represent when considering commercial fishing operations. 63

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LIST OF REFERENCES Allen, M. S., and L. E. Miranda. 1995. An eval uation of the value of harvest restrictions in managing black crappie fisheries. North Am erican Journal of Fisheries Management 15:766-772. Allen, M. S. 1997. Effects of variable recruitment on catch-cur ve analysis for black crappie populations. North American Journal of Fisheries Management 17:202-205. Allen, M. S., and L. E. Miranda. 1998. An ag e-structured model for erratic black crappie fisheries. Ecological Modeling 107:289-303. Allen, M. S., L. E. Miranda, and R. E. Brock. 1998. Implications of compensatory and additive mortality to the management of selected sportfish populations. Lakes & Reservoirs: Research and Management 3:67-69. Allen, M. S., M. V. Hoyer, and D. E. Canfield, Jr. 2000. Factors related to gizzard shad and threadfin shad occurrence and abundance in Florida lakes. Journal of Fish Biology 57:291-302. Anderson, R. O., and R. M. Neumann. 1996. Length, weight, and associated structuralindices. Pages 447-482 in B. R. Murphy and D. W. Willis, editors. Fisheries techniques, 2nd edition. American Fisheries Society, Bethesda, Maryland. Bachmann, R. W., B. L. Jones, D. D. Fox, M. V. H oyer, L. A. Bull, and D. E. Canfield, Jr. 1996. Relations between trophic state indicators and fish in Florida (U.S.A.) lakes. Canadian Journal of Fisheries and A quatic Sciences 53:842-855. Beerkircher, L. R., E. Corts, and M. Shivji. 2002. Characteristics of shark bycatch observed on pelagic longlines off the Southeastern United States, 1992-2000. Marine Fisheries Review 64(4):40-49. Belda, E. J., and A. Snchez. 2001. Seabird mortality on longline fisheries in the western Mediterranean: factors affecting bycatch and proposed mitigating measures. Biological Conservation 98:357-363. Benton, J. 2005. Recreational angler survey of Lakes Dora/Beauclair and Eustis for November 2004 through June 2005. Florida Fish and W ildlife Conservation Commission, Technical Report F2485-04-05-F, Eustis. Caswell, H., B. Solange, A. J. Read, and T. D. Smith. 1998. Harbor porpoi se and fisheries: An uncertainty analysis of incidental mortalit y. Ecological Applica tions 8(4):1226-1238. Clark, W. G., and S. R. Hare. 1998. Accountin g for bycatch in management of the Pacific Halibut fishery. North American Journal of Fisheries Management 18:809-821. Clark, W. G. 2002. F35% revisited 10 years later. North American Journal of FisheriesManagement 22:251-257. 64

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Cox, S. P., C. J. Walters, and J. R. Post. 2003. A model-based evaluation of active management of recreational fishing effort. North American Journal of Fisheries Management 23:1294-1302. Crouse, D. T., L. B. Crowder, and H. Caswell. 1987. A stage-based population model for loggerhead sea turtles and implications for conservation. Ecology 68(5):1412-1423. Crowder, L. B., and S. A. Murawski. 1998. Fi sheries bycatch: Implications for management. Fisheries 23(6):8-17. Crowder, L. B., D. T. Crouse, S. S. Heppell, an d T. H. Martin. 1994. Predicting the impact of turtle excluder devices on loggerhead sea tu rtle populations. Ecol ogical Applications 4(3):437-445. Crumpton, J. E. Unpublished. Bycatch and mortal ity in gill nets. Flor ida Fish and Wildlife Conservation Commission, unpublished report. DeVries, D. R., and R. A. Stein. 1992. Comp lex interactions between fish and zooplankton: Quantifying the role of an ope n-water planktivore. Canadi an Journal of Fisheries and Aquatic Sciences 49:1216-1227. DeVries, D. R., and R. V. Frie. 1996. Determin ation of age and growth. Pages 483-512 in B. R. Murphy and D. W. Willis, edito rs. Fisheries techniques, 2nd edition. American Fisheries Society, Bethesda, Maryland. Diamond, S. L., L. B. Crowder, and L. G. Cowell. 1999. Catch and bycatch: The qualitive effects of fisheries on populati on vital rates of Atlantic cr oaker. Transactions of the American Fisheries Society 128:1085-1105. Diamond, S. L., L. G. Cowell, a nd L. B. Crowder. 2000. Population effects of shrimp trawl bycatch on Atlantic croaker. Canadian J ournal of Fisheries and Aquatic Sciences 57:2010-2021. Edwards, G. P. Jr., R. M. Neumann, R. P. Jacobs and E. B. ODonnell. 2004. Factors related to mortality of black bass caught during small club tournaments in Connecticut. North American Journal of Fish eries Management 24:801-810. Fischer, A. J., M. S. Baker, Jr., and C. A. Wilson. 2004. Red snapper ( Lutjanus campechanus ) demographic structure in the northern Gulf of Mexico based on spatial patterns in growth rates and morphometrics. Fi shery Bulletin 102(4):593-603. Fischer, A. J., M. S. Baker, C. A. Wilson, a nd D. L. Neiland. 2005. Age, growth, mortality, and radiometric age validation of gray snapper ( Lutjanus griseus ) from Louisiana. Fishery Bulletin 103(2):307-319. Florida LAKEWATCH. 2004. Florida LAKEWATCH Annual Data Summaries 2004. Department of Fisheries and Aquatic Sciences University of Florida/Institute of Food and Agricultural Sciences. Library, Universi ty of Florida, Gainesville, Florida. 65

