Growth and reproduction of Southern Flounder (Paralichthys lethostigma) in the north-central Gulf of Mexico

Growth and reproduction of Southern Flounder (Paralichthys lethostigma) in the north-central Gulf of Mexico A Master s Thesis Prospectus submitted by: Morgan M. Corey Department of Coastal Sciences The University of Southern Mississippi Ocean Springs, MS June 29, 2015 Dr. Robert T. Leaf Major Professor Nancy J. Brown-Peterson Committee Member Dr. Mark S. Peterson Committee Member

Ecology of Southern Flounder Southern Flounder (Paralichthys lethostigma) is the most commonly harvested flatfish species that occurs in the north-central Gulf of Mexico (GOM) (Hensley and Ahlstrom 1984). Southern Flounder are found as far north as Albermarle Sound, North Carolina on the Atlantic 5 10 15 coast and throughout the GOM (Reagan and Wingo 1985). However, the Atlantic and GOM populations are separated geographically around the southernmost Florida peninsula. There is evidence for genetic distinction between the Atlantic and GOM Southern Flounder populations, and some small-scale genetic differences have been reported within the GOM (Blandon et al. 2001, Anderson and Karel 2012). Southern Flounder are a euryhaline, estuarine-dependent species with variable spatial dynamics (Deubler 1960, Etzold and Christmas 1979). Southern Flounder migrate to offshore continental shelf waters for spawning in winter months and larvae are transported to lowersalinity inshore waters in the late winter and spring (Stokes 1977, Shepard 1986, Ditty et al. 1988). Southern Flounder spawning may also occur in freshwater, and this behavior is supported by otolith microchemistry analyses in the Mobile-Tensaw River Delta of Alabama and in Texas coastal waters (Lowe et al. 2011, Farmer et al. 2013, Nims and Walther 2014). However, little is known about the spawning habitats and seasonal migrations of Southern Flounder in the GOM. The Southern Flounder stock is a valuable marine resource in the GOM and supports 20 25 both a recreational and commercial fishery. Although Southern Flounder and Gulf Flounder (Paralichthys albigutta) are managed as a single stock, Southern Flounder is the more abundant of the two species harvested in the north-central GOM (VanderKooy 2000) and is primarily harvested recreationally using hook-and-line fishing or gigging (Riechers 2008). The Gulf-wide recreational harvest averaged over 400,000 kg per year for the past decade (National Marine Fisheries Service, http://www.st.nmfs.noaa.gov/st1/recreational/ queries, accessed February 2015). However, long-term declines in population size were observed in Texas between 1975 and 2008 (Froeschke et al. 2011). Despite the economic value of this species and evidence for overfishing, life-history information for Southern Flounder in the north-central GOM is limited. 1

An understanding of life history improves the ability to manage a population sustainably 30 35 40 (Adams 1980). Age-specific growth parameters and fecundity estimates are particularly valuable for predicting future stock biomass and the effects of fishing mortality on a species in stock assessment models. However, characteristics of growth and reproduction for Southern Flounder in the north-central GOM have not been reported. Further research on the life history of Southern Flounder is therefore beneficial for management of the stock. The objective of this research is to describe the growth and reproduction of Southern Flounder in the north-central GOM. A field sampling effort will be conducted using multiple gear types to collect monthly fish samples. Size measurements and otoliths will be collected from each fish to estimate maximum length, sex-specific length-at-age and weight-at-length relationships, and to describe condition over time. Reproductive tissue will be processed using histological techniques to estimate age- and length-at-maturity, spawning frequency, and spawning duration. Fecundity will be estimated by counting oocytes from actively-spawning females. The knowledge gained from this research will improve understanding of Southern Flounder life history and the ability to manage the GOM stock. 45 50 2

Chapter I: Age and Growth of Southern Flounder in the north-central Gulf of Mexico 55 Introduction Growth is a fundamental life-history characteristic that reflects the ecology and evolutionary history of a species. An understanding of individual growth is valuable for studying fish population dynamics and informing fisheries management (Denney et al. 2002). Specifically, individual growth parameter estimates are used in some stock assessment models 60 65 to calculate mortality rates and predict future stock biomass (Pauly 1980). Growth of Southern Flounder is variable within the Gulf of Mexico (GOM) and may be determined in part by environmental conditions (Midway et al. 2015). The length-at-age and weight-at-length relationships of Southern Flounder (Paralichthys lethostigma) have been reported in the GOM but have not been described in Mississippi (Table 1 and 2). In this chapter, I will describe sexspecific age and growth characteristics of Southern Flounder. Counting otolith annuli is a widely-used method of age estimation in teleost fishes (Campana 2001). Otolith annuli deposition varies due to changes in individual growth rates, which are influenced by environmental factors (Campana and Neilson 1985). For example, timing and rate of annuli deposition was related to water temperature in North Sea Cod (Gadus 70 75 80 morhua) otoliths using linear models (Pilling et al. 2007). Because annuli deposition is variable, age validation should be used to confirm that annual increment formation occurs consistently (Beamish and McFarlane 1983). Marginal increment analysis (MIA) is one age validation technique in which the periodicity of annuli formation is determined based on the growth area beyond the most recently-formed annulus (Hyndes 1992). MIA has been used as an age validation technique for Southern Flounder otoliths with consistent annuli deposition reported (Shepard 1986, Wenner et al. 1990). In the GOM stock, researchers in Texas and Louisiana compared monthly mean marginal increment distances from Southern Flounder otolith crosssections to determine that annuli form between January and May (Stunz et al. 2000, Fischer and Thompson 2004). However, I observed high variability in marginal increment widths during the months of annuli formation in age-one Southern Flounder otoliths (Figure 1). This indicates that there may be individual or inter-annual variability in timing of annuli deposition, which 3

