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ARTICLE
Demographic, Taxonomic, and Genetic Characterization of the Snook
Species Complex (Centropomus spp.) along the Leading Edge of Its Range
in the Northwestern Gulf of Mexico
Joel Anderson* and Damon Williford
Texas Parks and Wildlife Department, Perry R. Bass Marine Fisheries Research Station and Hatchery, 3864 FM 3280,
Palacios, Texas 77465, USA
Alin González
Oklahoma Cooperative Fish and Wildlife Research Unit, Oklahoma State University, 007 Agriculture Hall, Stillwater,
Oklahoma 74078, USA
Chris Chapa
U.S. Fish and Wildlife Service, Partners for Fish and Wildlife, 10711 Burnet Road, Suite 200, Austin, Texas 78758, USA
Fernando Martinez-Andrade
Texas Parks and Wildlife Department, Corpus Christi Field Office, Natural Resources Center Building, Suite 2500,
6300 Ocean Drive, Corpus Christi, Texas 78412, USA
R. Deborah Overath
Division of Science, Technology, Engineering, and Mathematics, Texas Southmost College, 80 Fort Brown, Brownsville,
Texas 78520, USA
Abstract
A recent increase in the abundance of snook species (Centropomus spp.) in Texas has been generally associated with a
broad-scale warming trend of Texas’inshore waters, closure of the commercial fishery in 1987, and fairly conservative
restrictions on recreational catch implemented at the same time. Despite this observed increase in abundance, little is known
about the snook species complex in Texas, including uncertainty about recent changes in distribution and abundance, taxon-
omy, and population structure. Here, abundance and distribution data from a long-running fishery-independent (FIN) data
set were analyzed in synergy with mitochondrial DNA (mtDNA) and microsatellite genotypes to answer basic questions
about the snook species complex in Texas. The main findings from this work are as follows: (1) based on trends observed in
FIN data, snook are increasing in abundance and expanding their range northward in Texas; (2) based on mtDNA sequenc-
ing, the two most common species of snook in Texas are the Common Snook C. undecimalis and Largescale Fat Snook C.
mexicanus; (3) a third species, the Mexican Snook C. poeyi, occurs but only rarely; and (4) patterns from microsatellite
genotypes suggest that the two predominant species, Common Snook and Largescale Fat Snook, probably constitute single
genetic stocks in Texas, although evidence of chaotic genetic patchiness was also observed. This latter finding might be a gen-
eral expectation for populations that are on the leading edge of an expanding species range and implies that management
measures in Texas should be directed toward conservation of suitable habitat corridors offering environmental and habitat
refugia as well as measures (e.g., stock enhancement) that increase the probability of survival of small, localized populations.
*Corresponding author: joel.anderson@tpwd.texas.gov
Received August 12, 2019; accepted November 19, 2019
North American Journal of Fisheries Management
©2019 American Fisheries Society
ISSN: 0275-5947 print / 1548-8675 online
DOI: 10.1002/nafm.10394
1
Centropomidae (snook) is a family of popular game
fish that are distributed along the western Atlantic Ocean,
Gulf of Mexico, and eastern Pacific Ocean, where they
inhabit warm temperate to tropical estuarine systems
(Rivas 1986). In the western Gulf of Mexico, central
Texas (near Aransas Bay) has previously been considered
the northernmost edge of the centropomids’range (Rivas
1986). The inshore waters of Texas represent a latitudinal
environmental cline of salinity and temperature, both of
which increase from north to south (Table 1). Snook spe-
cies are vulnerable to acute temperature extremes, particu-
larly cold/freeze events (Shafland and Foote 1983; Adams
et al. 2012; Stevens et al. 2016), such that their distribution
has historically been limited to lower latitudes in Texas
where water temperatures are more tolerable (Matlock
and Osburn 1987; Pope et al. 2006). It has been suggested
that the natural range of snook species could expand fur-
ther north given the current climatic warming trend; such
a range expansion has been reported in another subtropi-
cal/tropical-associated species in Texas, the Gray Snapper
Lutjanus griseus, and has been hypothesized for other spe-
cies with similar thermal tolerance ranges (Tolan and
Fisher 2009). Recent fishery-independent (FIN) monitor-
ing by the Texas Parks and Wildlife Department (TPWD,
unpublished data) suggests that the range of snook in
Texas may indeed be expanding northward, although no
formal assessment of this potential range expansion has
been made.
Snook were the target of a short-lived commercial fish-
ery in Texas. The fishery crashed in the late 1920s, and
recreational catch declined within the same general time
frame (Matlock and Osburn 1987). It was hypothesized
that this dramatic decline in snook abundance was driven
by two factors: cold weather events and overfishing,
although it is unknown whether other factors may have
also played a role (Matlock and Osburn 1987). In any
event, the decline in abundance seemed to persist through
the entirety of the 1900s—so much so that Pope et al.
(2006) observed continued low abundance and erratic
recruitment for the most heavily targeted species, the
Common Snook Centropomus undecimalis. In 1987, the
commercial snook fishery in Texas was officially closed
and harvest was restricted to recreational fishing, with a
bag limit of 3 snook/d and a slot limit of 51–71 cm. In
1995, the bag limit was reduced to 1 snook/d and the slot
size was also narrowed to 61–71 cm. Subsequently, snook
abundance has increased dramatically in Texas over the
last three decades (González 2015). In particular, increases
in both abundance and presence have been observed dur-
ing FIN sampling undertaken by TPWD over that time
period (Supplemental Figures S1, S2 available in the
online version of this article).
The snook species complex in Texas is poorly charac-
terized compared to that in Florida, which is the north-
eastern range limit of the genus Centropomus (Huber et al.
2014). For instance, while it has long been assumed that
the Common Snook comprises the bulk of the snook com-
plex in Texas, other species have been noted—particularly
the Smallscale Fat Snook C. parallelus (Martin and King
1991), Largescale Fat Snook C. mexicanus, and Mexican
Snook C. poeyi (Chapa 2012). Although there are morpho-
logical characteristics that might distinguish these species,
in general these characteristics tend to overlap, making
identification based on morphology alone very difficult,
particularly for juveniles. Hybridization could further con-
tribute to the difficulty in identifying snook species based
on morphology. Pfennig et al. (2016) demonstrated that
hybridization could play a key role in the persistence of
some populations at the edge of their range. Although
hybridization among species of snook has not been
observed in the wild (Tringali et al. 1999a), there is indi-
rect evidence that hybridization may occur at least occa-
sionally. Hybrids of Common Snook ×Smallscale Fat
Snook have been produced in captivity (Ferraz et al.
2012), and juvenile residency patterns of each species in
Puerto Rico imply that spawning times are likely
TABLE 1. Mean (SD in parentheses) of annual water temperature, winter water temperature (December–February), and salinity in each of the major
bay systems of Texas, organized from north to south. Data are from monthly water samples taken by the Texas Parks and Wildlife Department dur-
ing the course of fishery-independent inshore trawls and are averaged over the entire period observed in this study (1980–2018).
Bay Annual temperature (°C) Winter temperature (°C) Annual salinity (‰)
Sabine Lake 22.45 (6.86) 14.07 (3.21) 7.73 (6.77)
Galveston Bay 23.36 (6.65) 15.18 (3.3) 17.22 (9.33)
East Matagorda Bay 24.1 (6.73) 15.87 (3.59) 22.41 (8.74)
West Matagorda Bay 23.7 (6.48) 15.96 (3.7) 19.98 (9.61)
San Antonio Bay 23.71 (6.47) 15.89 (3.59) 19.28 (11.27)
Aransas Bay 24.11 (6.41) 16.32 (3.67) 20.33 (9.97)
Corpus Christi Bay 24.34 (6.27) 16.64 (3.49) 29.47 (7.26)
Upper Laguna Madre 25.17 (6.21) 17.99 (3.91) 37.21 (11.34)
Lower Laguna Madre 25.43 (5.85) 18.52 (3.87) 31.16 (8.21)
2ANDERSON ET AL.
coincident with one another (Aliaume et al. 1997). In addi-
tion to the uncertain taxonomy of snook, there is also a
lack of knowledge regarding the genetic stock structure of
snook in Texas waters. Knowledge of genetic stock struc-
ture would aid in proper management of this species
group by helping to define management units (Waples et
al. 2008) and to prioritize conservation needs in a spatial
context (Du Toit 2010).
In this study, three key elements of snook biology in
Texas were explored. First, trends in abundance and dis-
tribution of snook in Texas over the last 38 years (1980–
2018) were related to trends in environmental variables
using a long-running FIN data set collected by the
TPWD. This was done in an effort to identify environ-
mental trends that might be driving changes in snook
inshore abundance in space and time. Second, the taxo-
nomic designation and relative abundance of snook spe-
cies in Texas were directly assessed by using DNA
sequence data in order to inform “unit stock”definitions
and future management measures that could presumably
target individual species. Third, intraspecific patterns of
genetic population structure were assessed in the two pre-
dominant snook species, the Largescale Fat Snook and
Common Snook, by using microsatellite DNA markers.
The findings associated with these data represent a base-
line upon which future assessments of distribution, abun-
dance, taxonomy, and stock structure in Texas snook
populations can be anchored.
