Resilience despite overfishing? Microsatellites reveal high genetic diversity and connectivity for Umbrina canosai (Acanthuriformes: Sciaenidae) in the Southwestern Atlantic

Davi M. Vasconcellos^1,2,3 , Ana Luzia Lacerda^2,4, Maíra Carneiro Proietti^1,2,5 and Manuel Haimovici^1,2

PDF: Download Here | Supplementary: Sup | Cite this article

Associate Editor: Osmar Luiz

Section Editor: Osmar Luiz

Editor-in-chief: José Birindelli

Abstract​

---

Introduction​

---

Understanding marine connectivity and demographic changes is important for the conservation and management of marine species, especially for marine fishes with commercial interest (Lowe, Allendorf, 2010; Cadrin et al., 2014). Genetic markers offer important information about ecological questions and are well-established techniques in fisheries science (Mariani, Bekkevold, 2014). Microsatellites are repetitive, noncoding regions of the DNA with high mutation rates, and have become popular tools for investigating genetic diversity, population structure, gene flow and demographic changes, as they can reflect the recent past of organisms (10–100 generations ago) (Selkoe, Toonen, 2006; Mariani, Bekkevold, 2014). Microsatellites have been applied to investigate the population genetics of important fisheries resources, including fishes of the family Sciaenidae, which are exploited worldwide (Vasconcellos et al., 2015; Almeida et al., 2022; Franco et al., 2023; Yu et al.,2024). Studies using these markers have indicated that fishing pressure can reduce the genetic diversity of marine fishes due to depleted population sizes, genetic drift and selection (Hauser et al., 2002; Pinsky, Palumbi, 2014).

The Warm Temperate Southwestern Atlantic Province (22°–41°S) includes three important marine ecoregions: Southeastern Brazil, Rio Grande and Uruguay-Buenos Aires Shelf (Spalding et al., 2007), which are limited by marine fronts (Acha et al., 2004). This Province supports highly productive fisheries in its ecoregions: Southeastern Brazil, Southern Brazil, and the Argentine-Uruguayan Common Fishing Zone (Defeo et al., 2025). Sciaenid fishes represent a significant portion of the captures in this Province (Haimovici, Cardoso, 2017; Defeo et al., 2025), among which is the Argentine croaker, Umbrina canosai Berg, 1895. The Argentine croaker is a demersal fish endemic to the Warm Temperate Southwestern Atlantic Province, with a continuous distribution between São Tomé Cape in Brazil (22°S) and the Gulf of São Matias in Argentina (41°30’S) (Cousseau, Perrota, 2013). Umbrina canosai is a relatively small fish that reaches over 400 mm in total length, may live up to over 25 years, and has a relatively slow growth (Haimovici, Reis, 1984). The species produces high number of pelagic eggs (Haimovici, Cousin, 1989) and feeds on benthic organisms, mainly crustaceans and polychaetes (Haimovici et al., 1989). As a typical sciaenid, U. canosai produces species-specific croaking sounds in agonistic and reproductive situations (Tellechea et al., 2017).

During the austral summer, U. canosai is abundant in Northern Argentina, which is used as feeding ground. In the autumn, large numbers of breeders migrate towards Southern Brazil (28°40’ to 33°50’S) to reproduce during winter and spring (Gonzalez Alberdi, Nani, 1967; Haimovici, Cousin, 1989), and the juveniles use this region as a nursery ground (Haimovici et al., 1996). Spawning females are also observed off the coast of Uruguay and Argentina in late spring (Militelli et al., 2013) and some adults remain along Southern Brazil in the summer, suggesting that there are possibly other shorter migratory patterns (Kikuchi et al., 2021). Additionally, the presence of both juveniles and adults in Southeastern Brazil (Zaneti-Prado, 1979; Haimovici et al., 2007, 2008, 2017), including individuals with mature gonads (DMV, 2019, pers. obs.), indicates that the species also reproduces along this region.

The occurrence of two U. canosai stocks was proposed by Zaneti-Prado, Vazzoler (1976) based on first maturity size: a north stock in Southeastern Brazil and a south stock from Southern Brazil to Northern Argentina. The two-stocks hypothesis was maintained in subsequent works (Haimovici, Cousin, 1989; Haimovici et al., 2006, 2008). Posteriorly, ecological and phenotypic approaches supported it: Canel et al. (2019) identified distinct parasitic assemblies, while Kikuchi et al. (2021) found different growth rates and sagittal otoliths shapes from these two regions.

Fisheries exploit the Argentine croaker throughout its entire distribution: Southeastern Brazil (FIPERJ, 2016; Instituto de Pesca/SP, 2026, www.propesq.pesca.sp.gov.br), South Brazil (FURG/SEMA/MPA, 2026), Uruguay (Uruguay/DINARA, 2019) and Northern Argentina (Navarro et al., 2022). The recorded catches peaked twice over time: more than 25,000 tons in the 1970’s and more than 15,000 tons in the 2000’s, both followed by a drastic decrease in catches (Haimovici et al., 2006, 2022). The industrial fleets include bottom trawls, gillnets and, since the 2000’s, midwater trawls equipped with sonars, which target spawning adults on the external shelf of Southern Brazil (Haimovici, Cardoso, 2016). Consequently, this fishery is considered to be at risk of commercial collapse (Haimovici et al., 2006, 2022).

