Stock boundaries for spotted seatrout (Cynoscion nebulosus) in Florida based on population genetic structure

Item Type monograph

Authors Seyoum, Seifu; Tringali, Michael D.; Barthel, Brandon L.; Villanova, Vicki; Puchulutegui, Cecilia; Davis, Michelle C.; Alvarez, Alicia C.

Publisher Florida Fish and Wildlife Conservation Commission, Fish and Wildlife Research Institute

Download date 04/10/2021 17:27:27

Link to Item http://hdl.handle.net/1834/41131 Florida Fish and Wildlife Research Institute TECHNICAL REPORTS

Stock Boundaries for Spotted Seatrout (Cynoscion nebulosus) in Florida Based on Population Genetic Structure

Seifu Seyoum, Michael D. Tringali, Brandon L. Barthel, Vicki Villanova Cecilia Puchulutegui, Michelle C. Davis, Alicia C. Alvarez Rick Scott Governor of Florida

Florida Fish and Wildlife Conservation Commission

Nick Wiley Executive Director

The Fish and Wildlife Research Institute (FWRI) is a division of the Florida Fish and Wildlife Conservation Commission (FWC). The FWC is “managing fish and wildlife resources for their long-term well-being and the benefit of people.” The FWRI conducts applied research pertinent to managing fishery resources and species of special concern in Florida.

Programs at FWRI focus on obtaining the data and information that managers of fish, wildlife, and ecosystem resources need to sustain Florida’s natural resources. Topics include managing recreationally and commercially important fish and wildlife species; preserving, managing, and restoring terrestrial, freshwater, and marine habitats; collecting information related to population status, habitat requirements, life history, and recovery needs of upland and aquatic species; synthesizing ecological, habitat, and socioeconomic information; and developing educational and outreach programs for classroom educators, civic organizations, and the public.

The FWRI publishes three series: Memoirs of the Hourglass Cruises, Florida Marine Research Publications, and FWRI Technical Reports. FWRI Technical Reports contain information relevant to immediate resource-management needs.

Gil McRae, FWRI Director

Bland Crowder, Shad Run, Word and Graphic Editor Stock Boundaries for Spotted Seatrout (Cynoscion nebulosus) in Florida Based on Population Genetic Structure

Seifu Seyoum Michael D. Tringali Brandon L. Barthel Vicki Villanova Cecilia Puchulutegui Michelle C. Davis Alicia C. Alvarez

Florida Fish and Wildlife Conservation Commission Florida Fish and Wildlife Research Institute 100 Eighth Avenue Southeast St. Petersburg, Florida 33701

Florida Fish and Wildlife Conservation Commission FWRI Technical Report 18 2014 Cover Photograph Spotted Seatrout in net — By Tim Donovan, FWC

Copies of this document may be obtained from MyFWC.com

Florida Fish and Wildlife Conservation Commission Fish and Wildlife Research Institute 100 Eighth Avenue SE St. Petersburg, FL 33701-5095 Attn: Librarian

Document Citation Seyoum, S., M.D. Tringali, B.L. Barthel, V. Villanova, C. Puchulutegui, M.C. Davis, and A. C. Alvarez. 2014. Stock boundaries for spotted seatrout (Cynoscion nebulosus) in Florida based on population genetic structure. Fish and Wildlife Research Institute Technical Report TR-18 + 26 p. Document Production This document was composed in Microsoft Word® and produced using InDesign® on Apple Macintosh® computers.The headline font is Adobe® Avant Garde, the text font is Adobe® Palatino, and the cover headline is Adobe® Avant Garde.

The cover and text papers used in this publication meet the minimum requirements of the American National Standard for Permanence of Paper for Printed Library Materials Z39.48—1992. Florida Fish and Wildlife Research Institute Technical Report TR-18. Table of Contents

ACKNOWLEDGMENTS...... 6

EXECUTIVE SUMMARY...... 7

INTRODUCTION...... 8

METHODS...... 10

Sampling and DNA Extraction...... 10

Development of Microsatellite Markers...... 10

Microsatellite Genotyping...... 10

Data Analyses...... 11

Phenetic Clustering...... 11

Genetic Structure...... 11

RESULTS...... 12 Microsatellite Marker Assays...... 12 Standard Genetic Measures and Distances ...... 12 Phenetic Clustering...... 12 Analysis of Molecular Variance (AMOVA)...... 17

Bayesian Population Assignment...... 17 Mantel Test...... 19

IMPLICATIONS FOR THE FISHERY...... 19

FWC Spotted Seatrout Regional Boundaries...... 19

Genetic Regional Boundaries...... 19 Incongruencies Between the Boundaries of FWC Management Units and the Genetic Stocks...... 21

Stock assessment of the Spotted Seatrout in Florida...... 21

Recommendations...... 21 LITERATURE CITED...... 23

FIGURES......

Figure 1. Locations and abbreviations of 18 Spotted Seatrout sampled for this study...... 10

Figure 2. Unrooted neighbor-joining phenogram...... 14

Figure 3. Mean likelihood L determined from 10 replicates of each value of K...... 17

Figure 4. Genetic population structure among Spotted Seatrout samples...... 18

Figure 5. Fish and Wildlife Conservation Commission Spotted Seatrout stock boundaries...... 20

Figure 6. Mantel tests between pairwise genetic and geographic distances...... 20

TABLES......

Table 1. Characterization of 29 polymorphic microsatellite loci...... 13

Table 2. Average standard measures of genetic diversity...... 15

Table 3. Estimates of genetic and geographic distances in Spotted Seatrout samples...... 15

Table 4. Analysis of molecular variance for 18 Spotted Seatrout samples...... 16

Acknowledgments We thank the staff of the Fisheries Independent Monitoring Program at the Tequesta Field Laboratory of the Florida Fish and Wildlife Research Institute for collecting the samples. We are especially grateful to Caitlin Curtis for help in the development of the microsatellite markers, Joel Bickford and Sarah Walters for collecting samples, and Mike Murphy and Bob Muller for insightful comments on a draft of this report. This work was supported by the U.S. Department of the Interior, U.S. Fish and Wildlife Service, under the Federal Aid in Sport Fish Restoration Program, Grant F-69. The statements, findings, and conclusions are those of the authors and do not necessarily reflect the views or policies of the Department of the Interior. Mention of trade names or commercial products does not constitute their endorsement by the U.S. government. Seifu Seyoum et. al Stock Boundaries for Spotted Seatrout

Executive Summary The Spotted Seatrout Cynoscion nebulosus (Sciaenidae) is an estuarine fish of economic importance, commercially and recreationally, in Florida. Harvesting of this fish has been steadily decreasing since the 1950s. In the late 1980s, the Florida Fish and Wildlife Conservation Commission (FWC) implemented a major effort to stop the decline in landings and classified the species as restricted, regulating the importation, transportation, and possession of these fish. Over the period 1981–2012, combined recreational and commercial landings of Spotted Seatrout have been flat, primarily because of regulation of the fishery. In the absence of a well-resolved population genetic structure for the Spotted Seatrout, the FWC has relied on coastal watershed features and reproductive differences among estuaries to demarcate regions for management purposes. Identification of the geographic boundaries between biological units (stocks) is a crucial first step in the implementation of sound management strategies.Although many investigators have attempted to define the population genetic structure of the Spotted Seatrout using allozyme electrophoresis, mtDNA RFLP, mtDNA control region sequencing and microsatellite markers, the results from these studies have been contradictory and unclear. An important exception is that most studies have identified structural differences between Spotted Seatrout in the Gulf of Mexico and those in the Atlantic Ocean. The failure to identify further substructure may be due to 1) the Gulf- Atlantic break’s being the only significant structure for the species, 2) the limited power of the genetic techniques used to robustly sample gene pools, or 3) insufficient sampling coverage. Microsatellite markers are highly polymorphic nuclear-DNA markers that provide a statistically powerful indicator for estimating genetic distances among samples. In this study, we 1) developed 29 polymorphic microsatellite markers for Spotted Seatrout, 2) employed 21 of these markers to resolve the genetic population structure among Spotted Seatrout collected in Florida, and 3) demarcated boundaries of the distinct stocks. The markers surveyed provided 363 alleles for a robust examination of population genetic structure. Multilocus microsatellite genotypes were obtained from a total of 438 Spotted Seatrout collected from 18 locations (from Texas to North Carolina), which constitutes the portion of the species range where it is most abundant and most heavily targeted by anglers. In the present study we identify three genetic stocks of Spotted Seatrout in Florida waters, each with a unique range: 1) from the western border of Florida to Apalachicola Bay, 2) east of Apalachicola Bay through Biscayne Bay, and 3) from Sebastian Inlet to the northeast border of the state. The genetic patterns observed indicate that little if any contemporaneous reproductive exchange takes place between these stocks and that recruitment usually occurs in the natal estuary. The geographic boundaries that frame the FWC’s periodic stock assessments and other demographic evaluations of Spotted Seatrout are not a perfect match with those of the genetically identified stocks. We recommend that, in its assessments of Florida stock of the Spotted Seatrout, the FWC use the genetic stock boundaries that we describe here.

