Dissecting Mortality Components for Recreational Fisheries With High Rates of Released Fish

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Dissecting Mortality Components for Recreational Fisheries With High Rates of Released Fish
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english
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Kerns, Janice A
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University of Florida
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Degree:
Doctorate ( Ph.D.)
Degree Grantor:
University of Florida
Degree Disciplines:
Fisheries and Aquatic Sciences, Forest Resources and Conservation
Committee Chair:
ALLEN,MICHEAL S
Committee Co-Chair:
PINE,WILLIAM E,III
Committee Members:
MURIE,DEBRA JEAN
ST MARY,COLETTE MARIE
HIGHTOWER,JOSEPH

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Subjects / Keywords:
bayesian -- dynamics -- fisheries -- modeling -- mortality -- population
Forest Resources and Conservation -- Dissertations, Academic -- UF
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Fisheries and Aquatic Sciences thesis, Ph.D.
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Abstract:
The purpose of this study was to assess the components of mortality for fish populations with high rates of angler release and to evaluate the potential impact of all sources of mortality for a recreational sport fish. My first objective was to estimate the cumulative fishing mortality through direct estimates of harvest fishing mortality and indirect estimates of mortality associated with catch-and-release fishing across a large number of lakes. My second objective was to directly estimate all components of total mortality: (a) natural mortality, and (b) total fishing mortality including harvest, catch-and-release and tournament components of a fishery within a single lake. My third objective was to investigate how seasonal trends in fishing and natural mortality influence mortality estimates derived from annual tag-return data. I used a passive tag reward study spread over one bass management regulation area within central Florida and then utilized a combined telemetry-tag return study on Lake Santa Fe, a fishery within north central Florida. All fish were tagged with a $5, $100, or $200 total reward amount. The last objective was met by simulating a number of mortality scenarios using both theoretical and field-based seasonal estimates. Results of this study indicated that overall average fishing mortality for Florida bass Micropterus floridanus in central Florida is relatively low, but much higher fishing mortality at Lake Santa Fe. Additionally, this study found simulated seasonal fluctuations in mortality could bias fishing mortality estimates derived from annual tag-return information. This was especially true for scenarios that simulated relatively high natural mortality rates. Thus, there is a need to evaluate fishing mortality at multiple spatial and temporal scales. For some fisheries this may be easy, as mortality rates remain relatively constant over time, but others may be more difficult. Thus, I suggest a two-tiered approach with 1) periodic (e.g., 5-10 year intervals) estimation of regional fishing mortality across lakes, and 2) site-specific estimates of mortality components when fishing mortality is suspected to be high, or when evaluating management regulations.
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In the series University of Florida Digital Collections.
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Includes vita.
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Statement of Responsibility:
by Janice A Kerns.
Thesis:
Thesis (Ph.D.)--University of Florida, 2013.
Local:
Adviser: ALLEN,MICHEAL S.
Local:
Co-adviser: PINE,WILLIAM E,III.

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1 DISSECTING MORTALITY COMPONENTS FOR RECREATIONAL FISHERIES WITH HIGH RATES OF RELEASED FISH By JANICE ANNETTE KERNS A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF T HE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY UNIVERSITY OF FLORIDA 2013

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2 2013 Janice Kerns

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3 To my friends and family for all your support and inspiration

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4 ACKNOWLEDGMENTS The Sport Fish Resto ration Program funded this research and I was supported by a Grinter Graduate School Fellowship for three years Travel funds for professional conferences were provided by Graduate Student Council, Florida Chapter of the American Fisheries Society, Black Bass Diversity Symposium, and Students United in the Research of Fisheries Other funds were provided by a Roger Rottman Scholarship. I would like to thank my advisor Dr. Micheal Allen and members of my graduate committee, doctors William Pine Debra Muri e, Joseph Hightower and Colette St. Mary who helped with development of my project. I specifically appreciate Dr. Allen who inspired me to pursue and had the confidence that I would complete my PhD. I also appreciate the guidance, statistical, and comput ing knowledge of Dr. Hightower who was always available and ready for a challenge. This study was conducted with cooperation and input from Jim Estes, Wes Porak, and Jason Dotson, who all helped extensively with stu dy design and implementation. The Florid a Fish and Wildlife Conservation Tag Return Hotline provided valuable assistance with collecting tag return information. I thank Bryan Matthias, Erin Bradshaw Settevendemio, Kyle Wilson, Matt Hangsleben Stephanie Shaw, Dan Gwinn, Nick Cole, Zak Slagle, a nd the numerous FW C biologists and Long Term Monitoring personnel for help with data collection and processing. Lastly, exceptional thanks goes to my friends and family, Emily Sampson, Jennifer Ortiz, Tara Engeron, Elizabeth Farney, Jynessa Dutka Gianelli, and Linda Lombardi for keeping me focused on what is important in life and for always being there.

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5 TABLE OF CONTENTS page ACKNOWLEDGMENTS ................................ ................................ ................................ .. 4 LIST OF TABLES ................................ ................................ ................................ ............ 7 LIST OF FIGURES ................................ ................................ ................................ .......... 8 A BSTRACT ................................ ................................ ................................ ..................... 9 CHAPTER 1 IMPORTANCE OF ASSESSING POPULATION LEVEL IMPACT OF CATCH AND RELEASE MORTALITY ................................ ................................ ................. 11 Introduction ................................ ................................ ................................ ............. 11 Components of Fish Mortality ................................ ................................ ................. 11 Future Research and Manag ement Needs ................................ ............................. 13 2 FISHING MORTALITY DUE TO HARVEST IN FLORIDA LAKES .......................... 16 Introduction ................................ ................................ ................................ ............. 16 Methods ................................ ................................ ................................ .................. 17 Sampling ................................ ................................ ................................ .......... 17 Model Structure a nd Analysis ................................ ................................ ........... 20 Results ................................ ................................ ................................ .................... 23 Discussion ................................ ................................ ................................ .............. 25 3 COMPONENTS OF TOTAL MORTALITY WITHIN A HIGH RELEASE RECREATIONAL FISHERY ................................ ................................ ................... 44 Introduction ................................ ................................ ................................ ............. 44 Methods ................................ ................................ ................................ .................. 45 Study Area ................................ ................................ ................................ ........ 45 Sampling ................................ ................................ ................................ .......... 46 Analysis ................................ ................................ ................................ ............ 48 Results ................................ ................................ ................................ .................... 54 Discussion ................................ ................................ ................................ .............. 56 4 DEGREE OF TEMPORAL SYNCHRONY BETWEEN FISHING AND NATURAL MORTALITY INFLUENCES MORTALITY ESTIMATES ................................ ......... 69 Introduction ................................ ................................ ................................ ............. 69 Methods ................................ ................................ ................................ .................. 70 Results ................................ ................................ ................................ .................... 71 Discussion ................................ ................................ ................................ .............. 73

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6 5 SYNTHESIS AND FUTURE RESEARCH ................................ ............................... 80 LIST OF REFERENCES ................................ ................................ ............................... 82 BIOGRAPHICAL SKETCH ................................ ................................ ............................ 89

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7 LIST OF TABLES Table page 2 1 Lake size categories and target tagging numbers for regional fishing mortality study. ................................ ................................ ................................ .................. 31 2 2 Number of Florida bass tagged within large lakes from Oc tober through December of 2009 ................................ ................................ ............................. 32 2 3 Number of Florida bass tagged within large lakes from October through December of 2010 ................................ ................................ .............................. 33 2 4 Number of Florida bass tagged within small lakes from October through December of 2010 ................................ ................................ .............................. 34 2 5 Number of Florida bass tagged and reported caught from October 2009 through September 2011 throughout lakes in central Florida ............................ 35 2 6 Mean annual instantaneous capture and mortality rates of Florida bass tagged in central Florida between October 2009 through September 201 1 ...... 36 2 7 Deviance information criterion values of alternative models fitted to tag return data in OpenBUGS.. ................................ ................................ ........................... 37 3 1 Number of dart ta gged Florida bass reported returned by release type within Lake Santa Fe, Florida from November 2010 through October 2012 ................ 61 3 2 Mortality, release, and survival status of telemetry dart tag ged Florida bass Lake Santa Fe, Florida between November 2010 through October 2012 .......... 62 3 3 Deviance information criterion values for alternative models fitted to combined tag return telemetry data ................................ ................................ ... 63 3 4 Florida bass instantaneous mortality estimates from a combined tag return telemetry model ................................ ................................ ................................ 64 4 1 Percent biases comparison of fishing mortality values derived from data that utilized true seasonal information or annual harvest information ....................... 76

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8 LIST OF FIGURES Figure page 2 1 Ma p of study area for Chapter 2. ................................ ................................ ....... 38 2 2 Plastic tipped dart tags used to tag Florida bass to determine regional fishing mortality in central Florida ................................ ................................ .................. 39 2 3 Sign placed at the boat ramp of all lakes sampled for the regional fishing mortality study within central Florida. ................................ ................................ .. 40 2 4 Comparison of mean annual natural mortality rates obtained from combined tag return telemetry data on Lake Santa Fe, Florida ................................ ......... 41 2 5 Comparison of mean annual capture and fishing mortality rates obtained from c ombined tag return telemetry data on Lake Santa Fe, Florida ................. 42 2 6 Comparison of mean annual capture and fishing mortality rates of Florida bass by lake and fish size groups ................................ ................................ ...... 43 3 1 Map of Lake Santa Fe, Florida. ................................ ................................ ......... 65 3 2 Tagging procedure and setup for Florida bass tagged with plastic dart tipped tags and telemetry transmitters on Lake Santa Fe, Florida ............................... 66 3 3 A nnual instantaneous mortality rates estimated by a tag return model, a telemetry model and a combin ed telemetry tag return model. ............................ 67 3 4 Quarterly mortality rates for Florida bass from November 2010 to October 2012 ................................ ................................ ................................ .................. 68 4 1 Simulated mortality scenarios developed to ex amine the influence of seasonal mortality on annual mortality estimates ................................ .............. 77 4 2 Percent biases of fishing mortality estimates that only use annual harvest information ................................ ................................ ................................ ......... 78 4 3 Seasonal mortality values obtained from the literature. ................................ ...... 79

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9 Abstract of Dissertation Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy DISSECTING MORTALITY COMPONENTS FOR RECREATIONAL FISHERIES WITH HIGH RATES OF RELEASED FISH By Janice Kerns December 2013 Chair: Micheal Allen Major: Fisheries and Aquatic Sciences The purpose of this study was to assess the components of mortality for Florida Bass Micropterus floridanus with high rates of angler release and to evaluate the potential impact of all sources of mortality on bass populations My first objective was to estimate the total fishing mortality through direct estimates of harvest fishing mortality and indirect estimates of mortality associated with catch and release fishing across a large number of lakes. My second objective was to directly estimate all compo nents of total mortality for a single lake : (a) natural mortality, and (b) fishing mortality due to harvest, catch and rel ease and tournament components My third objective was to investigate how seasonal trends in fishing and natural mortality influence mortality estimates derived from annual tag return data. I used a passive t ag reward study spread over one management regulation area within central Florida and then utilized a combined telemetry tag return study on Lake Santa Fe, a fishery within north c entral Florida. All fish were tagged wit h either a $5, $100, or $200 total reward amount. The last objective was met by simulating a number of mortality scenarios using both theoretical and field based seasonal estimates. Results of this study indicated that overall average fishing mortality for Florida bass in central Florida is relatively low, but

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10 much higher fishing mortality at Lake Santa Fe. Additionally, this study found simulated seasonal fluctuations in mortality could bias fishing mortality est imates derived from annual tag return information. This was especially true for scenarios that simulated relatively high natural mortality rates. Thus, there is a need to evaluate fishing mortality at multiple spatial and temporal scales. For some fishe ries this may be easy, as mortality rates remain relatively constant over time, but others may be more difficult. Thus, I suggest a two tiered approach with 1) periodic (e.g., 5 10 year intervals) estimation of regional fishing mortality across lakes, and 2) site specific estimates of mortality components when fishing mortality is suspected to be high, or when evaluating management regulations

