Genetic connectivity in the spotted rose snapper Lutjanus guttatus (Lutjaniformes: Lutjanidae) between Mexico and Panama throughout the Tropical Eastern Pacific

Noé Díaz-Viloria1 , Adriana Max-Aguilar2, Mailin I. Rivera-Lucero3, Elaine Espino-Barr4, Nicole Reguera-Rouzaud1, Andrea Casaucao-Aguilar1 and Ricardo Perez-Enriquez2

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Abstract​


EN

The spotted rose snapper, Lutjanus guttatus, is an important fishery species with high potential for aquaculture. Genetic characterization of its natural populations is necessary to avoid stock collapse and loss of genetic diversity. Previous studies carried out in the Tropical Eastern Pacific (TEP), however, have shown contrasting results in the genetic structure of fish populations, particularly in species of Lutjanidae. Therefore, to understand the genetic structure of spotted rose snapper in the TEP, twelve microsatellite loci were used to assess the genetic diversity and explore the hypothesis of population genetic structure in samples of the species collected throughout the TEP. Fin clips from 186 sampled individuals (27 to 49 per site) were analyzed from five sites in the three regional biogeographic provinces, delimited by shoreline reef habitat breaks: La Paz (Cortez province), Colima and Oaxaca (Mexican province), Chiriqui and Port of Panama (Panamic province). Results of global Analysis of Molecular Variance (AMOVA), population pairwise FST, hierarchical AMOVA, and a discriminant analysis of principal components (DAPC) reflected a panmictic population involving the entire set of sampled sites. The role of larval dispersal, post-recruitment migration, and marine current dynamics as drivers of genetic connectivity in this species is discussed.

Keywords: Biogeographic province, Gene flow, Microsatellites, Population genetic structure.

ESP

El pargo lunarejo, Lutjanus guttatus, es una importante especie pesquera, con alto potencial para la acuicultura. La caracterización genética de sus poblaciones naturales es necesaria para evitar el colapso del stock y la pérdida de diversidad genética. Sin embargo, estudios previos realizados en el Pacífico Oriental Tropical (TEP), han mostrado resultados contrastantes en la estructura genética de poblaciones de peces, particularmente en especies de Lutjanidae. Por lo tanto, para entender la estructura genética del pargo lunarejo en el TEP, se usaron 12 loci microsatélites para evaluar la diversidad genética y explorar la hipótesis de estructura genética poblacional en muestras de la especie colectada a lo largo del TEP. Se analizaron fragmentos de aletas de 186 individuos (27 a 49 por sitio) de cinco localidades en las tres provincias biogeográficas regionales, delimitadas por las discontinuidades de hábitat de arrecife costero: La Paz (Provincia de Cortés), Colima y Oaxaca (Provincia Mexicana), Chiriquí y Puerto de Panamá (Provincia Panámica). Los resultados del Análisis de Varianza Molecular (AMOVA) global, FST de poblaciones pareadas, AMOVA jerárquico y un análisis discriminante de componentes principales (DAPC) reflejaron una población panmíctica que involucraba todo el conjunto de sitios muestreados. Se discute el papel de la dispersión larvaria, migración post-reclutamiento y la dinámica de las corrientes marinas como propulsores de la conectividad genética en esta especie.

Palabras clave: Estructura genética poblacional, Flujo genético, Microsatélites, Provincia biogeográfica.

Introduction​


The spotted rose snapper, Lutjanus guttatus (Steindachner, 1869), is distributed throughout the Tropical Eastern Pacific (TEP) from the Gulf of California, Mexico, to Peru, including oceanic islands. It is an important food and recreational fishery species with a high market price of US$6-8 per kg (Sarabia-Méndez et al.,2010; Ibarra-Castro et al.,2012) and a high potential for aquaculture in Mexico (García-Ortega et al.,2005). Despite its great economic importance, there are few studies that use molecular techniques to identify stocks in L. guttatus, critical information for the to managing exploitation of mixed or locally discrete stocks and avoiding loss of genetic diversity and possible stock collapse (Pauly et al., 1996).

The TEP is a region that extends along 2,500 km from the equator north to the southernmost tip of the Baja California Peninsula (Kessler, 2006). The TEP coast is a highly dynamic environment, with sea temperatures ranging from warm to temperate, upwelling systems and various large gyres, alternating currents, and large rocky-habitat discontinuities that may greatly influence the genetic connectivity of populations (Robertson, Cramer, 2009; Sandoval-Huerta et al., 2019). These physical characteristics can affect distributions of species with narrow environmental tolerances and influence the dispersal of pelagic larvae, resulting in variable gene flow (from reproductive isolation to high connectivity) between adjacent populations (García-De León et al., 2018; Sandoval-Huerta et al., 2019).

There are several hypotheses about biogeographic partitioning in the TEP, where environmental and ecological differences have promoted speciation in the absence of isolation of diverging populations (Briggs, Bowen, 2012). Robertson, Cramer (2009) determined that three biogeographic provinces exist: the Cortez (Gulf of California and the southernmost Pacific Baja California), the Panamic (southward) and the Island province. However, Walker (1960) had previously defined two provinces in Mexico based on the distribution of locally endemic reef fishes: a Cortez Province from the Pacific coast of Baja California below 25° N, including the Gulf of California, and a Mexican province for the remainder. Within the TEP, there are also two major breaks in the distribution of shoreline reef habitats, consistent with shoreline extensions of sand and mud, named the Sinaloan Gap (370 km in the SE Gulf of California), and the Central American Gap (around 1000 km, from the Gulf of Tehuantepec, Mexico, to El Salvador). These gaps separated three mainland provinces (Cortez, Mexican, and Panamic) defined by Hastings (2000). Finally, the TEP was split into two provinces, the Galapagos and the remainder of the region, by Spalding et al. (2007).

Many studies carried out in the TEP have obtained measures of gene flow to explore levels of population genetic differentiation and evaluate the influence of habitat gaps and oceanographic processes, with contrasting results. Gene flow rates among coral and North Pacific hake (Merluccius productus (Ayres, 1855)) populations along coast are high, although populations at the northernmost and the southernmost peripheries appear to be more genetically isolated (Lessios, Baums, 2017; García-De León et al., 2018). Strong subdivisions between populations of the goby Elacatinus puncticulatus (Ginsburg, 1938) were better explained by local oceanographic processes than the largest habitat discontinuities (Sandoval-Huerta et al., 2019). Meanwhile, basin-wide connectivity and shallow population structure in the olive ridley sea turtle (Lepidochelys olivacea (Eschscholtz, 1829)) seems due, in part, to their low nesting site fidelity and broad foraging ranges (Silver-Georges et al., 2020).