FWRI Technical Report - TR18 ------7 Stock Boundaries for Spotted Seatrout Seifu Seyoum et. al

Introduction embayments and estuaries (Iversen and Tabb, 1962; Tabb, 1966; Weinstein and Yerger, 1976). The Spotted Seatrout (Cynoscion nebulosus) is a common marine fish that primarily inhabits Several investigators have attempted to estuaries from Massachusetts to Isla del Carmen characterize the genetic population structure of in the lower Bay of Campeche, Mexico, with a Spotted Seatrout using various morphological, center of abundance from Florida to Texas (Lassuy, physiological, and genetic methods, but the 1983; Mercer, 1984; Robins et al., 1986; Johnson results have been inconsistent or conflicting. and Seaman, 1986). It has also been reported On the basis of tagging and growth rates, from Holbox Island (21°43′N, 87°13′W), Quintana Iverson and Tabb (1962) characterized the Roo, off the northeastern Yucatan peninsula in Spotted Seatrout in Florida as five independent Mexico (Aguilar-Salazar et al., 1993, 1995). estuarine populations. In a study of reproductive physiology using samples from Texas and Commercially and recreationally, Spotted Florida, Brown-Peterson (2002) also suggested Seatrout is important in many parts of its range that regional differences in reproduction (NMFS, 2007). But commercial and recreational between samples from Texas and Florida might harvests have steadily declined, at least partly have been due to environmental or genetic because of a loss of key habitat and intense differences. Using allozyme electrophoresis, overfishing (Merriner, 1980; Johnson and Seaman, Weinstein and Yerger (1976) identified seven 1986; Pattillo et al., 1997; Murphy et al., 2009). For distinct populations in estuaries from Texas to this reason, Spotted Seatrout has been classified the Indian River in Florida. But a subsequent as a restricted species in Florida, and regulations allozyme analysis by Paschall (1986) found little restricting importation, transportation and genetic differentiation among samples from Port possession have been imposed on commercial Aransas, Texas, to St. Augustine, Florida. Ramsey and recreational harvests. For 1981–2012, and Wakeman (1987), in a study that utilized 40 combined recreational and commercial landings allozyme loci, failed to find genetic differences of Spotted Seatrout have stopped from declining, between regions or estuaries from the northern primarily because of regulations (Murphy et al., Gulf of Mexico to eastern Florida. King and Pate 2009). (1992) and King and Zimmerman (1993) also The Spotted Seatrout has an estuarine- used allozyme electrophoresis and reported dependent larval lifespan of 7–10 days (Lassuy, isolation by distance and clinal variation in mean 1983), and adults rarely move farther than 48 km heterozygosity for Spotted Seatrout inhabiting from their natal estuary (Moffett, 1961; Iversen the northwestern Gulf of Mexico; clinal variation and Moffett, 1962; Iversen and Tabb, 1962; and heterogeneity, however, do not correspond Beaumariage, 1969; Music, 1981). Such limited to genetic discontinuities or to discrete genetic adult dispersal has the potential of producing stocks. population structure between groups inhabiting But studies based on mitochondrial spatially disjunct estuaries. Differences in DNA (mtDNA) restriction site variation have ecological and environmental parameters could shown significant heterogeneity and isolation by promote divergence between subpopulations distance, with a strong genetic break between the inhabiting different estuaries. In the section of the Atlantic and Gulf of Mexico, and the presence Gulf of Mexico from southern Texas to eastern of significantly subdivided populations within Florida, individuals from different coastal bays these regions (Gold and Richardson, 1998; were found to have low dispersal and different Gold et al., 1999). Gold et al. (1999) also found growth rates. Furthermore, complete genetic that the genetic divergence between Mosquito mixing could be hampered by coastlines that lack Lagoon (FL) and Bulls Bay (SC) in the Atlantic

8 ------FWRI Technical Report - TR18 Seifu Seyoum et. al Stock Boundaries for Spotted Seatrout

region was almost as large as that between the microsatellite loci with a limited number of Atlantic Ocean and the Gulf of Mexico. This alleles cannot be expected to provide adequate could indicate a significant genetic break in resolution of genome-wide relationships the Atlantic region. Gold et al. (2003) found no underlying spatial population structure. Poor significant microsatellite DNA allele frequency sampling of the gene pool may fail to detect real differences among Spotted Seatrout in Texas differences or, worse, lead to spurious results. In bays. Wiley and Chapman (2003) used different studies of genetic population structure, loci with microsatellite loci and found evidence of stock numerous alleles provide better allele frequency differentiation between Spotted Seatrout of the estimates and produce better genetic distance Florida Gulf coasts and those of the U.S. Atlantic. estimates, with more power to discriminate But genetically, specimens from the Indian River between populations (Kalinowski, 2002, 2005; Lagoon on the Florida Atlantic coast differed less Wilson and Rannala, 2003). In general, the greater from those from the Choctawhatchee embayment the total number of independent loci with high along the Florida Gulf than they did from those allele diversity employed, the greater the chances from Georgia and South Carolina (Wiley and of resolving the relationship of populations with Chapman, 2003). This study indicated a stronger lower magnitude of differentiation. Thus, the use genetic discontinuity between Indian River of a large number of microsatellite markers that Spotted Seatrout and the northerly Atlantic Coast have a large number of independent alleles is of specimens, similar to what was seen between fish paramount importance to accurately estimate the from Mosquito Lagoon (FL) and Bulls Bay (SC) genetic distance between populations. by Gold et al. (1999). The objectives of this study were 1) to Ward et al. (2007) reported that, develop species-specific microsatellite markers genetically, Spotted Seatrout in Florida differed for the Spotted Seatrout, 2) to use those markers significantly from those in Louisiana and Texas to resolve the population genetic structure of based on frequencies of only five microsatellite Spotted Seatrout collected from Texas to North markers. But in that study, due to the lack of Carolina, and 3) to identify the geographic sampling sites between Louisiana and Tampa boundaries of discrete stocks in Florida for stock Bay, the location where the difference occurs assessment purposes. (genetic break) cannot be determined. Ward et al. (2007) also reported a strong genetic discontinuity between samples from Tampa Bay and Charlotte Harbor; the sample from Charlotte Harbor was most similar to Spotted Seatrout from the St. John’s River on the Atlantic Coast. Wilson et al. (2002) used sequences from a 335-base-pair segment of the mtDNA control region and found a strong genetic break between Gulf of Mexico and Atlantic samples as well as between numerous other subdivisions in Florida. Specimens from Cedar Key clustered with those from the Atlantic, but the authors offered no explanation. Assaying a single region of the mtDNA genome, with only a single genealogical history of the organism, or a small number of

FWRI Technical Report - TR18 ------9 Stock Boundaries for Spotted Seatrout Seifu Seyoum et. al