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11 CHAPTER 1 IMPORTANCE OF ASSESSING POPULATION LEVEL IMPACT OF CATCH AND RELEASE MORTALITY Introduction Many stud ies have measured the mortality of fish that are recreationally caught and released (i.e., catch and release (CR) mortality); however, little work has explored methods to understand the population level impact s of CR mortality on fish stocks. Despite cons iderable examination of biological, ethical, and practical aspects of CR fisheries (Arlinghaus et al. 2007; Cooke and Schramm 2007; Arlinghaus et al. 2012), little research has evaluated the cumulative effects of the different sources of mortality on recre ational fisheries. The purpose of this essay is to provide a brief discussion of the different components of mortality for fisheries with high rates of CR and the possible cumulative impacts of CR mortality on the quality of these fisheries. We demonstra te the need for studies that evaluate the impacts of CR mortality on fish stocks and estimate the fishing mortality rates associated with CR ( F cr ). Components of F ish M ortality The instantaneous total mortality ( Z ) of a fished population is described by th e equation (1 1) where F is the instantaneous fishing mortality and M is the instantaneous natural mortality. Fishing mortality is the rate at which fish are removed from a population due to fishing. Natural mortality is the rate at w hich individuals are lost from a population due to natural causes (i.e., predation, senescence, disease, or other natural causes). Components of fishing mortality include harvest and deaths of fish that are caught and

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12 released (e.g., CR mortality from ei ther immediate or delayed release of caught fish). To account for these components, the above equation can be expanded to (1 2) where F h is the instantaneous fishing mortality rate from harvest and F cr is the instantaneous fishing mortality rate via CR mortality. Fishing mortality from harvest is one of the most commonly estimated parameters in fisheries investigations via tagging studies, stock assessment models, and other approaches. It is important to make the distinction between F cr and CR mortality: F cr is the instantaneous fishing mortality rate resulting from CR mortality, whereas CR mortality is the proportion of individuals that die after being caught and released. Bartholomew and Bohnsack (2005) and Muoneke and Childress (1994) reviewed hundreds of published estimates of CR mortality, but we found no synthesis or literature reviews of F cr values. Relatively few studies have measured F cr for fish stocks. For some stocks, F cr can be a significant source of mortality resulting from harvest regulations or behavior of anglers (e.g., voluntary release, Driscoll et al. 2007). Harvest reg ulations can cause F cr to be a substantial mortality source, particularly if the CR mortality rate is high and a large portion of the age structure is protected from harvest (Coggins et al. 2007). Even if CR mortality is not high, impacts can be substanti Centropomus undecimalis ) fisheries have been managed with increasingly stringent harvest regulations to prevent overfishing, which has increased release rates from 31% in 1981 to over 90% in the late 1990s (Muller and Taylor 2006). Common snook have relatively low CR mortality (approximately 3%), but owing to increasing fishing effort about 35% of the total fishery

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13 related deaths are attributed to F cr (Muller and Taylor 2006). Many recreational fisheries (e.g., tr out [Family: Salmonidae ] or black bass [ Micropterus spp.]) have high release rates of fish that are legal to harvest; thus, traditional measures of F h may not indicate the full impact of fishing on fish abundance, size, or age structure. Although estimat es of F cr are not common, this mortality source has not been completely ignored. Most marine and anadromous stock assessments incorporate indirect estimates of F cr by estimating the number of fish released in a fishery and multiplying this by an average C R mortality rate obtained from experimental studies. The resulting estimate of dead releases is then added to the catch to determine total fishing mortality in stock assessment models (i.e., F h + F cr ). Similarly, Driscoll et al. (2007) used a tag return study and a range of CR mortality rates from literature to understand the impact tournament fishing was having on a largemouth bass ( Micropterus salmoides ) fishery in Sam Rayburn Reservoir, Texas. The combined mortality associated with CR fishing (i.e., m ortality of tournament released and fish immediately caught and released) accounted for 19 50% of the total fishing mortality. Future Research and Management Needs Future research needs to move past estimating CR mortality to developing more intensive fi eld studies to measure F cr for a wide range of fisheries. In our experience, many fisheries professionals report CR mortality as if high values are harmful and low values are not a concern. However, the ultimate impact of CR mortality on fish populations is known only through estimates of F cr because low CR mortality can have large population impacts (common snook example above). Only by estimating F cr will we understand the impacts of CR mortality on fish stocks.

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14 There are two basic options for estim ating F cr First, applying literature derived CR mortality rates in stock assessments or tag return studies as per Driscoll et al. (2007) would provide estimates of F cr This may be the only feasible option for evaluating F cr for recreational fisheries t hat occur in the open ocean or on some of the larger inland lake and riverine systems. However, for many freshwater and estuarine fisheries a second method is possible. We suggest using a combination of telemetry and tag return methods shown to provide u nbiased estimates of fishing and natural mortality rates (Pollock et al. 2004). In this framework, a fishery dependent high reward tag return study is primarily used to estimate F h whereas telemetry or fishery independent tags are used to estimate M (Poll ock et al. 2004; Bacheler et al. 2009). Pollock et al. (2004) illustrated that combining the two tagging methods incorporated the advantages of both approaches and provided more precise estimates of F h and M than either method would individually. This de sign could be expanded to include additional mortality components if the fates of all caught telemetered fish are known. For example, tagging all telemetered fish with an additional external high reward tag would allow researchers to document when fish ar e caught. If the fish is harvested it would contribute to F h in the typical way. If the fish is released, then its survival could be monitored to estimate F cr This method also assumes reporting rate to be 100%, which is realistic owing to use of high r eward tags. Non reporting may still occur but it would be considered negligible. Although this method is not infallible (e.g., tag loss, incorrect fate determination, and tag failure), it is an improvement upon the resolution and uncertainty of conductin g tag return and telemetry studies independently (Pollock and Pine 2007).

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15 Thus, we contend that future fisheries research should be less directed at estimating CR mortality where estimates exist under a range of environmental conditions for well studied sp ecies (examples in Cooke and Suski 2005). Instead, efforts should shift toward measuring F cr which could be compared to F h to understand whether F cr could be a significant component of total fishing mortality. Using this information, biologists could ex plore the population level effects of both voluntary and regulatory release of fish. Managers can then incorporate this information into comprehensive management plans and future data collection needs to further reduce uncertainty in understanding stock s tatus.

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16 CHAPTER 2 FISHING MORTALITY DUE TO HARVEST IN FLORIDA LAKES Introduction Fisheries m anagement plans for individual freshwater fisheries are uncommon because most state agencies lack the funding to conduct stock assessments for individual fisheries within a region, and relatively few recreational fisheries are of such singular importance that they provide strong sociopolitical or economic motives for active management (Pereira and Hansen 2003). Consequently, fisheries management plans are often app lied across broad spatial regions (Pereira and Hansen 2003). This is particularly true for lake rich landscapes such as Florida, which has over 8,000 named lakes. Therefore, the Florida Fish and Wildlife Conservation Commission (FWC) has focused fisherie s research on large (> 405 ha) high priority water bodies that receive the most fishing effort to make management decisions for large spatial areas. As a result, many lakes are sampled infrequently, or not at all, and it is unknown whether regional regula tions are appropriate for desired fishery outcomes (e.g., trophy fish production, high catch rates) on individual lakes. Thus, it is beneficial to explore economical ways to evaluate the efficacy of management strategies across broad spatial scales. Flori economy (Smithwick Associates 2012), the majority of which is dedicated to 16 million anglers days spent targeting black bass Micropterus spp. (USFWS 2011). A voluntary catch and rel ease ethic has increas ed through the 1990s and 2000 s for largemouth bass M. salmoides anglers (Myers et al. 2008), and this has caused fishing mortality rates to decline through time although rates remain high for some sizes (Allen et al. 2008). Henry ( 2003) found that even when overall fishing mortality was low,

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17 exploitation of the largest fish still exceeded 30%. Therefore, even when exploitation rates are low enough that traditional recruitment overfishing is not of concern, fishing mortality could s till alter the population size structure and substantially reduce the number of trophy fish. In recent years, anglers have expressed a greater interest in catching trophy size fish (Chen et al. 2003; Margenau and Petchenik 2004). This has strong economic implications in Florida where trophy fisheries are important, and thus, there is a need to evaluate how fishing mortality rates vary with fish size. The objective of this study was to estimate fishing mortality due to harvest for largemouth bass in a larg e number of lakes. Additionally I was interested in evaluating whether fishing mortality varied between lake and fish size groups. The results of this study will provide critical information needed to identify management strategies (e.g., harvest restric tions) that could improve recreational fisheries which are an important economic aspect of fisheries in Florida Methods Sampling I employed a passive tag reward study spread over a large number of lakes managed with a 356 mm total length (TL) minimum size limit in central Florida (Figure 2 1) to measure fishing mortality due to harvest. The lakes within this region potentially represented a wide range of exploitation rates, trophic levels, fish abundance, and recruitment levels. The study design did not provide e xploitation estimates on any specific lake due to the low number of tagged fish per lake, but was intended to provide an estimate of the average level of fishing mortality for the overall management region. Florida bass were tagged in the fall of 2009 and 2010 on 30 lakes as part of the FWC long term monitoring (LTM) program (Bonvechio et al. 2009). Lakes within the LTM

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18 program are generally large (4,314 ha on average) high priority fishing lakes deemed significant fisheries at the state wide level. To e valuate whether fishing mortality varied with lake size, Florida bass were also tagged in the fall of 2010 at 29 additional small lakes (< 405 ha) that are not part of the FWC LTM program such that in 2010 there were fish tagged in 59 lakes in total All Florida bass collected were measured to the nearest millimeter TL, and a subsample was tagged with plastic tipped dart tags (PDAT, Hallprint) and released in the same area where they were captured. The PDAT tags were 124 mm in length, with a barb length of 18.5 mm and have an outside diameter of 4.0 mm (Figure 2 2) To evaluate whether fishing mortality varied with fish size, I attempted to tag a minimum of 200 fish per two size groups (350 500 mm TL and > 500 mm TL). Because large fish are relatively ra re, fish over 500 mm TL were tagged at a higher proportion than the smaller size group to obtain an adequate sample size for estimating fishing mortality for both size groups. To insure some spatial distribution of fish within each lake, a maximum of two total fish were tagged per transect during LTM on large lakes, and all fish were tagged immediately after capture during sampling on small lakes. The entire perimeter of the lake was generally sampled on small lakes. Additional information on the standar dized protocol and sampling design of the LTM lakes can be found in Bonvechio et al. (2009). The passive tagging approach required several assumptions that are common to tagging models. These include 1) the tagged sample is representative of the target po pulation, 2) the fate of each fish is independent, and 3) all tagged fish within a cohort have the same annual survival (adapted from Pollock et al. 2001). To ensure the

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19 tagged fish were a representative sample of the target population, the number of fish tagged at each lake was based on lake size, so that the number of tagged fish in each lake was roughly proportional to the fish population size (Table 2 1). To ensure all tagged fish had the same possibility of survival due to fishing mortality, all lake s had the same harvest regulation. Additional assumptions of the passive tagging approach are 1) all fish with high reward tags caught by anglers are reported, and 2) tag loss and tagging mortality are minimal. All fish were tagged with either a $5 or $ 200 reward amount. The relative return rates were used to estimate the reporting rate for $5, based on the assumption that all $200 tags are reported by anglers (Nichols et al. 1991; Taylor et al. 2006). The variable reward amount for tag returns were use d to estimate the reporting rate of $5 tags by assuming that all $200 tags are reported by anglers. This assumption is reasonable based on previous work by Nichols et al. (1991), Taylor et al. (2006), and Meyer et al. (2012). Tags had printed instruction s indicating the reward amount and how to report catches of tagged fish (Figure 2 2). Tagging mortality is generally very low for Florida bass tagged with dart tags in the fall (Henry 2003). I estimated short term tagging mortality by placing tagged fish in mesh cages for 72 hours in four private lakes. Tag loss was determined by releasing fish in four private lakes that were double tagged with dart tags and implanted with internal passive integrated transponders that have been shown to have a 100% reten tion rate (Harvey and Campbell 1989; Hangsleben et al. 2012). Recapture trips occurred in conjunction with another study (Hangsleben et al. 2012) where sampling occurred two times in fall (early December) of 2009, spring (February March), and summer (June ) and three times in fall of 2010 and