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Forsburg, C., and S. O. Ryding. 1980. Eutrophicati on parameters and trophic state indices in 30 Swedish waste-receiving lakes. Archive for Hydrobiologie 88:189-207. FWC 2005. Florida Fish and Wildlife Conservati on Commission. 2005. Fish ID and biology of panfishes. Florida Fish and Wildlife Conservation Co mmission, Black crappie. Available; http://floridafisheries.com/Fishes/panfish.html (September 2005). Gallinat, M. P., H. H. Ngu, and J. D. Shively. 19 97. Short-term survival of lake trout Released from commercial gill nets in Lake Superi or. North American Journal of Fisheries Management 17:136-140. Gido, K. B. 2003. Effects of gizzard shad on benthi c communities in reservoirs. Journal of Fish Biology 62:1392-1404. Goodwin, N. B., A. Grant, A. L. Perry, N. K. Dulvy, and J. D. Reynolds. 2006. Life history correlates of density-dependent recruitment in marine fish es. Canadian Journal of Fisheries and Aquatic Sciences 63(3):494-509. Goodyear, C. P. 1980. Compensation in fish populat ions. Pages 253-280 in C. H. Hocutt and J. R. Staufer, Jr., editors. Biological Monitoring of Fish. Lexington Books, Lexington, Massachusetts. Goodyear, C. P. 1993. Spawning stock biomass per recruit in fisheries management:Foundation and current use. Pages 67-81 in S. J. Smith, J. J. Hunt, and D. Rivard, editors. Risk evaluation and biological reference points fo r fisheries management. Canadian Special Publication of Fisheries a nd Aquatic Sciences 120. Hale, M. M., J. E. Crumpton, and W. F. Goodwi n. 1981. Game fish by-catch in commercially fished hoop nets in the St. Johns River, Flor ida. Proceedings of the Annual Conference of the Southeastern Asso ciation of Fish and Wildlife Agencies 35:408-415. Hale, M. M., J. E. Crumpton, and D. J. Renfro. 1983. Catch composition of pound nets and their impact on game fish populations in the St Johns River, Florida. Proceedings of the Annual Conference of the Sout heastern Association of Fi sh and Wildlife Agencies 37:477-483. Hale, M. M., R. J. Schuler, Jr., and J. E. Crumpton. 1996. The St. Johns River, Florida freshwater striped mullet gill net fi shery: Catch com position, status, and recommendations. Proceedings of the A nnual Conference of the Southeastern Association of Fish and W ildlife Agencies 50:98-106. Hammers, B. E., and L. E. Miranda. 1991. Comp arison of methods for estimating age, growth, and related population characteri stics of white black crappies North American Journal of Fisheries Management 11:492-498. Henry, K. R. 2003. Evaluation of largemouth bass exploitation and potential harvest restrictions at Rodman Reservoir, Florida. M. S. th esis, Department of Fisheries and Aquatic Sciences, University of Fl orida, Gainesville, FL. 66