METHODS
Demographic analysis.—Trends in abundance of Cen-
tropomus spp. were evaluated using a long-running FIN
monitoring data set collected by TPWD. Gill nets have
been deployed by TPWD since the 1970s to measure
trends in abundance of all bay-associated finfish species in
each of the state's major bays. Gill nets were deployed in
10 major inshore bays (Figure 1) for 10 weeks in the spring
(April–June) and 10 weeks in the fall (September–Novem-
ber) each year throughout the period 1980–2018. A total
of 45 nets were deployed in each system across each 10-
week season, with the exception of East Matagorda Bay
(n=20 nets) and Cedar Lakes (n=10 nets). Each bay sys-
tem was subsectioned into 1-min
2
grids aligned with the
geographic coordinate system, and nets were deployed
overnight along shorelines in grids chosen using a strati-
fied random sampling design (stratified by bay). Each net
extended 182.9 m from shore and consisted of equally
sized panels with four different mesh sizes (76, 102, 127,
and 152 mm). Upon retrieval of each net, specimens were
enumerated and the TL of each specimen was measured
to the nearest millimeter. Additionally, two water
quality variables—temperature (°C) and salinity (‰)—
were assessed concurrently with net deployment. These
sampling protocols remained unchanged during the entire
duration of the sampling period; as a result, changes in
catch can be reasonably assumed to be related to changes
in the abundance and distribution of snook species over
the sample period. Due to difficulty in properly identifying
species, including overlap in key characteristics for almost
all specimens observed during a previous study in Texas
(Chapa 2012), snook species were combined into a single
data set for the demographic analysis.
Logistic regression was used to evaluate the presence/
absence of snook in any single net, with year and latitude
used as predictors of temporal and spatial change. Each
gill net set during the period 1980–2018 represented a sin-
gle observation, with a sample size of 29,276 total data
points for the regression. Temperature and salinity were
originally included in the logistic model described above;
however, both variables were collinear with latitude, and
neither variable was as important as latitude in the final
model. The coastal waters of Texas represent a cline of
cooler, less saline waters in the north and warmer, higher
salinity waters in the south. Temperature is driven primar-
ily by a natural climatic cline, whereas increasing salinity
from north to south is driven by decreasing rainfall and
freshwater inflows into southern bays (Tolan 2007). As
such, we would expect latitude to be correlated signifi-
cantly with both temperature and salinity. We used
ANOVA to evaluate differences in mean temperature and
salinity between nets with zero catch and nets with non-
zero catch. This analysis was broken up into seasons
(spring versus fall) to account for different water condi-
tions during these time periods.
Latitude was evaluated further by plotting each individ-
ual observation onto a map of the Texas coastline using
ArcMap version 10.1 (Environmental Systems Research
Institute, Redlands, California). It was noted that catches
in higher latitudes were observed more commonly during
later years in the study; therefore, the map plot was bro-
ken down by two eras, 1980–2009 and 2010–2018, to eval-
uate changes in snook distribution during the most recent
10-year period. The mean latitude of all catches was calcu-
lated for each year, and the relationship between year and
mean latitude was evaluated with simple linear regression.
Sample collection and DNA extraction.—Fin tissue
samples were collected from Centropomus spp. captured in
a variety of sampling areas between Aransas Pass, Texas,
and the Rio Grande along the Texas–Mexico border
between 2009 and 2013 (Table 2). Adults were collected
via hook and line, and juveniles were collected by using
seines; all individuals were sampled separately from the
FIN sampling program described above. Additional sam-
ples from Campeche, Mexico (n=24), and Jacksonville,
Florida (n=7), were collected for inclusion in the mito-
chondrial DNA (mtDNA) phylogenetic analysis. Genomic
DNA was extracted from tissues using the DNeasy Blood
CHARACTERIZATION OF THE SNOOK SPECIES COMPLEX 3
and Tissue Kit (Qiagen, Germantown, Maryland) and the
Puregene DNA Isolation Kit (Gentra Systems, Min-
neapolis, Minnesota) following the manufacturers’proto-
cols. The final rehydration volume varied between 75 and
200 μL depending on DNA pellet size.
It should be noted that genetic sample selection was
not identical between mtDNA and microsatellite data sets.
This was the result of combining two data sets generated
in two different laboratories during the course of indepen-
dent projects (Chapa 2012; González 2015). Although
there was overlap between studies in a large number of
samples (n=443 individuals that were sequenced for 16S
mtDNA were also genotyped, including 18 individuals
from Mexico), a subset of individuals (n=138, including
samples from Florida [n=7] and Mexico [n=6]) was
assigned mtDNA haplotypes but not genotyped and a sec-
ond subset (n=112, all from Texas) was genotyped but
not assigned mtDNA haplotypes.
16S mitochondrial DNA sequencing and phylogenetics.—
Mitochondrial DNA haplotypes were obtained from 581
unique individual snook (Texas: n=550; Mexico: n=24;
Florida: n=7). We used DNA primers described by
Palumbi (1996) to amplify a fragment (440 bp) of the mito-
chondrial 16S ribosomal RNA gene using the PCR protocol
of Tringali et al. (1999b). The PCRs were carried out using
Ready-To-Go PCR beads (GE Healthcare, Piscataway,
New Jersey) and reaction mixes of the following: 1 μLof
template DNA, one Ready-To-Go bead, 12 μL of 0.4-μM
FIGURE 1. Distribution of snook catch in Texas fishery-independent gill-net sets, 1980–2018. Ten major inshore areas (labeled) were sampled using a
stratified random design. Three of those areas (Aransas Bay, Upper Laguna Madre, and Lower Laguna Madre) were subsampled using hook-and-line
sampling as well as bag seines targeting snook. Specific targeted sampling locations are described in Table 2. Eras were differentiated in order to
qualitatively assess the distribution of the catch before and after 2010.
4ANDERSON ET AL.
forward primer, and 12 μL of 0.4-μM reverse primer for a
total volume of 25 μL. After amplification, PCR products
were purified using ExoSAP-IT (USB, Cleveland, Ohio) via
the manufacturer's recommended protocol. Sequencing
reactions were then carried out in 10-μL volumes using
Quick Start Master Mix DTCS (Beckman Coulter, Fuller-
ton, California). Primers for sequencing were the same as
those used in PCR. Sequencing reactions were precipitated
by adding 1/20 volume of a cocktail containing 2 μLof
sodium acetate (3 M), 2 μL of EDTA (100 mM), and 1 μL
of glycogen, followed by 2 volumes of 95% ethanol. Precipi-
tated sequence extracts were then centrifuged at 3,700 revo-
lutions/min for 30 min to form pellets. The resulting pellets
were then rinsed twice with 70% ethanol, dried, and rehy-
drated by using a formamide sample loading solution
(Beckman Coulter). Finally, the sequences were separated
and analyzed on a Beckman CEQ8000 capillary sequencer
(Beckman Coulter) using default sequencing module
parameters. Forward and reverse sequence traces were
aligned with Sequencher version 5.4 (Gene Codes, Ann
Arbor, Michigan).
The program Clustal X version 2.0.3 (Larkin et al.
2007) was used to align 16S mtDNA sequences. The num-
ber of haplotypes in the entire data set was computed
using DnaSP version 6.12.01 (Rozas et al. 2017). The spe-
cies identity of each haplotype was determined first by
using the Basic Local Alignment Search Tool (BLAST)
algorithm and the MegaBLAST optimization (Altschul et
al. 1990; Zhang et al. 2000; Morgulis et al. 2008) in the
GenBank database (National Center for Biotechnology
Information; https://blast.ncbi.nlm.nih.gov/Blast.cgi). To
further assess the species identity and evolutionary rela-
tionships of the haplotypes, a maximum likelihood phylo-
genetic analysis was performed in MEGA7 (Kumar et al.
2016) by using the haplotypes and 16S mtDNA sequences
of Centropomus species and outgroup taxa from previous
studies available in GenBank (Table 3). The phylogenetic
analysis employed Kimura's (1980) two-parameter gamma
distribution model, which was selected as the best-fitting
model of DNA sequence evolution based on the Bayesian
information criterion (Schwarz 1978) implemented in the
MEGA7 model selection tool. The reliability of the
inferred relationships was assessed using 1,000 bootstrap
replicates (Felsenstein 1985).
Microsatellite genotyping and population genetic analysis.—
Genotypes were generated using microsatellite markers for
555 unique snook individuals (Texas: n=537; Mexico: n=
18). Eight microsatellite loci developed by Seyoum et al.
(2005) were amplified and fluorescent-labeled using the
M13-tail labeling procedure described by Schuelke (2000).
Volumes for PCR were as follows: 5 μL of GoTaq Green
Master Mix (Promega Corp., Madison, Wisconsin), 0.25 μL
of 10-μM forward primer, 0.50 μL of 10-μM reverse primer,
0.25 μL of 10-μM FAM-M13 labeled primer, 3 μLof
TABLE 2. Collection locations and sample sizes (n)ofCentropomus species that were used for genetic analyses.