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20 spring of 2011. Tag loss was estimated independently of the mortality model as the expected proportion of tag loss in a binomial distribution given the number of fish recaptured. Press releases and signs were used to inform the public about the study (Pollock et al. 2001). Signs were posted at fishing access points around each lake and at local bait and tackle shops, but the dollar amount of the reward s were not specified on signage (Figure 2 3). Anglers were told to Return Hotline. Information collected from anglers included date and location of catch, and fate of the fish (i.e., harvested or released). Model Structure and Analysis s used to estimate fishing mortality within a Bayesian framework. This model was developed from an instantaneous rates formulation of the Brownie tag return model (Brownie et al. 1985; Hoenig et al. 1998) and improved upon past tag return studies by allo wing for catch and release of fish as well as harvest. This was accomplished by separating deaths due harvest and the of low reward tags ($5) returned E[R Hij ] from fish t agged and released in year i and harvested (H) in year j was (2 1) where (2 2)

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21 in which N i is the number of fish tagged in year i the subscript v referring to the tags that survived ( S ), j refers to the year a tag was returned R Hij is the number of tags reported as ha rvested, is the probability of a tagged fish being harvested and reported by an angler, is t he estimated reporting rate for low reward tags, S j is the annual survival rate, F H is the instantaneous rate of fishing mortality for tags of fish harves ted, represents the instantaneous fishing mortality for tags of fish caught and released, and M is the instantaneous natural mortality rate. The expected number of standard tag returns (from fish tagged and released in year i and caugh t and released without a tag in year j was (2 3) where (2 4) where is the probability of a tagged fish being caught, released and reported by an because of an assumed 100% reporting rate. Regional, lake and fish size capture and fishing mortality rates were estimated from their respective data sets with the same model structure. The multinomial likelihood function ( L tag ) of fish tagged in year i and subsequently harvested or caught and r eleased ( R Hij and R CRij respectively) follows Hoenig et al. (1998) and is

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22 (2 5) Equation 2 5 can be simply described as the number of ways the observed events can occur, multiplied by the probabilities of the events. The probabilities within this li kelihood are determined by the parameter values within the model, specifically, F H and Unless stated specifically, all prior distribution s used in the model were uninform ative uniform distribution s (McCarthy 2007) Posterior distributions of th e model parameters were sampled using OpenBUGS (http://www.openbugs.info/w/), a free software program for implementing Bayesian analyses. The Bayesian approach was chosen because it has been shown to be a statistically robust way of integrating multiple d ata sources, and it can consider prior information about a problem thus improving statistical inference (Walters and Martell 2004; Kurota et al. 2009). Additional derived parameters were also calculated from estimated parameters. The instantaneous catch and release fishing mortality rate F CR is then explained by: (2 6) To account for catch and release mortality, total F was adjusted upward ( using the following equation (Jiang et al. 2007): (2 7 ) where CR is t he average discard mortality rate of caught and released fish, and is the instantaneous capture rate of fish that are caught and released. Catch and release mortality was estimated using an uninformed binomial distribution and telemetry data from Chapter 3 as the total number of deaths due to catch and release (n= 5) divided by the total number released (n = 67) within the regional tagging model. The total capture rate ( F o ) within the fishery was calculated as the sum probability of being harvest ed ( F H )

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23 and the probability of being caught and released ( ). Deviance information criterion (DIC) was u sed to evaluate the likelihood of different models that incorporate fish and lake size groups (Spiegelhalter et al. 2002). Model assumptions fol l independent tag return model (i.e., released vs. harvested), 2) all tagged fish were fully recruited to the fishery, 3 ) M was constant over years, a nd 4 ) fates of tagged fish are independent. For a full discussion of model assumptions and potential biases inherent in high reward multiyear tagging studies see Pollock et al. (2001). Within this two year study, information about natural mortality was only obtained from the second year of tag returns as the ratio of tag returns between the first and even over long term (e.g., 20 years) tagging studies (Pollock et al. 20 04). To overcome this issue, an alternative estimate M was considered using an informative prior from a combined telemetry tag return study. The informative prior provided an alternate estimate of natural mortality, because the estimate from the passive tagging study resulted from low sample size and had low precision. Results A total of 497 Florida bass were tagged in the fall (October through December) of 2). During the fall of 20 10 Florida bass were collected and tagged from 30 LTM lakes (N=561) (Table 2 3) and 29 small lakes (N=247) within the same regulation zone (Table 2 4). A total of 247 tags (18%) with a total reward cost of $31,850 were reported for fish caught

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24 and release d or harvested by anglers across all lakes sampled over the two years of the study (Table 2 5). After the completion of the study an additional 66 tags valued at $10,470 were reported as caught or harvested as of April 2013. Tag loss and tagging mortalit y estimates were minimal. Within one year of release, 91 of 195 double tagged fish released into four private lakes were recaptured at least once after initial release. Seven recaptured fish lost their dart style tag. Thus, estimated annual instantaneou s tag loss was 0.09 (SD = 0.032). No deaths were observed among 127 double tagged fish placed within cages during the 72 h holding period, and thus, short term tagging mortality was assumed to be nil. A comparison of uninformed ( M ~ uniform, a = 0, and b = 1) and informed ( M ~ mortality estimates (Figure 2 4) of 2 and a slight compression of the residuals indicating a slightly better fit using uninformative priors. Natural mortality estimates ranged from 0.77 ( 95% credible intervals [CI] = 0.48 0.98 ) using an uninformative prior to 0.42 ( 95% CI = 0.32 0.52 ) with an informative prior estimated from Chapter 3 of this study (Figure 2 4). Thus, the M estimate was higher for the uninformed estimate, but this estimate a lso contained substantial uncertainty (Figure 2 4). However, which M estimate was used had relatively minor influence on fishing mortality rates. Annual m ean estimates of fishing mortality ( F H ) ranged from 0.08 ( 95% CI = 0.06 0.11 ) to 0.10 ( 95% CI = 0.08 0.13 ) depending on whether M was informed, and values exhibited extensive overlap of the credible intervals (Figure 2 5). A ll estimates of fishing mor tality described below utilized the informed M estimate as I

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25 believed the informed model contained better information about M based on results from Chapter 3. Annual mean capture rate F o across both years was 0.30 ( 95% CI = 0.25 0.36 ) with a directed fi shing mortality rate F H of 0.10 ( 95% CI = 0.08 0.13 ), and estimates were similar between years (Table 2 6). The average instantaneous catch and release fishing mortality ( F CR ) was 0.02, with voluntary release rates as high as 72% to 84%. This means tha t F CR ranged from 18% to 22% of the total adjusted fishing mortality estimate. All mortality and capture rates were estimated with a reporting rate of 0.55 (SD = 0.07) for low reward tags and a of 0.09 (SD = was estimated as a parameter within the tagging model as the ratio between the high and low reward tags. Fishing mortality rates were similar in both years and across fish size and lake size groups. Alt hough the mean capture rate estimate was slightly higher for small rather than large lakes, there was substantial overlap of the 95% credible intervals of both fishing mortality and capture rates among different lake and fish size groups (Figure 2 6). The best fit model using DIC selected the null model that did not incorporate fish size information in estimating mortality values (Table 2 7). The best fit model comparing lake size information found the model utilizing size information to be a slightly bet ter fit model but with an additional 2 parameters needed, parsimony would suggest the null model to more appropriate (Table 2 7) These results suggest that directed fishing mortality rate s w ere similar for both fish and lake size groups Discussion This is the first study to measure a regional capture and fishing mortality rate for a recreational fish across a large number of lakes. Compared to individual lake studies,

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26 the annual fishing mortality rates from harvest from this study were below average fo r largemouth bass in the United States but comparable to other estimates from Florida lakes. Two reviews of largemouth bass mortality rates showed annual exploitation rates to range from 7 72% (Allen et al. 1998; Allen et al. 2008), with estimates from ce ntral Florida ranging from 11 17% (Renfro et al. 1999; Henry 2003). However, the lower rates I obtained of about 0.08 for F H which is equivalent to an exploitation rate (u) of 0.07 ( ; Ricker 1975 ) were expected with the reported downward trend i n largemouth bass exploitation as voluntary release rates increase (Myers et al. 2008; Allen et al. 2008). The estimated average capture rate of 0.30 between both years was either comparable or lower than other studies. Henry (2003) found a maximum retur n rate of 33% in Rodman Reservoir, Florida, compared to Driscoll et al. (2007) who found that 62% of the tagged largemouth bass population was caught annually at Sam Rayburn Reservoir, Texas. Sam Rayburn reservoir is a popular fishery with relatively few alternate fishing sites, and thus it is not unexpected that the capture rate would be higher there than the regional average in central Florida because there are thousands of potential fishing sites in central Florida. Overall, my estimates of capture and fishing mortality rates were not strongly dissimilar to values in the literature. I found no difference in exploitation with fish size. Henry (2003) found that even mm TL) was almost double that of quality (300 379 mm TL) and preferred (380 509 mm T L) sized fish. However, similar to the findings of Driscoll et al. (2007), my estimates comparing size selective fishing mortality revealed no differences. Past studies on largemouth bass fisheries have found angling can influence the size structure of a

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27 population even when there is preference for catch and release fishing (Hayes et al. 1995; Carlson and Isermann 2010). I did not find higher exploitation rates for large fish, but the cumulative impacts of fishing mortality as fish grow could influence t he number of fish reaching large sizes (Dotson et al. 2013). I also found no difference in fishing mortality with lake size. Thus, tagging studies conducted in combination with long term monitoring programs that focus on ram) could provide reasonable estimates of exploitation across broad spatial regions that include both small and large lakes. This provides a cost effective sampling design to evaluate the efficacy of regional and/or state wide harvest regulations. To my knowledge, there are no previous studies that directly compare capture or exploitation rates as a function of lake size. Further study should evaluate whether this relationship is maintained through time and other geographic locations for smaller lakes. The cost associated with a sampling design could be further improved with a better understanding of the high reward value necessary to achieve 100% reporting rate. The high reward value was established based on the seminal study by Nichols et al. (1991) t hat estimated the response rate of mallard duck Anas playrhynchus hunters and a study of common snook Centropomus undecimalis anglers in Florida by Taylor et al. (2001). Both these studies found $100 to be sufficient to elicit 100% reporting rate. The hi gh reward value utilized in this study adjusted their estimates for inflation and rounded up to insure the reporting rate assumptions were met but this may or may not have been needed. A recent study conducted by the Idaho Department of Fish and Game fou nd tag values ranging from $50 $100 was enough to elicit over 96% tag

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28 reporting rate for largemouth bass anglers (Meyer et al. 2012). Additionally, in Chapter 3 I utilized both $200 and $100 tags and found the $100 tags were enough to elicit a 97% report ing rate. Considering this new information, future studies should evaluate the reward amount needed in Florida to ensure high dollar reward values are high enough but not too high, potentially saving costs. Tagging fish in a large number of lakes had ben efits in terms of analysis and management implications. From an analysis perspective, the wider dispersal range ensures a greater likelihood of tag return independence. With only a few tags placed in any individual lake, anglers are less likely to catch and hold on to multiple low reward tags before reporting them once a high reward tag is caught and thereby inflating the reporting rate. I believe this assumption was met with no more than two tags reported by any individual angler. There were only two o ccasions when anglers reported multiple tag returns on the same day and both times the tag values were identical (one angler reported two $5 tags and the other reported two $200 tags). From a management perspective, there is also a reduced chance of artif icially increasing angler effort via fishers fishing for profitable tags (Pollock et al. 2001). If management objectives are to monitor and/or measure fishing mortality, dispersing high reward tags across a large geographic range and multiple lakes is unl ikely to attract fishing effort on any individual system. Thus, the regional estimates of fishing mortality were robust to issues of independence among fish and problems with attracting fishing effort with high rewards. The drawback of a regional mortalit y estimate is that the estimate may not represent any one lake. This means that setting harvest regulations based on the regional values may not be optimal for any particular water body. However, obtaining

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29 mortality rates for even a moderate number of in dividual lakes would be costly, and harvest regulations are usually set for a region rather than each individual lake. Thus, obtaining a regional estimate of mortality is insightful for management on the scale where management is typically applied. The r egional study such as this provides little information on the range of fishing mortality rates among lakes, and perhaps fishery managers should combine regional estimates like this study with limited individual lakes to obtain some information at both spat ial scales. My estimate of natural mortality from the passive tags was possibly biased high but also had substantial uncertainty Past reviews of largemouth bass natural mortality found mean instantaneous rates to vary from 0.46 to 0.55 (Beamesderfer an d North 1995; Allen et al. 2008). While the credible intervals around my M values contained these rates, t he estimate from the uninformed model was on average 50% larger and exhibited a high level of uncertainty (95% CI = 0.48 0.98). This uncertainty c an occur in the model through the difficulty of allocating fates for the large number of fish never seen (i.e., fish are alive and never seen or die naturally and are never seen ) Because natural mortality is informed from the ratio of tagged cohorts caug ht in subsequent years, a two year tagging study has limited abilities to parse out the fate of unseen fish (Pollock et al. 2004). Although the estimates of natural mortality were within the predicted range based on latitude (Beamesderfer and North 1995), both Waters et al (2005) and the combined telemetry tagging model (Chapter 3 of this study) found lower natural mortality rates than would be expected based on their regional temperatures and locations. Future studies should consider either multi year t agging studies or telemetry methods (Chapter 3) to estimate M.