complicates the assignment of birthdates for stock assessment analyses. In this research, variability in otolith annuli deposition will be examined in relation to water temperature and annuli formation will be validated with MIA to improve the precision of birthdate assignment in 85 Southern Flounder. An understanding of the length-at-age relationship is valuable for estimating growth parameters used in age-structured stock assessment models. Fishing can affect population-level dynamics, such as changes in age-structure or mean length, by selective removal of fish. For example, both average age and length decreased in Chinook Salmon (Oncorhynchus 90 95 100 105 110 tshawytscha) due to harvest of immature individuals between the 1920s and the 1970s (Ricker 1981). Because many fish stocks are managed based on minimum length limits (Allen and Pine 2000), it is critical to understand individual growth dynamics. The length-at-age relationship can also be used to estimate ages from a length frequency distribution. The von Bertalanffy growth function (VBGF) is a non-linear model that is widely used to describe the length-at-age relationship (von Bertalanffy 1938). The longest mean length (L ) for Southern Flounder was estimated as 1461 mm standard length (SL) using the VBGF (Nall 1979). However, this estimate is far greater than the longest observed length from any location in the GOM (Table 3). Other approximations of L for Southern Flounder from South Carolina and Texas (Wenner et al. 1990, Stunz et al. 2000) are based on age estimates validated by MIA (Table 1). Although the VBGF has been used to model the length-at-age relationship in other locations, the length-atage relationship for Southern Flounder in Mississippi has not been described (Table 1). The VBGF is one candidate model that will be used to understand the length-at-age relationship for Southern Flounder because its parameters can be easily compared to published parameter values. Description of the weight-at-length relationship is also useful for informing stock assessments. Weight-at-length relationships are modeled using a power function characterized by two parameters controlling the shape of the curve (Le Cren 1951). Although the weight-atlength relationship often follows a cubic power function (Froese 2006), the parameter b is species-specific and should be described in order to understand a species growth. Sex-specific differences in the weight-at-length relationships have been documented for Southern Flounder 4

in Atlantic waters (Wenner et al. 1990) and in Texas (Stunz et al. 2000). The weight-at-length parameter estimates vary within the GOM stocks, and Texas and Louisiana Southern Flounder have generally greater b values than those in the eastern (Florida) Gulf (Table 2). However, confidence intervals of parameter estimates were not reported so it is uncertain whether sex- 115 120 and location-specific differences are significant. The Southern Flounder weight-at-length parameters and confidence intervals derived from samples collected in this study will be compared to previously published weight-at-length mean parameter estimates to determine if significant differences exist (Table 2). Modeling the weight-at-length relationship in Southern Flounder from the north-central GOM will improve understanding of this species individual growth. Condition is a measure of weight relative to length that can be used to evaluate the fitness of an individual (Froese 2006). Heavier fish at a given length are assumed to be in better condition than lighter individuals (Le Cren 1951). Relative condition varies spatially and temporally. For example, Atlantic Cod (Cadus morhua) stocks have different condition due to 125 130 mean regional water temperatures. Specifically, warmer-water Atlantic Cod stocks have generally greater observed average condition than cold-water stocks (Rätz et al. 2003). Seasonal changes in length-weight relationships were observed for the Comber (Serranus cabrilla), indicative of changes in reproduction or feeding activity (Moutopoulos and Stergiou 2002). The use of condition as an indicator of growth and development has not been reported for Southern Flounder. Sexual dimorphism exists in Southern Flounder. Males have shorter life spans than females, and maximum ages of four years for males and eight years for females have been observed in both the Atlantic Ocean and GOM (Table 3). Total length (TL) is similar between sexes during the first year post-hatch, but diverges during the second year of growth. For 135 example, age-zero females and males in Texas waters reached an average 253 mm and 243 mm TL, respectively. However, age-one females grew to an average 374 mm TL compared to an average 291 mm TL in age-one males (Stunz et al. 2000). Maximum average total length is greater for females than for males (Table 1). Because of the differences observed between 5