Recently, three studies of population genetics in different snapper species (Lutjanidae) in the TEP were completed. The first study carried out using sequencing of the mtDNA control region on Lutjanus peru (Nichols & Murphy, 1922)(~800 bp)and L. guttatus (576 bp) found high overall levels of genetic diversity and a lack of genetic differentiation for both species (Hernández-Álvarez et al., 2020). These results indicate that equatorial and subtropical residents display high levels of connectivity and highlight that no significant effect of environmental differences between Cortez and Panamic provinces exist. In contrast, a study using 13 microsatellite loci on L. peru and 11 microsatellite locion L. argentiventris (Peters, 1869) evaluated genetic diversity across 10 and five locations, respectively (Reguera-Rouzaud et al., 2021). Significant genetic structure was identified in both species, but the pattern of genetic structure differed between species. These authors suggested two possible drivers, including isolation by distance (IBD) at a spatial scale of more than 2,500 km and the presence of potential barriers to gene flow at smaller scales (< 250 km). The most recent study of L. guttatus, covering nearly all its distributional area, used 2003 single nucleotide polymorphisms (SNPs); including neutral loci (1858 SNPs) and outlier loci (145 SNPs) to assess genetic variation and population genetic structure (Mar-Silva et al., 2023). For neutral loci (NL), no differences were found, but with outlier loci (OL) two clusters were found dividing at the Gulf of Panama, suggesting the role of selection in generating genetic differences in L. guttatus.

Because microsatellites are assumed to be neutral markers, codominant with Mendelian inheritance, have higher mutation rates than mtDNA (Liu, Cordes, 2004; Hernández-Álvarez et al., 2020), and higher levels of genetic diversity in number of alleles per locus (NA) and heterozygosities (HO and HE) than SNPs (Mar-Silva et al., 2023), they are better suited to studying mutation-drift equilibrium and gene flow among populations. The hypothesis of the existence of population genetic structure in L. guttatus in the TEP was therefore retested using a set of 12 mostly tetranucleotide microsatellite loci to explore genetic diversity and neutral population genetic structure among five sites distributed throughout the TEP.

Material and methods


Sampling. Fin clips were collected from 30–50 individuals from each of five locations across the three putative mainland provinces in the TEP (three from Mexico and two from Panama) and preserved in ethanol (80%) (Fig. 1; Tab. 1). Note that this study did not include specimens from the Galapagos Islands, an island province where the species is also reported (Robertson, Allen, 2015), because originally was focused on the three mainland provinces scheme. Captures of L. guttatus at every site were supported by commercial fishermen.

TABLE 1 | Lutjanus guttatus tissue collections. Samples sizes (n), mean standard lengths (centimeters), standard deviations (inside parentheses), collection dates, and biogeographic province of origin in the Tropical Eastern Pacific.

Location

n

Mean sizes (± S.D.)

Date

Biogeographic Province

La Paz

30

30 (± 3.4)

July 2018

Cortez

Colima

50

27.8 (± 2.5)

April 2018

Mexican

Oaxaca

50

25.6 (± 7.4)

April 2017

Mexican

Chiriquí

30

25.5 (± 1.8)

August 2017

Panamic

Port of Panama

30

34 (± 6.4)

August 2017

Panamic

 

FIGURE 1| Sampling sites for adult Lutjanus guttatus along the Tropical Eastern Pacific. La Paz (PAZ), Colima (COL), and Oaxaca (OAX) are in Mexican waters and Chiriquí (CHI) and Panama Port (PAN) are in Panama.

DNA extraction and PCR. Genomic DNA was obtained by a salt extraction technique from Aljanabi, Martinez (1997) that contained a modified homogenizing buffer (5M NaCl, 1M Tris-HCl, 0.5M EDTA, 10% SDS, pH 8.0), and DNA extracts were standardized to 30 ng/µl. A set of 12 microsatellite loci were selected based on their numbers of alleles (moderate to high polymorphism) and general conformation to Hardy Weinberg Equilibrium (HWE) expectations (Perez-Enriquez et al., 2020; Tab. 2). The PCR was carried out following Schuelke (2000) in an 11 μl volume containing 1 μl of DNA (30 ng/μl), Taq Buffer (1×), MgCl2 (1.5 mM), dNTPs (0.25 mM), forward primer (0.1 μM), reverse primer (0.4 μM), M13+dye (0.4 μM; 6-FAM, VIC, NED, or PET (Applied Biosystems; Tab. 2), and Taq polymerase (0.04 U/μl). The PCR thermal cycling conditions were as follows: 94ºC for 5 min; 30 cycles at 94ºC for 30 sec, annealing temperature (Tab. 2) for 45 sec, and 72ºC for 45 sec; eight cycles at 94ºC for 30 sec and 53ºC for 45 sec; and a final extension at 72ºC for 10 min. The PCR products were mixed in poolplex (Tab. 2), and 2.0 μl of every poolplex were used in fragment analyses on an ABI3130 automated DNA sequencer (Applied Biosystems) at the University of Arizona Genetics Core. Fragments sizes were obtained relative to the GeneScan 500 LIZ Size Standard (Applied Biosystems).

TABLE 2 | The twelve microsatellite loci selected for population genetic analysis on Lutjanus guttatus. Three poolplexes were combined using amplicons from four loci having similar annealing temperatures and tagged with different flouorescent dyes.

Loci

Fluorescent dyes

Annealing temperatures (°C)

Poolplex

Allelic size range (pb)

Lgut18

FAM

60

Poolplex 1

231–339

Lgut21

PET

60

 

207–353

Lgut34

NED

60

 

349–467

Lgut39

VIC

60

 

265–367

Lgut19

VIC

62

Poolplex 2

277–426

Lgut26

PET

62

 

181–325

Lgut38

FAM

62

 

171–309

Lgut46

NED

62

 

208–331

Lgut44

VIC

63

Poolplex 3

252–457

Lgut16

NED

63

 

190–329

Lgut37

FAM

58

 

334–441

Lgut43

PET

56

 

215–306

 

Microsatellite genotyping and data analysis. Alleles were sized using the GeneMarker version 2.4.0 (Softgenetics, 2012). Individuals with more than 15% missing data were removed (two from Colima, one from Oaxaca, and one from Port of Panama), and 186 individual L. guttatus were retained for further analysis (Tab. S1). Binning within each allelic class was carried out with Flexibin version 2.0 (Amos et al., 2007), with all 186 retained individuals successfully genotyped at all 12 loci (2,232 genotypes). Allelic frequencies and null allele frequencies were obtained with Arlequin version 3.5 (Excoffier, Lischer, 2010) and FreeNA (Chapuis, Estoup, 2007), respectively. To test if the alleles were drawn from the same distribution in all populations, Fisher exact tests were carried out using Genepop version 4.7 (Raymond, Rousset, 1995; Rousset, 2008). Genetic diversity indices, including the number of alleles (NA), effective number of alleles (NEA), number of private alleles (NPA), and observed and expected heterozygosities (HO and HE), were assessed with GenAlEx ver. 6.5 (Peakall, Smouse, 2012). Kruskal-Wallis tests for NA, NEA, HO, and HE looking for differences among locations were performed with STATISTICA version 8 (StatSoft. Inc., Tulsa, OK, USA). The HWE and Linkage disequilibrium (LD) were evaluated through exact tests with Arlequin, and sequential Bonferroni adjustment at α = 0.05 was carried out to control the effects of multiple testing (Rice, 1989).