Location Major bay system State or country Latitude Longitude n
Jacksonville Jacksonville Florida 7
East Yucatan Campeche Mexico 24
Brazos River Galveston Bay Texas 28°52.771′N95°22.898′W1
Bridge Harbor surfside Galveston Bay Texas 28°57.852′N95°17.539′W1
Oyster Creek Galveston Bay Texas 29°01.079′N95°22.641′W1
Bastrop Bayou Galveston Bay Texas 29°05.875′N95°12.190′W4
Hitchcock diversion canal Galveston Bay Texas 29°20.129′N95°01.389′W1
Colorado River West Matagorda Bay Texas 28°40.797′N95°58.603′W2
Carancahua Bay West Matagorda Bay Texas 28°44.265′N96°24.107′W5
Aransas Pass ditch Aransas Bay Texas 27°53.566′N97°09.188′W73
Packery Channel Upper Laguna Madre Texas 27°36.829′N97°11.907′W55
Rio Grande/San Martin Lower Laguna Madre Texas 26°00.113′N97°17.911′W87
Brownsville Ship Channel Lower Laguna Madre Texas 26°00.125′N97°17.913′W29
South Bay Lower Laguna Madre Texas 26°01.529′N97°10.272′W82
Mexiquita flats Lower Laguna Madre Texas 26°04.045′N97°11.826′W1
Brazos Santiago Pass Lower Laguna Madre Texas 26°04.356′N97°09.994′W21
White Sands boat ramp Lower Laguna Madre Texas 26°04.462′N97°12.873′W3
Laguna Vista ditch Lower Laguna Madre Texas 26°05.705′N97°17.024′W 169
Arroyo Colorado Lower Laguna Madre Texas 26°20.998′N97°23.483′W 122
Port Mansfield jetties Lower Laguna Madre Texas 28°33.799′N97°16.483′W2
Exact location unknown Texas 3
Total 693
CHARACTERIZATION OF THE SNOOK SPECIES COMPLEX 5
deionized water, and 1.5 μL of genomic DNA. Fragment
length analysis was conducted by the Genomics Core Labo-
ratory at Texas A&M University–Corpus Christi with an
ABI 3730 DNA Analyzer (Applied Biosystems, Foster City,
California). Allele lengths were scored using the program
GeneMapper version 5.0 (Applied Biosystems) and binned
using Tandem (Matschiner and Salzburger 2009) with the
default settings.
For microsatellite analyses, individuals were initially
assigned to species based on their mtDNA haplotype. If
no haplotype information was available, individuals were
grouped as “unknown”and were assigned species based
on genotype cluster analysis (see below). Since only four
Mexican Snook were observed and since microsatellite
amplification of these individuals resulted in poorly
resolved genotypes, this species was excluded from
microsatellite analyses. We used Structure version 2.3.4
(Pritchard et al. 2000) to test for hybrids between Com-
mon Snook and Largescale Fat Snook and to assign indi-
viduals for which there were no mtDNA haplotype data
(n=112) to species. To determine the number of signifi-
cant genetic clusters, Structure was run iteratively while
varying Kfrom 1 to 5 clusters under the admixture model.
For each level of K, 10 independent iterations were run,
with each iteration consisting of 50,000 “burn-in”steps,
followed by 950,000 run steps. All runs (total n=50 runs)
were then used to calculate the ΔKstatistic of Evanno et
al. (2005), which more consistently resolves true Kthan
the log-likelihood probability values generated by Struc-
ture (Evanno et al. 2005). The ΔKstatistic for each level
of Kwas determined using Structure Harvester (Earl and
vonHoldt 2012). Individuals that had Structure Q-scores
TABLE 3. Species identity, accession numbers, and references of 16S mitochondrial DNA sequences downloaded from GenBank that were used in
the phylogenetic analysis.
Species Accession number Reference
Longspine Snook Centropomus armatus HQ731414 Li et al. (2011)
HQ731415 Li et al. (2011)
U85010 Tringali et al. (1999b)
Swordspine Snook C. ensiferus HQ731408 Li et al. (2011)
HQ731418 Li et al. (2011)
U85008 Tringali et al. (1999b)
Blackfin Snook C. medius EF120864 Smith and Craig (2007)
HQ731409 Li et al. (2011)
HQ731413 Li et al. 2011
JQ939047 Betancur-R et al. (2013)
U85019 Tringali et al. (1999b)
Largescale Fat Snook C. mexicanus KU745737 S. Seyoum, M. D. Tringali,
J. Dutka-Gianelli, and R. G. Taylor,
unpublished
Black Snook C. nigrescens U85015 Tringali et al. (1999b)
Smallscale Fat Snook C. parallelus U85016 Tringali et al. (1999b)
Tarpon Snook C. pectinatus U85018 Tringali et al. (1999b)
Mexican Snook C. poeyi U85014 Tringali et al. (1999b)
Yellowfin Snook C. robalito DQ307688 Peregrino-Uriarte et al. (2007)
U85011 Tringali et al. (1999b)
Common Snook C. undecimalis AF247436 Orrell and Carpenter (2004)
HQ731428 Li et al. (2011)
U85012 Tringali et al. (1999b)
Humpback Snook C. unionensis U85009 Tringali et al. (1999b);
White Snook C. viridis DQ307689 Peregrino-Uriarte et al. (2007)
DQ532849 Peregrino-Uriarte et al. (2007)
JQ939047 Betancur-R et al. (2013)
U85013 Tringali et al. (1999b)
Barramundi Lates calcarifer (outgroup) DQ010541 Lin et al. (2006)
Nile Perch L. niloticus (outgroup) KY213963 Gann et al. (2017)
Waigieu Seaperch Psammoperca
waigiensis (outgroup)
HQ731401 Li et al. (2011)
6ANDERSON ET AL.
less than 0.7 for all structure clusters were assumed to rep-
resent potential hybrids. Additionally, individuals having
a microsatellite genotype that clustered with one species
and mtDNA representative of the alternate species were
assumed to be late-generation hybrids (back-crossed).
An exploratory biplot of the microsatellite genotype
data was generated using a principal components analysis
(PCA) as implemented in the R package Adegenet (Jom-
bart 2008). Individuals with the representative mtDNA
haplotype of one species but a microsatellite genotype that
appeared (qualitatively) to cluster with the alternate spe-
cies in a plot of the first two axes from the PCA were
again considered to be potential hybrids. The PCA was
used to compare and contrast the results of a parametric
analysis (Structure) with those of a nonparametric multi-
variate approach (PCA).
Once species assignments were resolved via Structure,
microsatellites were checked within each species for devia-
tion from Hardy–Weinberg equilibrium (HWE) expecta-
tions as well as linkage disequilibrium using Genepop
(Raymond and Rousset 1995; Rousset 2008). Significant
deviation from HWE was tested using simulations as
implemented in Genepop, with 1,000 dememorizations,
100 batches, and 1,000 iterations/batch. Loci that had
inbreeding coefficients (F
IS
) values greater than 0 were con-
sidered to have deviated from HWE. Significant linkage
disequilibrium was also detected via simulation with the
same simulation parameters, although in this case a Holm–
Bonferroni adjustment (Holm 1979) was used to adjust the
targeted alpha (initial α=0.05) downward to account for
results from 28 simultaneous tests between pairs of loci.
Species-specific population structure was tested using
additional Structure runs. Individuals that were identified
as putative hybrids were removed, the remaining individu-
als were pooled by species, and Structure was run inde-
pendently for each species. For each level of K(K=1–5
genetic clusters), 10 iterations were run with 50,000 burn-
in steps and 950,000 run steps. Cursory Structure runs
suggested that results were generally similar among vari-
ous admixture models; therefore, the results from the
model assuming admixture among clusters and no prior
probabilities are reported here. The appropriate value of
Kwas again chosen by using the ΔKstatistic of Evanno
et al. (2005). In each species run, individuals were orga-
nized by bay to test for differences in cluster proportions
between different bays; F-statistics were generated using
the R package hierfstat (Goudet 2004), and statistical sig-
nificance of divergence among bays was determined using
1,000 data simulations. For Largescale Fat Snook, sample
locations were (from north to south) Aransas Bay, Upper
Laguna Madre, and Lower Laguna Madre. For Common
Snook, only individuals from Aransas Bay and Lower
Laguna Madre were compared (this species was not cap-
tured in the sample from Upper Laguna Madre).
A discriminant analysis of principal components
(DAPC) was used to validate additional population struc-
ture that was observed in the species-specific Structure
runs. First, a PCA was used to reduce redundancy in the
data set; a discriminant analysis of the first 20 principal
components was then performed among clusters identified
by Structure. The DAPC was carried out with the number
of groups constrained to the same value of Kthat was
observed in Structure runs. Individual scores on the first
two discriminant axes were plotted by Structure assign-
ment, and a chi-square (χ
2
) test with a null assumption of
random assignment among groups was used to determine
whether there was significant correlation between assign-
ment results from DAPC and Structure.
RESULTS
Snook Abundance and Distribution
Fishery-independent gill-net sets resulted in the observa-
tion of 1,129 snook during the period 1980–2018 (Table
4). The size range of snook encountered in nets was 270–
872 mm TL, and the length frequency plot suggested a
wide distribution in length, with most individuals occur-
ring between 350 and 650 mm TL (Figure 2).
Logistic regression results suggested that both year and
latitude significantly impacted snook presence/absence,
and the overall model was significant (r
2
=0.26, P<
0.0001; Table 5). Latitude was negatively correlated (i.e.,
presence was higher in lower latitudes) and was the most
important variable in the model (χ
2
=1,029.7, P<0.0001);
year was positively correlated (i.e., later years had higher
presence; χ
2
=215.2, P<0.0001). The three southernmost
bays (Corpus Christi Bay, Upper Laguna Madre, and
Lower Laguna Madre) yielded 1,017 of all snook landed
in FIN sampling (~90%; Table 4). However, the coastwide
relative distribution of snook changed significantly in later
years of the study based on two observations: (1)
TABLE 4. Total catch of all species of snook, by bay and overall, in
fishery-independent (FIN) sampling by the Texas Parks and Wildlife
Department, 1980–2018. Bays are organized from north to south.
