Preliminary genetic population structure of southern flounder, Paralichthys lethostigma, along the Atlantic Coast and Gulf of Mexico.
Declines in southern flounder absolute abundance in some regions (e.g. Texas during the 1980s; Fuls and McEachron (4)) have prompted some management agencies to institute restrictions on recreational and commercial fisheries including reductions in bag limits and minimum size. Should these measures fail to recover this fishery, other measures may be considered by managers, including further restrictions on harvest, or artificial propagation and stocking, or both. Implementation of such enhancement programs requires that genetic surveys be conducted to determine genetic variability and stock structure of managed fish populations (King et al., 1995). Failure to understand underlying genetic structure prior to implementing stocking programs places the genetic resources of target species at risk (Allendorf et al., 1987) and may result in the reduction or loss of among-population variability and changes in within-population genetic characteristics. Genetic analyses of population structure may also provide insight into management options that do not require stocking (Nelson and Soule, 1987).
The objective of our study was to characterize population structure of southern flounder in coastal regions of the northern Gulf of Mexico and north-western Atlantic Ocean and to test the null hypothesis of no genetic differentiation within the region surveyed. If this hypothesis was rejected, a number of processes would operate to structure southern flounder population(s). Genetic differentiation in some nearshore organisms in the northern Gulf of Mexico (e.g. Sciaenops ocellatus; Gold and Richardson, 1999) has been explained as isolation by distance (Wright, 1943). This model describes a population structured by isolation caused by limited individual migration potential in relation to the size of the species' distribution (Kimura and Weis, 1964). The hypothesis of isolation by distance is supported when geographic distance and genetic distance are positively correlated. Alternatively, differentiation may arise as an adaptive response to localized environmental conditions (King and Zimmerman, 1993) or from the operation of physical barriers, such as current patterns (King et al., 1994). Support for competing models comes from examination of specific patterns observed in structured populations. Such patterns, if observed, may have important management implications.
Materials and methods
Southern flounder were collected during the summers of 1996 and 1997 by rod and reel, flounder gigs, or Texas Parks and Wildlife (TPW) gill nets in four Texas bays (Sabine Lake, Galveston Bay, Matagorda Bay, and lower Laguna Madre), by pound nets in Core Sound, North Carolina, from gill nets in estuarine waters near Biloxi, Mississippi, and from commercial fish houses in Dauphin Island, Alabama, and St. Augustine, Florida (Fig. 1). Southern flounder from commercial fish houses were reported by the house operator to be caught locally. A majority of individuals collected were adult and were not reliably assignable to year classes. Samples were screened by using isoelectric focusing (IEF) of sarcoplasmic proteins (methods of Ward et al., 1995) to insure that individuals belonging to other Paralichthys species were not included in our analyses (necessary because of accidental inclusion of congenerics both in samples obtained from commercial sources and in samples of juvenile flounder obtained during routine resource sampling in Texas).
Skeletal muscle, liver, heart, and kidney tissues were excised from fresh or frozen fish. Sample preparation and electrophoretic techniques and conditions followed those of King and Pate (1992). Gel and electrode buffers used were tris-borate-EDTA, pH 8.0 (Selander et al., 1971), triscitrate, pH 8.0. (Selander et al., 1971), lithium hydroxide, pH 8.0 (Selander et al., 1971), borate buffer, pH 9.0 (modified from Sackler, 1966), and Poulik's discontinuous system, pH 8.7 (Selander et al., 1971). Histochemical methods were primarily taken from Manchenko (1994). Genetic nomenclature followed the recommendations of Shaklee et al. (1990).