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Hooe, M. L. 1991. Black crappie biology and management. North American Journal of Fisheries Management 11:483-484. Irwin, B. J., D. R. DeVries, and G. W. Kim. 2003. Responses to gizzard shad recovery following selective treatment in Walker County Lake, Alabama, 1996-1999. North American Journal of Fish eries Management 23:1225-1237. Krebs, C. J. 1999. Ecological Methodology, 2nd edition. Benjamin/Cummings, Menlo Park, CA. Kwak, T. J., and M. G. Henry. 1995. Largemouth bass mortality and related causal factors during live-release fishing tournaments on a large Minnesota lake. North American Journal of Fisheries Management 15:621-630. Larson, S. C., B. Saul, and S. Schleiger. 1991. Exploitation and survival of black crappies in three Georgia reservoirs. North American Jo urnal of Fisheries Management 11:604-613. Lewison, R. L., L. B. Crowder, A. J. Read, a nd S. A. Freeman. 2004. Understanding impacts of fisheries bycatch on marine megafauna. Tr ends in Ecology and Evolution 19(11):598604. Magley, W. 2003. Total maximum daily load fo r total phosphorous for Lake Dora and Dora Canal Lake County, Florida. Florida Depart ment of Environmental Protection Watershed Assessment Section, Technical Report, Tallahassee. Majluf, P., E. A. Babcock, J. C. Riveros, M. A. Schreiber, and W. Al derete. 2002. Catch and bycatch of sea birds and marine mammals in the small-scale fishery of Punta San Juan, Peru. Conservation Biology 16(5):1333-1343. Malvestuto, S. P., W. D. Davies and W. L. Shelton. 1978. An evaluation of the roving creel survey with non-uniform probability sampling. Transactions of the American Fisheries Society 107:255-262. Malvestuto, S. P. 1996. Sampling the recreationa l creel. Pages 591-623 in B. R. Murphy and D. W. Willis, editors. Fisheries Techniques, 2nd edition. American Fisheries Society, Bethesda, Maryland. Mangel, M. 1993. Effects of high-seas driftn et fisheries on the north ern right whale dolphin Lissodelphis borealis. Ecological Applications 3(2):221-229. Miranda L. E., and B. S. Dorr. 2000. Size select ivity of black crappie an gling. North American Journal of Fisheries Management 20:706-710. Miranda, L. E., R. E. Brock, and B. S. Dorr. 2003. Uncertainty of expl oitation estimates made from tag returns. North American Jo urnal of Fisheries Management 22:1358-1362. Murphy, D. M., G. A. Nelson, and R. G. Muller. 1999. An update of the assessment of spotted seatrout. Florida Marine Research Institute, St. Petersburg, Florida. 67

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Murray, J. D., J. J. Bahen, and R. A. Rulifson. 1992. Management considerations for by-catch in the North Carolina and Southeast sh rimp fishery. Fisheries 17(1):21-26. Myers, R. A., K. G. Bowen, and N. J. Barrowm an. 1999. Maximum reproductive rate of fish at low population sizes. Canadian Journal of Fisheries and Aquatic Sciences 56:24042419. Neal, J. W., and D. Lopez-Clayton. 2001. Mo rtality of largemouth bass during catch-andrelease tournaments in a Puerto Rico reserv oir. North American Journal of Fisheries Management 21:834-842. Nichols, J. D., R. J. Blohm, R. E. Reynolds, R. E. Trost, J. E. Hines, and J. P. Bladen. 1991. Band reporting rates for mallards with reward ba nds of different dollar values. Journal of Wildlife Management 55(1):119-126. Patterson, W. F., J. H. Cowan, C. A. Wilson, a nd R. L. Shipp. 2001. Age and growth of red snapper, lutjanus campechanus from an artificial reef area off Alabama in the northern Gulf of Mexico. Fishery Bulletin 99(4):617-627. Pereira D. L., and M. J. Hansen. 2003. A pers pective on challenges to recreational fisheries management: summary of the symposium on activ e management of recreational fisheries. North American Journal of Fish eries Management 23:1276-1282. Pikitch, E. K., J. R. Wallace, E. A. Babcock, D. L. Erickson, M. Saelens, and G. Oddsson. 1998. Pacific halibut bycatch in the Washington, Or egon, and California groundfish and shrimp trawl fisheries. North American Jour nal of Fisheries Management 18:569-586. Pollock, K. H., and W. E. Pine, III. 2007. The de sign and analysis of field studies to estimate catch-and-release mortality. Fisheries Management and Ecology 14:1-8. Pollock, K. H., C. M. Jones, and T. L. Brown. 1994. Angler survey methods and their applications in fisheries management. Amer ican Fisheries Society. Special Publication 25, Bethesda, Maryland. Potts, J. C., C. S. Manooch, III, and D. S. Vaughan. 1998. Age and Growth of vermillion snapper from the Southeastern United States Transactions of th e American Fisheries Society 127:787-795. Potts, J. C., and C. S. Manooch, III. 1999. Obse rvations on the age and growth of graysby and coney from the Southeastern United States. Transactions of the American Fisheries Society 128:751-757. Renfro, D. J., M. M. Hale, and J. E. Crumpton. 1989. Estimating annual game fish bycatch in commercial fishing devices from harvest data Proceedings of the Annual Conference of the Southeastern Association of Fi sh and Wildlife Agencies 43:75-79. Ricker, W. E. 1975. Computation and interpretation of biological statistics in fish populations. Bulletin 191 of Fisheries Research Board of Canada. 68