male and female growth (Fischer and Thompson 2004), the use of sex-specific models will likely 140 result in more accurate descriptions of individual growth. The description of a biological relationship involves uncertainty associated with model structure, parameter estimates, and natural variation (Chatfield 1995). Model misspecification can result from choosing only one model based on convenience or the frequency with which the model is used to describe the relationship. Fitting multiple statistical models and choosing 145 150 the best model based on an objective criterion reduces model selection uncertainty and improves accuracy of parameter estimates (Burnham and Anderson 2004, Katsanevakis 2006). The three-parameter von Bertalanffy growth function is the most commonly used model for describing the length-at-age relationship of Southern Flounder (Table 1). However, other models for the length-at-age relationship do exist, including the two-parameter von Bertalanffy, the Gompertz growth model, and the logistic model. Therefore, multiple models should be evaluated to avoid model misspecification and to improve Southern Flounder growth parameter estimates. The objectives of this research are: (1) to determine factors influencing otolith growth and to validate the formation of annuli in Southern Flounder otoliths using MIA methods; (2) to 155 quantify the sex-specific length-at-age relationships using multiple models for Southern Flounder; (3) to quantify the sex-specific weight-at-length relationships of Southern Flounder; (4) to compare results with previously-published growth parameter estimates; and (5) to evaluate seasonal changes in condition of Southern Flounder. 160 Materials and Methods Southern Flounder will be sampled in the north-central GOM using primarily hook and line fishing and gigging. A target sample size of 30 fish will be collected each month, but the objective will be to collect a sufficient sample of fish to represent the population dynamics. Collection will occur at multiple locations primarily within Mississippi waters (Figure 2). Fish 165 caught in other Gulf-states and offshore will be included when possible. The gear type used for collection will vary throughout the study with maximum effort used for each as appropriate. 6

Additional samples will also be obtained from local fishing tournaments or incidental catch from research surveys. Fish will be immediately placed on ice following collection and processed in the laboratory. 170 175 180 185 190 195 Each fish will be measured for TL (mm), standard length (SL, mm) and wet body weight (g). The paired sagittal otoliths will be removed from each fish by exposing the brain cavity with a transverse cut. Otoliths will be rinsed to remove membranous tissue and stored in a labeled envelope. Following methods presented by VanderKooy (2009), the left sagittal otolith from each fish will be processed for age determination. The otolith will be embedded in a mold with Epoxicure resin and allowed to harden for a minimum of 24 hours. Once the resin is hardened, the resin block will be marked to target the otolith core and several sections will be cut at a thickness of about 0.4 mm with a Buehler diamond blade saw. Otolith sections will then be polished to increase the visibility of annuli and mounted on slides with Crystalbond and Flo- Texx mounting mediums. Age estimates will be determined using annuli counts from otoliths and validated with MIA. Southern Flounder scales were reported to have inconsistent markings (Palko 1984), making otoliths the preferred structures for age estimation. Otolith-based age estimation is recommended for this species (VanderKooy 2009), and therefore will be used in this research. Annuli will be counted from images taken at 2x to 5x magnification under transmitted light with a Stemi 2000-C microscope. Two independent readers will record an age estimate by counting fully-formed annuli, and a third reader will reexamine otoliths in case of a discrepancy between initial readings. If an agreement cannot be reached between readers, the age estimate will not be used for analysis. The otolith radius, annuli width, and translucent area formed on the outer edge margin will be measured from images using i-solution Lite. Otoliths will be assigned a margin code (one = 0% translucent area, two = 33%, three = 66%, four = 99%) based on the percentage of outer margin width relative to the width of the last fully-formed annuli, where a margin code of one indicates opaque ring formation (VanderKooy 2009). The proportions of otoliths with each margin code will be examined as a function of capture month to determine timing of annuli formation. Linear models will be used to determine the influence of year of capture, month of capture, degree-days (the cumulative water temperature experienced over 7