Major bay Catch
Sabine Lake 1
Galveston Bay 16
East Matagorda Bay 33
West Matagorda Bay 19
San Antonio Bay 23
Aransas Bay 20
Corpus Christi Bay 75
Upper Laguna Madre 67
Lower Laguna Madre 875
Total catch, FIN sampling 1,129
CHARACTERIZATION OF THE SNOOK SPECIES COMPLEX 7
individuals were caught in the northern bays between East
Matagorda Bay and Sabine Lake more commonly in the
latest 9-year period (2010–2018) than in all other years of
the study combined (1980–2009; Figure 1); and (2) there
was a significant and positive relationship between year
and mean latitude (Figure 3), as the mean latitude of catch
increased by 0.017 decimal degrees/year from 1980 to
2018 (r
2
=0.40, P<0.0001). Latitude is generally corre-
lated negatively with both temperature and salinity in Tex-
as’inshore waters. As a result, catch of snook was
correlated with relatively high temperature and salinity in
both spring and fall (Table 6).
Mitochondrial DNA Phylogenetics
Overall, 428 bp of the 16S mtDNA gene were observed
from 581 Centropomus specimens, and these were col-
lapsed into 21 haplotypes (Table 7). Haplotype sequences
were submitted to GenBank as accession numbers
MN068225–MN068245. Haplotype 1 was the most abun-
dant haplotype and occurred in 56% of samples. Haplo-
types 2 and 3 were less common, occurring in 23% and
15% of the samples, respectively. The BLAST searches
revealed that 13 of the haplotypes most closely matched
sequences of Common Snook (accession number
HQ731428 or U85012), whereas 6 haplotypes most closely
matched a sequence of Largescale Fat Snook
(KU745737). Haplotypes 7 and 19 most closely matched a
sequence of Mexican Snook (U85014). The results of the
phylogenetic analysis supported the BLAST search results
(Figure 4). Most haplotypes clustered with sequences from
specimens identified as Common Snook (AF247436,
HQ731428, and U85012). This clade was closely related
to another clade composed of haplotypes 7 and 19 and
the GenBank sequence of Mexican Snook (U85014).
Haplotypes 1, 11, 12, 13, 17, and 18 clustered with the
Largescale Fat Snook sequence (KU745737).
Of the total 581 specimens that were assigned a haplo-
type in this study, 336 were identified via BLAST and phy-
logenetic analysis as Largescale Fat Snook (n=332 from
Texas; n=4 from Mexico). An additional 241 specimens
were identified as Common Snook (n=215 from Texas; n=
19 from Mexico; n=7 from Florida). Finally, four individu-
als were identified as Mexican Snook (n=3 from Texas; n
=1 from Mexico). The top results from the BLAST search
were concordant with the phylogenetic analysis in all cases.
Interestingly, there were qualitative differences in haplo-
type distribution for specimens from outside of Texas that
were identified as Common Snook. For instance, 6 of 7
specimens (86%) sampled from Florida possessed haplo-
type 3, whereas only 6 of 18 individuals (33%) from Mex-
ico possessed this haplotype. In contrast, 1 of 7 individuals
(14%) from Florida possessed haplotype 2, compared to 10
of 18 individuals (56%) from Mexico. Qualitatively, Texas
had high numbers of both haplotypes 2 and 3 (n=126 and
74, respectively), suggesting that it may receive migrants
from both areas. Due to small sample sizes in areas outside
of Texas, a more quantitative statistical assessment of stock
contribution was not possible.
Genetic Variation and Population Structure
When individual Common Snook, Largescale Fat
Snook, and unknowns were included in the initial Struc-
ture run, the ΔKstatistic suggested that there were two
genetic clusters (K=2). An examination of each group
under the K=2 scenario yielded the following findings: (1)
257 of 259 individuals that were identified as Largescale
Fat Snook with mtDNA generally fell into a single cluster
(Figure 5); (2) 182 of 184 individuals that were identified
as Common Snook with mtDNA fell into the alternate
cluster; and (3) the estimated genetic divergence between
clusters was high and indicative of what might be expected
between congeneric species (F
ST
=0.183). The four
remaining individuals with an unknown species assign-
ment showed evidence of hybrid background. One of these
individuals (SN550) had the mtDNA haplotype of Larges-
cale Fat Snook but a genotype that indicated Common
Snook. Three additional individuals (SN31, SN522, and
SN573) had genotypes that indicated admixture (Q<0.7
for any genetic cluster) despite being assigned a diagnostic
mtDNA haplotype (Common Snook: n=2; Largescale
Fat Snook: n=1). Of the 112 individuals with no mtDNA
FIGURE 2. Length frequency (mm TL) of snook (all species combined)
captured during fishery-independent sampling in Texas, 1980–2018.
TABLE 5. Results of logistic regression evaluating the impact of year
and latitude on presence/absence of snook in fishery-independent gill nets
deployed during 1980–2018. Both variables contributed significantly to
presence/absence in the model.
Term Estimate SE χ
2
P(>χ
2
)
Intercept −79.68 8.262 93.0 <0.0001
Year 0.06 0.004 215.2 <0.0001
Latitude −1.65 0.051 1,029.7 <0.0001
8ANDERSON ET AL.
data (i.e., no haplotype) included in the Structure analysis,
73 had genotypes that were consistent with cluster 1 (Lar-
gescale Fat Snook), while an additional 39 had genotypes
that were consistent with cluster 2 (Common Snook).
Results from the PCA supported those from the Struc-
ture analysis. Most of the variance observed in the
microsatellite data set was aligned with the first principal
component (Figure 6). Clustering of individuals along this
axis generally coincided with taxonomic expectations based
on mtDNA haplotypes, with three notable exceptions. One
individual (SN550) had a genotype that clustered with
Common Snook but had a Largescale Fat Snook mtDNA
haplotype. An additional two individuals (SN522 and
SN573), both with mtDNA representative of Common
Snook, had genotypes that did not cluster tightly with either
group. All three individuals coincided with those identified
as potential hybrids in Structure. The fourth individual
identified as a potential hybrid by Structure (SN31) clus-
tered tightly with other Largescale Fat Snook in the PCA,
in contrast to the results from Structure.
Most microsatellite loci deviated from HWE, and this
was observed in both species, including deviation from
HWE at 7 of 8 loci in Largescale Fat Snook and at 6 of 8
loci in Common Snook (Table 8). As a result, both species
deviated significantly from HWE across all loci combined
(Largescale Fat Snook: F
IS
=0.134, P<0.0001; Common
Snook: F
IS
=0.187, P<0.0001). Four pairs of loci also
deviated significantly from linkage expectations after
adjustment for multiple tests (CUN14–CUN19,CUN14–
CUN16,CUN4A–CUN22, and CUN4A–CUN16). An
additional nine pairs of loci showed evidence of linkage
disequilibrium under a static alpha of 0.05 (with no Bon-
ferroni adjustment).
Examination of the ΔKstatistic from the species-speci-
fic Structure analysis of Common Snook suggested that
there were four genetic clusters (K=4). The plot of Q-
scores from this model suggested a high degree of admix-
ture within most individuals and complex population
structure within and among sample areas (Figure 7). The
divergence among sample areas (Aransas Bay versus
Lower Laguna Madre) was small but significant (F
ST
=
0.010, P=0.003). Both the multivariate PCA and the dis-
criminant analysis based on the first 20 principal compo-
nents demonstrated clustering that validated observable
FIGURE 3. Mean latitude of the snook catch (all species combined) in fishery-independent gill-net samples in Texas, 1980–2018. Gray filled circles
represent point means of each year. The dashed line is a trend line (linear regression) fit to the data (regression parameters and fit are reported in the
upper left corner).
TABLE 6. Mean values of temperature and salinity for samples that had zero catch ( =0) or positive catch (>0) of snook in spring and fall sampling
efforts. Analysis of variance was used to determine whether mean differences in catch were significantly greater than zero.
Variable Snook catch =0 Snook catch >0FP
Mean spring temperature (°C) 26.8 27.9 19.6 <0.0001
Mean fall temperature (°C) 25.7 26.3 8.4 0.004
Mean spring salinity (‰) 22.2 31.6 120.8 <0.0001
Mean fall salinity (‰) 23.9 29.2 82.9 <0.0001
CHARACTERIZATION OF THE SNOOK SPECIES COMPLEX 9
differences between individuals assigned to Structure clus-
ters 1–4 (Figure 8). The chi-square test comparing Struc-
ture clusters to DAPC assignment was highly significant
(χ
2
=182.4, df =9, P<0.0001).
The species-specific Structure analysis of Largescale Fat
Snook also suggested a complex population structure. The
ΔKstatistic implied that Kwas equal to 3, and as in Com-
mon Snook the plot of Q-scores from this model suggested a
high degree of admixture within most individuals and com-
plex population structure within and among populations
(Figure 9). The global F
ST
(among sample areas: Aransas
Bay, Upper Laguna Madre, and Lower Laguna Madre) was
small but significant (F
ST
=0.006, P=0.005). The PCA and
DAPC both seemed to validate observable differences among
individuals assigned to Structure clusters 1–3 (Figure 10),
and the comparison of assignments from Structure and
DAPC was highly significant (χ
2
=290.0, df =4, P<0.0001).