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Ross, J. R., J. D. Crosby, and J. T. Kosa. 2005. Accuracy and precision of age estimation of black crappies. North American Jour nal of Fisheries Management 25:423-428. SAS (Statistical Analyses Systems). 1996. SAS stat istics users guide. SAS Institute,Inc., Cary, North Carolina. Schaus, M. H., and M. J. Vanni. 2000. Eff ects of gizzard shad on phytoplankton and nutrient dynamics: Role of sediment feeding and fish size. Ecology 81(6):1701-1719. Schaus, M. H., M. J. Vanni, and T. E. Wissi ng. 2002. Biomass-dependent diet shifts in omnivorous gizzard shad: Implications for gr owth, food web, and ecosystem effects. Transactions of the American Fisheries Society 131:40-54. Schramm, H. L. Jr., and J. F. Doerzbacher. 1982 Use of otoliths to age black crappie from Florida. Proceedings of the Annual Conferen ce of the Southeastern Association of Fish and Wildlife Agencies 36:95-105. Schramm, H. L. Jr., J. V. Shireman, D. E. Hammond, and D. M. Powell. 1985. Effect of commercial harvest of sport fish on the bl ack crappie population in Lake Okeechobee, Florida. North American Journal of Fisheries Management 5:217-226. Schramm, H. L. Jr., P. J. Haydt, and K. M. Por tier. 1987. Evaluation of prerelease, postrelease and total mortality of largemouth bass caught during tournaments in two Florida lakes. North American Journal of Fi sheries Management 7:394-402. Stein, R. A., D. R. DeVries, and J. M. Dettm ers. 1995. Food-web regulation by a planktivore: Exploring the generality of the trophic cascade hy pothesis. Canadian Journal of Fisheries and Aquatic Sciences 52:2518-2526. Stein, A. B., K. D. Friedland, and M. Sutherla nd. 2004. Atlantic sturgeon marine bycatch and mortality on the continental shelf of the Northeast United States. North American Journal of Fisheries Management 24:171-183. Taylor, R. G., J. A. Whittington, W. E. Pine, III, and K. H. Pollock. 2006. Effect of different reward levels on tag reporti ng rates and behavior of common snook anglers in Southeast Florida. North American Journal of Fisheries Management 26:645-651. Tuck, G. N., T. Polacheck, J. P. Croxall, and H. Weimerskirch. 2001. Modelling the impact of fishery by-catches on albatross populations. Journal of Applied Ecology 38:1182-1196. U.S. Department of Labor. 2006. Bureau of Labor Statistics, Division of Consumer Prices and Price Indexes, Washington D.C. Walters, C. J. 1986. Adaptive management of renewable resources. Macmillan, New York. Walters, C.J., and S. J. D. Martell. 2004. Fisheries Ecology and Management. Princeton University Press, Princeton, New Jersey. 69

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Walters, C. J., S. J. D. Martell, and J. Korma n. 2006. A stochastic approach to stock reduction analysis. Canadian Journal of Fish eries and Aquatic Sciences 63:212-223. Walters, C. J., R. Hilborn, and R. Parrish. 2007. An equilibrium model for predicting the efficacy of marine protected areas in coasta l environments. University of Florida. Available; http://floridarivers.ifas.ufl.edu/Carl%20Class/MPA%20evaluation%20paper.doc (April 2007). Weathers, K. C., and M. J. Newman. 1997. Effects of organizational procedures on mortality of largemouth bass during summer tournaments. North American Journal of Fisheries Management 17:131-135. Wilde, G. R., M. I. Muoneke, P. W. Bettoli, K. L. Nelson, and B. T. Hysmith. 2000. Bait and temperature effects on striped bass hooking mortality in freshwater. North American Journal of Fisheries Management 20:810-815. 70

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BIOGRAPHICAL SKETCH Jason Randall Dotson was born February 11, 1980, in Manassas, Virginia, the son of Paul Randall and Rae Lynn Dotson. He was raised wi th his younger sister Jennifer on Lake Jackson, a private reservoir in Northern Virginia. Under the influence of his father Randy and grandfather Paul, Jason acquired a love and appreciation for the outdoors at an early age. In 1998, Jason began his collegiate studies at Vi rginia Tech, where he would earn a Bachelor of Science degree in fisheries science in 2003. While at Virginia Tech, he worked on a variety of research projects involving smallmouth bass, muske llunge, striped bass, and the federally endangered Roanoke logperch. After graduation, Jason ex perienced a short-lived career as an insurance adjuster. In August of 2004, he decided to pursu e a career in fisheries scienc e and served as a fisheries technician for the University of Florida, wher e he worked on a variety of projects involving gizzard shad, American shad, black crappie, spot ted sunfish, and largemouth bass. In August of 2005, Jason began his own research as a graduate student in the Department of Fisheries and Aquatic Sciences at the University of Florida. He graduated in August 2007 with a Master of Science degree, and is currently working as a fisheries biologist for Florida Fish and Wildlife Conservation Commission. 71


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