BIOSYS-1 (Swofford and Selander, 1981) was used to generate an allele frequency table, to estimate the proportion of loci heterozygous (H) in the average individual, the proportion of polymorphic loci in individuals from each bay population, and genetic divergence using the F-statistics of Wright (1978). Exact tests calculated by GENEPOP (v. 3.1; Raymond and Rousset, 1995) were used to test for conformance of genotypic frequencies at each locus within a sample to Hardy-Weinberg expectations, genotypic linkage disequilibrium, and allelic and genotypic heterogeneity. Pairwise differences between samples were tested by using the genic differentiation randomization test in GENEPOP. Results were combined over loci with Fisher's method (Sokal and Rohlf, 1994). Differences between each pair of populations were summarized by using the chord distance of Cavalli-Sforza and Edwards (1967). An unrooted phylogenetic tree was fitted to the chord distance matrix by using the neighbor-joining (NJ) algorithm. TreeView (Page, 1996) was used to visualize the tree. The strength of support for each node in the tree was tested by bootstrapping over loci with NJBPOP (Cornuet et al., 1999). To further quantify spatial heterogeneity, the fixation index ([F.sub.ST]) was calculated for each locus to provide measures of interpopulation differentiation and estimates of reduction in heterozygosity of a subpopulation due to population subdivision. A [chi square] test was used to test the null hypothesis, [F.sub.ST]=0 (Workman and Niswander, 1970). All [chi square] probability values from tests for conformance to Hardy-Weinberg expectations, heterogeneity, linkage disequilibrium, and [F.sub.ST] were adjusted for multiple simultaneous tablewide tests by using sequential Bonferroni adjustments to minimize type-I statistical inference errors (Rice, 1989).
Partitioning of variance components among geographic regions and within samples was accomplished by using a hierarchical analysis of molecular variance (AMOVA, Cockerham, 1969, 1973) with the package ARLEQUIN (version 2, Schneider et al., 1999). Sample sites were nested into regional groupings for separate analyses (Atlantic versus Gulf of Mexico, and Atlantic combined with eastern Gulf sites versus western Gulf). A phenogram was generated from the chord distance matrix with the neighbor-joining (N-J) algorithm. The N-J phenogram, with bootstrap estimates (as percentage of 10,000 replications) obtained by resampling loci within samples, was generated with NJBPOP (Cornuet, et al., 1999). The significance of the relationship between genetic (i.e. chord) and geographic (bay to bay shoreline distance) distance matrices was determined by sampling the randomization distribution generated from 1000 replications with the MXCOMP (matrix comparison) routine in NTSYS-PC 2.0 by Rohlf (1997) to allow a Mantel test (Mantel, 1967). An assignment test (WHICHRUN 4.1, Banks and Eichert, 2000) tested the ability to discriminate population of origin based on an individual's multilocus genotypic profile.
Clinal trends in heterozygosity and allele frequency were examined by using nonparametric correlation analyses (SAS Institute, 1989). Significance of Spearman's correlation coefficients was determined as the probability a correlation differed from zero. Probabilities less than 0.05 were considered statistically significant (Snedecor and Cochran, 1980).
Misidentifications detected by IEF resulted in a reduced sample size in some samples especially from Alabama and Florida. Examination of 46 enzymes and structural protein systems in southern flounder produced scorable phenotypes for 68 putative gene loci. Two dimeric esterase loci (ESTD-1* and ESTD-2*, 3.1.1 .. [IUBMBNC, 1992]) a tripeptide aminopeptidase locus (PEPB-2*, 3.4.13 ..), glycerol-3-phosphate dehydrogenase (G3PDH*, 126.96.36.199), two glucose-6-phosphate isomerase loci (GPI-A* and GPI-B*, 188.8.131.52), phosphoglucomutase (PGM*, 184.108.40.206) and two glucose-6-phosphate dehydrogenase loci (G6PDH-1* and G6PDH-2*, 220.127.116.11) were resolved, scored as variable, and included in analyses. The remaining 59 loci were monomorphic or could not be scored consistently and were omitted. All polymorphic loci had the same common allele across all localities (Table 1). ESTD-1* ESTD-2*, GPI-B* and G6PD-1* each expressed an allele unique to a single locality. The percentage of polymorphic loci ([P.sub.0.99]) averaged 7.5% and ranged from 4.41% to 10.29% (Table 1). Mean individual heterozygosity ranged from 0.03 (SE=0.02) in North Carolina and Florida to 0.12 (SE=0.12) in Matagorda Bay. Statistically significant clinal relationships were found in the Gulf of Mexico between the frequency of the common allele of the G6PDH-2* locus and degrees west longitude ([r.sub.s]=0.829, P<0.05) and degrees north latitude ([r.sub.s]=0.829, P<0.05). Further differentiation of populations in the western Gulf of Mexico was observed at the G3PDH* locus. The G3PDH*1 allele occurred at a frequency of 12.0% in the Laguna Madre, declined to a frequency of 1.4% in Galveston Bay, and was absent in all collections east of Galveston Bay. Four loci had alleles confined to a single locality in this same region of Gulf of Mexico (Table 1), including G6PDH-1*2 which was limited to the Laguna Madre. Samples were in Hardy-Weinberg equilibrium at the nine allozyme loci surveyed for each of the 72 possible comparisons of variable loci for each locality, except ESTD-2* in Galveston Bay. No statistically significant genotypic linkage disequilibrium was observed between any loci for any population.