DISCUSSION
Snook Range Expansion and Abundance
Two different trends observed here suggest that the dis-
tribution and abundance of snook species in Texas have
changed dramatically in the 38-year span of this data set.
First, snook have increased in general abundance; there
was a significant positive correlation with year in the logis-
tic regression, indicating a higher frequency of occurrence
in later years. In fact, the last 2 years of the observed per-
iod (2017 and 2018) had the two highest coastwide fre-
quencies of occurrence compared to all other years of the
study. Second, the distribution of snook has expanded
northward over the observed period at a rate of approxi-
mately 0.017 decimal degrees/year (between 1 and 2 km).
Latitude was in fact the most important variable in the
logistic regression of catch, and most catches occurred in
southern bays. However, examination of the relationship
between year and the mean latitude of catch demonstrated
a clear range expansion of snook into northern latitudes—
areas where these species were only rarely observed prior
to 2010. This range expansion has coincided with a warm-
ing climate and the development of tropical conditions in
temperate and subtropical waters (Staten et al. 2018),
resulting in the range expansion of tropical marine fish
species into temperate zones worldwide (e.g., Figueira and
Booth 2010; Nakamura et al. 2013; Verges et al. 2014;
Heck et al. 2015). In this context, perhaps the most parsi-
monious conclusion is that the northward expansion of
snook species in Texas is driven by warming trends that
are also likely to sustain this new distribution. With that
TABLE 7. Basic Local Alignment Search Tool (BLAST) results for each of the 21 haplotypes observed in 581 samples of Centropomus spp. collected
from the Texas coast, including the number of individuals (N) that carried each haplotype, the top matching species in GenBank, the total BLAST
score and percent identical of that match, and the GenBank accession number of the top matching sequence.
Haplotype NTop match Total score % Identical Accession number
1 328 Largescale Fat Snook
C. mexicanus
773 99.07 KU745737
2 134 Common Snook C. undecimalis 739 97.49 HQ731428
3 86 Common Snook 745 97.72 U85012
4 5 Common Snook 739 97.49 U85012
5 1 Common Snook 739 97.49 U85012
6 1 Common Snook 734 97.26 HQ731428
7 1 Mexican Snook C. poeyi 747 98.15 U85014
8 6 Common Snook 734 97.26 HQ731428
9 2 Common Snook 739 97.49 U85012
10 1 Common Snook 739 97.49 U85012
11 1 Largescale Fat Snook 767 98.84 KU745737
12 1 Largescale Fat Snook 767 98.84 KU745737
13 2 Largescale Fat Snook 767 98.84 KU745737
14 1 Common Snook 739 97.49 U85012
15 1 Common Snook 734 97.26 HQ731428
16 1 Common Snook 734 97.26 HQ731428
17 1 Largescale Fat Snook 767 98.84 KU745737
18 3 Largescale Fat Snook 767 98.84 KU745737
19 3 Mexican Snook 752 98.38 U85014
20 1 Common Snook 734 97.26 HQ731428
21 1 Common Snook 739 97.49 U85012
10 ANDERSON ET AL.
FIGURE 4. Maximum likelihood condensed tree of Centropomus taxa (see Table 3) inferred from 428 bp of 16S mitochondrial DNA sequence data.
Values on branches are bootstrap values. Nodes with bootstrap values less than 50% were collapsed into polytomies. Individual haplotypes observed
in this study are designated with “Haplotype #”and correspond to the haplotypes listed in Table 7.
CHARACTERIZATION OF THE SNOOK SPECIES COMPLEX 11
said, the current data are inadequate to distinguish the
effects of long-term climate change versus the positive
impact of restrictive fishery regulations that were
implemented in the 1980s, including a ban on commercial
entanglement gears in Texas, a daily bag limit of 1 snook/
d, and a conservative slot limit of 610–711 mm (24–28 in).
FIGURE 5. Cluster results from Structure analysis of all snook individuals genotyped in this study, sorted by mitochondrial DNA haplotype results
from the Basic Local Alignment Search Tool (generally by species). Individuals with no haplotype available were sorted by Q-scores from Structure.
The four labeled individuals showed evidence of admixture: SN550 had a Largescale Fat Snook Centropomus mexicanus haplotype and a Common
Snook C. undecimalis genotype (Q>0.7); SN31 had a Largescale Fat Snook haplotype and a hybrid genotype (Q<0.7); and SN522 and SN573 had a
Common Snook haplotype and a hybrid genotype (Q<0.7).
FIGURE 6. Principal components analysis of snook microsatellite genotype data. Each color represents individuals whose mitochondrial DNA
haplotype implied a taxonomic designation of Largescale Fat Snook Centropomus mexicanus (black) or Common Snook C. undecimalis (red). Three
individuals (labeled) that we identified as putative hybrids with Structure also clustered with the alternate species group (SN550) or did not cluster
tightly with either group (SN522 and SN573).
12 ANDERSON ET AL.
Previous literature suggests that the historical distribution
of Common Snook included areas as far north as Galve-
ston Bay (Rivas 1986; Pope et al. 2006); thus, the combi-
nation of favorable climate, conservative fishing
regulations, and perhaps other factors might be driving
the range expansion of Centropomus. However, the
observed presence of snook in Sabine Lake in this study
implies that they are expanding beyond even their histori-
cal distribution, which emphasizes the importance of cli-
mate in the observed expansion.
In addition to year and latitude, salinity and tempera-
ture appear to influence the probability of encountering a
snook in Texas. Snook are associated with warmer, saltier
water. From a qualitative standpoint, temperature may be
the most important factor driving the distribution of
snook in Texas, as these species are susceptible to cold dis-
turbances that can significantly increase mortality (Adams
et al. 2012; Stevens et al. 2016). The likelihood of snook to
be found more commonly in southern latitudes in Texas
underscores that these relatively warmer bays historically
have proven to be more suitable habitat for Centropomus
than cooler northern bays.
Snook Species Composition and Hybridization
Based on mitochondrial haplotypes and microsatellite
genotypes, the Largescale Fat Snook is the most com-
monly encountered snook in Texas (63%), followed clo-
sely by Common Snook (36%) and occasional
observations of Mexican Snook (0.4%). To our knowl-
edge, this is the first study documenting Mexican Snook
as far north as Texas. A systematic review by Rivas
(1986) suggested that this species is generally found from
Tampico, Tamaulipas, Mexico, southward to Belize;
hence, this species has been generally thought to have the
narrowest range of any snook species (Kubicek et al.
2018). We observed two specimens from Lower Laguna
Madre and a third specimen in Aransas Bay, suggesting
occasional incursions of this species into Texas. Given the
distance between Tampico and Aransas Bay (~630 km),
these data indicate that the known natural range of Mexi-
can Snook may have expanded into more northerly areas
than previously noted.
These data also suggest that Common Snook and Lar-
gescale Fat Snook may occasionally hybridize in Texas.
To our knowledge, evidence for wild hybrids has not been
demonstrated (Tringali et al. 1999a), but hybrid snook
(Common Snook ×Smallscale Fat Snook) have been pro-
duced in the laboratory (Ferraz et al. 2012), indicating the
potential for occasional hybridization events in the wild.
The species diversification in Centropomus was a relatively
recent event (Tringali et al. 1999b); therefore, it is reason-
able to assume that viable hybrids could occasionally be
produced, particularly in relatively sparsely populated
TABLE 8. Expected heterozygosity (H
e
), F
IS
, and the P-value of F
IS
at
each microsatellite locus assayed in this study for Largescale Fat Snook
and Common Snook. The P-values in bold italics indicate that loci were
significantly different from the null expectation of F
IS
=0.
Locus
Largescale Fat Snook Common Snook
H
e
F
IS
PH
e
F
IS
P
CUN4A 0.808 0.058 <0.0001 0.74 0.0292 0.9641
CUN9 0.816 0.003 0.9268 0.93 0.4147 <0.0001
CUN14 0.799 0.096 <0.0001 0.86 0.114 <0.0001
CUN16 0.577 0.208 0.0017 0.85 0.0555 <0.0001
CUN17 0.669 0.151 <0.0001 0.77 0.1997 <0.0001
CUN19 0.661 0.362 <0.0001 0.69 0.0914 0.2134
CUN20 0.789 0.085 <0.0001 0.78 0.0876 0.0065
CUN22 0.889 0.163 <0.0001 0.95 0.4185 <0.0001
FIGURE 7. Results from Structure cluster analysis of Common Snook genotypes. Individuals are aligned on the x-axis and grouped by sample
location (Aransas Bay and Lower Laguna Madre, Texas). The four colors represent the proportions of contribution to each individual from the K=4
genetic clusters observed in the Structure analysis.
CHARACTERIZATION OF THE SNOOK SPECIES COMPLEX 13
areas at the edge of the species’range (Pfennig et al.
2016). Nevertheless, the finding of hybrids in this data set
could also be the result of the poor statistical resolution
expected from a small genetic data set. This question
could be answered with a larger genetic or genomic data
set, as the number of loci employed here (n=8) is proba-
bly not sufficient to conclusively resolve hybrids. It should
be noted also that one of the putative hybrids identified
by Structure clustered with its expected species in the PCA
model, which underscores the weakness of these data in
truly identifying hybrids with any reliability.