time as a continuous variable), otolith radius, and annuli count on margin width. Inter-annual variability will be assessed by including measurements from Southern Flounder otoliths collected by the Mississippi Department of Marine Resources between 2007 and 2013. Water temperature data will be obtained from the USGS National Water Information System Web 200 Interface for the Mississippi Sound. The length-at-age relationships of Southern Flounder will be described using non-linear models. A three-parameter VBGF will be used to estimate length-at-age: 205 210 L t = LL [1 ee kk(t tt 0 ) ], where t represents time (y), Lt is the length (mm) at a given time, LL is the mean hypothetical maximum TL (mm), k is the growth coefficient (y -1 ), and tt 0 is a theoretical age at length of zero (y). Other candidate models to describe length-at-age, including the two-parameter von Bertalanffy growth function, Gompertz growth model, and logistic model, will also be fit to the data. The two-parameter von Bertalanffy growth function is described by the following equation: L t = LL (1 ee kkt ). The Gompertz growth model (Gompertz 1825) is: L t = LL e( 1 kk ee kk(t 1 kk llllll) ), where λ is the theoretical initial relative growth rate at age zero (y -1 ) and k is the rate of exponential decrease of the relative growth rate with age (y -1 ). The logistic length-at-age model 215 220 (Ricker 1975) is: L t = LL (1 + ee kk(t tt ii ) ), where k is a relative growth rate parameter (y -1 ) and ti corresponds to the inflection point of the sigmoidal curve. These candidate length-at-age models will be evaluated for goodness-of-fit and parsimony using Akaike information criterion (AIC). Calculated AIC values will be compared to determine the best-fit model, indicated by the lowest AIC value. The weight-at-length relationship will be modeled using a power function: W = aal bb, where W represents wet weight (g), L represents TL (mm), a is a coefficient term and b is an exponent describing change in length relative to weight. The 95% confidence intervals will be 8

225 230 calculated for each mean parameter estimate. The mean parameter estimates will be compared to published mean parameter estimates using the 95% confidence intervals. Mean parameter estimates that are within the confidence interval range of published values indicate that no significant difference exists. The relative condition of individuals and temporal changes in condition will be evaluated using Fulton s condition factor. Condition is calculated based on the relationship between weight and length: KK = 100 W L bb, where K represents Fulton s condition factor and b is an exponent parameter. Mean monthly condition will be calculated with 95% confidence intervals. The condition values will be tested 235 240 for normality with a Shapiro-Wilk test and for homogeneity of variance with a Bartlett s test. If the condition data are not normally distributed, the data will be arcsine square root transformed before analysis. If the condition data are normally distributed and meet the homogeneity of variance assumption, a parametric one-way analysis of variance (ANOVA) test will be carried out to determine if condition is significantly different between months. If the assumptions of normality and homogeneity of variance are violated, a non-parametric Kruskal- Wallis ANOVA test will be used. A post-hoc Tukey s test will be used to determine which months condition values are significantly different. The significance level will be set to P < 0.05. 245 250 9

Chapter II: Reproductive Biology of Southern Flounder in the north-central Gulf of Mexico Introduction Reproduction is a fundamental aspect of a species life history. In fisheries science, an understanding of reproductive biology is essential because reproduction greatly influences fish 255 260 population dynamics and the resilience of stocks (Beverton and Holt 1957). Length-at-maturity and fecundity estimates are particularly valuable to inform stock assessment models of spawning stock biomass and egg production (Lowerre-Barbieri et al. 2011a). Characteristics of Southern Flounder reproduction, including length-at-maturity, spawning season timing, spawning frequency, and fecundity estimates, have not been described in the north-central Gulf of Mexico (GOM). This chapter will describe the reproductive biology of Southern Flounder in the north-central GOM. Timing of reproductive maturity is a life-history trait that affects population dynamics (Lowerre-Barbieri et al. 2011b). Age- and length-at-maturity vary within populations, as well as temporally and spatially (Trippel 1995). The description of accurate age- and length-based 265 270 275 maturity estimates would be beneficial for Southern Flounder management because changes in maturity were shown to greatly affect biological reference points for this species (Midway and Scharf 2012). Southern Flounder females reach greater lengths and have longer life spans than males (Stokes 1977, Stunz et al. 2000, Fischer and Thompson 2004), which complicates reported estimates of age- and length-at-maturity when both sexes are considered together. For example, Southern Flounder spawned at an estimated two years of age in Texas waters (Stokes 1977). Southern Flounder in Mississippi waters were reported mature at three years of age (Etzold and Christmas 1979). However, in South Carolina, males were reported mature at two to three years of age and females were reported mature at three to four years (Wenner et al. 1990). Dimorphism was also reported in length-at-maturity, which ranges from about 230 mm to 310 mm total length (TL) for males and 320 mm to 380 mm TL for females in South Carolina. These sex-specific differences in age- and length-at-maturity have not been reported for Southern Flounder in the GOM. A 305 mm (12 inch) minimum size limit was established for the Mississippi Southern Flounder fishery in 2002 (Mississippi Department of Marine Resources, 10