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30 Lack of information regarding largemouth bass fisheries is a consistent problem in evaluating management regulations (Wilde 1997). Carlson and Isermann (2010) noted that lake rich states like Minnesota often lack fishery data due to logistics and budgetary constraints that only allow individual studies to be conducted on large high priority lakes. Estimates of fishing mortality for many largemouth bass populations are lacking. This issue has led to poor understanding of how fishing is impacting most largemouth bass populations throughout the United States. This leaves fishery managers with a gap in understanding the relationship between fishing mortality and its impact on population dynamics Study methods such as these will help reduce this gap

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31 Table 2 1. Lake size categories (ha) and target tagging numbers for regional fishing mortality study.

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32 Table 2 2. Number of Florida bass tagged within large lakes (>405 ha) from October through December of 2009. Total numbers of tagged fish for each lake are separated by fish size group (mm TL, total length) and tag value ($US).

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33 Table 2 3. Number of Florida bass tagged within large lakes (> 405 ha) from October through December of 2010. Total numbers of tagged fish for each lake are separated by fish size group (mm TL, total length) and tag value ($US).

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34 Table 2 4. Number of Florida bass tagged wi thin small lakes (< 405 ha) from October through December of 2010. Total numbers of tagged fish for each lake are separated by fish size group (mm TL, total length) and tag value ($US).

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35 Table 2 5. Number of Florida bass tagged and reported caught fro m October 2009 through September 2011 throughout lakes in central Florida. Returns categorized by the release status of the fish (i.e., harvested or released).

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36 Table 2 6. Mean annual instantaneous capture ( F o ) and mortality rates (total fishing [ ], harvest [ F H ], and catch and release [ F CR ]) of Florida bass tagged in central Florida between October 2009 through September 2011. Tags were dispersed among 30 large (> 405 ha) lakes in 2009 and a total of 59 lakes (30 large and 29 small) in 2 010. The standard deviation (SD) and 95% credible intervals are shown. Parameter estimates accounted for non reporting of caught fish, tag mortality and tag loss.

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37 Table 2 7 Deviance information criterion (DIC) values for alternative models fitted to tag return data in OpenBUGS. Mortality was allowed to vary by fish or lake size (s) or held constant (.). The fish size model utilized data that was collected for Florida bass tagged in central Florida from October 2009 through September 2011. Tags were dispersed among 30 large (> 405 ha) lakes in 2009 and a total of 59 lakes (30 large and 29 small) in 2010. The lake size model only utilized data from fish tagged in 2010. Parameters (pD) in the model are the effective number of estimated parameters

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38 Figure 2 1. Map of study area for Chapter 2 Lake sites are highlighted in dark grey. Light grey area indicates the 356 mm minimum length limit Florida bass management regulation zone.

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39 Figure 2 2. Plastic tipped dart tags (PDAT, Hallprint) used to tag Florida bass to determine regional fishing mortality in central Florida. Photo courtesy of David Hall

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40 Figure 2 3. Sign placed at the boat ramp of all lakes sampled for the regional fishing mortality study within central Florida.

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41 Figure 2 4. Comp arison of mean annual natural mortality rates estimated from a tag return model that utilized an uninform ative ( M ~ uninformed, a = 0, and b = 1)) and inform ative ( M ~ normal, mu = 0.37, and SD = 0.053) prior distribution for the natural mortality rate. T he informative prior distribution for M was obtained from combined tag return telemetry data on Lake Santa Fe, Florida. Black bars indicate 95% credible intervals.

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42 Figure 2 5. Comparison of mean annual capture F o and fishing mortality rates F H estima ted from a tagging model that utilized an uninform ative ( M ~ uninformed, a = 0, and b = 1) and inform ative ( M ~ normal, mu = 0.37, and SD = 0.053) prior on natural mortality. The informative prior distribution for M was obtained from combined tag return t elemetry data on Lake Santa Fe, Florida. Black bars indicate 95% credible intervals.

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43 Figure 2 6. Comparison of mean annual capture F o and fishing mortality rates F H of Florida bass by lake (left panels) and fish size (right panels) groups. Rates e stimated from tag return data within 30 large lakes and 30 small lakes throughout central Florida from October 2010 through September 2011. Bar represent 95% credible intervals.

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44 CHAPTER 3 COMPONENTS OF TOTAL MORTALITY WITHIN A HIGH RELEASE RECREATIONAL FISHERY Introduction Mortality resulting from recreational fishing may occur through directed harvest or mortality of fish caught and released by non tournament or tournament anglers. For many recreational fisheries, high release rates occur primarily du e to regulations, but release rates have also increased to due voluntary release of fish by anglers (Quinn 1996; Myers et al. 2008). Educational outreach by state and federal agencies along with an increased emphasis on catch and release by the outdoor me dia has likely influenced angler behavior and increased voluntary release of fish that are legal to harvest (Quinn 1996). Given that stringent size limits and voluntary releases are increasingly common, reliable information about all components of fish mo rtality is needed for informed management of recreational fisheries (Kerns et al. 2012). A combined telemetry and tag return model is an effective technique for obtaining estimates of F and M In this framework, a passive tagging study is primarily used to estimate F and active tags are used to estimate M (Pollock et al. 2004; Bacheler et al. 2009). Information about F is primarily obtained from returns of high reward tags, whereas information about M is primarily from monitoring movements of telemetere d fish, where natural mortality is indicated when fish cease movement (Pollock et al. 2004). Via simulation, Pollock et al. (2004) suggested that the combined methods utilize advantages of both approaches and provide more precise estimates of F and M than either method individually. Further, use of high reward tags in telemetered fish can allow estimation of catch and release mortality, such that mortality of fish can be

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45 measured in the days following catch by an angler (Kerns et al. 2012). This approach also allows for precise and relatively unbiased reporting and mortality rates to be estimated over shorter time intervals (relative to multi period tag return studies that only provide annual information). While this method requires extensive effort, it allows for direct differentiation between sources of mortality, provides information on the seasonality of mortality, imparts data in a timely manner, and therefore, allows managers to develop informed management approaches that consider all forms of morta lity influencing fish stocks. The objective of this study was to estimate components of total mortality including natural mortality, and total fishing mortality including harvest, catch and release (discard deaths) and tournaments (discard deaths) for a si ngle largemouth bass fishery. As a result of this study, alternative harvest regulations (e.g., permit requirements for harvest of large fish, maximum size limits, or effort restrictions) may be identified to maintain high quality fisheries. This study w ill also serve as an example for designing new methodologies for recreational fisheries where directed harvest is only one component of the fishing related mortality Methods Study Area Lake Santa Fe (Figure 3 1) is a 2,011 ha mesotrophic lake that is the headwaters of the Santa Fe River (LAKEWATCH 2009). The lake has a mean depth of 5.2 m, a maximum depth of 9.1 m, and its littoral edge is dominated by maiden cane Panicum hemitomon and bald cypress trees Taxodium distichum The Florida bass fishery is ch aracterized by being average to above average for its region in terms of directed

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46 angler effort (5.81 angler hours/ha/100 days), fish harvested (0.79 fish/ha/100 days), and catch success (0.45 fish/angler hour; FWC 2009). The lake experiences a year round evening tournament during the week and periodic weekend tournaments. Sampling Fish were collected via angling and boat electrofishing in the fall (October) of 2010 and 2011 and boat electrofishing in the spring (March) of 2012. Electrofishing covered th e entire shore line and occurred within 50 m of the vegetation line. Angled fish were caught offshore primarily from artificial structures. All fish larger than 350 mm total length were tagged with external variable reward dart tag s (PDAT, Hallprint), a nd a minimum of 75 fish per year were tagged with an internal radio transmitter (Advanced Telemetry Systems, F1835). Dart tags had reward values of high ($200), medium ($100) and low reward ($5) printed on them along with instructions on how to redeem the reward. The radio transmitters had a life e xpectancy of 502 days and weigh ed 14 grams. To insert transmitters, fish were first placed in an inverted position within a v shaped cradle. This cradle was positioned at 45 angle within an aerated tank so th at 3 2). In this position, an approximate 5 cm incision was made along the ventral side of the body cavity. Tags were inserted so that the tag and external anten na were completely inside the body cavity. Two to three stiches were made with monofilament suture to close the incision. Following procedures outlined in Dutka Gianelli et al (2011), cyanoacrylate adhesive was applied to incisions, allowed to dry, and t he n covered with antibiotic ointment. All transmitters and surgery equipment were sterilized

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47 with isopropyl alcohol prior to surgery. After surgery fish were placed in an aerated holding tank (Figure 3 2) and allowed to recover before releasing the fish near its capture location. The telemetry portion of the study was similar to the field study conducted by Thompson et al. (2007). After fish were tagged and released they were tracked biweekly for approximately one year (expected life span of the tag) to determine their fate s All tagged fish were tracked using ATS R410 receivers with a hand held Yagi antenna and locations of each fish were recorded using GPS receivers. R adio transmitters were used to reduce impacts of submersed aquatic plants on the ab ility to identify fish locations. The Thompson et al. (2007) approach was modified so that all telemetered fish were tagged with $200 external reward dart tags. This modification allowed us to verify if a fish was caught and released by an angler. Trans mitters also had contact information, such that fish caught and released but subsequently harvested were distinguishable. Mortality was assumed if fish were repeatedly located in the same position over multiple search events (Hightower et al. 2001) or des ignated as harvested if telemetered fish disappeared over successive months. Fish that disappeared were also confirmed as harvested if anglers reported finding the internal radio transmitter th at carried contact information f or research personnel. Deaths due to catch and release or tournaments were assigned if fish died within 30 days of being captured by an angler. The first month of tracking data was censored to account for any tagging mortality that may otherwise appear as natural mortality I informe d anglers that tagged fish were in the system through press releases and signs (Pollock et al. 2001). Signs were posted at all fishing access points around

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48 the lake and at local bait and tackle shops, but the dollar amount of the reward were not specified Hotline. Information collected from anglers included date and location of the catch, fate of the fish (i.e., harvested or released), and the type of fishing being conducted (i.e ., tournament or non tournament). Analysis The objective of this study was to measure all components of mortality within a Florida bass fishery. The specific goal was to measure the components of mortality as: (3 1) where Z is total instantaneous mortality, M is instantaneous natural mortality, F H is instantaneous fishing mortality due to harvest, F R is instantaneous fishing mortality due to recreational (non tournament) cat ch and release of fish, and F T is instantaneous fishing mortality due to tournament release of fish. Components of catch and release fishing mortality ( F CR ) were subdivided into tournament ( F T ) and recreational catch and release ( F R ) of fish due to potent ial difference in mortality rates between these two types of fisheries (Muoneke and Childress 1994; Wilde 1998) caused by stressors associated with handling and holding of fish during a tournament (summarized by Gilliland and Schramm 2002). A modified vers ion of Pollock et al. (2004) model was used to estimate the components of mortality via a multi tiered tagging program, including an active telemetry and passive tag return model. This model incorporates a passive tagging program following the Jiang et al (2007) model and an extension of the Pollock (1995) and Hightower (2001) telemetry tagging model. In typical tag return studies estimating