Afinal note regarding the taxonomy of Texas snook
is the unexpected lack of Smallscale Fat Snook.This
contrasts with the results of Martin and King (1991),
FIGURE 8. Results from multivariate clustering of individual Common Snook Centropomus undecimalis (PCA =principal components analysis;
DAPC =discriminant analysis of principal components; DA =discriminant analysis). The figure on the left presents a PCA of microsatellite
genotypes. Individuals are colored based on their assigned cluster from Structure analysis (clusters 1–4). The x- and y-axes represent the first and
second axes of ordination in the PCA. The figure on the right is a DA of the first 20 principal components, with individuals again colored based on
their assigned genetic cluster from Structure.
FIGURE 9. Results from Structure cluster analysis of Largescale Fat Snook genotypes. Individuals are aligned on the x-axis and grouped by sample
location (Aransas Bay, Upper Laguna Madre, and Lower Laguna Madre, Texas). The three colors represent the proportions of contribution to each
individual from the K=3 genetic clusters observed in the Structure analysis.
14 ANDERSON ET AL.
who observed both juveniles and advanced-stage female
Smallscale Fat Snook near the mouth of the Rio
Grande. There are two potential explanations for this
disparity. First, the small group observed by Martin
and King (1991) could have been transient. To our
knowledge, prior to the findings of Martin and King
(1991) there were no records of Smallscale Fat Snook in
the literature from Texas. These historical catches
occurred during 1986–1989, a time period that was
bookended by two historic freezes (in February and
December 1989), which killed an estimated 17 million
fish combined (TPWD, unpublished data). It is possible
that a small but established population of Smallscale
Fat Snook near the Rio Grande was extirpated as a
result of these freeze events. A second, more plausible
explanation is that the taxonomic designation of Smalls-
cale Fat Snook in the earlier study was incorrect and
that the specimens described by Martin and King (1991)
were actually Largescale Fat Snook. Morphologically,
these two species are very similar and can only be sepa-
rated using differences in lateral line scale counts and
size (Rivas 1986). These species are also very closely
related biochemically, with approximately 0.5% diver-
gence at the mtDNA 16S locus (Tringali et al. 1999b).
Anecdotally, the taxonomic designation of the C.
parallelus/mexicanus group in Texas has historically been
equivocal; as such, it is likely that the specimens
described by Martin and King (1991) and the specimens
described in the current work are in actuality members
of the same species.
Population Genetics of Snook in Texas
For the two more common snook species in Texas,
analysis of genetic population structure suggested equivo-
cal results. In both cases (Largescale Fat Snook and Com-
mon Snook), the Structure analysis and multivariate
statistical analyses suggested the presence of multiple
genetic clusters within species, but there was little actual
geographic structure that could be associated with these
clusters. The complex admixture pattern observed in pop-
ulation analyses was coupled with very high rates of
genetic disequilibrium. The weak and irregular differentia-
tion among individual cluster assignments, coupled with
deviation from equilibrium expectations, has been identi-
fied before in marine fishes and has previously been attrib-
uted to chaotic genetic patchiness caused by small
effective population size and localized kin aggregations
(Selkoe et al. 2006; Selwyn et al. 2016). Simulation studies
have suggested that chaotic genetic patchiness is driven by
(1) genetic drift created by small local effective population
FIGURE 10. Results from multivariate clustering of individual Largescale Fat Snook Centropomus mexicanus (PCA =principal components analysis;
DAPC =discriminant analysis of principal components; DA =discriminant analysis). The figure on the left presents a PCA of microsatellite
genotypes. Individuals are colored based on their assigned cluster from Structure analysis (clusters 1–3). The x- and y-axes represent the first and
second axes of ordination in the PCA. The figure on the right is a DA of the first 20 principal components, with individuals again colored based on
their assigned genetic cluster from Structure.
CHARACTERIZATION OF THE SNOOK SPECIES COMPLEX 15
sizes and (2) collective dispersal at the larval phase (Bro-
quet et al. 2013). Selwyn et al. (2016) noted that patchiness
in the marine realm is generally characterized by very low
but significant genetic divergence among areas as well as
deviation from equilibrium expectations, both of which
were observed for Largescale Fat Snook and Common
Snook in the present study. The patchiness pattern
observed in microsatellite data could be further reinforced
by multiple factors associated with the distribution of
snook species at the edge of their population ranges. First,
Texas represents the leading northern edge of the snook
species’range in the western Gulf of Mexico, and previous
studies on population leading edges have also observed
patterns of disorganized but significant genetic divergence
over small spatial scales (Eschbach et al. 2014; Shirk et al.
2014; Hagen et al. 2015; Tollefsrud et al. 2016). Second,
the demographic data reported here suggest persistent
expansion and contraction of snook in Texas, and these
pulses of abundance at the edge of the species’range can
be expected to be coupled with complementary pulses of
gene flow. Persistent gene flow into sparsely occupied
areas at the edge of the range could drive genetic patterns
similar to that expected from repeated founder events,
resulting in genetic disequilibrium. In the case of Common
Snook in particular, a previous study noted spawning
ground fidelity in this species (Adams et al. 2009), which
would also be expected to reinforce a pattern of genetic
patchiness (Selwyn et al. 2016). Finally, it should be noted
that the unique reproductive strategy of snook species
may also play a role in the population genetic patterns
observed here. Snook are protandric hermaphrodites, with
all individuals beginning life as males but switching to
females upon reaching maximum size (Vidal-López et al.
2019 and references therein). Sequential hermaphroditism
has the potential to impact effective population size by
limiting the number of individuals that successfully spawn
as one sex or the other, but it is poorly understood how
this strategy may impact genetic drift. A previous study of
sequential hermaphrodites suggested that species exhibit-
ing this strategy are not more genetically structured than
other species (Chopelet et al. 2009), and there is no evi-
dence from previous snook population genetics work that
would indicate an impact of this unique reproductive
strategy (Tringali and Bert 1996).
Taken together, although these patterns suggest a lack
of temporally stable population structure over a broad
geographic scale in Texas, neither Largescale Fat Snook
nor Common Snook can be reasonably characterized as
entirely panmictic. It is more likely that while some degree
of genetic divergence might be expected among local pop-
ulations in larvae or younger individuals, over broader
spatial scales both of the predominant snook species in
Texas probably represent single genetic stocks. As these
populations continue to expand and as more individuals
become involved in localized spawning events (i.e.,
increased effective population size), this pattern may lose
some effect over time since genetic disequilibrium would
be expected to break down.
Management Implications
The low but significant values of F
ST
between areas
and the lack of geographical resolution in the Structure
models imply that both Largescale Fat Snook and Com-
mon Snook in Texas exist as single genetic stocks. As pre-
viously discussed, the statistical significance of measured
divergence among areas is more likely driven by a pattern
of chaotic genetic patchiness reinforced by low effective
population size at the edge of each species’range than by
traditional population structure (i.e., isolated populations).
Thus, we recommend that each of the two predominant
snook species in Texas be managed as a single stock unit.
One caveat to this recommendation is that the genetic
data set used to generate this interpretation was very small
(n=8 loci). New genomic-based methods (e.g., Peterson
et al. 2012) allow for simultaneous locus discovery and
genotyping of potentially thousands of genetic markers for
a reduced per-sample cost. These “genomic”methods have
frequently demonstrated a pattern whereby small numbers
of loci (presumably under directional selection) show ele-
vated divergence relative to the genomic mean in marine
fishes (Portnoy et al. 2015; Hollenbeck et al. 2018; Ander-
son et al. 2019), a finding that underscores the risk of
overinterpreting smaller genetic data sets. Nevertheless, in
the absence of such genomic data, the present data suggest
a tentative single-stock management unit for each of the
predominant snook species in Texas.
Large-scale changes in climate present challenges for the
management of wild species (Hulme 2005; Thomas et al.
2010), and range expansion can be included among these
challenges as tropical species can be generally expected to
advance poleward (Lawson et al. 2012). It has been sug-
gested that management of local populations should shift
toward facilitation of their inevitable range expansion
(Galatowitsch et al. 2009; Lawson et al. 2012). Although
Texas historically supported a commercial snook fishery,
the fishery crashed after the 1920s; it has been hypothesized
that this crash was driven by an interplay of freezes and
overfishing (Matlock and Osburn 1987), followed by low
abundance and erratic recruitment (Pope et al. 2006).
Warming of inshore water temperatures over the last several
decades, coupled with no commercial fishing pressure and
limited recreational pressure, has resulted in the increasing
abundance and range of snook species in Texas. In this con-
text, management efforts should be focused on further facil-
itating this expansion by (1) increasing connectivity between
suitable habitat patches and (2) enhancing population sur-
vival (Lawson et al. 2012). Regarding the latter, Stevens et
al. (2016) noted that snook population resilience in Florida
16 ANDERSON ET AL.
after cold events was variable across estuaries and that this
variability in resilience was likely tied to estuary geomor-
phology and the accessibility of thermal refugia. Using the
Stevens et al. (2016) study as a model for the expected resili-
ence of snook in Texas after cold events, one might expect
that bay-specific variability in available thermal refugia and
localized extinction during extreme cold are the most
important challenges to snook expansion. Thus, efforts to
enhance population survival may have the most promise for
facilitating snook expansion in Texas. In 2005, the TPWD
enacted regulations to close easily accessible, deepwater
refugia to fishing during extreme freeze events. The spatial
and temporal extent of these closures could be expanded to
include known snook habitat areas that are not currently
protected. Additionally, snook species in Texas represent
excellent candidates for future stock enhancement efforts,
as mortality caused by persistent acute cold events could
potentially be offset by the release of captive-raised juve-
niles. In any event, the increasing abundance and range of
snook species in Texas represent increased opportunity for
anglers, and measures to facilitate this expansion should
balance conservation and recreational access. This work
represents a baseline upon which future assessments of
snook can be anchored, and management efforts going for-
ward should take into account the diversity and distribution
of snook species in coastal waters of Texas.