www.dmr.state.ms.us/recreational-fishing/recreational-catch-limits, accessed June 2015). 280 285 290 295 300 However, the length-at-first-maturity for female Southern Flounder reported by Wenner et al. (1990) is greater than the minimum size limit, which suggests that some proportion of females may be harvested before spawning. Improved understanding of the length-at-maturity relationship for Southern Flounder would therefore be valuable knowledge to GOM state management agencies. Spawning seasonality can be determined by monitoring gonadal development throughout the year. One measure used to describe temporal gonad development patterns is the gonadosomatic index (GSI), which is a ratio of gonad weight relative to gonad-free body weight. Gonad weight can be used as an indicator of reproductive maturation (Htun-Han 1978), so the observation of monthly GSI values is used to describe annual reproductive development and spawning preparedness. The use of GSI is advantageous because weight measurements are easily obtained, but requires a continuous sampling effort for a minimum of one year to accurately describe annual maturation patterns. In the GOM, the Southern Flounder spawning season occurs from late autumn through early winter (Reagan and Wingo 1985, Ditty et al. 1988). Increasing gonadal development from August through November was indicated by GSI values measured from Southern Flounder in Louisiana, suggesting that peak spawning activity occurs in December (Shepard 1986). However, Shepard (1986) only recorded GSI from May to December, which does not fully detail the annual trends in maturation for this species. Fischer (1995) used both GSI and ovarian histology to determine that the Southern Flounder spawning season lasts about 60 days from December through January in Louisiana. Further research on Southern Flounder GSI would provide a better understanding of temporal gonadal development and an estimate of the spawning season duration. Histological analyses are more time- and cost-intensive than GSI measures, but provide a precise characterization of gonad developmental phase and frequency of spawning events 305 (Lowerre-Barbieri et al. 2011a). Histology involves examination of gonadal tissue at the cellularlevel. The use of histology is preferable to macroscopic maturity-stage classification because defining characteristics can be clearly identified (Hunter and Macewicz 1985). Spawning frequency can be determined with histological analysis based on the presence of postovulatory 11

follicle complexes (POFs) or oocytes undergoing oocyte maturation (OM) from reproductivelyactive ovaries (Hunter and Macewicz 1985). Batch spawning behavior (multiple spawning 310 315 320 events per individual in a season) can be identified by examining developmental stages throughout the spawning season. Batch spawning throughout the spawning season is common in flatfishes, including the North Sea Dab, Limanda limanda (Htun-Han 1978), Dover Sole, Microstomus pacificus (Hunter et al. 1992), Tasmanian Greenback Flounder, Rhombosolea tapirina (Barnett and Pankhust 1999), and Summer Flounder, Paralichthys dentatus, in the Middle Atlantic Bight (Morse 1981). Batch spawning was observed in laboratory-reared Southern Flounder and each female spawned more than three times throughout the spawning season duration (Arnold et al. 1977). However, spawning behavior in a laboratory setting likely does not reflect spawning behavior in a natural population. In Southern Flounder collected from Louisiana waters, the presence of different oocyte stages throughout the spawning season was indicative of batch spawning (Fischer 1995). To my knowledge, this is the only example of batch spawning behavior documented in wild-caught Southern Flounder from the GOM population. Examination of gonadal development with histology is needed to confirm batchspawning behavior in Southern Flounder. Assessment of egg production requires an understanding of fecundity, which is a 325 330 335 measure of individuals potential reproductive capability each reproductive season. The fecundity of individuals and the recruitment of their offspring to the population has a great effect on population growth potential (Beverton and Holt 1957, Goodyear 1993). There are two types of life-history strategies for fecundity in fishes defined by oocyte recruitment patterns (Lowerre-Barbieri et al. 2011a). Determinate fecundity is characterized by all oocytes in a reproductive cycle being recruited to secondary growth prior to the beginning of the spawning period, and indeterminate fecundity is characterized by oocytes continuously entering secondary growth throughout the spawning period (Hunter et al. 1992, Ganias et al. 2015). Determinate fecundity has been observed in flatfish, such as the common Sole, Solea solea, in the Atlantic Ocean (Witthames and Walker 1995), and the Dover Sole, Microstomus pacificus, in the Pacific Ocean (Hunter et al. 1992). Most flatfish species occur in cold-water regions, and winter-spawning fish tend to have a determinate fecundity strategy (Rijnsdorp and Witthames 12