Tests for homogeneity of allelic frequencies at variable loci across all localities were statistically significant at six of the nine loci: ESTD-2* (P<0.01), G3PDH* (P<0.01), GPI-A* (P=0.01), GPI-B* (P<0.01), PGM* (P<0.01), and G6PDH*-2 (P<0.01). Tests for homogeneity of genotypic frequencies across all localities were significant at four loci: G3PDH* (P<0.01), GPI-B* (P<0.01), PGM* (P<0.01), and G6PDH-2* (P<0.01). [F.sub.ST] averaged 0.088 and varied from 0.181 for G6PDH-2* to 0.001 for G6PDH-1*. All estimates of [F.sub.ST] were found to be statistically different from 0, except for loci G6PDH-1*, PEPB-2*, and GPI-A*.
Regional differentiation among southern flounder was demonstrated (Fig. 2) by using chord distance and N-J clustering. The greatest discontinuity was between all samples from Galveston Bay eastward and a cluster composed of Matagorda Bay and the Laguna Madre. No apparent differentiation exists between Atlantic Coast populations and populations collected from the eastern Gulf of Mexico. This pattern is supported by the outcome of the assignment test (Table 2). Percentage correct assignment ranged from 0% for Florida to 78% for Matagorda Bay. However, when assignments between groups identified in the cluster analysis were examined, the overall correct percentage increased to 81%. The assignment test supports the interpretation of the cluster analysis, suggesting within-region differentiation was minimal and not geographically consistent, but between-region differences were considerable. A different pattern was discerned by using AMOVA (Table 3). The majority of the variance (>99%) was distributed within samples and the among-sites-within-coasts component was nonsignificant. Comparison of Atlantic versus Gulf of Mexico sample sites was statistically significant (P=0.04) and comparison of western Gulf of Mexico sample sites with those from the eastern Gulf and the Atlantic coast approached significance (P=0.07). The results of chi-square tests for population differentiation (Table 4) allowed a pairwise examination of differences among the eight sampling sites. Eastern sample sites were not significantly different (P>0.01) from their nearest geographic neighbors. Western sites, from Sabine Lake to the lower Laguna Madre, were statistically different from at least one neighbor, suggesting more pronounced genetic differentiation in the western Gulf of Mexico. Among Gulf of Mexico samples, no statistically significant correlations were observed between chord distances and geographic distances (r=0.416, t=1.549, P=0.06).
Genetic differentiation was not extensive over most of the range of P. lethostigma examined in this study. Samples collected from Core Sound in North Carolina to Sabine Lake on the upper Texas coast were genetically similar. However, a discontinuity in allele frequencies was identified on the Texas coast between Galveston and Matagorda Bays. In addition, statistically significant clines in allele frequencies at the G6PDH-2* locus and in average individual heterozygosity were observed across the Gulf of Mexico. These observations do not suggest the occurrence of independent stocks of southern flounder in the Gulf of Mexico but do support the hypothesis that genetic structuring is present. Southern flounder samples collected off St. Augustine, FL, and off North Carolina cluster with samples in the Gulf of Mexico from Galveston Bay, Texas, eastward, despite a modern-era distributional gap that encompasses the southern reaches of Florida from the Loxahatchee River on the Atlantic Coast to the Caloosatchee Estuary on the Gulf Coast (NOAA (5)). This apparent gap may not represent an effective barrier to gene flow or may be of such recent origin that differentiation has been minimal. It is also possible that differences existed that were undetected by techniques used in our study.
Significant correlation between genetic and geographic distance was not found, lending no support to application of the isolation by distance model (Wright, 1943) to population structuring in southern flounder. The estimated [F.sub.ST] value of 0.088 for southern flounder was greater than that found for Sciaenops ocellatus ([F.sub.ST]=0.022, Gold et al., 1994), Cynoscion nebulosus ([F.sub.ST]=0.009, King and Pate, 1992), or Pogonius chromis ([F.sub.ST]=0.013, Karel, unpubl. data) in this region. Physical or biotic factors may have caused greater isolation for southern flounder than for other nearshore species. Cluster analysis suggested that the region of greatest differentiation occurs along the middle Texas coast. Similar genetic structure in Crassostrea virginica may be explained by seasonal current patterns in Corpus Christi Bay, Texas (King et al., 1994). A comparable mechanism may operate in southern flounder; offshore currents may have resulted in reduced dispersion of eggs and larvae between the upper and middle Texas coasts. Currents off the Texas coast are seasonally variable and complex (Cochrane and Kelly, 1986) and may aid in reducing egg and larval dispersion between regions of the western Gulf of Mexico.