ACKNOWLEDGMENTS
The following individuals assisted with preparation and/or
internal review of this work: M. Fisher of TPWD, A. Landry
formerly of Texas A&M Galveston, C. Bird of Texas A&M
Corpus Christi, and M. Iacchei of Hawai'iPacificUniversity.
We thank the various teams within TPWD, TAMU-Corpus
AndTAMU-Galvestonthatassistedwithcollectionand
identification of field specimens and lab work, especially C.
Barnes. This work was supported by a grant from the Sport
Fish Restoration Program (U.S. Fish and Wildlife Service).
A. González was supported by a fellowship from the Hispa-
nic Leaders in Agriculture and the Environment. C. Chapa
was supported by the Southeast Texas Sportfishing Associa-
tion and McDaniel Charitable Foundation. There is no con-
flict of interest declared in this article.
ORCID
Alin González https://orcid.org/0000-0003-4041-0496
REFERENCES
Adams, A. J., J. E. Hill, B. N. Kurth, and A. B. Barbour. 2012. Effects
of a severe cold event on the subtropical, estuarine-dependent Com-
mon Snook, Centropomus undecimalis. Gulf and Caribbean Research
24:13–21.
Adams, A., R. Wolfe, N. Barkowski, and D. Overcash. 2009. Fidelity to
spawning grounds by a catadromous fish, Centropomus undecimalis.
Marine Ecology Progress Series 389:213–222.
Aliaume, C., A. Zerbi, and J. M. Miller. 1997. Nursery habitat and diet
of juvenile Centropomus species in Puerto Rico estuaries. Gulf of
Mexico Science 1997:77–87.
Altschul, S. F., W. Gish, W. Miller, E. W. Myers, and D. J. Lipman.
1990. Basic local alignment search tool. Journal of Molecular Biology
215:403–410.
Anderson, J. D., S. J. O'Leary, and P. T. Cooper. 2019. Population
structure of Atlantic Croakers (Micropogonias undulatus) from the
Gulf of Mexico: evaluating a single stock hypothesis using a genomic
approach. Marine and Coastal Fisheries: Dynamics, Management,
and Ecosystem Science [online serial] 11:3–16.
Betancur-R, R., C. Li, T. A. Munroe, J. A. Ballesteros, and G. Orti.
2013. Addressing gene tree discordance and non-stationarity to
resolve a multi-locus phylogeny of the flatfishes (Teleostei: Pleuronec-
tiformes). Systematic Biology 62:763–785.
Broquet, T., F. Viard, and J. M. Yearsley. 2013. Genetic drift and collec-
tive dispersal can result in chaotic genetic patchiness. Evolution
67:1660–1675.
Chapa, C. J. 2012. Early life history and resurgence of snook (family
Centropomidae) in Texas. Master's thesis. A&M University, Galve-
ston, Texas.
Chopelet, J., R. Waples, and S. Mariani. 2009. Sex change and the genetic
structure of marine fish populations. Fish and Fisheries 10:329–343.
Du Toit, J. T. 2010. Considerations of scale in biodiversity conservation.
Animal Conservation 13:229–236.
Earl, D. A., and B. M. vonHoldt. 2012. STRUCTURE HARVESTER:
a website and program for visualizing STRUCTURE output and
implementing the Evanno method. Conservation Genetics Resources
4:359–361.
Eschbach, E., A. W. Nolte, K. Kohlmann, P. Kersten, J. Kail, and R.
Arlinghaus. 2014. Population differentiation of Zander (Sander luciop-
erca) across native and newly colonized ranges suggests increasing
admixture in the course of an invasion. Evolutionary Applications
7:555–568.
Evanno, G., S. Regnaut, and J. Goudet. 2005. Detecting the number of
clusters of individuals using the software STRUCTURE: a simulation
study. Molecular Ecology 14:2611–2620.
Felsenstein, J. 1985. Confidence limits on phylogenies: an approach using
the bootstrap. Evolution 39:783–791.
Ferraz, E. M., R. L. Petersen, G. Passini, and V. R. Cerqueira. 2012.
Híbridos recíprocos obtidos por cruzamentos entre os robalos Cen-
tropomus parallelus eCentropomus undecimalis. Boletim do Instituto
de Pesca 39:53–61.
Figueira, W. F., and D. J. Booth. 2010. Increasing ocean temperatures
allow tropical fishes to survive overwinter in temperate waters. Global
Change Biology 16:506–516.
Galatowitsch, S., L. Frelich, and L. Philips-Mao. 2009. Regional climate
change adaptation strategies for biodiversity conservation in a mid-
continental region of North America. Biological Conservation
142:2012–2022.
Gann, H. M., H. Takahashi, M. P. Hammer, H. H. Tan, Y. P. Lee, J.
M. Voss, and C. M. Austin. 2017. Mitochondrial genomes and phylo-
genetic relationships of Lates japonicus,Lates niloticus, and Psam-
moperca waigiensis (Perciformes: Latidae). Mitochondrial DNA Part
B: Resources 2:73–75.
González, A. 2015. Population genetic structure of the crashed snook
fishery. Master's thesis. Texas A&M University, Corpus Christi.
Goudet, J. 2004. hierfstat, a package for R to compute and test hierarchi-
cal F-statistics. Molecular Ecology Notes 5:184–186.
Hagen,S.B.,A.Kopatz,J.Aspi,I.Kojola,andH.G.Eiken.
2015. Evidence of rapid change in genetic structure and diversity
CHARACTERIZATION OF THE SNOOK SPECIES COMPLEX 17
during range expansion in a recovering large terrestrial carnivore.
Proceedings of the Royal Society B: Biological Sciences
282:20150092.
Heck, K. L., Jr., F. J. Frodrie, S. Madsen, C. J. Baillie, and D. A.
Byron. 2015. Seagrass consumption by native and a tropically associ-
ated fish species: potential impacts of the tropicalization of the north-
ern Gulf of Mexico. Marine Ecology Progress Series 520:165–173.
Hollenbeck, C. M., D. S. Portnoy, and J. R. Gold. 2018. Evolution of
population structure in an estuarine-dependent marine fish. Ecology
and Evolution 9:3141–3152.
Holm, S. 1979. A simple sequential rejective multiple test procedure.
Scandinavian Journal of Statistics 6:65–70.
Huber, C. G., T. B. Grabowski, R. Patiño, and K. L. Pope. 2014. Distri-
bution and habitat associations of juvenile Common Snook in the
lower Rio Grande, Texas. Marine and Coastal Fisheries: Dynamics,
Management, and Ecosystem Science [online serial] 6:170–180.
Hulme, P. E. 2005. Adapting to climate change: is there scope for ecolog-
ical management in the face of a global threat? Journal of Applied
Ecology 42:784–794.
Jombart, T. 2008. Adegenet: an R package for the multivariate analysis
of genetic markers. Bioinformatics 24:1403–1405.
Kimura, M. 1980. A simple method for estimating evolutionary rate of
base substitutions through comparative studies of nucleotide
sequences. Journal of Molecular Evolution 16:111–120.
Kubicek, K. M., C. A. Alvarez-Gonzalez, R. Martinez-Garcia, W. M.
Contreras-Sánchez, C. Pohlenz, and K. W. Conway. 2018. Larval
development of the Mexican Snook, Centropomus poeyi (Teleostei:
Centropomidae). Neotropical Ichthyology 16:e170014.
Kumar, S., G. Stecher, and K. Tamura. 2016. MEGA7: molecular Evo-
lutionary Genetics Analysis version 7.0 for bigger datasets. Molecular
Biology and Evolution 33:1870–1874.
Larkin, M. A., G. Blackshields, N. P. Brown, R. Chenna, P. A. McGet-
tigan, H. McWilliam, F. Valentin, I. M. Wallace, A. Wilm, R. Lopez,
J. D. Thompson, T. J. Gibson, and D. G. Higgins. 2007. Clustal W
and Clustal X version 2.0. Bioinformatics 23:2947–2948.
Lawson, C. R., J. J. Bennie, C. D. Thomas, J. A. Hodgson, and R. J.
Wilson. 2012. Local and landscape management of an expanding
range margin under climate change. Journal of Applied Ecology
49:552–561.
Li, C., R. Betancur-R, W. L. Smith, and G. Orti. 2011. Monophyly and
interrelationships of snook and barramundi (Centropomidae sensu
Greenwood) and five new markers for fish phylogenetics. Molecular
Phylogenetics and Evolution 60:463–471.
Lin, G., L. C. Lo, Z. Y. Zhu, F. Feng, R. Chou, and G. H. Yue. 2006.
The complete mitochondrial genome sequence and characterization of
single-nucleotide polymorphisms in the control region of the Asian
Seabass (Lates calcarifer). Marine Biotechnology 8:71–79.