2005, Lowerre-Barbieri et al. 2011b). The fecundity strategy of Southern Flounder, a warmwater flatfish species, is currently undescribed and will be determined through this research. Estimation of individual fecundity is necessary for informing stock assessment models. 340 345 350 For example, egg-per-recruit models are used to evaluate changes in egg production in response to fishing (Prager et al. 1987), and accurate estimates of fecundity improve the accuracy of these models. Fecundity estimates are commonly obtained using the relationships between ovary weight or volume to the density of oocytes in the ovary (Murua et al. 2003). Fecundity varies with body size, and larger fish produce more eggs relative to body mass than smaller fish (Buckley et al. 1991). However, variability in body size and individual spawning capabilities was not reported in previous Southern Flounder fecundity estimates. In laboratoryspawned Southern Flounder, 13 spawning events from three large females (each weighing more than 2,000 g) produced about 120,000 eggs total (Arnold et al. 1977). Another laboratory experiment showed that each spawning event yielded about 5,000 fertilized eggs in hormoneinduced spawning Southern Flounder females (Lasswell et al. 1978). The only known fecundity estimate from Southern Flounder collected in the GOM is a mean batch fecundity of 44,000 to 62,000 ova per batch (Fischer 1995). Better classification and estimation of Southern Flounder fecundity will be useful to inform stock assessment for this species. 355 The objectives of this research are: (1) to describe sex-specific age- and length-atmaturity; (2) to determine the approximate spawning season using monthly GSI values and histology; (3) to describe characteristics of gonadal development in males and females and to estimate spawning frequency using histological analyses; (4) to determine fecundity type; and (5) to estimate batch fecundity in Southern Flounder. 360 Materials and Methods Southern Flounder will be sampled in the north-central GOM using primarily hook and line fishing and gigging. A target sample size of 30 fish will be collected each month, but the objective will be to collect a sufficient sample of fish to represent the population dynamics and all reproductive phases. Collection will occur at multiple locations primarily within Mississippi 13

365 370 375 380 waters (Figure 2). Fish caught in other Gulf states and offshore will also be included when possible. Gear used for collection will vary throughout the study with maximum effort used for each technique as necessary. Additional samples will also be obtained from local fishing tournaments or incidental catch from research surveys. Fish will be immediately placed on ice following collection and processed in the laboratory within 24 hours. Each specimen will be measured for TL (mm), standard length (SL, mm), and total weight (TW, g). The sex of each fish will be determined by macroscopic examination of gonads. Whole gonads will be removed and weighed to the nearest 0.01 g. A cross section no larger than 1 cm 3 from the middle of one gonad will be placed into a histology cassette and fixed in 10% neutral buffered formalin for at least one week. A 1:20 ratio of tissue volume to formalin volume will be maintained to ensure adequate penetration and preservation of the gonadal tissue. Any gonad tissue samples that cannot be weighed fresh will be preserved whole in 10% neutral buffered formalin. A regression analysis will be used to examine the relationship between fresh gonad weight and gonad weight as a function of time in solution, and a conversion factor will be used to account for any shrinkage in sample weight over time. Tissue will be examined from the anterior, middle, and posterior sections of both the left and right gonad in three spawning capable females to determine if oocyte development is homogenous throughout the gonad. Mean TL at 50% maturity (MTL) will be estimated using a 2-parameter logistic model: 385 390 MM TTTT = 1 1+ee rr(tl TTTT 50 ), where r is the instantaneous growth rate and TL50 is the TL at 50% maturity. Maturity will be coded as immature (0) or mature (1) and the 95% confidence interval of the MTL estimate reported. Age-at-maturity will be back-calculated using the length-at-age relationship of Southern Flounder. The gonadosomatic indices (GSI) will be calculated for each sex using the following equation: GSI = GW 100, GFBW where GW is the gonad weight (g) and GFBW is the gonad-free body weight of the fish (g). A linear regression will be performed to determine if there is a relationship between GSI and 14

GFBW, with no relationship meaning that GSI is an indicator of spawning preparedness (Jons and Miranda 1997). The GSI values for both females and males will be tested for normality with a Shapiro-Wilk test and for homogeneity of variance with a Bartlett s test. If the GSI data are 395 400 not normally distributed, the proportional data will be arcsine square root transformed before analysis. If the GSI data are normally distributed and meet the homogeneity of variance assumption, a parametric one-way analysis of variance (ANOVA) test will be carried out for GSI differences among months by sex. If the assumptions of normality and homogeneity of variance are violated, a non-parametric Kruskal-Wallis ANOVA test will be used. A post-hoc Tukey s test will be used to determine which months GSI values are significantly different. The significance level for all tests will be set to P < 0.05. Preparation of gonads for analysis will follow standard histology procedures, which include rinsing and dehydrating the preserved gonad tissue, embedding in paraffin, sectioning into thin slices, differentially staining tissue, and mounting sections to slides for examination. To 405 410 415 420 prepare for dehydration of the gonad samples and embedding in paraffin, the sample cassettes will be rinsed overnight with low-flowing tap water. After rinsing, samples will be placed in 60% ethanol for two hours, drained, placed in 70% ethanol for two hours, drained, and replaced in 70% ethanol for a minimum of two hours. Next, the preserved gonad samples will be dehydrated using various dilutions of ethanol up to 100%, cleared using Shandon Xylene substitute, and impregnated with Paraplast Plus in a Shandon Excelsior Tissue Processor (Table 4). All steps will be performed under vacuum to maximize the penetration of reagents into the tissues. Tissues will be embedded within one hour of cycle completion using a Shandon Histocentre 2 Embedding Center. To embed tissues, a small amount of Paraplast will be placed in the bottom of a stainless-steel mold and the gonad tissue will be positioned in a manner to obtain the best cross-section. The tissue will be secured by briefly cooling the paraffin and the cassette base placed on top of the mold. The mold will then be completely filled with Paraplast. The cooled Paraplast and tissue block will be removed from the mold and the excess paraffin trimmed off. To prepare for tissue sectioning, an S/P Brand Tissue Flotation Bath will be filled with distilled water. One cap-full of Surgipath STAY ON, a tissue section adhesive, will be added and the bath will be heated to 37-42 C. Prior to sectioning, the blocks will be placed on ice. 15