Many marine species spawn in the open ocean, have eggs and larvae with an extensive planktonic stage, or are highly mobile as adults. It is not surprising that such organisms are often panmictic, or exhibit subdivision on only broad levels (e.g. Sciaenops ocellatus; Gold et al., 1994). When genetic differentiation is found in marine organisms (e.g. Cynoscion nebulosus; King and Pate, 1992), extensive regions of clinal change in allele frequency may be seen and may be an adaptive feature (King and Zimmerman, 1993). A critical question for fishery management is how much differentiation is necessary to indicate biologically significant population structuring. Gold et al. (1994), for instance, found that red drum were subdivided (albeit weakly) between Atlantic and Gulf of Mexico subpopulations despite relatively high levels of gene flow between the populations and failure of cluster analyses to consistently segregate localities into proper geographic regions. However, significant differences in allele frequencies for two loci were found and the researchers were able to demonstrate, through hierarchical gene-diversity analysis, that 20% of the variation in gene diversity was related to within-region differences.
In our study, allele frequency discontinuities and clinal variation in genetic characters were identified among southern flounder inhabiting the western portion of the species' range. Short-term goals (e.g. supplementing exploited populations) that fail to account for this structuring could result in management programs that undermine the long-term resource management objective: maintaining the evolutionary potential of this species. The results of this survey suggest that southern flounder populations should be considered potentially distinct pending further resolution of population differentiation and clinal variation. Stocking efforts, especially those involving interbay transfers, should be undertaken only after careful consideration of all pertinent information, and be contingent upon careful studies of genetic variation at the appropriate local level. Observed discontinuities and clinal variations in allele frequencies may indicate adaptation to localized conditions and should be incorporated into comprehensive management strategies developed for southern flounder.
Table 1 Allele frequencies, sample sizes (n), percentage of polymorphic loci per population, and mean heterozygosity per locus. LM = Laguna Madre, Texas; MAT = Matagorda Bay, Texas; GAL = Galveston Bay, Texas; SAB = Sabine Lake, Texas; MIS = off Biloxi, Mississippi; ALA = Mobile Bay, Alabama; STA = off St. Augustine, Florida; NC = Core Sound, North Carolina. Locus Allele LM MAT ESTD-1 * (1,3) * 1 0.972 0.918 * 2 0.028 0.082 * 3 0.000 0.000 n 54 61 ESTD-2 * (1,3) * 1 0.009 0.000 * 2 0.981 1.000 * 3 0.009 0.000 n 54 61 PEPB-2 * (1,3) * 1 0.074 0.016 * 2 0.926 0.984 * 3 0.000 0.000 n 54 61 G3PDH * (2,3) * 1 0.120 0.098 * 2 0.778 0.779 * 3 0.102 0.123 n 54 61 GPI-A * (1,3) * 1 1.000 0.992 * 2 0.000 0.008 n 54 61 GPI-B * (1,4) * 1 0.000 0.000 * 2 1.000 1.000 * 3 0.000 0.000 n 54 61 PGM * (1,4) * 1 0.009 0.008 * 2 0.991 0.992 * 3 0.000 0.000 n 54 61 G6PDH-1 * (1.3) * 1 0.991 1.000 * 2 0.009 0.000 n 54 61 G6PDH-2 * (1,3) * 1 0.000 0.000 * 2 0.639 0.648 * 3 0.361 0.352 n 36 61 % polymorphic loci 10.29 