Methods contained 15–25 ng of nDNA, 50 μM of dNTP mix, 0.25 μl of 0.1mg/ml BSA; two primers Sampling and DNA Extraction randomly chosen from a set of 120 10-mer Total genomic DNA was purified by phenol/ RAPD primers (Operon Technologies Inc.); 5 chloroform extraction (Sambrook et al., 1989) μl of Taq polymerase buffer (10x) containing from liver or muscle tissue from 438 Spotted 15mM MgCl2 (Promega); and 1.25 units of Taq Seatrout collected from 18 locations from Texas to polymerase (Promega). The reaction profile was North Carolina (Figure 1). In this study we used 94°C for 2 min, and 30 * (94°C for 40 s, 35°C for the word “sample” to refer to a discrete collection 40 s, 72°C for 45 s) and final extension at 72°C of specimens from a specified area. for 30 min. Purified PCR products (Stratagene) Development of Microsatellite Markers were cloned into plasmid T-vectors (Bluescript Microsatellite loci were isolated following the PBC KS‑, Stratagene). Recombinant colonies PIMA (PCR-based isolation of microsatellite were screened by performing PCR (12.5 μl total arrays) method of Lunt et al. (1999), modified reaction volume) containing T3 and T7 vector as reported in Seyoum et al. (2005). Briefly, primers and two repeat-specific primers [5¢- nuclear DNA (nDNA) was first purified from (AC) 10-3¢, 5¢-(AG) 10-3¢, 5¢-(AGC) 5-3¢, 5¢-(ACT) liver tissue from a single Spotted Seatrout via 12-3¢, 5¢-(ACC) 6-3¢]. Here, the reaction profile density-gradient ultracentrifugation and used was 94°C for 2 min, 35 ´ (94°C for 30 s, 55°C for in multiple RAPD PCRs. Each 50-μl RAPD PCR 30 s, 72°C for 30 s) and final extension 72°C for 7 min. If PCR products were found to have two or more bands when run through a 1.5% low- EEO agarose gel, another PCR was performed using only the vector primers. PCR products were cycle-sequenced from both directions using BigDye (version 1.3; Applied Biosystems Inc.). Sequencing products were visualized on an Applied Biosystems PrismTM 3130-Avant Genetic Analyzer. Primers were designed for candidate loci using OligoPerfect (Invitrogen), with forward primers 5¢ end-labeled with a fluorescent dye. Multiplex PCRs were performed in 12.5-μl volumes with combinations of three loci that had annealing temperatures of 58°C and Figure 1. Locations and abbreviations of with the reaction profile as above. Total DNA 18 Spotted Seatrout (Cynoscion nebulosus) extracted from 137 samples of Spotted Seatrout samples collected for this study: From- Texas - collected from Tampa Bay was used to screen the [PI = South Padre Island, (1); PA = Port Arthur, markers. Fragments were visualized on an ABI (2)]: Mississippi - MS = (3)]: Florida – [FW = 3130 XL genetic analyzer and genotyped using Fort Walton, (4) ; AP = Apalachicola, (5) ; ST = GeneMapper (version 4.0; Applied Biosystems Steinhatchee, (6) ; CK = Cedar Key, (7); TP = Inc.). For fragment assays, we used Gene Tarpon Springs, (8) ; TB = Tampa Bay, (9) ; CH= Scan-500 ROX-labeled size standard (Applied Charlotte Harbor, (10) ; FB = Florida Bay, (11) Biosystems Inc.). ; KY = Big Pine Keys, (12) ; BB = Biscayne Bay, (13) ; SI = Sebastian Inlet, (14) ; SA = Saint Johns’ Microsatellite Genotyping River, (15)] – Georgia- GA = St. Andrew, (16)]- Specimens were genotyped using 21 of 29 South Carolina - SC = Charleston, (17)] – and microsatellite markers identified (Table 1: Cneb6, North Carolina - NC = Morehead City(18)]. Cneb15, Cneb29, Cneb34, Cneb39, Cneb40,

10 ------FWRI Technical Report - TR18 Seifu Seyoum et. al Stock Boundaries for Spotted Seatrout

and Cneb 41 were not used mostly because of Genetic Structure difficulty in incorporating them in multiplex The genetic structure among samples was reactions). Multiplex PCR amplifications were examined using three analytical approaches. carried out in an Eppendorf thermal cycler as in The first was analysis of molecular variance the above procedure containing a combination (AMOVA) using ARLEQUIN (version 3.5.1.3; of three optimally selected primers of three loci 100,000 permutations; Excoffier and Lischer, with each forward primer labeled with different 2010). AMOVA follows an a priori hierarchical fluorescent dye. Fragments were visualized on approach in which correlations among genotypes an ABI 3130 XL genetic analyzer and genotyped at various levels are partitioned as F-statistics.

using GeneMapper software (version 4.0; The proportion of variation among groups (FCT),

Applied Biosystems Inc.). within groups (FSC), and within samples (FST) Data Analyses was computed and the φ statistic assessed by GENEPOP data files were generated from the permutation method of Excoffier et al. (1992). fragment sizes recorded using the Microsatellite The a priori hierarchical structure analyzed was Marker Toolkit add-on (version 3.1.1; Park, based on the results of above phenetic clustering. 2001): http://animalgenomics.ucd.ie/sdepark/ The second method was explored using ms-toolkit/); these were converted to other the Bayesian clustering algorithm employed formats as needed using the conversion tool by STRUCTURE (version 2.3.4; Pritchard et PGDspider (version 2.0.1.9; Lischer and Excoffier, al., 2000). In this model, individuals were 2012). Hardy-Weinberg equilibrium (HWE) probabilistically assigned to one or more expectations, sample genotypic disequilibrium, clusters (K) based in the manner that minimizes

and observed (HO) and unbiased expected (HE) Hardy-Weinberg and linkage disequilibrium heterozygosity estimates averaged over all loci among multilocus microsatellite genotypes were obtained using GENEPOP (version 3.4; of the individuals included in the study. Ten Raymond and Rousset, 1995; Rousset, 2008). replicate simulations were conducted using a Bonferroni corrections were applied in multiple 1.0 × 106 Markov-Chain Monte Carlo (MCMC) comparisons (Rice, 1989). Genetic diversity, the simulation after a 2.0 × 106 burn-in period for number of alleles, and allelic richness (a diversity each value of K from 1 (the null hypothesis of measure that corrects for differences in sample panmixia) to 18 (where each sample would size) were calculated over all loci in each sample be a distinct genetic cluster). We used the using FSTAT (version 2.9.3.2; Goudet, 2001). admixture model and the independent allele T-tests were performed using QI Macros for frequencies option to minimize the chance of Excel (http://www.qimacros.com/Macros.html) overestimating the number of groups present to determine whether average genetic diversity in the data set (Pritchard et al., 2009). The differed significantly between samples. output result file for 10 replicated runs for Phenetic Clustering each cluster from STRUCTURE was archived into a zip file and uploaded to a web-based Pairwise genetic distances (FST) between samples (Weir and Cockerham, 1984) were estimated with program, STRUCTURE HARVESTER (version 10,000 permutations using GENETIX (Belkhir 0.56.3; Earl et al., 2012), which uses the posterior probabilities from STRUCTURE to calculate et al., 2000). The pairwise FST estimates were then used to create a phenogram illustrating lnP (D) and the magnitude change of LnP (D), K the relationships among samples via neighbor- that is, the log likelihood for each relative joining using NEIGHBOR (Saitou and Nei, to the standard deviation, called ΔK (Evanno 1987) in the PHYLIP package (version 3.6.8; et al. (2005). Evanno et al. (2005) suggest that K Felsenstein, 2009). the Δ parameter is a reliable measure of the relative support for each level of K. Once the

FWRI Technical Report - TR18 ------11 Stock Boundaries for Spotted Seatrout Seifu Seyoum et. al

most likely number of clusters was identified, the be polymorphic with a larger sample size or in a average proportions of membership from the 10 different sample or for other species ofCynoscion. replicated runs were aligned and summarized Twenty-one of the 29 informative markers were using CLUMPP (version 1.1.2; Jakobsson and selected based on optimal performance and Rosenberg, 2007) under the Greedy algorithm number of alleles and were used to genotype 438 with 1000 replicates. The average proportion of Spotted Seatrout collected at 18 locations from each cluster in each of the 18 samples and in the Texas to North Carolina (Figure 1, Table 2). 438 individuals under the optimal level of K was Standard Genetic Measures and Distances visualized using Excel. The average genotype The average of the genetic standard membership across runs for the sample and measurements over all loci for each sample individuals from CLUMPP was plotted using is given in Table 2. There were no significant DISTRUCT (version 1.1; Rosenberg, 2004) and the differences between the average diversity postscript visualized using Ghost View (http:// levels of any pair of samples. Using the 21 pages.cs.wisc.edu/_ghost). microsatellite loci, a total of 363 alleles were Finally geographic distances between sampled from the Spotted Seatrout gene pool samples were estimated by measuring the in the study region, and, of these, an average of shortest distance between sites following the 180 alleles were recorded in a single sample for coastline in GOOGLE EARTH. To test whether estimating genetic distances and relationships the genetic relationships among samples fit the among the 18 geographic samples. Of 153 pattern of isolation by distance, we estimated pairwise comparisons of genetic distance, 130 the Mantel correlation between the genetic were significantly different from zero at a 0.05