The statistical power to find genetic differentiation among populations was tested with POWSIM version 4.1 (Ryman, Palm, 2006) with the following parameters: sample size from 29 to 49 individuals (based on mean n, Tab. S2) and one to 12 loci, with ~20 alleles each. In addition, a predefined FST = 0.0025 was employed, representing the lower non-significant FST value found in this study. Although, in L. peru (FST = 0.0159, P = 0) and L. argentiventris (FST = 0.019, P = 0) the significant FST values were higher (Reguera-Rouzaud et al., 2021). Finally, interaction/permutation factors for the Fisher’s exact test (10,000, 1,000, 10,000), five populations, an effective population size of 2,000, 10 generations under drift, and 1,000 runs were also included in simulations.

Genetic population structure was evaluated through a global Analysis of Molecular Variance (AMOVA), pairwise FST, and a hierarchical AMOVA using Arlequin. Three groups were created for the hierarchical AMOVA: 1) La Paz, 2) Colima and Oaxaca, and 3) Chiriqui, and Port of Panama. The significance of the covariance components associated with the different possible levels of genetic structure (within populations, within groups of populations, among groups) was tested using non-parametric permutation procedures (10,100 permutations). These three groups were in agreement with the biogeographic provinces proposed by Hasting (2000). A phylogenetic tree was constructed using Nei genetic distances, the UPGMA method, and 10,000 bootstrap replicates with PHYLIP (Felsenstein, 2005). Statistical support was obtained by bootstrap percentages in every branch.

Discriminant Analysis of Principal Components (DAPC) was performed in R (R Development Core Team, 2011) package adegenet (Jombart, 2008) to explore clustering in the data (Jombart, Collins, 2021). The data were transformed through a PCA and a discriminant analysis was applied to the retained principal components to minimize intra-group variability while maximizing inter-group variability (Jombart, Collins, 2021). First, genotype data were imported using import2genind, using a genetix file (.gtx). Second, the best number of K clusters was determined de novo using find.clusters, we keep all the information, specifying to retain 200 PCs. After obtaining the graph of Bayesian information criterion (BIC), and no clear elbow was observed, the best number of K clusters was obtained by the difference of Ki+1-Ki. Third, DAPC was run with dapc(obj, grp$pop), 80 PCs and four discriminant functions retained, accounting for 82% of variance. Each DAPC was cross-validated and rerun with suggested PCs according to the best proportion of successful outcome prediction. Two STRUCTURE-like plots with membership of all individuals and with mixed individuals having no more than 0.5 probability of membership were created using compoplot in adegenet.

Gene flow estimates (Nm) were inferred from FST, in agreement with Wright (1969), as implemented in Genetix ver. 4.05.2 (Belkhir et al., 2004) and using private alleles (Slatkin, 1985). A Mantel test with 100,000 permutations was also performed using Genetix to assess if Reynold’s genetic distances (Weir, Cockerham, 1984) were correlated to geographical distances (km).

Results​


The allelic frequencies showed two patterns. First, most medium and low frequency alleles (< 10%) were present at most locations (data not shown). Second, there were frequency differences among locations for some of the most common alleles in nearly all loci. However, after Fisher exact tests, such differences in distribution of allelic frequencies were significant only for Lgut38 and Lgut44 (P < 0.05), and none were significant after sequential Bonferroni adjustment (P > 0.006).

Higher values in NA and NEA were found at the center of the species distribution range (Colima and Oaxaca, Mexico) than in locations at the extremes of the sampled range. The opposite was observed in HO, where higher values were found for La Paz and the Port of Panama than for the rest of locations (Fig. 2). Such results were an effect of the sample size per location (Tabs. 3, S2). However, after Kruskal-Wallis tests, no differences among locations were observed in NA (P = 0.28), NEA (P = 0.74), HO (P = 0.56), or HE (P = 0.99). Heterozygote deficits were observed but not significant (P > 0.006) in most loci except Lgut37, Lgut38, and Lgut44. These three loci had null allele frequencies greater than 0.05 and deviated from HWE in most locations. After sequential Bonferroni adjustment, however, zero loci showed significant HWE deviations (P < 0.004) (Tab. S2) or signs of linkage disequilibrium (LD) (P > 0.0005), through the five locations.

TABLE 3 | Genetic diversity in Lutjanus guttatus from the five sample locations. Sample sizes (n), number of alleles (NA), observed (HO), and expected (HE) heterozygosities. Note that values with asterisk showed significant deviations from Hardy-Weinberg Equilibrium after Bonferroni adjustment (P < 0.004, Tab. S2).

Locus

Mexico

Panama

La Paz

Colima

Oaxaca

Chiriqui

Port of Panama

Lgut16

n

30

48

47

30

29

NA

23

24

24

24

24

HO

0.933

0.938

0.851

0.967

1

HE

0.950

0.951

0.949

0.961

0.955

Lgut18

n

29

48

49

29

28

NA

19

20

16

15

18

HO

0.966

0.833*

0.735

0.862

0.857

HE

0.932

0.923

0.918

0.914

0.934

Lgut19

n

30

48

48

30

29

NA

27

31

30

21

24

HO

1

0.979

0.979

0.967

1

HE

0.959

0.961

0.962

0.953

0.958

Lgut21

n

29

47

48

30

29

NA

17

22

26

19

17

HO

0.793

0.787*

0.688*

0.800

0.897

HE

0.933

0.936

0.950

0.907

0.938

Lgut26

n

29

48

49

29

29

NA

23

24

25

25

21

HO

0.862

0.958

0.939

0.897

0.966

HE

0.952

0.952

0.957

0.961

0.947

Lgut34

n

30

48

48

30

28

NA

19

26

22

19

20

HO

0.833

0.958

0.833

0.900

0.857

HE

0.942

0.949

0.942

0.942

0.945

Lgut37

n

30

48

49

30

29

NA

18

17

20

18

13

HO

0.567*

0.708*

0.735

0.700*

0.759

HE

0.944

0.927

0.929

0.927

0.899

Lgut38

n

30

48

49

29

29

NA

24

26

27

20

26

HO

0.767*

0.688*

0.755*

0.517*

0.862

HE

0.963

0.954

0.955

0.937

0.958

Lgut39

n

30

48

49

30

29

NA

15

16

16

16

14

HO

0.9

0.938

0.816

0.800

0.724

HE

0.927

0.917

0.926

0.925

0.914

Lgut43

n

30

47

48

28

27

NA

18

18

18

18

16

HO

0.933

0.745*

0.854

0.714*

0.889

HE

0.928

0.933

0.933

0.942

0.934

Lgut44

n

30

44

49

29

29

NA

25

22

27

20

22

HO

0.833

0.568*

0.673*

0.586*

0.724*

HE

0.956

0.940

0.934

0.949

0.953

Lgut46

n

30

48

49

30

29

NA

13

17

16

13

18

HO

0.9

0.854

0.898

0.933

0.897

HE

0.886

0.905

0.896

0.888

0.917

 

FIGURE 2| Genetic diversity in Lutjanus guttatus from five sampled locations. Numbers of alleles (NA), effective alleles (NEA), and private alleles (NPA) and observed (HO) and unbiased expected heterozygosities (uHE).