Blocks will be sectioned at a thickness of 4 µm using an AO Rotary Microtome with a disposable Accu-Edge Low Profile Microtome Blade. Sections will be placed in the water bath and the best two from each specimen will be floated onto a slide. Each slide will be labeled and placed on a slide warmer for a minimum of two hours to completely dry. The staining process will include 425 430 435 removing the paraffin, rehydrating the sample, staining the various tissue components, and then dehydrating the section. Slide baths will be created in a sequence with varying solutions and soak times (Table 5). Slides will be stained following a regressive method of hematoxylin staining (Luna 1968) using Hematoxylin 2 and counterstained with Eosin Y (Richard-Allan Scientific). Solution baths will be rotated or discarded and replaced as needed. Slides will be cover-slipped using a mounting medium (Richard-Allan Scientific) and allowed to dry completely. Stained slides will be evaluated microscopically to define developmental phases for both males and females. Each sample will be sorted in one of five reproductive phases (immature, developing, spawning capable, regressing, and regenerating), including the subcategories of early developing and actively spawning (Table 6 & 7), based on the classification scheme presented by Brown-Peterson et al. (2011). This analysis will provide a definitive classification of reproductive phase for each individual. A chi-square contingency table will be used to determine if the frequency distributions of reproductive phases are different among months. The reproductive development of males will be examined using histological classification 440 445 of samples. Males will be classified sexually mature when primary spermatocytes are observed (Brown-Peterson et al. 2011). The spermatogenic maturity index (SMI) will be used in combination with GSI to describe the gonadal development of males (Tomkiewicz et al. 2011). The SMI method involves estimation of the area fractions of various tissue categories characterized by progressive gamete development stages in histological sections of the testes. The entire testis tissue section will be imaged at 10x magnification with a Nikon compound microscope and three areas will be randomly selected from each slide for examination using an Image J software point grid. The number of squares of coverage for each testis tissue type (testicular somatic cells, spermatogonia, spermatocytes, spermatids, spermatozoa) and atresia 16

will be counted and divided by the total number of counts, resulting in a percentage of area 450 covered by each. The SMI will be calculated using the following equation: SMI = 0.0F Ts + 0.4F Sg + 0.6F Sc + 0.08F St + 1.0F Sz + 0.2F atresia, where F is the frequency of occurrence for the indicated cell type (Ts = testicular somatic cells, Sg = spermatogonia, Sc = spermatocytes, St = spermatids, Sz = spermatozoa, and atresia). The index weighs the volume fractions of the different tissues (somatic cells and germ cell stages) 455 and describes testis development on a scale of 0 to 1. The monthly proportions of female samples in each ovarian phase will be used in combination with GSI data to determine the spawning season timing. Females will be classified sexually mature when fish enter the developing phase and cortical alveoli oocytes are observed (Brown-Peterson et al. 1988, Brown-Peterson et al. 2011, Lowerre-Barbieri 2011b). The percent 460 465 470 coverage of each oocyte stage present in female ovarian sections will be determined using images taken at 4x magnification with a Nikon compound microscope. The entire tissue section will be imaged and three areas will be randomly selected from each slide for oocyte examination using an Image J software point grid. All oocytes, postovulatory follicle complexes (POF), and atretic oocytes will be counted. The number of grids of coverage will be counted and divided by the total number of grid points, resulting in a percentage of total area for each oocyte, POF, and atresia stage (modified from Tomkiewicz et al. 2011). A qualitative descriptive analysis of the oocyte stage frequency distributions will be used to examine any changes in the most-frequently occurring oocyte stage among months. The oocyte stage frequency distributions will be tested for normality using a Shapiro-Wilk test to determine if it is appropriate to calculate error within oocyte stages. Histological data will be used to determine spawning frequency of Southern Flounder females. Two methods will be used to determine the spawning frequency. One method uses samples from fish undergoing oocyte maturation (OM) whereas the other method uses samples with POFs less than 24 hours old (Hunter and Macewicz 1985). The OM method is based on the 475 observation of fish that are going into the final stages of oocyte maturation. The POF method is 17