distance (FST) and the geographic distance (km) level of significance (Table 3). using the program GenAlEx (version 6.5; 9000 Phenetic Clustering randomizations; Peakall and Smouse, 2006, 2012). The genetic cluster of the 18 samples based on the F pairwise genetic distances showed Results ST three distinct clusters (Figure 2). These clusters Microsatellite Marker Assays corresponded to the western Gulf of Mexico, Thirty-two microsatellite markers were isolated from Texas to Fort Walton, Florida (4 samples), and characterized from 137 Spotted Seatrout the eastern (Florida) Gulf, from Steinhatchee specimens collected from Tampa Bay, Florida to Florida Bay (8 samples), and the Atlantic (Table 1). Twenty-nine of the 32 loci were Ocean, from Sebastian Inlet to North Carolina polymorphic. The average number of alleles per (5 samples). Specimens from Apalachicola were polymorphic locus was 11 (range, 2–35). The placed in the phenogram between the western mean observed and expected heterozygosities Gulf and the Florida Gulf. Genetic distances for were 0.56 (range, 0.02–0.98) and 0.58 (range, every pair of samples between different clusters 0.02–0.96), respectively. Significant departure were significant, but within each cluster 30–50% from HWE was detected at a single locus (Table of the pairwise differences were significant. The 1). Analyses using MICROCHECKER (version genetic distance between the western and the 2.2; Van Oosterhout et al., 2004) suggested eastern Gulf samples was 0.021; that between that the nonconformance to HWE may have the western Gulf and the Atlantic samples was resulted from the presence of null alleles at 0.056; and that between the eastern Gulf and the this locus. No pairs of loci were found to be in Atlantic was 0.052. The genetic distance between linkage disequilibrium. The three monomorphic the combined Gulf samples and the Atlantic was (Table 1) loci have been submitted to GenBank 0.048. accession, since these markers may be shown to

12 ------FWRI Technical Report - TR18 Seifu Seyoum et. al Stock Boundaries for Spotted Seatrout

Table 1. Characterization of 29 polymorphic microsatellite loci in 137 specimens of Spotted Seatrout (Cynoscion nebulosus) from Tampa Bay, Florida. Repeat motif, allele size range, number of alleles (K),

and observed and unbiased expected heterozygosity (HO and HE , respectively) are reported for each locus.

(Cont. next page)

FWRI Technical Report - TR18 ------13 Stock Boundaries for Spotted Seatrout Seifu Seyoum et. al

* indicates significant departure from Hardy-Weinberg equilibrium. GenBank accession numbers for monomorphic loci: Cneb05 (JF495376); Cneb14 (JF495381); Cneb28 (JF495391)

Figure 2. Unrooted neighbor-joining phenogram based on estimated values of FST between each pair of Spotted Seatrout samples using the NEIGHBOR program of PHYLIP. Sample abbreviations are presented in Figure 1.

14 ------FWRI Technical Report - TR18 Seifu Seyoum et. al Stock Boundaries for Spotted Seatrout

Table 2. Average standard measures of genetic diversity based on 21 microsatellite genotypes of

Spotted Seatrout (Cynoscion nebulosus) samples from 18 locations. (Estimates of HE [unbiased expected heterozygosity], HO [observed heterozygosity], and allelic richness based on total sample size at each location.)

Gene Alleles/ Allelic Location Code N diversity locus richness HE HO 1 South Padre Island, TX PI 29 0.71 9.6 8.9 0.70 0.71 2 Port Arthur, TX PA 25 0.72 9.5 9.3 0.71 0.72 3 Pascagoula, MS MS 21 0.73 9.1 8.8 0.71 0.73 4 Fort Walton, FL FW 27 0.73 10 9.8 0.71 0.72 5 Apalachicola Bay, FL AP 28 0.72 9.6 9.2 0.71 0.72 6 Steinhatchee, FL ST 23 0.70 8.2 8.2 0.68 0.70 7 Cedar Key, FL CK 35 0.71 10.4 10.2 0.70 0.71 8 Tarpon Springs, FL TP 24 0.70 8.9 8.7 0.68 0.70 9 Tampa Bay, FL TB 33 0.71 10.2 9.7 0.70 0.71 10 Charlotte Harbor, FL CH 20 0.72 8.5 8.3 0.70 0.72 11 Florida Bay, FL FB 22 0.71 8.8 8.3 0.70 0.71 12 Big Pine Key, FL KY 23 0.70 9.2 8.9 0.70 0.71 13 Biscayne Bay, FL BB 17 0.69 7.0 6.4 0.67 0.69 14 Sebastian Inlet, FL SI 18 0.65 6.3 6.1 0.63 0.65 15 St. Augustine, FL SA 18 0.66 6.5 6.3 0.64 0.66 16 St. Andrew, GA GA 28 0.66 8.0 7.7 0.64 0.66 17 Charleston, SC SC 29 0.66 8.2 7.9 0.64 0.66 18 Morehead City, NC NC 18 0.61 6.2 5.7 0.60 0.61

Table 3. Estimates of genetic distance (FST ; below diagonal) and geographic distance (km; above diagonal) between pairs of Spotted Seatrout (Cynoscion nebulosus) samples collected from Texas to North Carolina. FST values were estimated based on the 21 microsatellite genotypes. FST value between samples marked with an asterisk were not significantly different from zero at a 0.05 level of significance. ______PI PA MS FW AP ST CK TP TB CH FB KY BB SI SA GA SC NC ______

PI --- 588 1345 1548 1714 1934 2016 2171 2240 2348 2626 2718 2744 3006 3257 3508 3646 4059 PA 0.0075 --- 757 960 1126 1346 1428 1583 1652 1760 2039 2130 2156 2418 2670 2920 3058 3471 MS 0.0089 0.0004* --- 203 369 590 671 826 895 1003 1282 1373 1399 1661 1913 2163 2301 2714 FW 0.0163 0.0054 0.0014* --- 166 387 468 623 692 800 1079 1170 1196 1458 1710 1960 2098 2511 AP 0.0233 0.0125 0.0084 0.0124 --- 221 302 457 526 634 913 1004 1030 1292 1544 1794 1932 2345 ST 0.0323 0.0270 0.0242 0.0244 0.0096 --- 81 236 305 413 692 783 809 1071 1323 1573 1712 2125 CK 0.0317 0.0268 0.0256 0.0215 0.0162 0.0000* --- 155 224 332 611 702 728 990 1242 1492 1630 2043 TP 0.0276 0.0299 0.0257 0.0331 0.0201 0.0000* 0.0000* --- 69 177 456 547 573 835 1087 1337 1475 1888 TB 0.0297 0.0285 0.0244 0.0257 0.0117 0.0000* 0.0007* 0.0000* --- 108 387 478 504 766 1018 1268 1406 1819 CH 0.0334 0.0208 0.0196 0.0188 0.0115 0.0010* 0.0066 0.0007* 0.0054* --- 279 370 396 658 910 1160 1298 1711 FB 0.0284 0.0267 0.0249 0.0234 0.0165 0.0042* 0.0060 0.0057* 0.0067 0.0056* --- 91 117 379 631 881 1020 1433 KY 0.0355 0.0360 0.0285 0.0292 0.0097 0.0022* 0.0090 0.0053* 0.0014* 0.0030* 0.0056* --- 171 433 684 935 1073 1486 BB 0.0524 0.0469 0.0403 0.0456 0.0304 0.0264 0.0186 0.0182 0.0252 0.0111 0.0024* 0.0143 --- 262 514 764 902 1315 SI 0.0607 0.0598 0.0472 0.0611 0.0657 0.0709 0.0574 0.0548 0.0521 0.0527 0.0626 0.0617 0.0591 --- 252 502 640 1053

SA 0.0520 0.0522 0.0488 0.0607 0.0661 0.0734 0.0599 0.0630 0.0530 0.0613 0.0590 0.0657 0.0594 0.0168 --- 250 388 663 GA 0.0722 0.0641 0.0573 0.0667 0.0673 0.0836 0.0734 0.0721 0.0672 0.0618 0.0736 0.0786 0.0655 0.0232 0.0065* --- 138 551 SC 0.0559 0.0515 0.0450 0.0579 0.0610 0.0699 0.0591 0.0586 0.0531 0.0556 0.0657 0.0659 0.0665 0.0091 0.0017* 0.0029* --- 413 NC 0.0860 0.0844 0.0778 0.0932 0.0919 0.1048 0.0885 0.0918 0.0847 0.0901 0.0874 0.0957 0.0900 0.0509 0.0219 0.0108 0.0209 --- ______

FWRI Technical Report - TR18 ------15 Stock Boundaries for Spotted Seatrout Seifu Seyoum et. al

Table 4. Analyses of molecular variance (AMOVA) for the 18 Spotted Seatrout (Cynoscion nebulosus) samples collected from Texas to North Carolina, based on 21 microsatellite loci genotypes. Samples were partitioned into three clusters, as western Gulf of Mexico (4 samples from South Padre Island, TX, to Fort Walton, FL); eastern Gulf of Mexico (9 samples from Apalachicola Bay, FL, to Biscayne Bay, FL), and Atlantic Ocean (5 samples from Sebastian Inlet, FL to (Morehead City, NC). The Gulf of Mexico group included both western and eastern Gulf samples. (Samples from Apalachicola are mixtures, 46% western Gulf of Mexico, 44% eastern Gulf of Mexico and 10% Atlantic; see Fig. 4A).