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49 reliable F and M estimates are difficult when tag reporting rates are not known with any certainty. The problem arises when unseen natural deaths cannot be separated from unreported harvest. Pollock et al. (2004) resolve this concern by combining telemetry data with a variable reward tagging program to directly observe both fishing and natural deaths that would not be seen independently. A detailed description of the Jiang et al (2007) model is provided in Chapter 2 of this study, and here I describe where differences occurred. Estimated parameters of the model were comparable to Jiang et al. (2007) (i.e., tag rep orting, survival, natural, and fishing mortality were estimated) with the addition of multiple fishing mortality components ( F H F R and F T ) such that '. (3 2) where is the total fis CR is subdivided into catch and release components including the immediate recreational release of fish R T ). The instantaneous capture rate ( ) of fish that are caught and re leased, analogous to the mortality rate of tags due to catch and release as described in Jiang et al. (2007), was also subdivided into recreational ( ) and tournament ( ) release components. Note that the instantaneous fishing mortality rates due to catch and release and tournaments are F R = and F T = Estimates of F R and F T were obtained by asking anglers when a fish was reported as caught and released whether it was caught in a tournament or released immediately after capture. Fates of telemetered fish that died wi thin 30 days of release were

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50 recorded according to release status (i.e., recreational or tournament release mortality). The total capture rate ( F o ) within the fishery was calculated as: (3 3) where harvest ( F H ), recreational ( ), and tournament ( ) release for both telemetry and dart tag only fish. Estimates were corrected for tag loss based on the results from Chapter 2 where n o external dart tag mortality was found and therefore was not incorpor ated. To interpret the telemetry data, Pollock et al. (2004) combined the Pollock (1995) tagging model with the Hightower (2001) modification for telemetry fish. I used the same method with additional mortality components whereby parameter estimation wa s based on the expected numbers of each possible outcome (i.e., alive, harvested, caught and released by a recreational or tournament angler, or dead via natural mortality) for all fish released at period i If a fish was first relocated alive at period j it became a part of a new (virtual) release at period j + 1. The new (virtual) release ( R j ) was the sum of new releases and those found alive from previous periods ( a j ). Then the expected number from release R j first relocated alive at period j + 1 was (3 4) where (3 5) P A being the probability of being alive, F Hj being the instantaneous fishing mortality rate due to harvest, F Rj being the in stantaneous fishing mortality rate of fish that die after being recreationally caught and released, and F Tj being the instantaneous fishing mortality rate of fish that die after being caught in a tournament and released, and M j

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51 being the instantaneous natu ral mortality rate. A detection probability of 1 was assumed for all telemetered fish. The expected number of fish from release R Mj relocated dead due to natural causes at period j + 1 was (3 6) where the probability of natural mortality ( P M ) was (3 7) The expected number of fish from release R Hi that died due to harvest and reported by an angler was given by (3 8) where the probability of harvest ( P H ) was (3 9) The expected number of fish from release R j relocated dead after being caught and released recreationally was given by (3 10) where the probability of release ( P R ) wa s (3 11) The expected number of fish from R j relocated alive after being caught and released recreationally was given by (3 12) where the probability of surviving release ( P R ) was

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52 (3 13) The expected n umber of fish from release R j relocated dead or alive after being caught and released from a tournament followed the same format as recreationally caught and released fish. Some of the fish with transmitters were caught and released, with the external tag removed, and subsequently died in the lake. For these fish there was uncertainty whether the fish died of natural mortality or from non harvest fishing sources (i.e., catch and release or released after tournaments). This secondary type of information h as not been gained from past tagging studies but is important as it helps inform both the total mortality and fishing mortality estimates by allowing additional mortality to be detected after the initial release and survival of tagged fish by anglers. The model does this by pooling together fates that can no longer be distinguished such that the expected number of fish that survived initial capture and release by anglers R NHi that later died due to non harvest sources of mortality therefore becomes (3 14) where the probability of post release non harvest mortality ( P NH ) was (3 15) These expressions were expanded for the probability of fish from release R i found alive or dead on later search occasions (Hightower et al. 2001). The number of fish tagged and fates determined in period j again followed a multinomial distribution similar to the tag return model from Chapter 2 and the likelihood would therefore be

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53 (3 16) where the number of individuals within different fates include the number of individuals that survived ( a ), or died due natural causes ( m ), harvest ( h ), catch and release ( r ), or tournament release ( t ) Individual relocation histories were transformed into a full m array (Burnham et al. 1987) and were used to summarize the probab ilities of fish falling into each of the model categories for each release. The summary table was used within OpenBUGS (http://www.openbugs.info/w) to estimate model parameters from the multinomial likelihood. Because the two likelihoods obtained from th e tag return model ( L tag ) and the telemetry model ( L tel ) are independent, the combined likelihood was therefore the product of the two: (3 17) Deviance information criterion (DIC) was utilized to evaluate the likelihoo d of different models (Spiegelhalter et al. 2002). The global model allowed natural and fishing

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54 mortality rates to vary by occasion. Reduced models assumed rates remained constant over search occasion, varied by year, quarter, or both year and quarter. R esults Between the fall of 2010 and the spring of 2012, 345 Florida bass were tagged with passive external dart tags at Lake Santa Fe (Table 3 1). An additional 181 fish were also tagged with a combination of external dart tags and internal radio transmit ters. The telemetered fish were captured during three sampling events : October of 2010 (n = 82), October of 2011 (n = 79), and March of 2012 (n = 20). Due to premature transmitter failure, the 2010 cohorts were tracked through July of the following year. No tag failures were observed among the 2011 and 2012 tagged cohorts. Overall, 247 recoveries (47%) were reported by anglers. Tag reporting rates varied from 0.45 (SD = 0.06) for $5 tags to 0.97 (SD = 0.03) for $100 tags. Mortality and survival statu s classifications were possible for 96% of all telemetered fish (Table 3 2). Between the two years of telemetry data, 93 telemetered fish were caught at least once by anglers. Of those, 48 were recreationally released and 19 were released from tournament s. In total across the two years, 34 fish died due to natural mortality, 40 died from harvest, three fish died after release from tournaments and two died after recreational catch and release, and 13 fish died after initially surviving a catch and release event (non harvest, Table 3 2). The non harvest mortalities were fish that were caught once and later died in the lake, giving us uncertainty into their mortality source (description of equation 3 14). A total of eight telemetered fish (4%) were exclude d from analysis. Among these, three fish were

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55 removed due to mortality or lost tag within the first month, and five fish never encountered or reported by anglers after the initial release. The tag return model comparing tag types estimated average annua l F H at 0.71 ( 95% CI = 0.18 1.52 ) across the two years of the study compared to 0.49 obtained by telemetry model ( 95% CI = 0.36 0.65 ; Figure 3 3 ) The combined model had the greatest amount of precision with 95% CI ranging from 0.29 to 0.50 and an aver age F H value of 0.39. Similar in trends in in values and precision between the models were found for F R F T and M values (Figure 3 3). The best model fitted to the combined telemetry tag return data provided quarterly estimates of all mortality component s (Table 3 3). Natural mortality estimates varied seasonally with increases in the early to mid summer in both years (Figure 3 4 ). Fishing mortality was also seasonal with highest values in spring and early summer (Figure 3 4 ). Fishing mortality due to recreational non tournament and tournament release followed a similar pattern but sustained mortality through the summer months (Figure 3. 4 ). A nnual mortality rates are typically used for management, and thus I estimated annual rates in both years. Both M and F H were higher in 2011 2012 than in the first year, with F H increasing from 0.17 in the first year to 0.6 0 in the second (Table 3 4). The average M and F H across both years was 0.37 and 0.39, indicating that directed fishing mortality was similar in magnitude to natural mortality on average. Discard mortality sources ( F R and F T ) were low relative to F H and M but still combined to average about 0.10 across both years (Table 3 4) and accounted for 21% of total fishing mortality (Table 3 4). Mean annual estimates of F T were consistently higher than

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56 F R (Table 3 4) even though considerably more fish were released from non tournament anglers than tournament anglers (Table 3 3 and Table 3 4), indicating the impact of tournaments was higher than catch and release. The reason for this is the marked difference in release mortality. The estimated mortality due to recreation catch and release ( R ) was 6% (SD = 0.03), whereas the mortality of fish caught and released in tournaments ( T ) was over three times higher at 20% (SD = 0.09). Fish capture was much higher in year 2 than in year 1. The instantaneous capture rate ( F o ) was 0.85 in 2010 201 1, but this increased to 1.83 in 2011 2012 (Table 3 4). This means that the proportion of Florida bass caught by anglers due to all fishing (i.e., 1 e Fo ) was 0.57 in 2010 2011 and 0.84 in 2011 2012. Thus, anglers caught a substantial portion of fish at Lake Santa Fe in both years. Discussion Directed fishing mortality F H at Lake Santa Fe was substantially higher than averages compared to a recent review by Allen et al. (2008) and higher than the regional results reported in Chapter 2. This indicates tha t harvest could be a serious constraint to the number of large fish at Lake Santa Fe, and potentially in other systems. These higher rates coincided with a drought that brought water levels down to the lowest Lake Santa Fe has experienced in 10 years. Th e drought ended with tropical storm Debby that occurred over north Florida in late June 2012. Prior to this storm event, access to surrounding lakes was highly limited, with three popular surrounding Orange 12,700 acres) were completely inaccessible to anglers. Thus, angler effort could have been relatively high at Lake Santa Fe during the study years, possibly resulting in high capture and fishing

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57 mortality rates. Recent creel survey data suggest t hat during the peak of the drought, the fishery experienced a 150% increase in angler effort from past estimates when water levels were almost 1 m higher (FWC 2009; FWC 2012). However, it is possible that the increase in angler effort was also compounded with the existence of the high rewards tags dispersed in this study attracting angler effort (Pollock et al. 2001; Pine et al. 2003). Although I gave great care to not advertise the reward values within the study, word of mouth and online bass forums lik ely spread this information to anglers. Thus, the fishing mortality estimates could have been inflated by attracting angler effort. However, anglers could have obtained the reward and released the fish. Thus, the relatively high harvest I found at Lake Santa Fe suggests that fishing mortality ( F H ) can be high on some systems, making regulations important if trophy bass are a consideration (Dotson et al. 2013). Clearly there is a need to understand the spatial and temporal extent of fishing mortality for Florida bass populations. The overall increase in F H compared to other lakes also comes from the nature of the data and the study design of this project. Through the use of both dart and telemetry tags I was able to obtain additional information about th e mortality of fish after they were released by anglers and survived. If the transmitter disappeared from the lake (in some cases, confirmed by an angler reporting the transmitter) I was able to designate the fish as harvested. For the telemetry fish, t his increased the total number of fish reported harvested from 25 to 40 fish over the course of the study. If a fish died in the lake after initially surviving a catch and release event, I was able to assign some probability of non harvest mortality (i.e. natural or catch and release mortality). In

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58 traditional high reward tagging studies, the external tag is removed from the fish once it is caught and therefore no information about subsequent captures is obtained. This is the first study to document thi s additional mortality information and is important as it allows a better estimate of total mortality within a fishery. My estimates of M were within expected values compared to averages reported by Allen et al. (2008) and Beamesderfer and North (1995). A llen et al. (2008) found an average M of 0.55 in a review of Florida bass populations, which was included in the 2011 2012 estimate credible intervals but higher than the values found in the first year (95% credible intervals of 0.19 0.42). Waters et al. (2005) conducted a telemetry study on Florida bass within a tropical reservoir and found an estimate of M of 0.31 (SE = 0.122), with similar trends in the seasonality of M Their estimate was included in the credible intervals of M for both years 1 and 2. Like Waters et al. (2005), higher natural mortality in late spring and early summer in this study was most likely influenced by spawning activity that occurs from March through May in Florida populations (Chew 1974). Alternate means of obtaining estima tes of M include predicting M based on life history metrics such as longevity, growth rates, etc (Alverson and Carney 1975; Hoenig 1983; Jensen 1996; Hewitt and Hoenig 2005). Recent estimate s of these parameters for Florida bass ( L = 458 mm, t max = 8, and k = 0.47; FWC 2009) at Lake Santa Fe allow predicted M values to range between 0.35 0.71 and averaging 0.50. These values are within the range of estimates I obtained with telemetry in both years. However, I still had consider able uncertainty in M estimates in both years with credible intervals extending +/ 0.1 to 0.2 in both years. Comparatively, Thompson et al.