Statistical power testing showed that there was greater than 95% capacity to detect significant genetic structure with ten or more loci (Fig. 3). Yet, global AMOVA (FST= 0.00012, P = 1), population pairwise FST (Tab. 4), and hierarchical AMOVA (FCT = 0.00126, P = 0.1215) showed no evidence of population genetic structure. Exclusion of the three loci with high frequencies of null alleles (Lgut37, Lgut38, and Lgut44) did not change these results, and subsequent analyses therefore included all 12 loci. The topology of the UPGMA tree did group the sampled populations by their respective biogeographic provinces, but this result is tempered by low bootstrap support for Chiriquí-Port of Panama (Fig. 4).

TABLE 4 | Pairwise FST values using 12 loci for sampled Lutjanus guttatus populations (above diagonal) and P values (below diagonal). Note that all comparisons were not significant (P > 0.05).

 

La Paz

Colima

Oaxaca

Chiriqui

Port of Panama

La Paz

0.0023

0.0003

0.0026

0.0031

Colima

0.2412

0.0002

0.0018

-0.0012

Oaxaca

0.7741

0.8011

0.0038

0.0028

Chiriqui

0.3269

0.4208

0.0974

0.0010

Port of Panama

0.1527

0.9385

0.1833

0.6074

 

FIGURE 3| Estimates of the statistical power (percent) for finding significant population genetic structure when using different numbers of microsatellite loci.

FIGURE 4| UPGMA dendrogram of Lutjanus guttatus from five sampled locations along the Pacific coast of Mexico (La Paz, Colima, Oaxaca) and Panama (Chiriquí, Port of Panama) based on Nei’s genetic distance (1972). Numbers on the nodes indicate the percent of times the illustrated topology was found with 10,000 bootstrap replicates.

DAPC showed five mixed genetic groups (Fig. 5), and most individuals in each cluster were found to have a very high (90-100%) membership probability to their own group, despite a variable but lower proportion of samples exhibiting mixed memberships. No transitional zones with higher proportions of putatively admixed individuals were observed between pairs of neighboring sites (Fig. 6). Of 23 individuals with membership probabilities lower than 0.5, most exhibited membership to more than two clusters (Fig. 7).

FIGURE 5| Discriminant Analysis of Principal Components (DAPC) of Lutjanus guttatus from five sampled sites based on 12 microsatellite loci, 80 PCs, and four DA eigenvalues. Sampled locations include La Paz (LAP), Colima (COL), Oaxaca (OAX), Chiriquí (CHI), and Port of Panamá (PP).

FIGURE 6| STRUCTURE-like plot with estimated cluster memberships for all individuals derived from the DAPC. Sampled locations include La Paz (LAP), Colima (COL), Oaxaca (OAX), Chiriquí (CHI), and Port of Panama (PP).

FIGURE 7| STRUCTURE-like plot illustrating cluster membership for all admixed individuals having < 0.5 probability of membership to any group. Sampled locations include La Paz (LAP), Colima (COL), Oaxaca (OAX), Chiriquí (CHI), and Port of Panama (PP).

Gene flow was moderate to high (Nm: 121 – 1×10) between pairs of locations (Tab. 5), while estimates of gene flow obtained using private alleles were lower for La Paz (Nm = 16) and Chiriqui (Nm = 17) than Port of Panama (Nm = 21), Colima (Nm = 40), and Oaxaca (Nm = 37). Finally, a Mantel test did not support the presence of IBD (r = 0.108, P = 0.467) (Fig. 8).

TABLE 5 | Estimates of gene flow (Nm; after Wright, 1969) between sampled Lutjanus guttatus locations.

 

La Paz

Colima

Oaxaca

Chiriqui

Port of Panama

La Paz

207

1×106

405

131

Colima

 

1×106

1051

1×106

Oaxaca

 

 

121

186

Chiriqui

 

 

 

1×106

Port of Panama

 

 

 

 

 

FIGURE 8| Mantel test of correlation between genetic (y-axis) and geographic distances (x-axis) among sampled locations. The black line represents the central tendency among the dots in the scatterplot.

Discussion​


Lutjanus guttatus did not show any evidence for genetic population structure by traditional analysis, such as global AMOVA, population pairwise FST, hierarchical AMOVA, and IBD. However, DAPC showed distinctive local genetic subunits of a large population but with no clear cuts among them. The presence of admixed individuals, with membership to more than one external clusters, could be outlining contemporary connectivity that both a) connects the closest sampled populations and b) spans the entire geographic range (e.g., the major cluster found in La Paz; Cluster LAP) is also found present in admixed individuals in Panama, where Cluster PP predominates), with c) no transitional zones of admixture. These results as well as the low membership probabilities of 23 individuals reflect panmixia involving the entire set of sampled populations including all three mainland provinces, which implies that long-distance connectivity is as prevalent as short-distance exchanges in L. guttatus. These results were in agreement with those reported for Epinephelus labriformis (Jenyns, 1840) (Craig et al., 2006), Nerita funiculata Menke, 1850 (Hurtado et al., 2007), Lepidochelys olivacea (Silver-Georges et al., 2020), Rhincodon typus Smith, 1829 (Guzmán et al., 2021), and earlier studies in L. guttatus (Hernández-Álvarez et al., 2020; Mar-Silva et al., 2023) in the TEP. Nevertheless, they were also different from those reported in Nerita scabricosta (Hurtado et al., 2007), Pavona gigantea (Verrill, 1869) (Saavedra-Sotelo et al., 2010), Sybiodinium glynnii Wham, LaJeunesse, 2017 (Pettay, LaJeunesse, 2013), Merluccius productus (García-De Leon et al., 2018), Elacatinus puncticulatus (Sandoval-Huerta et al., 2019), and L. peru and L. argentiventris (Reguera-Rouzaud et al., 2021).