(Φ(F) statistic: ΦCT (FCT ), among clusters; ΦSC (FSC ), among samples within clusters; ΦST (FST ), within samples).

Observed Partition

Variance Component df Variance % Total Φ P All Samples Among samples 17 0.24767 3.42 0.03422 <0.0001 Within samples 858 6.98976 96.58 ------Gulf of Mexico vs. Atlantic Ocean Among clusters 1 0.34217 4.60 0.04598 <0.0001 Among samples within clusters 16 0.11021 1.48 0.01552 <0.0001 Within samples 858 6.98976 93.92 0.06079 <0.0001 Western Gulf of Mexico vs. Eastern Gulf of Mexico vs. Atlantic Ocean Among clusters 2 0.29026 3.96 0.03956 <0.0001 Among samples within clusters 15 0.05733 0.78 0.00814 <0.0001 Within samples 858 6.98976 95.26 0.04737 <0.0001 Western Gulf of Mexico vs. Eastern Gulf of Mexico Among clusters 1 0.14732 2.01 0.02008 <0.0001 Among samples within clusters 11 0.04913 0.67 0.00683 <0.0001 Within samples 641 7.14104 97.32 0.02677 <0.0001 Western Gulf of Mexico vs. Atlantic Ocean Among clusters 1 0.38995 5.35 0.05355 <0.0001 Among samples within clusters 7 0.06328 0.87 0.00918 <0.0001 Within samples 417 6.82949 93.78 0.06223 <0.0001 Eastern Gulf of Mexico vs. Atlantic Ocean Among clusters 1 0.37646 5.10 0.05099 <0.0001 Among samples within clusters 12 0.06182 0.84 0.00882 <0.0001 Within samples 658 6.94396 94.06 0.05937 <0.0001 Western Gulf of Mexico Among samples 3 0.04110 0.57 0.00572 0.00293 Within samples 200 7.14044 99.43 ------Eastern Gulf of Mexico Among samples 8 0.05221 0.73 0.00726 <0.0001 Within samples 441 7.14131 99.27 ------Atlantic Ocean Among samples 4 0.08377 1.26 0.01264 <0.0001 Within samples 217 6.54290 98.74 ------

16 ------FWRI Technical Report - TR18 Seifu Seyoum et. al Stock Boundaries for Spotted Seatrout

Anaysis of Molecular Variance (AMOVA) 3A Analysis of molecular variance with all samples combined showed that 96.58% of the variation existed within samples, i.e., between individuals,

and 3.42% among samples. The FST value of 0.03422 indicated significant differentiation among the samples (P < 0.0001). The AMOVA results of the various sample partitions are shown in Table 4. A number of sample groupings were analyzed by AMOVA, and all variance components among samples within each cluster and between clusters were significant (Table 4). The greatest among-group variance for any two- 3B grouping of samples was expected to be found when the samples were divided between the Gulf and Atlantic regions (4.6%, Table 4). But both subdivided groups of the Gulf of Mexico samples gave a higher percentage difference when each subdivision of the Gulf of Mexico samples was tested against the Atlantic samples (western Gulf 5.4% and eastern Gulf 5.1%). These changes probably reflect the effects of subsampling, which led to slightly different results, and did not reveal significant differences in the genetic distance estimates. The best supported delineation of Figure 3. Mean likelihood L (posterior three regional groupings was when samples probability, A) and ΔK, B) determined from were organized into western Gulf, eastern Gulf, 10 replicates of each value of K (from 1 to 18) and Atlantic groups just as they were in the statistics from STRUCTURE analysis of the 18 phenogram. The only uncertainty was whether Spotted Seatrout samples. the Apalachicola sample should be included with the western Gulf (3.82% variation partitioned among groups) or the eastern Gulf samples (3.96%). This analysis indicates that samples from peak at K = 3 (Figure 3B) indicate a hierarchical Apalachicola were an admixture of the first and substructure. At K = 2, the Spotted Seatrout is second clusters. partitioned at its highest differentiation, that Bayesian Population Assignment is, between the Gulf and the Atlantic samples, Reliable inferences can be derived by permuting into two separate genetic clusters, with the likelihood values and ΔK statistics for different genetic break being between samples from levels of K to determine optimal number of Biscayne Bay (assigned to the Gulf cluster) genetic clusters (K). In this study, the likelihood and those from Sebastian Inlet (assigned to values increased quickly from 1 to 2, and then the Atlantic cluster) (Figure 4A). At K = 3, to 3, before reaching the maximum at K = 4 and the second-highest likelihood scores for each decreasing in successive Ks (Figure 3A). But value of k genetic clusters from STRUCTURE the greatest change in LnP(D) relative to the are obtained (Figure 4B). These three clusters standard deviation, called ΔK by Evanno et al. differentiate the Spotted Seatrout from Texas to (2005), occurred at K = 2, and one other lower North Carolina congruently with the neighbor-

FWRI Technical Report - TR18 ------17 Stock Boundaries for Spotted Seatrout Seifu Seyoum et. al

4A

Figure 4. Genetic population structure among Spotted Seatrout samples according to posterior probability assignment produced by the STRUCTURE analysis of 21 polymorphic microsatellite loci 4B genotypes (4.5x105 burn-in, and 9.0x105 replications): The CLUMMP output from 10 replicates for the 438 individuals and the eighteen samples for the highest modal value (number of genetic cluster) at K=2 (A), for the next modal value 4C of cluster at K=3 (B), proportional values of each cluster in each of the 18 samples (C), and graphic display of the population structure using the program DISTRUCT (C, D). Figures are demarcated by vertical lines. Sample name of the numbers 4D in Figure 4C are given in Table 2.

18 ------FWRI Technical Report - TR18 Seifu Seyoum et. al Stock Boundaries for Spotted Seatrout