Martin, J. H., and T. L. King. 1991. Occurrence of Fat Snook (Cen-
tropomus parallelus) in Texas: evidence for a range extension. Contri-
butions in Marine Science 32:123–126.
Matlock, G. C., and H. R. Osburn. 1987. Demise of the snook fishery in
Texas. Northeast Gulf Science 9:53–58.
Matschiner, M., and W. Salzburger. 2009. TANDEM: integrating auto-
mated allele binning into genetics and genomics workflows. Bioinfor-
matics 25:1982–1983.
Morgulis, A., G. Coulouris, Y. Raytselis, T. L. Madden, R. Agarwala,
and A. A. Schäffer. 2008. Database indexing for production Mega-
BLAST searches. Bioinformatics 24:1757–1764.
Nakamura, Y., D. A. Feary, M. Kanda, and K. Yamaoka. 2013. Tropi-
cal fishes dominate temperate reef fish communities within western.
Japan. PLoS (Public Library of Science) ONE [online serial] 8:e81107.
Orrell, T. M., and K. E. Carpenter. 2004. A phylogeny of the fish family
Sparidae (porgies) inferred from mitochondrial sequence data. Molec-
ular Phylogenetics and Evolution 32:425–434.
Palumbi, S. R. 1996. Nucleic acids II: the polymerase chain reaction.
Pages 205–247 in D. M. Hillis, C. Moritz, and B. K. Mable, editors.
Molecular systematics. Sinauer, Sunderland, Massachusetts.
Peregrino-Uriarte, A. B., R. Pacheco-Aguilar, A. Varela-Romero, and G.
Yepiz-Plascencia. 2007. Differences in the 16S rRNA and cytochrome
oxidase csubunit I genes in the mullets Mugil cephalus and Mugil
curema, and snooks Centropomus viridis and Centropomus robalito.
Ciencias Marinas 33:95–104.
Peterson, B. K., J. N. Weber, E. H. Kay, H. S. Fisher, and H. E. Hoek-
stra. 2012. Double digest RADseq: an inexpensive method for de
novo SNP discovery and genotyping in model and non-model species.
PLoS (Public Library of Science) ONE [online serial] 7:e37135.
Pfennig, K. S., A. L. Kelly, and A. A. Pierce. 2016. Hybridization as a
facilitator of species range expansion. Proceedings of the Royal Soci-
ety B: Biological Sciences 283:20161329.
Pope, K. L., D. R. Blankenship, M. Fisher, and R. Patiño. 2006. Status
of the Common Snook (Centropomus undecimalis) in Texas. Texas
Journal of Science 58:325–332.
Portnoy, D. S., J. B. Puritz, C. M. Hollenbeck, J. Gelsleichter, D. Chap-
man, and J. R. Gold. 2015. Selection and sex-biased dispersal in a
coastal shark: the influence of philopatry on adaptive variation.
Molecular Ecology 24:5877–5885.
Pritchard, J. K., M. Stephens, and P. Donnelly. 2000. Inference of popu-
lation structure using multilocus genotype data. Genetics 155:945–
959.
Raymond, M., and F. Rousset. 1995. GENEPOP (version 1.2): popula-
tion genetics software for exact tests and ecumenicism. Journal of
Heredity 86:248–249.
Rivas, L. R. 1986. Systematic review of the perciform fishes of the genus
Centropomus. Copeia 1986:579–611.
Rousset, F. 2008. Genepop'007: a complete reimplementation of the Gen-
epop software for Windows and Linux. Molecular Ecology Resources
8:103–106.
Rozas, J., A. Ferrer-Mata, J. C. Sánchez-DelBarrio, S. Guirao-Rico, P.
Librado, S. E. Ramos-Onsins, and A. Sánchez-Gracia. 2017. DnaSP
v6: DNA sequencing polymorphism analysis of large data sets.
Molecular Biology and Evolution 34:3299–3302.
Schuelke, M. 2000. An economic method for the fluorescent labeling of
PCR fragments. Nature Biotechnology 18:233–234.
Schwarz, G. E. 1978. Estimating the dimension of a model. Annals of
Statistics 6:461–464.
Selkoe, K. A., S. D. Gaines, J. E. Caselle, and R. R. Warner. 2006. Cur-
rent shifts and kin aggregation explain genetic patchiness in fish
recruits. Ecology 87:3082–3094.
Selwyn, J. D., J. D. Hogan, A. M. Downey-Wall, L. M. Gurski, D. S.
Portnoy, and D. D. Heath. 2016. Kin-aggregations explain chaotic
genetic patchiness, a commonly observed genetic pattern, in a marine
fish. PLoS (Public Library of Science) ONE [online serial] 11:e015338.
Seyoum, S., M. D. Tringali, and J. G. Sullivan. 2005. Isolation and char-
acterization of 27 polymorphic microsatellite loci for the Common
Snook, Centropomus undecimalis. Molecular Ecology Notes 5:924–
927.
Shafland, P. L., and K. Foote. 1983. A lower lethal temperature for fin-
gerling snook, Centropomus undecimalis. Northeast Gulf Science
6:175–177.
Shirk, R. Y., J. L. Hamrick, C. Zhang, and S. Qiang. 2014. Patterns of
genetic diversity reveal multiple introductions and recurrent founder
effects during range expansion in invasive populations of Geranium
carolinianum (Geraniaceae). Heredity 112:497–507.
Smith, W. L., and M. T. Craig. 2007. Casting the percomorph net
widely: the importance of broad taxonomic sampling in the search for
the placement of serranid and percid fishes. Copeia 2007:35–55.
Staten, P. W., J. Lu, K. M. Grise, S. M. Davis, and T. Birner. 2018. Re-
examining tropical expansion. Nature. Climate Change 8:768–775.
18 ANDERSON ET AL.
Stevens, P. W., D. A. Blewett, R. E. Boucek, J. S. Rehage, B. L. Winner,
J. M. Young, J. A. Whittington, and R. Paperno. 2016. Resilience of
a tropical sport fish population to a severe cold event varies across
five estuaries in southern Florida. Ecosphere [online serial] 7:e01400.
Thomas, C. D., J. K. Hill, B. J. Anderson, S. Bailey, C. M. Beale, R. B.
Bradbury, C. R. Bulman, H. Q. P. Crick, F. Eigenbrod, H. M. Grif-
fiths, W. E. Kunin, T. H. Oliver, C. A. Walmsley, K. Watts, N. T.
Worsfold, and T. Yardley. 2010. A framework for assessing threats
and benefits to species responding to climate change. Methods in
Ecology and Evolution 2:125–142.
Tolan, J. M. 2007. El Niño-Southern Oscillation impacts translated to
the watershed scale: estuarine salinity patterns along the Texas
Gulf coast, 1982–2004. Estuarine, Coastal and Shelf Science
72:247–260.
Tolan, J. M., and M. Fisher. 2009. Biological response to changes in cli-
mate patterns: population increases of Gray Snapper (Lutjanus gri-
seus) in Texas bays and estuaries. U.S. National Marine Fisheries
Service Fishery Bulletin 107:36–44.
Tollefsrud, M. M., T. Myking, J. H. Sønstebø, V. Lygis, A. M. Hietala,
and M. Heuertz. 2016. Genetic structure in the northern range mar-
gins of common ash, Fraxinus excelsior L. PLoS (Public Library of
Science) ONE [online serial] 11:e0167104.
Tringali, M. D., and T. M. Bert. 1996. The genetic stock structure of
Common Snook (Centropomus undecimalis). Canadian Journal of
Fisheries and Aquatic Sciences 53:974–984.
Tringali, M. D., T. M. Bert, and S. Seyoum. 1999a. Genetic identifica-
tion of centropomine fishes. Transactions of the American Fisheries
Society 128:446–458.
Tringali, M. D., T. M. Bert, S. Seyoum, E. Bermingham, and D. Bar-
tolacci. 1999b. Molecular phylogenetics and ecological diversifica-
tion of the transisthmian fish genus Centropomus (Perciformes:
Centropomidae). Molecular Phylogenetics and Evolution 13:193–
207.
Verges, A., F. Tomas, E. Cebrian, E. Ballesteros, Z. Kizilkaya, P. Den-
drinos, A. A. Karamanlidis, D. Spiege, and E. Sala. 2014. Tropical
Rabbitfish and the deforestation of a warming temperate sea. Journal
of Ecology 2014:1518–1527.
Vidal-López, J. M., W. M. Contreras-Sánchez, A. Torres-Martínez, A.
A. Hernández-Franyutti, and M. Del Carmen-Aranzábal Uribe. 2019.
Early gonadal differentiation in the Mexican Snook Centropomus
poeyi (Centropomidae, Perciformes, Teleostei) suggests protandric
hermaphroditism. Marine and Freshwater Behaviour and Physiology
51:327–345.
Waples, R., A. E. Punt, and J. M. Cope. 2008. Integrating genetic data
into management of marine resources: how can we do it better? Fish
and Fisheries 9:423–449.
Zhang, Z., S. Schwartz, L. Wagner, and W. Miller. 2000. A greedy algo-
rithm for aligning DNA sequences. Journal of Computational Biology
7:203–214.
SUPPORTING INFORMATION
Additional supplemental material may be found online
in the Supporting Information section at the end of the
article.
CHARACTERIZATION OF THE SNOOK SPECIES COMPLEX 19