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59 (2007) studying striped bass Morone saxatilis using a similar telemetry method found confidence intervals of +/ 0 .02 while Waters et al. (2005) had considerably more uncertainty. Expected 95% confidence intervals for the Waters et al. (2005) study ranged from 0.07 to 0.55 (estimated as CI 2*SE) compared to the overall credible interval average of this study whic h ranged from 0.28 to 0.48. One factor that may explain the differences in uncertainty between these studies may be the level of variation in seasonal M values. The Thompson et al. (2007) study found constant annual M values compared to the Waters et al. (2005) study had M values that varied over smaller biweekly periods. Uncertainty in this study could have also been influenced by tag failure through the loss of sample size if the tag failure was detected. Misclassification of mortality status is a conce rn in many telemetry studies. Fish that are caught and released that subsequently die but are never reported caught are classified as a natural deaths. This misclassification could strongly affect the estimates of F CR when F CR is low, but the expected ch ange in M would be minimal. For example, adding 1 2 fish to the total number of natural deaths experienced within this study would only change the total number of natural deaths by a small percent, whereas adding a few fish to the F CR would change the tot al numbers considerably. Tag failure could have been a concern in this study if there were additional tags failures that were not detected. This non detection would lead to biased estimates of both F and M values. Until now, most field research examinin g the impacts of catch and release fishing have focused on estimating the mortality rate of f ish caught and released (Muoneke and Childress 1994; Bartholomew and Bohnsack 2005). This is the first study to directly measure the fishing mortality rate associ ated with catch and release mortality and

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60 tournament mortality. These results confirm previous modeling studies that showed the total effect of release mortality from either non tournament or tournament anglers to be small compared to other mortality sour ces (Hayes et al. 1995; Kwak and Henry 1995; Allen et al. 2004; Edwards et al. 2004; Driscoll et al. 2007). Even with tournament release mortality as high as 20% for released fish, it only accounted for 12% of the overall fishing mortality and 6% of the t otal mortality for the population. I conclude that despite high rates of voluntary release, total F CR remains only a small proportion of overall F and even smaller proportion of total mortality. Furthermore, F H values can still reach levels that may nega tively impact size structure and the abundance of large fish. Thus, studies like this provide key insights about the components of mortality for fished populations.

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61 Table 3 1. Number of dart tagged Florida bass reported returned by release type (harves t [H], recreational release [R], and tournament release [T]) and tag value within Lake Santa Fe, Florida from November 2010 through October 2012.

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62 Table 3 2. Mortality, release, and survival status of telemetry dart tagged Florida bass Lake Santa Fe, Florida between November 2010 through October 2012. Mortalities for natural deaths (M), harvest (H), released (R), tournament (T), and non harvest (NH) are shown. Status is partitioned into initial survival and first capture and secondary mortality data Non harvest deaths are fish that survived being caught once and released and subsequently died in the lake due to causes unrelated to initial capture. Due to tag removal, partitioning of non harvest mortality components after initial capture (i.e., sep arating natural from catch and release mortality) was not possible. Fates were not determined in August through October of first year of the study due to transmitter failure.

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63 Table 3 3. Deviance information criterion (DIC) values for alternative mode ls fitted to combined tag return telemetry data in OpenBUGS. Sources of mortality (natural [ M ], harvest [ F H ], recreational release [ F R ], and tournament [ F T ]) were allowed to vary by month ( m ), quarter ( q ), year ( y ), or held constant (.). Parameters (pD) in the model are the effective number of estimated parameters.

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64 Table 3 4. Florida bass instantaneous mortality (natural [ M ], harvest [ F H ], recreational release [ F R ], and tournament [ F T ]) estimates from a combined tag return telemetry model. Annual est imates were calculated from the summation of quarterly estimates. Data collected from the November 2010 through October 2012 on Lake Santa Fe, Florida.

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65 Figure 3 1. Map of Lake Santa Fe, Florida. Grey dots represent individual fish locations collecte d during both years, such that individual fish are shown more than once. Dots lying outside of the lake boarder were either found in canals or where transmitters found on land after a natural mortality event.

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66 Figure 3 2. Tagging procedure and setup for Florida bass tagged with plastic dart tipped tags and telemetry transmitters on Lake Santa Fe, Florida in the fall of 2010 and 2011. Photos courtesy of Janice Kerns.

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67 Figure 3 3. Average annual instantaneous natural mortality ( M ) and sources of fishin g mortality ( F H F R and F T ) estimated by a tag return model, a telemetry model and a combined telemetry tag return model. Average rates are across the two years of the study. Estimates of tournament ( T = 20%; SD = 0.09) and catch and release ( R = 6%;SD = 0.03) mortality obtained from telemetry estimates were used within the tag return model. Black bars represent 95% credible intervals.

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68 Figure 3 4 Quarterly mortality rates (solid lines; 95% credible intervals, dashed lines) for Florida bass from November 2010 to October 2012. Instantaneous natural mortality ( M ) and sources of fishing mortality ( F H F R and F T ) were estimated from a combined dart telemetry tagging model.

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69 CHAPTER 4 DEGREE O F TEMPORAL SYNCHRONY BETWEEN FISHING AND NATURAL MORTALITY INFLUENCES MORTALITY ESTIMATES Introduction Mark recapture (capture recapture) models that estimate fish mortality make many assumptions about the tagged and untagged sample populations. Elements of these assumptions have been reviewed in detail (reviews in Pollock et al. 1991; Pine et al. 2003; Miranda and Bettoli 2007; Allen and Hightower 2010). Two assumptions that have not been reviewed are violations to the constant mortality within periods where both natural ( M ) and fishing ( F ) mortality occur continuously. Until recently, very few tag return studies have estimated changes in these rates for anything less than annual periods. With the increasing use of telemetry data, estimating variations at smaller scales have been possible (Hightower et al. 2001; Waters et al. 2005; Thompson et al. 2007; Bacheler et al. 2009). Those studies showed considerable seasonal variation in mortality rates that could affect studies done on an annual scale Tradi tional mark recapture studies estimate annual exploitation as (4 1) where C is the number of fish caught (corrected for non reporting and tag loss) and T is the number of fish tagged in the population corrected for tag loss and immediate mortality due to capture, handing, and tagging If the t otal instantaneous annual mortality ( Z ) is known, fishing mortality is estimated as (4 2)

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70 Tags are dispersed during a single event and then collected over the following year via anglers reporting the capture of marked fis h. During that time, F and M are assumed to be independent and operating concurrently (Miranda and Bettoli 2007). The purpose of this chapter was to investigate how variations or seasonal trends in fishing and natural mortality influence estimates from tag return studies. I explored a number of scenarios using both theoretical and field based seasonal estimates of mortality. The specific interest was to investigate how traditional tag return studies that only collect annual information may be biased if F and M vary seasonally. Information gained from this simulation will help to better inform future tagging studies. Methods The simulation methods for this chapter used either and artificial set of seasonal patterns for mortality or observed seasonal pat terns from recent field studies. The artificial set of seasonal scenarios was constructed where F and M varied independently of each other (Figure 4 1). For each scenario, peak mortalities were adjusted to account for 90% or more of the F or M that could occur in t he year. For instance, scenario 1 had 90% of the fishin g mortality occurring within first quarter of the year. For the variable mortality, as seen in scenario 5 where there is more than one peak, the upper limit of mortality occurs over two pe aks (45% for each peak). For each scenario type, two opposing settings (a and b) were created such that the majority of mortality happens at the beginning or the end of a year. Tagging data were simulated by estimating the number of tag deaths due to harv est ( H p ) over seasonal periods as (4 3)

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71 And number of deaths from natural mortality ( D p ) over seasonal periods as (4 4) where N is the number of tags in the population F p is the instantaneous fishing mortality rate within a seasonal period and M p is the instantaneous natural mortality rate within a seasonal period From this information two values of F where derived. One estimate incorporated the true seasonal variat ions by taking the sum of F p over each period ( F true ) and an estimated F with no seasonal information as (4 5 ) where Ha is the total number of fish harvested annually and T is the total number of tagged fish. Percent bias of Fno seasonal compared to F true was estimated as (4 6 ) This information was then simulated over a wide range of annual F and M values. Similar analysis was then used with known seasonal variations of mortality across multiple species. I reviewed the litera ture to find studies where F and M were monitored at finer detail than annual rates. Only data from a full year were considered for the simulation and comparisons among years were made if multiple years of seasonal data were available. Result s Comparati vely, the magnitude of M had a greater influence on F estimates th a n annual F values (Figure 4 2). The greatest amount of bias occurred when peak M and F occurred at separate times of the year (e.g., scenario 3.a). The primary reason for this is the unob served change in sample size through time due to natural mortality. If a

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72 substantial amount of natural mortality occurs at the extremities of a year (as seen in scenario 3), an individual mortality at the beginning of the year will carry less weight than an individual mortality at the end of the year when sample size of the tagged population has been reduced. The amount of bias in F no seasonal values varied greatly between theoretical model scenarios (Figure 4 2). Biases ranged from as low as <10% over es timate in model 2 where the 90% of the mortality occurred over 3 periods to scenario 3.a where 90% of F and M occurred within one period at opposite ends of the year and had the possibility of > 80% bias over the range of values tested. An overestimate w as possible throughout all scenarios when peak F occurred at the beginning of the year and became under estimated when it occurred near the end. If F was held constant and M was allowed to vary, the relationship was reversed with over estimates occurring when peak M happened at the end of a year (e.g., scenario 9.a). A review of the literature found four telemetry studies (other studies then the one described in Chapter 3) that estimated both annual and seasonal estimates of both F and M (Figure 4 3; H ightower et al. 2001; Waters et al. 2005; Thompson et al. 2007; Bacheler et al. 2009). In Waters et al.(2005), radio telemetered Largemouth Bass Micropterus s almoides in a reservoir in Puerto Rico had consistent fishing mortality and seasonal natural mort ality around spawning seasons. In the first year of the Largemouth Bass study they found total annual F to be 0.56 and natural mortality to be 0.23. Conversely, three multiyear studies found Striped Bass Morone saxatilis (telemetry only) and Red Drum Sc iaenops ocellatus (combined tag return telemetry data) were found to have constant natural mortality and seasonal varia tions in fishing

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73 mortality. Thompson et al. (2007) estimate d that Striped Bass F to range between 0.65 and 0.77 in the two year s of the study with M estimated to be a co nstant 0.1. Hightower et al. ( 2001) estimate d F to range between 0.73 to 0.26 and a constant M of 0.14. Bacheler et al. (Bacheler et al. 2009) estimated annual F of Red Drum to vary from 0.26 to 0.32 within the first two y ears and a constant annual natural mortality of 0.04. Florida Bass Micropterus floridanus as described in Chapter 3 were found to have seasonal variations in both F and M with annual variations to be between 0.24 to 0.72 and 0.29 to 0.46, respectively. Thus, it appears that F and M can be highly seasonal in some cases based on the empirical studies. Species comparisons found that biases could change annually but overall had less than 7% bias across all species examined (Table 4 1). Striped Bass and Fl orida Bass had the greatest amount of variation with a percentage point spread of 11 and 7, respectively between the first and second year of data. Red Drum had the least amount of bias with the least amount of seasonal variation within both mortality so urces Discussion The temporal synchrony of F and M has rarely been quantified and therefore, the number of species examined here are few. Field based estimates are especially rare for natural mortality, even on an annual basis The information we do ha ve suggest s that M can vary periodically due to extreme weather events (Adams et al. 2012), sporadic changes in the environment (e.g., algal blooms; Julliard et al. 2001), or annually due to spawning activity (Waters et al. 2005). There are more estimates of F with seasonal variations known to occur for a variety of fish species (Reed and Davies 1991; Teisl et al. 1993; Muoneke 1994; Pegg et al. 1996; Parsons and Reed 1998;