joining tree (Figure 2) and decidedly showed 1:2.5:5 (western Gulf : eastern Gulf : Atlantic). that sample associations exist in the Spotted The relationships between the groups were Seatrout based on geographic location for the presented for samples from the western Gulf and three genetic clusters. These clusters are the the Atlantic (Figure 6D), the eastern Gulf and the western Gulf, eastern Gulf, and the Atlantic Atlantic (Figure 6E), and the western Gulf and samples, as identified in the cluster of the eastern Gulf (Figure 6F). genetic distances and as depicted in the visual graph from STRUCTURE. The assignment of Implications for the Fishery the 18 samples and the 438 individuals in their FWC Spotted Seatrout Regional Boundaries respective clusters are graphically depicted in The present delineation of FWC’s regional units Figure 4 (A, B, C, and D). Figure 4C shows the for managing Spotted Seatrout is not based graphic composition of the three clusters in each on genetic stocks. Instead, the boundaries are sample, corresponding to the cluster membership based primarily on watershed boundaries and distributions across individuals in percentage reproductive differences among estuaries. There values of the three clusters in each sample. At is evidence of biological differences among the population level the western Gulf samples estuaries, including growth characteristics (Bedee are grouped together by a 78–82% membership et al., 2002), size and age structure (Murphy and coefficient, the Florida Gulf by 78–88%, and Taylor, 1994), and reproductive biology (Brown- the Atlantic samples by 86–96% (Figure 4C). Peterson, 2002). In 2011, the FWC modified its The genotypes from the Apalachicola sample Spotted Seatrout management zones by splitting had nearly equal assignments to Cluster 1 and the south management zones into two, increasing Cluster 2 (46% and 44%, respectively), suggesting the total number of zones to four. These zones are that this may be an area where these two genetic 1) the northwest region, from Escambia County groups come into contact and mix (Figure 4C to Pasco County, 2) the southwest region, from #5). Figure 4D is a graphic display of the genetic Pasco County to the Miami Dade–Monroe county population structure of the Spotted Seatrout. line, 3) the southeast region, from the Miami Mantel Test Dade–Monroe county line to Flagler County, and The result of the Mantel test that included all the 4) the northeast region, from Flagler County to samples (i.e., from Texas to North Carolina) was the Georgia state line (Figure 5A). statistically significant (R = 0.581, P < 0.000). This Genetic Regional Boundaries statistical significance of the correlation between Our findings support the presence of three the genetic and the geographic distances is that discrete Spotted Seatrout stocks in Florida waters these samples conformed to expectations of (Figure 5B): 1) fish in the western border of genetic isolation by distance. But this significance Florida to Apalachicola Bay, 2) fish from east of might also result from the population structure. Apalachicola Bay through Biscayne Bay, and 3) When Mantel tests were conducted within the fish from Sebastian Inlet through the northeast clusters identified by the phenogram (Figure 2), border of Florida. At the adjoining sites between marginally significant isolation by distance was the first and second stock ranges at Apalachicola detected among the western Gulf samples (R Bay, the Spotted Seatrout stock appears to be an = 0.785, P = 0.050; Figure 6A) and the Atlantic admixture of the first and second stocks. That Ocean samples (R = 0.807, P = 0.04; Figure 6C). is, this area represents a small zone of genetic In contrast, there was a high significant isolation intergradation between the first and second by distance in the eastern Gulf samples stocks that may be maintained by migration (R = 0.515, P = 0.01; Figure 6B). The slope of the between them. regressions of genetic and geographic distances for the three groups had a ratio of approximately In contrast, the area between Biscayne

FWRI Technical Report - TR18 ------19 Stock Boundaries for Spotted Seatrout Seifu Seyoum et. al

5A

Figure 6. Mantel tests examining correlations

between pairwise genetic distance (FST) and geographic distance (km) within and between the groups of Spotted Seatrout samples identified by the cluster analyses within: the western Gulf of Mexico including samples from South Padre Island, TX, to Fort Walton, FL (N = 4, A) the eastern Gulf of Mexico from Apalachicola 5B Bay, FL, to Biscayne Bay, FL (N = 9, B), and the Atlantic Ocean from Sebastian Inlet, FL to Morehead, NC (N = 5, C); between groups; western Gulf of Mexico and Atlantic Ocean(N = 9, D), eastern Gulf of Mexico and Atlantic Ocean(N = 14, E), and western Gulf of Mexico and eastern Gulf of Mexico, (N= 13, F).

5C

Figure 5. Spotted Seatrout stock boundaries; based on watershed and Spotted Seatrout reproductive differences FWC-Fish and Wildlife Conservation Commission’s management zones: arrows indicate boundaries of management zones (A), genetic structure detected using 21 microsatellite loci: arrows indicate regions of gene flow restriction (genetic breaks) and transition zones between clusters (regions; B), and superimposition of the above two demarcations: Arrows indicate regions of differences between the FWC and genetic boundaries (C). (See text for definitions of boundaries).

20 ------FWRI Technical Report - TR18 Seifu Seyoum et. al Stock Boundaries for Spotted Seatrout

Bay and Sebastian Inlet, which separates genetic regions coincided with the eastern Gulf and regions 2 and 3, represents a more definitive Atlantic genetic stocks, respectively. But in the genetic break. Here, there is a high degree of assessment conducted for the northwest region, human development and minimal estuarine the western Gulf was combined with part of habitat for Spotted Seatrout. Landings of Spotted the eastern Gulf genetic stock. In the panhandle Seatrout in this area have fallen dramatically region, that is, in the western Gulf genetic stock, in recent years (Addis et al., 2013). Thus, the landings could be significant, but sampling of location of this strong genetic discontinuity the fishery in this relatively small area has been corresponds to a contemporary gap in the sparse, and so assessments are data-limited abundance of the species. (Addis et al. 2013). Thus, the stock assessment Incongruencies Between the Boundaries of of the northwest and southwest regions was FWC Management Units and the Genetic based essentially on data solely from the second Stocks genetic stock. Spotted Seatrout are much more The boundaries of genetic stocks demarcated abundant in locations west of Florida, but, except in this study differ from the FWC’s regional in Texas, regulations in these locations tend to management boundaries for Spotted Seatrout. be lax. This makes it all the more important that These differences are identified by numbers fishery biologists in Florida assess and formulate placed at three locations in Figure 5C. The first regulations for managing this portion of the difference, identified by arrow #1, is the most western Gulf genetic stock. The strict regulation significant, because it combines the western Gulf and intensive management of the Spotted genetic stock in Florida with part of the eastern Seatrout in Texas (Anderson and Karl, 2009) may Gulf genetic stock into a single zone, according be beneficial to the entire western Gulf genetic to FWC regional management boundaries. The stock, because the results of isolation by distance result is that the two genetic stocks are managed suggests that fish in this stock have considerable as one genetic stock. But Florida has the authority migratory potential (>200 km in range; Seyoum to manage only a small part of the western Gulf et al., unpublished data). On the other hand, genetic stock (100 ± 25 miles of shoreline). Most stocking activities in Texas or other states that of this stock’s range is located in other states’ may have an active hatchery program for Spotted waters. The second and the third differences, Seatrout could affect the integrity of fish in identified by arrows #2 and #3, are cases for western Florida if agencies released fish raised which FWC management boundaries divide from other genetic stocks. An interstate (and the eastern Gulf and the Florida Atlantic Ocean possibly even an international) assessment effort genetic stocks each into two management zones. would be needed to gather the data required to The division of a single genetic stock into two assess the western Gulf genetic stock from Cape management zones does not serve any purpose. San Blas, Florida, west to at least Port Isabel, It does not, however, entail any adverse genetic Texas, and possibly into Mexico (Mike Murphy, effects, as would be the case if two genetic stocks personal communication). were combined into one management zone. Recommendations Stock Assessment of the Spotted Seatrout in We recommend that FWC scientists adopt Florida genetically demarcated stock boundaries as Murphy et al. (2009) recently conducted a stock assessment boundaries for the Spotted Seatrout. assessment of the Spotted Seatrout in four Although current management practices have regions (identified as northwest, northeast, been successful, it is not advisable over the southwest, and southeast). The stock assessment long term to assess reproductively independent boundaries of the southwest and southeast populations as a single unit. Demarcations of management areas based on geography or even

FWRI Technical Report - TR18 ------21 Stock Boundaries for Spotted Seatrout Seifu Seyoum et. al

morphological or reproductive differences often resulted in overfishing of the more vulnerable fail to reflect the demographically meaningful subsample (Fu and Fanning, 2004; Sterner, 2007). ranges of genetic stocks. Such demarcations If for no other reason, the fact that there are may ultimately lead to overfishing, reduced significant landings of Spotted Seatrout in the recruitment, and population declines. For range of the western Gulf of Mexico genetic example, simulation studies for cod, using stock in Florida (Addis et al., 2013) warrants a a sample dynamics model, showed that the change to management strategies commensurate combined management of two subsamples with the genetic stock identity of the fish in this (which may be genetically distinct) as one unit region.

Kayla Michael releases a Spotted Seatrout. FWC photos by Tim Donovan.