8.82 Heterozygosity 0.10 0.12 Direct count (SE) (0.05) (0.06) Locus Allele GAL SAB ESTD-1 * (1,3) * 1 0.889 0.917 * 2 0.111 0.083 * 3 0.000 0.000 n 36 24 ESTD-2 * (1,3) * 1 0.014 0.000 * 2 0.917 1.000 * 3 0.069 0.000 n 36 24 PEPB-2 * (1,3) * 1 0.014 0.000 * 2 0.986 0.958 * 3 0.000 0.042 n 36 24 G3PDH * (2,3) * 1 0.014 0.000 * 2 0.972 0.848 * 3 0.014 0.152 n 36 23 GPI-A * (1,3) * 1 1.000 1.000 * 2 0.000 0.000 n 36 24 GPI-B * (1,4) * 1 0.000 0.125 * 2 0.986 0.875 * 3 0.014 0.000 n 36 24 PGM * (1,4) * 1 0.086 0.000 * 2 0.829 1.000 * 3 0.086 0.000 n 35 24 G6PDH-1 * (1.3) * 1 1.000 1.000 * 2 0.000 0.000 n 36 24 G6PDH-2 * (1,3) * 1 0.114 0.000 * 2 0.871 0.896 * 3 0.014 0.104 n 35 24 % polymorphic loci 10.29 7.35 Heterozygosity 0.09 0.08 Direct count (SE) (0.04) (0.03) Locus Allele MIS ALA ESTD-1 * (1,3) * 1 0.885 1.000 * 2 0.115 0.000 * 3 0.000 0.000 n 13 21 ESTD-2 * (1,3) * 1 0.000 0.000 * 2 1.000 1.000 * 3 0.000 0.000 n 13 21 PEPB-2 * (1,3) * 1 0.038 0.024 * 2 0.962 0.976 * 3 0.000 0.000 n 13 21 G3PDH * (2,3) * 1 0.000 0.000 * 2 0.808 0.833 * 3 0.192 0.167 n 13 21 GPI-A * (1,3) * 1 0.962 0.929 * 2 0.038 0.071 n 13 21 GPI-B * (1,4) * 1 0.000 0.000 * 2 1.000 1.000 * 3 0.000 0.000 n 13 21 PGM * (1,4) * 1 0.000 0.000 * 2 1.000 1.000 * 3 0.000 0.000 n 13 21 G6PDH-1 * (1.3) * 1 1.000 1.000 * 2 0.000 0.000 n 13 21 G6PDH-2 * (1,3) * 1 0.000 0.095 * 2 0.962 0.881 * 3 0.038 0.024 n 13 21 % polymorphic loci 7.35 5.88 Heterozygosity 0.08 0.09 Direct count (SE) (0.03) (0.04) Locus Allele STA NC ESTD-1 * (1,3) * 1 1.000 0.959 * 2 0.000 0.027 * 3 0.000 0.014 n 20 37 ESTD-2 * (1,3) * 1 0.000 0.054 * 2 1.000 0.946 * 3 0.000 0.000 n 20 37 PEPB-2 * (1,3) * 1 0.025 0.000 * 2 0.975 1.000 * 3 0.000 0.000 n 20 37 G3PDH * (2,3) * 1 0.000 0.000 * 2 0.875 0.946 * 3 0.125 0.054 n 20 37 GPI-A * (1,3) * 1 0.975 1.000 * 2 0.025 0.000 n 20 36 GPI-B * (1,4) * 1 0.000 0.000 * 2 1.000 1.000 * 3 0.000 0.000 n 20 37 PGM * (1,4) * 1 0.000 0.000 * 2 1.000 1.000 * 3 0.000 0.000 n 20 37 G6PDH-1 * (1.3) * 1 1.000 1.000 * 2 0.000 0.000 n 20 37 G6PDH-2 * (1,3) * 1 0.025 0.000 * 2 0.975 1.000 * 3 0.000 0.000 n 20 37 % polymorphic loci 5.88 4.41 Heterozygosity 0.03 0.03 Direct count (SE) (0.02) (0.02) (1) Buffer = TVB (Tris-borate-EDTA, pH 8.0). (2) Buffer = borate 9. (3) Tissue = muscle. (4) Tissue = liver. Table 2 Assignment test outcomes. Values indicate frequency of assignment of individuals from a collection locality (rows) to the locality to which it is most similar (columns). Numbers on the diagonal indicate correct assignments. The "East" category combines collections from North Carolina to Sabine Lake, Texas, and the "West" category combines Texas sites from Galveston Bay to Laguna Madre. NC = Core Sound, North Carolina; STA = off St. Augustine, Florida; ALA = Mobile Bay, Alabama; MIS = off Biloxi, Mississippi; SAB = Sabine Lake, Texas; GAL = Galveston Bay, Texas; MAT = Matagorda Bay, Texas; LM = Laguna Madre, Texas. Assigned to From NC STA ALA MIS NC 29 0 4 0 STA 13 0 5 1 ALA 8 0 6 3 MIS 4 1 0 4 SAB 15 1 4 2 GAL 9 0 2 1 MAT 8 0 6 6 LM 7 0 4 0 East 158 West 33 Assigned to From SAB GAL MAT LM NC 2 0 0 0 STA 0 1 0 0 ALA 0 0 0 0 MIS 3 1 0 0 SAB 10 5 0 0 GAL 2 22 1 0 MAT 1 0 14 25 LM 0 1 3 19 East 1 West 61 Table 3 Hierarchical analyses of molecular variation (AMOVA) among mtDNA composite haplotypes of