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74 Hightower et al. 2001; Julliard et al. 2001; Margenau et al. 2003; Isermann et al. 2005; Thompson et al. 2007; Bacheler et al. 2009; Smith et al. 2009). Highly seasonal F can result from a wide range of angler and fish behavior patterns. For instance, depending on a species life history or habitat type increases and decreases in F ha ve been observed during spawning seasons due to differences in vulnerability as fish move in and out of spawning areas (Pegg et al. 1996; Smith et al. 2009). Some seasonal trends in harvest may reflect patterns in total angling effort rather than amount o f effort targeted at specific species alone (Isermann et al. 2005). Changes in F could reflect effort trends influenced by the type of fishing activity desired (e.g., open water vs. ice fishing; Teisl et al. 1993) but changes in capture rates could also allow higher harvest during low angler effort periods (Margenau et al. 2003). Even within similar fisheries, the magnitude of temporal trends can vary by region (Isermann et al. 2005). Some species like crappie Pomoxis spp., have been shown to have a pea k season that last s as little as 2 months with seasonal changes in weather condition possibly influencing the magnitude of peak effort (Reed and Davies 1991). For recreational and commercial coastal cod Gadus morhua fisheries, seasonal trends in catch hav e also been shown to vary by seasonal changes in gear used (Julliard et al. 2001). Although limited seasonal biases of F were found for individual species, large biases were found to be possible under theoretical scenarios. This is especially true for sim ulations under of relatively high levels of M Overall, the species examined here experience d average to low levels of natural mortality Therefore, limited amount of bias uncovered is not unrealistic especially when compared to the theoretical simulatio ns

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75 that had comparable amounts of seasonal variation. For instance, both the black bass species examined had seasonal variations that were similar to scenario 7. Comparatively, the Red Drum and Striped Bass studies had seasonal variations that were close to scenarios 5 and 6. In summary, temporal patterns in M are most likely to influence F estimates if natural mortality is high and concentrated in certain time of year. Species with low M (e.g., below 0.3) are unlikely to have strong biases in F due to seasonal patterns in M To overcome the larger biases exposed in the simulations tagging studies will need to estimate mortality rates at biologically relevant intervals. This is particularly true for estimation of natural mortality, as this data is typi cally unobserved within most tag return studies. For some fisheries this may be easy, as natural mortality rates remain relatively constant over time, but others may be more difficult. If the rates are changing at less than annual intervals, the dispersa l of tags may have to occur at finer periods of time to account for these fluctuations or more intensive telemetry studies will need to be conducted

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76 Table 4 1. Percent biases comparison of fishing mortality values derived from data that utilized true se asonal information ( F true ) or annual harvest information ( F no seasonal ) only Over or under percent bias estimates are signified by plus or minus signs, respectively.

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77 Figure 4 1. Simulated mortality scenarios developed to examine the influence of v ariable or seasonal mortality on annual mortality estimates from mark recapture studies. Fishing (solid) and natural mortality (dashed) are presented as proportions of annual mortality. For each scenario 1 10, a and b represent mirror images such that th e majority of mortality happens at the beginning or end of a year.

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78 Figure 4 2. Perce nt bias for annual estimates of fishing mortality ( F ) when F and natural mortality vary seasonally Each graph (1.a 10.b) corresponds with the model scenarios describe d in Figure 4.1. Over or under estimation are signified by positive or negative numbers, respectively.

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79 Figure 4 3 Seasonal mortality values obtained from the literature where both fish (solid line) and natural (dashed line) where obtained over a one or two year time period. All mortality rates are presented as proportions of annual mortality. The Waters et al. (2005) study contained only one full year of data, all other contained at least two years of complete data.

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80 CHAPTER 5 SYNTHESIS AND FUTURE RESE ARCH The primary results of this study indicate there is a need to evaluate mortality at multiple spatial temporal and biological scales. Clearly the statewide average fishing mortality rates may not accurately represent all aquatic systems (lakes a nd/or rivers) within the region Furthermore, the simulation of seasonal trends in mortality highlighted the need to obtain information at biological relevant temporal scales to obtain unbiased estimates of fishing mortality. However, obtaining F estimat es across a large number of individual aquatic systems is infeasible, and thus a combination of methods is probably the best strategy for evaluating fishing mortality for fish stocks in lake rich landscapes. It would be prudent to combine regional estimat es at p eriodic intervals (e.g., every 5 10 years) combined with site specific estimates at lakes where fishing mortality is suspected to be high. Further, site specific studies could be used where management actions are being considered to increase the nu mber of large fish. Assuming that fishing mortality is not significant based on angling behavior could be a dangerous assumption based on my site specific results. Combining regional and site specific estimates will probably give fishery managers the bes t information for informed use of regulations and improvement of fisheries. The sim ulation exercise indicated the potential for either over or under estimation of estimates when M and F peaked at different times of the year. The magnitude of this bias inc reased drastically with increasing values of natural mortality. I therefore recommend conducting this type of simulation prior to beginning any annual mark recapture study to determine whether estimation at finer time scales is needed With

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81 current limit ed knowledge on how mortality changes seasonally for many fisheries, simulations such as these can be informed by life history information (e.g., seasonal spawning or migration periods) or with seasonal harvest information (e.g., creel surveys). Estimatin g natural mortality is among the most difficult parameters in fish stock assessment. The telemetry estimate of M from Lake Santa Fe was realistic relative to other field and life history based methods in the literature. However, my estimates still contai ned relatively high uncertainty, and larger sample sizes could be needed to improve precision of M with telemetry tags. However, the M estimates provided insight into the seasonality of M and the values were within reported ranges for largemouth bass. F uture studies shoul d consider the telemetry method for estimating M for fish stocks. I recommend use of this method for other species, specifically ones that experience relatively high levels of M and in different geographic areas, as M can vary with lat itude (Beamesderfer and North 1995) Overall, I began this study with the premise that catch and release research needs to move beyond the estimation of release mortality for well studied species and expand into understanding the population impacts occurri ng within these fisheries. This study only begins to reveal some of the complexity of these types of fisheries, and I found that even among fisheries with high rates of release and low rates of release mortality, relatively high rates of fishing mortality are still possible. Furthermore, F H values can still reach levels that may negatively impact abundance of large fish and size structure. Thus, studies like this provide key insights about the components of mortality for fished populations

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82 LIST OF REFE RENCES Adams, A. J., J. E. Hill, B. N. Kurth, and A. B. Barbour. 2012. Effects of a severe cold event on the subtropical, estuarine dependent common snook, centropomus undecimalis Gulf and Caribbean Research 24:13 21. Allen, M. S., and J. E. Hightower. 20 10. Fish population dynamics: Mortality, growth, and recruitment. Pages 43 79 in W. A. Hubert, and M. C. Quist, editors. Inland fisheries management in North America, 3rd edition. American Fisheries Society, Bethesda, Maryland. 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 & Management 3:67 79. Allen, M. S., M. W. Rogers, R. A. Myers, and W. M. Bivin. 2004. Simulated im pacts of tournament associated mortality on largemouth bass fisheries. North American Journal of Fisheries Management 24:1252 1261. Allen, M. S., C. J. Walters, and R. Myers. 2008. Temporal trends in largemouth bass mortality, with fishery implications. No rth American Journal of Fisheries Management 28:418 427. Alverson, D. L., and M. J. Carney. 1975. A graphic review of the growth and decay of population cohorts. ICES Journal of Marine Science. Arlinghaus, R., S. J. Cooke, J. Lyman, D. Policansky, A. Schwa b, C. Suski, S. G. Sutton, and E. B. Thorstad. 2007. Understanding the complexity of catch and release in recreational fishing: An integrative synthesis of global knowledge from historical, ethical, social, and biological perspectives. Reviews in Fisheries Science 15:75 167. Arlinghaus, R., A. Schwab, C. Riepe, and T. Teel. 2012. A primer on anti angling philosophy and its relevance for recreational fisheries in urbanized societies. Fisheries 37:153 164. Bacheler, N. M., J. A. Buckel, J. E. Hightower, L. M. Paramore, and K. H. Pollock. 2009. A combined telemetry tag return approach to estimate fishing and natural mortality rates of an estuarine fish. Canadian Journal of Fisheries and Aquatic Sciences 66:1230. Bartholomew, A., and J. A. Bohnsack. 2005. A revi ew of catch and release angling mortality with implications for no take reserves. Reviews in Fish Biology and Fisheries 15:129 154.

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83 Beamesderfer, R. C. P., and J. A. North. 1995. Growth, natural mortality, and predicted response to fishing for largemouth b ass and smallmouth bass populations in North America. North American Journal of Fisheries Management 15:688 704. Bonvechio, K. I., M. J. Catalano, R. E. Sawyers, and S. Crawford. 2009. Determining electric fishing sample size for monitoring fish communitie s in three Florida lakes. Fisheries Management and Ecology 16:409 412. Brownie, C., D. R. Anderson, K. P. Burnham, and D. S. Robson. 1985. Statistical inference from band recovery data a handbook, 2nd edition. Burnham, K. P., D. R. Anderson, G. C. White, C Brownie, and K. H. Pollock. 1987. Design and analysis methods for fish survival experiments based on release recapture. American Fisheries Society Monograph No. 5, Bethesda, Maryland, USA. Carlson, A. J., and D. A. Isermann. 2010. Mandatory catch and rel ease and maximum length limits for largemouth bass in Minnesota: Is exploitation still a relevant concern? North American Journal of Fisheries Management 30:209 220. Chen, R. J., K. M. Hunt, and R. B. Ditton. 2003. Estimating the economic impacts of a tr ophy largemouth bass fishery: Issues and applications. North American Journal of Fisheries Management 23:835 844. Chew, R. L. 1974. Early life history of the Florida largemouth bass. Florida Game and Freshwater Fish Commission, Federal Aid in Fish Restorat ion, Project F 24 R, Tallahassee, FL. Coggins, L. G., M. J. Catalano, M. S. Allen, W. E. Pine, and C. J. Walters. 2007. Effects of cryptic mortality and the hidden costs of using length limits in fishery management. Fish and Fisheries 8:196 210. Cooke, S. J., and H. L. Schramm. 2007. Catch and release science and its application to conservation and management of recreational fisheries. Fisheries Management and Ecology 14:73 79. Cooke, S. J., and C. D. Suski. 2005. Do we need species specific guidelines for catch and release recreational angling to effectively conserve diverse fishery resources? Biodiversity and Conservation 14:1195 1209. Dotson, J. R., M. S. Allen, J. A. Kerns, and W. F. Pouder. 2013. Utility of restrictive harvest regulations for trophy lar gemouth bass management. North American Journal of Fisheries Management 33:499 507. Driscoll, M. T., J. L. Smith, and R. A. Myers. 2007. Impact of tournaments on the largemouth bass population at Sam Rayburn Reservoir, Texas. North American Journal of Fish eries Management 27:425 433.