22 ------FWRI Technical Report - TR18 Seifu Seyoum et. al Literature Cited Stock Boundaries for Spotted Seatrout

Literature Cited BELKHIR K, P. BORSA, L. CHIKHI, N. RAUFASTE, AND F. BONHOMME. 2000. ADDIS, D., D. CHAGARIS, W. COOPER, GENETIX, Logiciel sous WindowsTM pour B. MAHMOUDI, R. G. MULLER, J. la génétique des samples. Laboratoire MUNYANDOREO, M. D. MURPHY, Génome, Samples, Interactions CNRS J. O’HOP, C. GUENTHER, AND M. UMR 5000, Université de Montpellier II, TYLER-JEDLUND. 2013. Florida inshore Montpellier, France. and nearshore species: 2012 status and trends report. Florida Fish and BROWN-PETERSON, N. J. 2002. The Wildlife Conservation Commission, Fish reproductive biology of the Spotted and Wildlife Research Institute, Saint Seatrout. Pp. 99–133 in S. A. Bortone, ed. Petersburg, FL 6p. Biology of the Spotted Seatrout. CRC Press, Boca Raton, FL. AGUILAR-SALAZAR, F. A., F. ARREGUÍN- SÁNCHEZ, J. A. SÁNCHEZ, AND J. D. EARL, DENT A., AND M. B. VONHOLDT. 2012. MARTÍNEZ-AGUILAR. 1993. Fishing STRUCTURE HARVESTER: a website mortality and sample size in the Spotted and program for visualizing STRUCTURE Seatrout Cynoscion nebulosus (Cuvier) from output and implementing the Evanno Holbox, Quintana Roo, Mexico. Ciencias method. Conservation Genetics Resources Marinas 19(3): 307–319. 4(2): 359–361. AGUILAR-SALAZAR, F. A., F. ARREGUÍN- EVANNO, G., S. REGNAUT, AND J. GOUDET. SÁNCHEZ, J. A. SÁNCHEZ, AND J. D 2005. Detecting the number of clusters MARTÍNEZ-AGUILAR. 1995. Sinopsis de of individuals using the software la pesquería de la corvina pinta Cynoscion STRUCTURE: a simulation study. nebulosus (Cuvier) de Holbox, Quintana Molecular Ecology 14: 2611–2620. Roo, México. Revista de Investigaciones EXCOFFIER, L., AND H. E. L. LISCHER. 2010. Marinas 16(1–3): 121–135. Arlequin suite (version 3.5.1.3): a new ANDERSON, J. D., AND W. J. Karl. 2009. A series of programs to perform population genetic assessment of current management genetics analyses under Linux and strategies for Spotted Seatrout in Texas. Windows. Molecular Ecology Resources Marine and Coastal Fisheries: Dynamics, 10: 564–567. Management, and Ecosystems Science EXCOFFIER, L., P. E. SMOUSE, AND J. M. 1:121–132. DOI: 10.1577/C09–001–1 QUATTRO. 1992. Analysis of molecular BEDEE, C. D., C. L. PALMER, S. A. BORTONE, variance inferred from metric distances AND D. A. DEVRIES. 2002. Estuary- among DNA haplotypes: applications to specific age and growth of Spotted human mitochondrial DNA restriction Seatrout in the northern Gulf of Mexico. data. Genetics 131: 479–491. Pp. 57–77 in S. A. Bortone, ed. Biology FELSENSTEIN, J. 2009. PHYLIP (Phylogeny of the Spotted Seatrout. CRC Press, Boca Inference Package; version 3.69). Raton, FL. Department of Genome Sciences and BEAUMARIAGE, D. S. 1969. Results from the Department of Biology, University of Schlitz tagging program including a Washington, Seattle. cumulative analysis of previous results. Florida Department of Natural Resources, Tech. Ser. No. 59:1–38.

FWRI Technical Report - TR18 ------23 Stock Boundaries for Spotted Seatrout Literature Cited Seifu Seyoum et. al

FU, C., AND L. P. FANNING. 2004. Spatial JOHNSON, D. R., AND W. SEAMAN. 1986. considerations in the management of Species profiles: life histories and Atlantic Cod off Nova Scotia, Canada. environmental requirements of coastal fish North American Journal of Fisheries and invertebrates (South Florida): Spotted Management 24(3): 775–784. Seatrout. U. S. Fish and Wildlife Service GOLD, J. R., AND L. R. RICHARDSON. 1998. Biological Report 82(11.72) U. S. Army Mitochondrial DNA diversification and Corps of Engineers TR EL-82-4. 16 pp. sample structure in fishes from the Gulf of KALINOWSKI, S. T. 2002. How many alleles per Mexico and western Atlantic. Journal of locus should be used to estimate genetic Heredity 89: 404–414. distances? Heredity 88: 62–65. GOLD, J. R., L. R. RICHARDSON, AND C. KALINOWSKI, S. T. 2005. Do polymorphic loci FURMAN. 1999. Mitochondrial DNA require large sample sizes to estimate diversity and sample structure of Spotted genetic distances? Heredity 94: 33–36. Seatrout (Cynoscion nebulosus) in coastal KING, T. L., AND H. O. PATE. 1992. Sample waters of the southeastern United States. structure of Spotted Seatrout inhabiting Gulf of Mexico Science 17: 41–50. the Texas Gulf coast: an allozyme GOLD, J. R., L. B. STEWART, AND R. WARD. perspective. Transactions of the American 2003. Sample structure of Spotted Seatrout Fisheries Society 121: 746–756. (Cynoscion nebulosus) along the Texas Gulf KING, T. L., AND E. G. ZIMMERMAN. coast, as revealed by genetic analysis. Pp. 1993. Clinal variation at aspartate 17–29 in S. A. Bartone, ed. Biology of the aminotransferase-2 in Spotted Seatrout, Spotted Seatrout. CRC Press, Boca Raton, Cynoscion nebulosus (Cuvier), inhabiting FL. the northwestern Gulf of Mexico. Animal GOUDET, J. 2001. FSTAT: a program to estimate Genetics 24: 59−61. and test gene diversities and fixation LASSUY, D. R. 1983. Species profile: life histories indices (version 2.9.3.2). Available and environmental requirements of coastal from http://www2.unil.ch/popgen/ fishes and invertebrates (Gulf of Mexico): softwares/FSTat.htm Spotted Seatrout. U. S. Fish and Wildlife IVERSEN, E. S., AND A. W. MOFFETT. 1962. Service, Division of Biological Sciences. Estimation of abundance and mortality of FWS/OBS-82/11.4. U. S. Army Corps of a Spotted Seatrout sample. Transactions Engineers TR EL–82–4. 14 pp. of the American Fisheries Society 91: 395– LISCHER, H. E. L., AND L. EXCOFFIER. 2012. 398. PGDSpider: an automated data conversion IVERSEN, E. S., AND D. C. TABB. 1962. tool for connecting sample genetics and Subsamples based on growth and tagging genomic programs. Bioinformatics 28: studies of Spotted Seatrout, Cynoscion 298–299. nebulosus, in Florida. Copeia 1962: 544–548. LUNT, D. H., W. F. HUTCHINSON, AND G. R. JAKOBSSON, M., and N. ROSENBERG. 2007. CARVALHO. 1999. An efficient method CLUMPP (version 1.1.2): a cluster for PCR-based isolation of microsatellite matching and permutation program arrays (PIMA). Molecular Ecology 8: 891– for dealing with label switching and 894. multimodality in analysis of sample structure. Bioinformatics 23(14): 1801–1806.

24 ------FWRI Technical Report - TR18 Seifu Seyoum et. al Literature Cited Stock Boundaries for Spotted Seatrout