southern flounder (Paralichthys lethostigma) from the U.S. Atlantic coast and Gulf of Mexico. Source of variation Variance % variation pi All sites Among coasts 0.00026 0.06 0.04 Among sites within coasts -0.00020 -0.04 0.92 Within sites 0.44985 99.99 0.74 Eastern versus western cluster Among clusters 0.00042 0.09 0.07 Among sites within regions -0.00034 -0.08 0.10 Within sites 0.44985 99.98 0.76 (1) Probability of finding a more extreme variance component by chance alone (1000 permutations). Table 4 Tests ([chi square]) of pairwise population differentiation for southern flounder (Paralichthys lethostigma) samples obtained from sites on the U.S. Gulf and Atlantic coasts based on variability at eight allozyme loci. The [chi square] value tests the hypothesis of no difference in allelic distribution across collection localities. The associated probability (P) is given in parentheses. Inf. = infinite. North Carolina Florida Florida 12.875 (0.38) Alabama 22.791 4.478 (<0.01) (0.81) Mississippi 28.820 13.350 (<0.01) (0.21) Sabine 26.087 17.208 (0.01) (0.14) Galveston 59.146 40.903 (<0.01) (<0.01) Matagorda Inf. Inf. (<0.01) (<0.01) Laguna Inf. Inf. Madre (<0.01) (<0.01) Alabama Mississippi Florida Alabama Mississippi 7.723 (0.66) Sabine 18.942 21.959 (0.09) (0.04) Galveston 42.314 37.970 (<0.01) (<0.01) Matagorda Inf. Inf. (<0.01) (<0.01) Laguna Inf. 42.178 Madre (<0.01) (<0.01) Sabine Galveston Florida Alabama Mississippi Sabine Galveston 44.407 (<0.01) Matagorda Inf. Inf. (<0.01) (<0.01) Laguna Inf. Inf. Madre (<0.01) (<0.01) Matagorda Florida Alabama Mississippi Sabine Galveston Matagorda Laguna 26.956 Madre (0.07)
Financial support for this research was received from the Sport Fish Restoration Program of the U. S. Fish and Wildlife Service. Field personnel of the TPW Coastal Fisheries Division provided invaluable assistance in sample collection. Additional samples were provided by Greg Stunz of Texas A&M University, and Bradley Randall and his colleagues in the Fishery Department of the Gulf Coast Research Laboratory of Ocean Springs, Mississippi. We thank Larry McEachron and Bob Colura for administrative support and assistance with sample collection, and Roberta Vickers, David Westbrook, and Eric Young for their laboratory assistance, as well as Andrew Shaw for the preparation of Figure 1.
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Ivonne R. Blandon Rocky Ward Perry R. Bass Marine Fisheries Research Station Coastal Fisheries Division Texas Parks and Wildlife Department Palacios, Texas 77465 E-mail address (for I. R. Blandon): firstname.lastname@example.org Tim L. King U.S. Geological Survey-Biological Resource Division Leetown Science Center Aquatic Ecology Laboratory 1700 Leetown Road Keameysville, West Virginia 25430 William J. Karel Perry R. Bass Marine Fisheries Research Station Coastal Fisheries Division Texas Parks and Wildlife Department Palacios, Texas 77465 James P. Monaghan Jr. North Carolina Division of Marine Fisheries 3441 Arendell Street Morehead City, North Carolina 28557
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|Author:||Blandon, Ivonne R.; Ward, Rocky; King, Tim L.; Karel, William J.; Monaghan, James P., Jr.|
|Article Type:||Statistical Data Included|
|Date:||Oct 1, 2001|
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