Notably, despite the lack of genetic population structure, the UPGMA tree showed three apparent genetic groups in L. guttatus, including La Paz, Colima with Oaxaca, and Chiriquí with Port of Panama, although this last grouping had low bootstrap support. Such apparent genetic groups were in good agreement with the Cortez, Mexican and Panamic provinces suggested by Hastings (2000).

Several factors may favor genetic differentiation in the TEP, such as IBD (e.g., Epinephelus clippertonensis Allen & Robertson, 1999, Merluccius productus, L. peru, and L. argentiventris; Craig et al., 2006; García-De Leon et al., 2018;Reguera-Rouzaud et al., 2021), the vertical range of larvae distribution and dispersal effects of the currents (e.g., Nerita scabricosta Lamarck, 1822; Hurtado et al., 2007), genetic drift in locations with low effective population sizes (e.g., Nerita scabricosta and Pavona gigantea; Hurtado et al., 2007; Saavedra-Sotelo et al., 2010), the presence of upwelling (e.g., Pavona gigantean and Elacatinus puncticulatus; Saavedra-Sotelo et al., 2010; Sandoval-Huerta et al., 2019), environmental gradients (e.g., Sybiodinium glynnii, Merluccius productus, Elacatinus puncticulatus, L. peru, and L. argentiventris; Pettay, LaJeunesse, 2013; García-De Leon et al., 2018; Sandoval-Huerta et al., 2019; Reguera-Rouzaud et al., 2021), and rocky reef habitat discontinuities (e.g., Elacatinus puncticulatus, L. peru, and L. argentiventris; Sandoval-Huerta et al., 2019; Reguera-Rouzaud et al., 2021). In contrast, there are also factors that may foster genetic connectivity in the TEP, like a long-lived larval stages with potential to long distance dispersal (e.g., 50 days in Epinephelus labriformis; Craig et al., 2006), the homogenizing effect of the complex surface currents (e.g., Sybiodinium glynnii; Pettay, LaJeunesse, 2013), and nomadic-female migration due to limited fidelity to nesting sites or changes in high productivity areas (e.g., Lepidochelys olivacea and Rhincodon typus; Silver-Georges et al., 2020; Guzmán et al., 2021).

Several of these factors did not contribute to population genetic differentiation in L. guttatus, including IBD, genetic drift, the presence of upwelling, environmental differences, and rocky reef habitat discontinuities. Otherwise, reproductive characteristics (Grimes, 1987; Cruz-Romero et al., 1991; Rojas, 1997; Correa-Herrera, Jiménez-Segura, 2013; Vega et al., 2016a), in connection with marine currents in the TEP (Kessler, 2006; Gómez-Valdivia et al., 2015), support the hypothesis of sufficient gene flow among populations, through larval dispersal, to explain the observed results in the Panamic, Mexican, and Cortez provinces.

The TEP is a region with suitable reef-fish habitat along the American continental coast and oceanic islands (Allen, Robertson, 1994; Glynn, Ault, 2000; Zapata, Herrón, 2002). Results in L. guttatus were different from those reported in the related species L. peru and L. argentiventris (Reguera-Rouzaud et al., 2021), despite similarities in reproductive characteristics, pelagic larval duration (PLD), and distribution ranges (Allen, 1995; Cruz et al., 1991; Zapata, Herrón, 2002; Peña et al., 2017). Such contrasting patterns of genetic structure between L. guttatus and related species are likely due to an ontogenetic habitat shift (Sala et al., 2003; Aburto-Oropeza et al., 2009; Vega et al., 2015).

Juveniles of L. guttatus can use the soft bottoms adjacent to rocky reefs (Mariscal-Romero, Van der Heiden, 2006; Saucedo-Lozano et al., 2006) or mangroves to feed (Allen, 1995; Gutierrez-Barreras, 1999; Vega et al., 2015; 2016b; Medina-Contreras et al., 2021). Such ecological adaptations to different environmental conditions in different habitats enable nomadic individuals of L. guttatus to migrate around habitat discontinuities that restrict movement in L argentiventris (absence of mangroves) and L. peru (absence of rocky reefs), possibly resulting in the connectivity between the Gulf of California-Colima and Oaxaca-Chiriquí and Port of Panama regions seen in this study.

Larval dispersal and possible migration of nomadic individuals were mentioned by Mar-Silva et al. (2023) as mechanisms that may have differentially contributed to the high contemporary genetic connectivity seen among locations. Mar-Silva et al. (2023) found no differences using neutral loci (dataset of 1858 SNPs) and genetic differences using outlier loci (dataset of 145 SNPs) suggesting the role of selection in this case. Results of the present study were in agreement with the conclusion of “no differences” or panmictic population, which is explained because microsatellite loci are assumed to be neutral markers. FST, when based on neutral genetic markers, estimates the degree to which populations have diverged from one another as a result of gene flow and genetic drift, without the selection effect in the equation (Freeland, 2006).

If genetic connectivity is the result of larval dispersal or migration of juveniles, preadults, or adults of L. guttatus every generation, it may result in greater resilience to local extirpation because fishing areas could be recolonized relatively quickly (Craig et al., 2006). On the other hand, local extinction could modify the pattern of connectivity, increasing the relative geographic distance among populations (Saavedra-Sotelo et al., 2010). Nevertheless, additional research that includes more sites within the distribution range of the species, such as the Galapagos islands, which represents a province not assayed in this study, uses potentially adaptive genetic markers (e.g., SNPs), and adequate sample size per site (Flesch et al., 2018) is required to both further improve our understanding of the population dynamics of L. guttatus in the TEP as well as the influence of environmental variables on its genetic makeup.

Acknowledgments​


We thank Silvie Dumas who supported the research and synthesis of microsatellite loci in L. guttatus. We thank Oswaldo Morales-Pacheco (CRIP Salina Cruz), “Mariscos Baja Sur”, “SPP Manuel Cabrera SC de RL”, to the fishermen of the ports of Remedios, Playa el Arenal, the Fiscal dock of Panama and J. A. Clarós (UMIP) for providing support during tissue sampling. This research was partially supported by grants from Consejo Nacional de Ciencia y Tecnología (CONACyT) (CB-2015-01, No. 257019) and IPN-SIP (20180339, 20195461, 20201032, 20210196) to Noé Díaz-Viloria, who is an EDI-IPN fellow. We thank to Kristen Gruenthal, who reviewed and improved the English edition of this manuscript.

References​


Aburto-Oropeza O, Domínguez-Guerreo I, Cota-Nieto J, Plomozo-Lugo T. Recruitment and ontogenetic habitat shifts of the yellow snapper (Lutjanus argentiventris) in the Gulf of California. Mar Biol. 2009; 156:2461–72. https://doi.org/10.1007/s00227-009-1271-5

Aljanabi SM, Martinez I. Universal and rapid salt-extraction of high quality genomic DNA for PCR-based techniques. Nucleic Acids Res. 1997; 25(22):4692–93. https://doi.org/10.1093/nar/25.22.4692

Allen GR. Lutjanidae Pargos. In: Fischer W, Krupp F, Schneider W, Sommer C, Carpenter KE, Niem VH, editors. Guía FAO para la identificación de especies para los fines de la pesca, Pacífico Centro-Oriental, Volumen III, Vertebrados-Parte 2, Roma: Organización de las Naciones Unidas para la Agricultura y la Alimentación; 1995. p.1231–44.