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84 Dutka Gianelli, J., R. Taylor, E. Nagid, J. Whittington, and K. Johnson. 2011. Habitat utilization and resource partitioning of apex predators in coastal rivers of southern Florida. Florida Fish and Wildlife Conservation Commis sion, Fish and Wildlife Research Institute, St. Petersburg, FL. Edwards, J. G. P., R. M. Neumann, R. P. Jacobs, and E. B. O'Donnell. 2004. Impacts of small club tournaments on black bass populations in Connecticut and the effects of regulation exemptions. North American Journal of Fisheries Management 24:811 821. FWC. 2009. Long term monitoring of Florida's important freshwater sportfisheries federal aid Wallop Breaux annual performance report. Florida Fish and W ildlife Conservation Commission, Tallahassee, FL. FWC. 2012. Long term monitoring of F Florida Fish and Wildlife Conservation Commission, Federal Aid Wallop Breaux Annual Performance Report, Project FL F 175 R 1 Tallahassee, FL. Gilliland, G., and H. Schramm. 2002. Keeping bass alive: A guidebook for anglers and tournament organizers. BASS/ESPN, Montgomery, Alabama. Hangsleben, M. A., M. S. Allen, and D. C. Gwinn. 2012. Evaluation of electrofishing catch per uni t effort for indexing fish abundance in Florida lakes. Transactions of the American Fisheries Society 142:247 256. Harvey, W. D., and D. L. Campbell. 1989. Technical notes: Retention of passive integrated transponder tags in largemouth bass brood fish. The Progressive Fish Culturist 51:164 166. Hayes, D. B., W. W. Taylor, and H. L. Schramm. 1995. Predicting the biological impact of competitive fishing. North American Journal of Fisheries Management 15:457 472. Henry, K. R. 2003. Evaluation of largemouth bas s exploitation and potential harvest restrictions at Rodman Reservoir, Florida. University of Florida, Gainesville. Hewitt, D. A., and J. M. Hoenig. 2005. Comparison of two approaches for estimating natural mortality based on longevity. Fisheries Bulletin 103:433 437. Hightower, J. E., J. R. Jackson, and K. H. Pollock. 2001. Use of telemetry methods to estimate natural and fishing mortality of striped bass in Lake Gaston, North Carolina. Transactions of the American Fisheries Society 130:557 567. Hoenig, J. M. 1983. Empirical use of longevity data to estimate mortality rates. Fisheries Bulletin 82:898 903.

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85 Hoenig, J. M., N. J. Barrowman, W. S. Hearn, and K. H. Pollock. 1998. Multiyear tagging studies incorporating fishing effort data. Canadian Journal of Fis heries Aquatic Sciences. 55:1466 1476. Isermann, D. A., D. W. Willis, D. O. Lucchesi, and B. G. Blackwell. 2005. Seasonal harvest, exploitation, size selectivity, and catch preferences associated with winter yellow perch anglers on South Dakota lakes. Nort h American Journal of Fisheries Management 25:827 840. Jensen, A. L. 1996. Beverton and Holt life history invariants result from optimal trade off of reproduction and survival. Canadian Journal of Fisheries and Aquatic Sciences 53:820 822. Jiang, H., K. H. Pollock, C. Brownie, J. M. Hoenig, R. J. Latour, B. K. Wells, and J. E. Hightower. 2007. Tag return models allowing for harvest and catch and release: Evidence of environmental and management impacts on striped bass fishing and natural mortality rates. No rth American Journal of Fisheries Management 27:387 396. Julliard, R., N. C. Stenseth, J. Gjster, K. Lekve, J. M. Fromentin, and D. S. Danielssen. 2001. Natural mortality and fishing mortality in a coastal cod population: A release recapture experiment. Ecological Applications 11:540 558. Kerns, J. A., M. S. Allen, and J. E. Harris. 2012. Importance of assessing population level impact of catch and release mortality. Fisheries 37:502 503. Kurota, H., M. K. McAllister, G. L. Lawson, J. I. Nogueira, S. L. H Teo, and B. A. Block. 2009. A sequential Bayesian methodology to estimate movement and exploitation rates using electronic and conventional tag data: Application to Atlantic Bluefin tuna ( thunnus thynnus ). Canadian Journal of Fisheries and Aquatic Scienc es 66:321 342. 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. Margenau, T. L., S. J. Gil bert, and G. R. Hatzenbeler. 2003. Angler catch and harvest of northern pike in northern Wisconsin lakes. North American Journal of Fisheries Management 23:307 312. Margenau, T. L., and J. B. Petchenik. 2004. Social aspects of muskellunge management in Wis consin. North American Journal of Fisheries Management 24:82 93. McCarthy, M. A. 2007. Bayesian methods for ecology. Cambridge University Press.

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86 Meyer, K. A., F. S. Elle, J. A. Lamansky, E. R. J. M. Mamer, and A. E. Butts. 2012. A reward recovery study to estimate tagged fish reporting rates by Idaho anglers. North American Journal of Fisheries Management 32:696 703. Miranda, L. E., and P. W. Bettoli. 2007. Mortality. Pages 229 277 in C. S. Guy, and M. L. Brown, editors. Analysis and interpretation of fre shwater fisheries data. American Fisheries Society, Bethesda, Maryland. Muller, R. G., and R. G. Taylor. 2006. The 2005 stock assessment update of common snook, centropomus undecimalis Florida Marine Research Institute, Fish and Wildlife Conservation Comm ission, St. Petersburg, FL. Muoneke, M. I. 1994. Dynamics of a heavily exploited Texas white bass population. North American Journal of Fisheries Management 14:415 422. Muoneke, M. I., and W. M. Childress. 1994. Hooking mortality: A review for recreational fisheries. Reviews in Fisheries Science 2:123 156. Myers, R., J. Taylor, M. Allen, and T. F. Bonvechio. 2008. Temporal trends in voluntary release of largemouth bass. North American Journal of Fisheries Management 28:428 433. 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 bands of different dollar values. The Journal of Wildlife Management 55:119. Parsons, B. G., and J. R. Reed. 1998. Angler exploitation of bluegi ll and black crappie in four west central M innesota lakes. Minnesota Department of Natural Resources, Section of Fisheries, St. Paul, Minnesota. Pegg, M. A., J. B. Layzer, and P. W. Bettoli. 1996. Angler exploitation of anchor tagged saugers in the lower T ennessee R iver. North American Journal of Fisheries Management 16:218 222. Pereira, D. L., and M. J. Hansen. 2003. A perspective on challenges to recreational fisheries management: Summary of the symposium on active management of recreational fisheries. No rth American Journal of Fisheries Management 23:1276 1282. Pine, W. E., K. H. Pollock, J. E. Hightower, T. J. Kwak, and J. A. Rice. 2003. A review of tagging methods for estimating fish population size and components of mortality. Fisheries 28:10 23. Pol lock, K. H., C. M. Bunck, S. R. Winterstein, and C. L. Chen. 1995. A capture recapture survival analysis model for radio tagged animals. Journal of Applied Statistics 22:661 672.

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87 Pollock, K. H., J. M. Hoenig, W. S. Hearn, and B. Calingaert. 2001. Tag repor ting rate estimation: 1. An evaluation of the high reward tagging method. North American Journal of Fisheries Management 21:521 532. Pollock, K. H., J. M. Hoenig, and C. M. Jones. 1991. Estimation of fishing and natural mortality when a tagging study is co mbined with a creel survey or port sampling. American Fisheries Society Symposium 12:423 434. Pollock, K. H., H. Jiang, and J. E. Hightower. 2004. Combining telemetry and fisheries tagging models to estimate fishing and natural mortality rates. Transaction s of the American Fisheries Society 133:639 648. Pollock, K. H., and W. E. Pine, III. 2007. The design and analysis of field studies to estimate catch and release mortality. Fisheries Management and Ecology 14:123 130. Quinn, S. 1996. Trends in regulatory and voluntary catch and release fishing. Pages 152 162 in R.E. Miranda and D.R. DeVries, editors. Multidimensional approaches to reservoir fisheries management. American Fisheries Society, Chattanooga, TN. Reed, J. R., and W. D. Davies. 1991. Population dy namics of black crappies and white crappies in Weiss Reservoir, A labama: Implications for the implementation of harvest restrictions. North American Journal of Fisheries Management 11:598 603. Renfro, D. J., W. F. Porak, and S. Crawford. 1999. Angler explo itation of largemouth bass determined using variable reward tags in two central Florida lakes. Proceedings of the Annual Conference Southeastern Association of Fish and Wildlife Agencies 51:175 183. Ricker, W. E. 1975. Computation and interpretation of bio logical statistics in fish populations. Fisheries Research Board of Canada Bulletin 191. Smith, W. E., F. S. Scharf, and J. E. Hightower. 2009. Fishing mortality in North C arolina's southern flounder fishery: Direct estimates of instantaneous fishing morta lity from a tag return experiment. Marine and Coastal Fisheries: Dynamics, Management, and Ecosystem Science: 283 299. Smithwick Associates. 2012. Sport fishing in A merica: An economic force of conservation. American Sportfishing Association under a U.S. F ish and Wildlife Service Sport Fish Restoration grant (F12AP00137, VA M 26 R) awarded by the Association of Fish and Wildlife Agencies. Spiegelhalter, D. J., N. G. Best, B. P. Carlin, and A. Van Der Linde. 2002. Bayesian measures of model complexity and fi t. Journal of the Royal Statistical Society: Series B (Statistical Methodology) 64:583 639.

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88 Taylor, R. G., J. A. Whittington, and D. E. Haymans. 2001. Catch and release mortality rates of common snook in Florida. North American Journal of Fisheries Managem ent 21:70 75. Taylor, R. G., J. A. Whittington, W. E. Pine, and K. H. Pollock. 2006. Effect of different reward levels on tag reporting rates and behavior of common snook anglers in southeast Florida. North American Journal of Fisheries Management 26:645 6 51. Teisl, M. F., K. J. Boyle, and O. C. Fenderson. 1993. Angler opinions regarding management options to balance open w ater and ice fishing effort in M aine. North American Journal of Fisheries Management 13:353 359. Thompson, J. S., D. S. Waters, J. A. Ri ce, and J. E. Hightower. 2007. Seasonal natural and fishing mortality of striped bass in a southeastern reservoir. North American Journal of Fisheries Management 27:681 694. USFWS. 2011. National survey of fishing, hunting, and wildlife associated recreati on. U.S. Department of the Interior, Fish and Wildlife Service and U.S. Department of Commerce, Census Bureau. Walters, C. J., and S. J. D. Martell. 2004. Fisheries ecology and management. Princeton University Press. Waters, D. S., R. L. Noble, and J. E. H ightower. 2005. Fishing and natural mortality of adult largemouth bass in a tropical reservoir. Transactions of the American Fisheries Society 134:563 571. Wilde, G. R. 1997. Largemouth bass fishery responses to length limits. Fisheries 22:14 23. Wilde, G. R. 1998. Tournament associated mortality in black bass. Fisheries 23:12 22.

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89 BIOGRAPHICAL SKETCH Janice Kerns was a twin born to Martha and the late Ernest Kerns in the winter of 1978 Growing up, her love of the outdoors was cultivated by spending most summers in summer camp exploring the backwoods of Ohio, Minnesota, and eventually the blue waters off the British Virgin Islands. As an undergraduate she majored in marine s cience and b iology with minors in e n vironmental science and chemistry. After comp leting her degree she went on to become a Peace Corps environmental/agroforestry volunteer in the The Gambia, West Africa. When she returned from her service she obtained her first fisheries position as research technician with the Ohio Departme nt of Natural Resources Fairport Harbor Lake Erie Research Unit This is where she found her true passion for applied management research. It was a field that she could continue her love of the outdoors, help the community around her, and pursue a career o f lifelong learning. Fisheries Research Unit. While there she studied the paddlefish commercial f isheries of Kentucky Lake in rural west Tennessee. She worked closely with commercial fishing community to understand the impact of release mortality had on individual fish and how this knowledge would improve fisheries management decisions After graduati on she went on to become a Fisheries Biologist with the Florida Fish and Wildlife Conservation Commission. She worked first to understand habitat and species conservation issues on Lake Okeechobee and then move to the Freshwater Fisheries Research Unit exp loring everything from the long term trends in fish populations within the St. Johns River, to

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90 evaluating stocking techniques to improve the survival of juvenile hatchery fish, to understanding apex predator dynamics. In the Fall of 2009 she continue d her research and education to complete a PhD at the University of Florida within the Fisheries and Aquatic Sciences Program. Her sole desire was to improve her quantitative skills and a greater understand ing of population dynamics Although she successfully co mpleted her degree in the Winter of 2013, she of 2012 where she continued to work upon graduation