based on the presence of a thinly-stretched and folded follicle that remains behind after the ovulated egg is released. All specimens that are categorized as spawning capable or actively spawning will be counted for each month. The sum of the total spawning capable and actively spawning fish within a month will then be divided by the number of specimens within that 480 485 month that contained 0 to 24 hour POFs or OM. The result gives an estimate of the number of days between spawns for each month. Annual spawning frequency will be calculated as the sum of the spawning capable and actively spawning fish within the spawning season divided by the total number of fish that contained 0-24 hour POFs or OM in a year. The total number of potential spawns per year will be calculated by dividing the total number of days in the spawning season by the annual spawning frequency. Differences in spawning frequency among months will be tested with a chi-square test. An oocyte size-frequency analysis will be used to determine whether Southern Flounder have a determinate or an indeterminate fecundity strategy. Oocytes from spawning capable females collected early and late in the spawning season will be sorted into 50 µm size bins. The 490 495 500 oocyte size-frequency distributions will be compared between early- and late-spawning individuals using a Kolmogorov-Smirnov test for differences between the distributions. A determinate fecundity strategy will be indicated by a low frequency of smaller oocytes and a high frequency of larger oocytes late in the spawning season. An indeterminate fecundity strategy will be indicated by the presence of smaller oocytes late in the spawning season. Samples classified as actively spawning will be used to estimate batch fecundity, relative batch fecundity, and total annual fecundity. If an ovary is identified as actively spawning, a subsample (~5 g) of the gonad will be removed, weighed (0.01 g), placed in a labeled jar, sliced into smaller sections, and preserved in modified Gilson s fluid (Table 8) for a minimum of three months (Bagenal 1966). All actively-spawning ovarian samples collected after January 2016 will be preserved in 10% neutral buffered formalin due to limited time. Gilson s fluid is used to harden the outer most layer of the oocyte and separate the oocyte from the ovarian tissue. Repeated shaking of the jar over the duration of storage helps break apart the ovarian tissues and aides in releasing and suspending the oocytes, thus allowing better fluid penetration and preservation. The volumetric method for estimating fecundity will be used in this study 18

505 510 515 520 (Bagenal and Braum 1971). Samples will be rinsed overnight in running water and oocytes will be teased from the tissue and placed in 100 ml of water. While the sample is being stirred, six aliquots of 1-2 ml each will be sub-sampled with replacement. An oocyte size frequency distribution will be developed for a spawning capable fish and an actively spawning fish and the two distributions will be compared. A distinct pattern of large oocyte frequency will be evident in actively spawning fish, indicating the size at which an oocyte undergoes maturation and thus the size at which oocytes need to be counted for fecundity analysis. The same analysis will be done for samples preserved in modified Gilson s fluid and in 10% neutral buffered formalin to account for differences in preservation methods. Both batch fecundity (number of eggs/female) and relative batch fecundity (number of eggs/g ovary free body weight) will be calculated and reported as a mean ± standard error (SE) of the mean fecundity estimate. Batch fecundity (BF) will be estimated using the following equation: BF = N DL GW, DLS PGW where N is the number of oocytes undergoing maturation, DL is dilution water volume (ml), DLS is the dilution water subsample volume (ml), GW is gonad weight (g), and PGW is the portion of the whole gonad used (g). Relative batch fecundity (RBF) will be estimated using the following equation: RBF = BF, OFBW where BF is the batch fecundity (number of eggs) and OFBW is the ovary-free body weight (g). Total annual fecundity will be estimated using the following equation: 525 530 Total Annual Fecundity = # spawning events (BF), where the total number of potential spawning events per year is defined as the total number of days in the spawning season divided by the spawning frequency, and BF is the batch fecundity (number of eggs). Linear regressions will be used to determine whether relationships exist between BF and TL, GFBW, or age. The data will be tested for normality with a Shapiro-Wilk test and for homogeneity of variance with a Bartlett s test to determine if the use of a linear model is appropriate. 19

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795 Figure 1: Boxplot of marginal increment widths relative to the width of the last fully-formed annuli by month for age-one Southern Flounder otoliths. Measurements were taken from Southern Flounder otoliths collected by the Mississippi Department of Marine Resources from 2007 to 2013. Dark bands indicate the median marginal increment proportion, shaded boxes indicate the first and third quartiles, dotted lines indicate the 95% confidence intervals, and open circles indicate outliers in the data. 27

800 Figure 2: Locations within the Mississippi Sound where Southern Flounder will be targeted for collection with hook and line fishing, gigging, gill netting, seining, and trawling from September 2014 through February 2016. Samples from other sites, including offshore Texas and Louisiana waters, will be collected as available. 28