Allen GR, Robertson DR. Fishes of the tropical eastern Pacific. Honolulu: University of Hawaii Press; 1994.

Amos W, Hoffman JI, Frodsham A, Zhang L, Best S, Hill AVS. Automated binning of microsatellite alleles: problems and solutions. Mol Ecol Notes. 2007; 7(1):10–14. https://doi.org/10.1111/j.1471-8286.2006.01560.x

Belkhir K, Borsa P, Chikhi L, Raufaste N, Bonhomme F. GENETIX 4.05 logiciel sous Windows TM pour la génétique des populations [Internet]. Montpellier: Laboratoire Génome, Populations Interactions Université de Montpellier II; 2004. Available from: http://www.genetix.univ-montp2.fr/genetix/genetix.htm

Briggs JC, Bowen BW. A realignment of marine biogeographic provinces with particular reference to fish distributions. J Biogeogr. 2012; 39(1):12–30. https://doi.org/10.1111/j.1365-2699.2011.02613.x

Chapuis MP, Estoup A. Microsatellite null alleles and estimation of population differentiation. Mol Biol Evol. 2007; 24(3):621–31. https://doi.org/10.1093/molbev/msl191

Correa-Herrera T, Jiménez-Segura F. Biología reproductiva de Lutjanus guttatus (Perciformes: Lutjanidae) en el Parque Nacional Natural Utría, Pacífico colombiano. Rev Biol Trop. 2013; 61(2):829–40.

Craig MT, Hastings PA, Pondella DJ, Robertson DR, Rosales-Casián JA. Phylogeography of the flag cabrilla Epinephelus labriformis (Serranidae): implications for the biogeography of the Tropical Eastern Pacific and the early stages of speciation in a marine shore fish. J Biogeogr. 2006; 33(6):969–79. https://doi.org/10.1111/j.1365-2699.2006.01467.x

Cruz-Romero M, Espino-Barr E, Mimbela-López J, García-Boa A, Obregón-Alcaraz LF, Girón-Botello E. Biología reproductiva en tres especies del género Lutjanus en la costa de Colima. México: Secretaría de Pesca Manzanillo; 1991.

Excoffier L, Lischer HEL. Arlequin suite ver 3.5: a new series of programs to perform population genetics analyses under Linux and Windows. Mol Ecol Resour. 2010; 10(3):564–67. https://doi.org/10.1111/j.1755-0998.2010.02847.x

Felsenstein J. PHYLIP (Phylogeny Inference Package) version 3.6. Department of Genome Sciences University of Washington [Internet]; Seattle, 2005. Available from: http://evolution.gs.washington.edu/phylip.html

Flesch EP, Rotella JJ, Thomson JM, Graves TA, Garrott RA. Evaluating sample size to estimate genetic management metrics in the genomics era. Mol Ecol Resour. 2018; 18(5):1077–91. https://doi.org/10.1111/1755-0998.12898

Freeland JR. Molecular ecology. Hoboken: John Wiley and Sons; 2006.

García-De León FJ, Galván-Tirado C, Sánchez-Velasco L, Silva-Segundo CA, Hernández-Guzmán R, Barriga-Sosa IA, Díaz-Jaimes P, Canino M, Cruz-Hernández P. Role of oceanography in shaping the genetic structure in the North Pacific hake Merluccius productus. PLoS ONE. 2018; 13:1–26. https://doi.org/10.1371/journal.pone.0194646

García-Ortega A, Abdo-de la Parra I, Duncan NJ, Rodríguez-Ibarra E, Velasco G, González-Rodríguez B, Puello-Cruz A, Martinez I. Larval rearing of Spotted Rose Snapper Lutjanus guttatus under experimental conditions. In: Hendry CI, Van Stappen G, Wille M, Sorgeloos P, editors. Larvi 05 – Fish & Shellfish Larviculture Symposium. Oostende: European Aquaculture Society; 2005. p.172–75.

Glynn P, Ault J. A biogeographic analysis and review of the far eastern Pacific coral reef region. Coral Reefs. 2000; 19:1–23. https://doi.org/10.1007/s003380050220

Gómez-Valdivia F, Parés-Sierra A, Flores-Morales AL. The Mexican coastal current: A subsurface seasonal bridge that connects the tropical and subtropical Northeastern Pacific. Cont Shelf Res. 2015; 110:100–107. https://doi.org/10.1016/j.csr.2015.10.010

Grimes CB. Reproductive biology of the Lujanidae: a review. In: Polovina JJ, Ralston S, editors. Tropical snappers and groupers: biology and fisheries management. Boulder: Westview press; 1987. p.239–94.

Gutierrez-Barreras JA. Ictiofauna de fondos blandos de la bahía de Topolobampo, Sinaloa, México. [Master Dissertation]. La Paz B.C.S.: Instituto Politécnico Nacional-Centro interdisciplinario de Ciencias Marinas; 1999. Available from: http://repositoriodigital.ipn.mx/handle/123456789/14842

Guzmán HM, Beaver CE, Díaz-Ferguson E. Novel insights in to the genetic population connectivity of transient whale sharks (Rhincodon typus) in Pacific Panama provide crucial data for conservation efforts. Front Mar Sci. 2021; 8: 744109. https://doi.org/10.3389/fmars.2021.744109

Hastings PA. Biogeography of the tropical eastern Pacific: distribution and phylogeny of chaenopsid fishes. Zool J Linn Soc Lond. 2000; 128(3):319–35. https://doi.org/10.1111/j.1096-3642.2000.tb00166.x

Hernández-Álvarez C, Bayona-Vásquez NJ, Domínguez-Domínguez O, Uribe-Alcocer M, Díaz-Jaimes P. Phylogeography of the pacific red snapper (Lutjanus peru) and spotted rose snapper (Lutjanus guttatus) in the inshore Tropical Eastern Pacific. Copeia. 2020; 108(1):61–71. https://doi.org/10.1643/CG-18-157

Hurtado LA, Frey M, Gaube P, Pfeiler E, Markow TA. Geographical subdivision, demographic history and gene flow in two sympatric species of intertidal snails, Nerita scabricosta and Nerita funiculata, from the tropical eastern Pacific. Mar Biol. 2007; 151:1863–73. https://doi.org/10.1007/s00227-007-0620-5