MERCER, L. P. 1984. A biological and fisheries PASCHALL, R. L. 1986. Biochemical systematics profile of Spotted Seatrout, Cynoscion of the seatrout of the western Atlantic nebulosus. North Carolina Department genus Cynoscion. Master’s Thesis, of Natural Resources and Community University of New Orleans (Louisiana). Development Divison. Marine Fisheries PATTILLO, M. E., T. E. CZAPLA, D. M. Special Scientific Report. 40:1– 87. NELSON, AND M. E. MONACO. 1997. MERRINER, J. V. 1980. History and management Distribution and abundance of fishes and of the Spotted Seatrout fishery. invertebrates in Gulf of Mexico estuaries. Proceedings of the Red Drum and Seatrout Vol. II. Species life history summaries. Colloquium. Gulf States Marine Fisheries Estuaries Living Marine Resources Commission 5: 55–61. Report no 11. NOAA/NOS Strategic MOFFETT, A. W. 1961. Movements and growth Environmental Assessments Division. of Spotted Seatrout, Cynoscion nebulosus Silver Spring, MD. (Cuvier), in west Florida. State of Florida PEAKALL, R., and P. E. SMOUSE. 2006. Board of Conservation Technical Series GENALEX (version 6): genetic analysis 36:35pp. in Excel. Sample genetic software for MURPHY, M. D., D. CHAGARIS, AND D. teaching and research. Molecular Ecology ADDIS. 2011. An assessment of the status Notes 6: 288–295. of Spotted Seatrout in Florida waters PEAKALL, R., AND P. E. SMOUSE. 2012. through 2009. Florida Fish and Wildlife GenAlEx (version 6.5): genetic analysis Conservation Commission Fish and in Excel. Sample genetic software for Wildlife Research Institute IHR 2011-002. teaching and research: an update. 53 pp http://myfwc.com/media/1355905/ Bioinformatics 28: 2537–2539. Spotted_seatrout.pdf. PRITCHARD, J. K., M. STEPHENS, AND P. MURPHY, M. D., AND R. G. TAYLOR. 1994. Age, DONNELLY. 2000. Inference of sample growth, and mortality of Spotted Seatrout structure using multilocus genotype data. in Florida waters. Transactions of the Genetics 155: 945–959. American Fisheries Society 123: 482–497. PRITCHARD, J. K., X. WEN, AND D. FALUSH. MUSIC, J. L., JR. 1981. Seasonal movement and 2009. Documentation for structure migration of Spotted Seatrout (Cynoscion software: (version 2.2). University of nebulosus) (Abstract). Estuaries 4(3): 280. Chicago. Available at pritch.bsd.uchicago. NMFS (National Marine Fisheries Service). 2007. edu/software. Fisheries of the United States 2006. NMFS, RAMSEY, P. R., AND J. M. WAKEMAN. 1987. Office of Science and Technology, Fisheries Sample structure of Sciaenops ocellatus and Statistics Division, Silver Spring, MD. 4 Cynoscion nebulosus (Pisces: Sciaenidae): pp. biochemical variation, genetic subdivision PARK, S. D. E. 2001. Trypanotolerance in and dispersal. Copeia 1987: 682–695. West African cattle and the population RAYMOND, M., AND F. ROUSSET. 1995. An genetic effects of selection. Ph.D. Thesis, exact test for sample differentiation. University of Dublin, Republic of Ireland. Evolution 49: 1280–1283. RICE, W. R. 1989. Analyzing tables of statistical tests. Evolution 43: 223–225.

FWRI Technical Report - TR18 ------25 Stock Boundaries for Spotted Seatrout Literature Cited Seifu Seyoum et. al

ROBINS, C., C. RICHARD, G. C. RAY, J. VAN OOSTERHOUT, C., W. F. HUTCHINSON, DOUGLAS, AND R. FREUD. 1986. A field D. P. M. WILLS, AND P. SHIPLEY. guide to Atlantic coast fishes of North 2004. MICRO-CHECKER: software for America. Peterson Field Guide Series No. identifying and correcting genotyping 32. Houghton Mifflin Co., Boston. 354 pp. errors in microsatellite data. Molecular ROUSSET, F. 2008. Genepop’ 007: a complete Ecology Resources 4: 535–538. reimplementation of the GenePop WARD, R., K. BOWERS, R. HENSLEY, B. software for Windows and Linux. MOBLEY, AND E. BELOUSKI. 2007. Molecular Ecology Resources 8: 103–106. Genetic variability in Spotted Seatrout Cynoscion nebulosus ROSENBERG, N. A. 2004. DISTRUCT (version ( ), determined with 1.1): a program for the graphical display microsatellite markers. Fisheries Bulletin of sample structure. Molecular Ecology 105: 197–206. Notes 4: 137–138. WEINSTEIN, M. P., AND R. W. YERGER. SAITOU, N., AND M. NEI. 1987. The neighbor- 1976. Electrophoretic investigation of joining method: a new method for subsamples of the Spotted Seatrout, Cynoscion nebulosus reconstructing phylogenetic trees. (Cuvier), in the Molecular Biology and Evolution 4: 406– Gulf of Mexico and Atlantic coast of 425. Florida. Comparative Biochemistry and Physiology 54B: 97–102. SAMBROOK, J., E. F. FRITSCH, AND T. MANIATIS. 1989. Molecular cloning: a WEIR, B. S., AND C.C. COCKERHAM. 1984. laboratory manual. 2nd ed. Cold Spring Estimating F-statistic for the analysis of Harbor Laboratory Press, Cold Spring sample structure. Evolution 38: 1358–1370. Harbor, NY. 1626 pp. WILEY, B. A., AND R. W. CHAPMAN. 2003. SEYOUM, S, M. D. TRINGALI, AND J. Sample structure of Spotted Seatrout, Cynoscion nebulosus, G. SULLIVAN. 2005. Isolation and along the Atlantic in characterization of 27 polymorphic Coast of the U.S. Pp. 31–40 S. A. microsatellite loci for the common snook, Bartone, ed. Biology of the Spotted Centropomus undecimalis. Molecular Seatrout. CRC Press, Boca Raton, FL. Ecology Notes 5: 924–927. WILSON, M. M, T. M. BERT, AND S. SEYOUM. STERNER, T. 2007. Unobserved diversity, 2002. Genetic stock structure of the Cynoscion nebulosus, depletion and irreversibility. The Spotted Seatrout, in importance of sub-samples for Florida. Florida Marine Research Institute management of cod stocks. Ecological Report Number IHR2002–005. Fish and Economics 61: 566–574. Wildlife Research Institute. 11 pp. TABB, D. C. 1966. The estuary as a habitat for WILSON, G. A., AND B. RANNALA. 2003. Spotted Seatrout, Cynoscion nebulosus. Bayesian inference of recent migration American Fisheries Society Special rates using multilocus genotypes. Genetics Publication 3: 59–67. 163: 1177–1191.

26 ------FWRI Technical Report - TR18 Fish and Wildlife Research Institute Technical Report Series A complete list of the Technical Report Series can be found at MyFWC.com/research/publications/scientific/technical-reports

TR-7 Weigle, B. L., I. E. Wright, M. Ross, and R. Flamm. 2001. Movements of Radio-Tagged Manatees in Tampa Bay and Along Florida’s West Coast, 1991–1996. Florida MarineResearch Institute Technical Report TR-7.ii + 156 p.

TR-8 Wakeford, A. 2001. State of Florida Conservation Plan for Gulf Sturgeon (Acipenseroxyrinchus desotoi). Florida Marine Research Institute Technical Report TR-8.ii + 100 p.

TR-9 Adams, D. H., R. H. McMichael, Jr., and G. E. Henderson. 2003. Mercury Levels in Marine and Estuarine Fishes of Florida 1989–2001. Second Edition, Revised. Florida Marine Research Institute Technical Report TR-9.ii + 57 p.

TR-10 McDonald, S. L., and R. O. Flamm. 2006. A Regional Assessment of Florida Manatees (Trichechus manatus latirostris) and the Caloosahatchee River, Florida. Fish and Wildlife Research Institute Technical Report TR-10. ii + 52 p.

TR-11 Hunt, J. H., and W. Nuttle, eds. 2007. Florida Bay Science Program: A Synthesis of Research on Florida Bay. Fish and Wildlife Research Institute Technical Report TR-11.iv + 148 p.

TR-12 Gerhart, S. D. 2007. A Review of the Biology and Management of Horseshoe Crabs, with Emphasis on Florida Populations. Fish and Wildlife Research Institute Technical Report TR-12.ii + 24 p.

TR-13 Swanson, K., D. Land, R. Kautz, and R. Kawula. 2008. Use of Least-Cost Pathways toIdentify Key Road Segments for Florida Panther Conservation. Fish and Wildlife Research Institute Technical Report TR-13.ii + 44 p.

TR-14 Abbott, G. M., J. H. Landsberg,A. R. Reich, K. A. Steidinger, S. Ketchen, and C. Blackmore. 2009. Resource Guide for Public Health Response to Harmful Algal Blooms in Florida. Fish and Wildlife Research Institute Technical Report TR-14.viii + 132 p.

TR-15 Endries, M., B. Stys, G. Mohr, G. Kratimenos, S. Langley, K. Root, and R. Kautz. 2009. Wildlife Habitat Conservation Needs in Florida. Fish and Wildlife Research Institute Technical Report TR-15.x + 178 p.

TR-16 Switzer, .T S., A. J. Tyler-Jedlund, K. R. Rogers, H. Grier, R. H. McMichael Jr., and S. Fox. 2011 Response of estuarine nekton to the regulated discharge of treated phosphate-production process water. Fish and Wildlife Research Institute Technical Report TR-16. + 24p.

TR-17 Yarbro, L. A., and P. R. Carlson, Jr., eds. 2013. Seagrass Integrated Mapping and Monitoring Program: Mapping and Monitoring Report No. 1. Fish and Wildlife Research Institute Technical Report TR-17. iv + 126 p.