Ibarra-Castro L, Alvarez-Lajonchére L, García-Aguilar N, Abdo de la Parra MI, Rodríguez-Ibarra LE. Generation cycle closure of the spotted rose snapper, Lutjanus guttatus, in captivity. Rev Biol Mar Oceanog. 2012; 47(2):333–37. http://dx.doi.org/10.4067/S0718-19572012000200015

Jombart T. adegenet: a R package for the multivariate analysis of genetic markers. Bioinformatics. 2008; 24(11):1403–05. https://doi.org/10.1093/bioinformatics/btn129

Jombart T, Collins C. A tutorial for discriminant analysis of principal components (DAPC) using adegenet 2.1.3. [Internet]. London: MRC Centre for Outbreak Analysis and Modelling; 2021. Available from: https://adegenet.r-forge.r-project.org/files/tutorial-dapc.pdf

Kessler WS. The circulation of the eastern tropical Pacific: a review. Prog Oceanogr. 2006; 69:181–217. https://doi.org/10.1016/j.pocean.2006.03.009

Lessios HA, Baums LB. Gene flow in coral reef organisms of the Tropical Eastern Pacific. In: Glynn PW, Derek PM, Ian CE, editors. Coral Reefs of the Eastern Tropical Pacific, Coral Reefs of the World 8: Springer Dordrecht; 2017. p.477–99. https://doi.org/10.1007/978-94-017-7499-4

Liu ZJ, Cordes JF. DNA marker technologies and their applications in aquaculture genetics. Aquaculture. 2004; 238:1–37. https://doi.org/10.1016/j.aquaculture.2004.05.027

MarSilva A, Diaz-Jaimes P, Domínguez-Mendoza C, Domínguez-Domínguez O, Valdiviezo-Rivera J, Espinoza-Herrera E. Genomic assessment reveals signal of adaptive selection in populations of the Spotted rose snapper Lutjanus guttatus from the Tropical Eastern Pacific. PeerJ. 2023; 11:e15029 http://doi.org/10.7717/peerj.15029

Mariscal-Romero J, Van der Heiden MA. Peces de importancia ecológica y comercial asociados a fondos blandos en la plataforma continental de Jalisco y Colima, México. In: Jiménez-Quiroz MC, Espino-Barr E, editors. Los recursos pesqueros y acuícolas de Jalisco, Colima y Michoacán. Manzanillo: SAGARPA-INP-CRIP; 2006. p.180–95.

Medina-Contreras D, Cantera-Kints J, Sánchez A. Trophic structure of fish communities in mangrove systems subject to different levels of anthropogenic intervention, Tropical Eastern Pacific, Colombia. Env Sci Pollut Res. 2021; 29:61608–22. https://doi.org/10.1007/s11356-021-16814-x

Nei M. Genetic distance between populations. Am Nat. 1972; 106(949):283–92. https://doi.org/10.1086/282771

Pauly D, Arreguín-Sánchez F, Munro JL, Balgos MC. Biology, fisheries and culture of Snappers and Groupers: workshop conclusions and updates to 1996. In: Arreguín-Sánchez F, Munro JL, Balgos MC, Pauly D, editors. Biology, fisheries and culture of tropical groupers and snappers. Manila: ICLARM; 1996. p.1–10.

Peakall R, Smouse PE. GenAlEx 6.5: genetic analysis in Excel. Population genetic software for teaching and research – an update. Bioinformatics. 2012; 28(19):2537–39. https://doi.org/10.1093/bioinformatics/bts460

Peña R, Dumas S, Contreras-Olguin M. Organogenesis of the digestive system in Pacific red snapper (Lutjanus peru) larvae. Aquac Res. 2017; 48(4):1561–75. https://doi.org/10.1111/are.12991

Perez-Enriquez R, Valadez-Rodríguez JA, Max-Aguilar A, Dumas S, Díaz-Viloria N. Parental contribution in a cultivated stock for the spotted rose snapper Lutjanus guttatus (Steindachner, 1869) estimated by newly developed microsatellite markers. Lat Am J Aquat Res. 2020; 48(2):247–56. http://dx.doi.org/10.3856/vol48-issue2-fulltext-2424

Pettay DT, LaJeunesse TC. Long-range dispersal and high-latitude environments influence the population structure of a “stress-tolerant” dinoflagellate endosymbiont. PLoS ONE. 2013; 8(11):e79208. https://doi.org/10.1371/journal.pone.0079208

R Development Core Team. R A language and environment for statistical computing. Vienna: R Foundation for Statistical Computing; 2011. Available from: https://www.r-project.org/

Raymond M, Rousset F. GENEPOP version 1.2: population genetics software for exact tests and ecumenicism. J Hered. 1995; 86(3):248–49. https://doi.org/10.1093/oxfordjournals.jhered.a111573

Reguera-Rouzaud N, Díaz-Viloria N, Pérez-Enríquez R, Espino-Barr E, Rivera-Lucero MI, Munguía-Vega A. Drivers for genetic structure at different geographic scales for Pacific red snapper (Lutjanus peru) and yellow snapper (Lutjanus argentiventris) in the tropical eastern Pacific. J Fish Biol. 2021; 98(5):1267–80. https://doi.org/10.1111/jfb.14656

Rice WER. Analyzing tables of statistical tests. Evolution. 1989; 43(1):223–25. https://doi.org/10.2307/2409177

Robertson DR, Allen GR. Shorefishes of the Tropical Eastern Pacific: online information system version 2.0. Smithsonian Tropical Research Institute [Internet]. Balboa; 2015. Available from: www.stri.org/sftep

Robertson DR, Cramer KL. Shore fishes and biogeographic subdivisions of the Tropical Eastern Pacific. Mar Ecol Prog Ser. 2009; 380:1–17. https://doi.org/10.3354/meps07925

Rojas MJR. Fecundidad y épocas de reproducción del “pargo mancha” Lutjanus guttatus (Pisces: Lutjanidae) en el Golfo de Nicoya, Costa Rica. Rev Biol Trop. 1997; 44:477–87.

Rousset F. GENEPOP’ 007: a complete reimplementation of the Genepop software for Windows and Linux. Mol Ecol Resour. 2008; 8(1):103–06. https://doi.org/10.1111/j.1471-8286.2007.01931.x

Ryman N, Palm S. POWSIM: a computer program for assessing statistical power when testing for genetic differentiation. Mol Ecol Notes. 2006; 6(3):600–02. https://doi.org/10.1111/j.1471-8286.2006.01378.x

Saavedra-Sotelo NC, Calderon-Aguilera LE, Reyes-Bonilla H, López-Pérez RA, Medina-Rosas P, Rocha-Olivares A. Limited genetic connectivity of Pavona gigantea in the Mexican Pacific. Coral Reefs. 2011; 30:677–86. https://doi.org/10.1007/s00338-011-0742-6

Sala E, Aburto-Oropeza O, Paredes G, Thompson G. Spawning aggregations and reproductive behavior of reef fishes in the Gulf of California. Bull Mar Sci. 2003; 72:103–121.

Sandoval-Huerta ER, Beltrán-López RG, Pedraza-Marrón CR, Paz-Velásquez MA, Angulo A, Robertson DR, Espinoza E, Domínguez-Domínguez O. The evolutionary history of the goby Elacatinus puncticulatus in the tropical eastern pacific: Effects of habitat discontinuities and local environmental variability. Mol Phylogenet Evol. 2019; 130:269–85. https://doi.org/10.1016/j.ympev.2018.10.020

Sarabia-Méndez M, Gallardo-Cabello M, Espino-Barr E, Anislado-Tolentino V. Characteristics of population dynamics of Lutjanus guttatus (Pisces: Lutjanidae) in Bufadero Bay, Michoacan, Mexico. Hidrobiológica. 2010; 20(2):147–57.

Saucedo-Lozano M, Raymundo-Huizar AR, Valadez-González C. Comparación de los hábitos alimentarios de juveniles de Lutjanus peru y Lutjanus guttatus en la costa de Jalisco y Colima, México. In: Jiménez-Quiroz MC, Espino-Barr E, editors. Los recursos pesqueros y acuícolas de Jalisco, Colima y Michoacán. Manzanillo: SAGARPA-INP-CRIP; 2006. p.209–18.

Schuelke M. An economic method for the fluorescent labeling of PCR fragments. Nat Biotechnol. 2000; 18:233–34. https://doi.org/10.1038/72708

Silver-Gorges I, Koval J, Rodriguez-Zarate CJ, Paladino FV, Jordan M. Large-scale connectivity, cryptic population structure, and relatedness in Eastern Pacific Olive ridley sea turtles (Lepidochelys olivacea). Ecol Evol. 2020; 10(16):8688–704. https://doi.org/10.1002/ece3.6564

Slatkin M. Rare alleles as indicators of gene flow. Evolution. 1985; 39(1):53–65. https://doi.org/10.1111/j.1558-5646.1985.tb04079.x

Softgenetics. Software power tools for genetic analysis [Internet]. 2012. Available from: https://softgenetics.com

Spalding MD, Fox HE, Allen GR, Davidson N, Fierdaña ZA, Finlayson M et al. Marine ecoregions of the world: a bioregionalization of coastal and shelf areas. Bioscience. 2007; 57(7):573–83. https://doi.org/10.1641/B570707

Vega AJ, Yolani A, Robles P, Kelvin G. El papel de los manglares como criaderos de pargo (Lutjanidae) en el Golfo de Chiriquí. Tecnociencia. 2015; 17(2):109–23.

Vega AJ, Maté JL, Yolani A, Robles P. First report of reproductive aggregations for Pacific red snappers Lutjanus peru (Nicholson y Murphy, 1992) and spotted rose snapper L. guttatus (Steindachner, 1869) in the Coiba National Park, Pacific of Panama. GCFI. 2016a; 68:112–17.

Vega AJ, Yolani A, Robles P, Maté JL. La pesca artesanal en el Parque Nacional Coiba y zona de influencia. Biología y pesquería de sus principales recursos, con recomendaciones de manejo. Ciudad de Panamá; Fundación MarViva; 2016b.

Walker BW. The distribution and affinities of the marine fish fauna of the Gulf of California. Syst Zool. 1960; 9(3–4):123–33. https://doi.org/10.2307/2411961

Weir BS, Cockerham C. Estimating F-statistics for the analysis of population. Evolution. 1984; 38(6):1358–70. https://doi.org/10.2307/2408641

Wright S. Evolution and the genetics of populations, vol. 2. The theory of gene frequencies. Chicago: University of Chicago Press; 1969.

Zapata FA, Herrón PA. Pelagic larval duration and geographic distribution of tropical eastern Pacific snappers (Pisces: Lutjanidae). Mar Ecol Prog Ser. 2002; 230:295–300. https://doi.org/10.3354/meps230295

Authors


Noé Díaz-Viloria1 , Adriana Max-Aguilar2, Mailin I. Rivera-Lucero3, Elaine Espino-Barr4, Nicole Reguera-Rouzaud1, Andrea Casaucao-Aguilar1 and Ricardo Perez-Enriquez2

[1]    Instituto Politécnico Nacional-Centro Interdisciplinario de Ciencias Marinas (IPN-CICIMAR), Departamento de Plancton y Ecología Marina, Av. Instituto Politécnico Nacional s/n Col. Playa Palo de Santa Rita, La Paz, B.C.S. 23096, Mexico. (NDV) ndiazv@ipn.mx (corresponding author), (NRR) nreguerar1500@alumno.ipn.mx, (ACA) acasaucaoa2000@alumno.ipn.mx.

[2]    Centro de Investigaciones Biológicas del Noroeste (CIBNOR), Instituto Pollitécnico Nacional 195, Colonia Playa Palo de Santa Rita Sur, La Paz, B.C.S. 23096, Mexico. (AMA) amax@pg.cibnor.mx, (RPE) rperez@cibnor.mx.

[3]    Universidad Marítima Internacional de Panamá (UMIP), La Boca, Ancón, Panamá. (MIRL) isabel.lin.06@gmail.com.

[4]    Instituto Nacional de Pesca y Acuacultura, CRIAP-Manzanillo, Playa Ventana, Colima, Mexico. (EEB) elespino@gmail.com.

Authors’ Contribution


Noé Díaz-Viloria: Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Writing-original draft, Writing-review and editing.

Adriana Max-Aguilar: Investigation, Methodology, Visualization.

Mailin I. Rivera-Lucero: Investigation, Resources.

Elaine Espino-Barr: Investigation, Methodology, Resources, Supervision, Validation, Visualization.

Nicole Reguera-Rouzaud: Investigation, Resources, Software, Validation, Visualization.

Andrea Casaucao-Aguilar: Investigation, Methodology.

Ricardo Perez-Enriquez: Conceptualization, Formal analysis, Funding acquisition, Investigation, Supervision, Validation, Writing-review and editing.

Ethical Statement​


All tissue samples were obtained from fishermen’s catches. Tissue samples from Panama were imported to Mexico through permit number SENASICA B00.02.04.657/2017.

Competing Interests


The authors declares no competing interests.

How to cite this article


Díaz-Viloria N, Max-Aguilar A, Rivera-Lucero MI, Espino-Barr E, Reguera-Rouzaud N, Casaucao-Aguilar A, Perez-Enriquez R. Genetic connectivity in the spotted rose snapper Lutjanus guttatus (Lutjaniformes: Lutjanidae) between Mexico and Panama throughout the Tropical Eastern Pacific. Neotrop Ichthyol. 2023; 21(2):e220113. https://doi.org/10.1590/1982-0224-2022-0113


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