The mitochondrial genome of U. canosai was recently sequenced, along with several other bony fishes from the South Atlantic Ocean (Alvarenga et al., 2024); however, to the best of our knowledge, there are no studies focusing on its population genetics. Considering the importance of understanding genetic factors for fisheries management, in this work we used microsatellite markers to investigate 1) genetic diversity, 2) potential genetic bottleneck events, and 3) genetic connectivity for U. canosai throughout most of its distribution area in the Southwest Atlantic. We also evaluated the gonadal maturity stage of some individuals from Brazil in order to infer spawning seasonality and to associate it with larval dispersion.

Material and methods

---

Study area. The Warm Temperate Southwestern Atlantic Province (22°–41°S) is dominated by the warm and saline Brazil Current flowing southwards, and the cold and less saline Malvinas Current flowing northwards, with both meeting at the Brazil-Malvinas Confluence at around 38°S; this latitude can vary according to the season (Fig. 1; map built in QGIS 3.34.11, QGIS Development Team, 2024) (Matano et al., 2010). On the continental shelf, these oceanic currents are diluted by fluvial drainage systems, and the Brazil Current originates the Subtropical Shelf Water, while the Malvinas Current originates Subantarctic Shelf Water (Piola et al., 2000). These shelf waters meet at the Subtropical Shelf Front at around 33°S, as an extension of the Brazil-Malvinas Confluence (Fig. 1) (Piola et al., 2000; Acha et al., 2004).

FIGURE 1 | Study area and sample design of Argentine croaker Umbrina canosai along its distribution range in the Warm Temperate Southwestern Atlantic Province. Genetic analyses considered the three regions in orange: 1) Southeastern Brazil (SEB), 2) Southern Brazil, and 3) Argentine-Uruguayan Common Fishing Zone (AU). Sampling locations are represented by black dots in the dark grey areas. In Brazil, samples were collected in Niterói, Rio de Janeiro State (RJ); Ilhabela, São Paulo State (SP); and Rio Grande, Rio Grande do Sul State (RS). In Argentina, samples were collected in San Clemente del Tuyú, Villa Gesell and Necochea in the Buenos Aires Province (BsAs). Adjacent areas in light gray: Uruguay (UY) and Brazilian States of Paraná (PR) and Santa Catarina (SC). Curved arrows indicate the warm (red) and cold (blue) marine currents of the Southwestern Atlantic.

Sampling. Adult specimens of U. canosai were collected along approximately 2,400 km of coastline in the Warm Temperate Southwestern Atlantic Province. Samples were obtained from commercial trawl and gillnet fisheries between 2015 and 2019, supported by partner institutions (Tab. 1). Three sampling regions were defined according to the marine ecoregions (Spalding et al., 2007), the main fishing areas of the species (Kikuchi et al., 2021; Defeo et al., 2025) and the previous studies evaluating their stocks (Canel et al., 2019; Kikuchi et al., 2021). The sampling regions are: 1) Southeastern Brazil (SEB), comprising samples from Niterói (Rio de Janeiro State) and Ilhabela (São Paulo State); 2) Southern Brazil (SB), comprising samples from Rio Grande (Rio Grande do Sul State); and 3) Argentine-Uruguayan Common Fishing Zone (AU), comprising samples from San Clemente del Tuyú, Villa Gesell and Necochea (Buenos Aires Province) (Fig. 1).

All fish have the sex, total length (TL, mm) and total weight (WT, 1 g precision) recorded. The gonads collected from each specimen from Brazil were weighed (WG, 0.01 g precision) and the maturity stage was recorded following the scale proposed by Haimovici, Cousin (1989), as was the gonadosomatic index: GSI = 100 x (WG/WT). The maturity stage and spawning seasonality were related to larval dispersion.

Voucher specimens. Muscle and DNA samples of Umbrina canosai (UC) were numbered from UCRS001 to UCSP251 and deposited in the Laboratório de Ecologia Molecular Marinha (LEMM) from Instituto de Oceanografia (IO), Universidade Federal do Rio Grande (FURG), Rio Grande, Brazil. The Tab. S1 lists the voucher specimens analyzed in this study, according to Tab. 1.

DNA extraction, amplification and genotyping. For each region, over 30 individuals were selected for molecular analysis, with a similar number of females and males to avoid putative sex biases (Tab. 1). Approximately 2 g of muscle was collected from each individual and stored in absolute ethanol at -20 °C for further analyses. Genomic DNA was extracted with the saline method (Aljanabi, Martinez, 1997), and its concentration and purity were quantified with a BioDrop™ spectrophotometer (Biochrom™). DNA concentrations were standardized to 35-65 ng/µL (~50 ng/µL) by diluting in ultrapure water prior to amplifications.

Microsatellite loci were amplified via Polymerase Chain Reaction(PCR). Since no microsatellite primers specific for Umbrina canosai have been reported, we used cross-species transferability of markers (Barbará et al., 2007). Twelve primers from other species of sciaenids were tested: 1) primers for eight loci developed for Micropogonias furnieri and polymorphic for U. canosai: Mfur02, Mfur03, Mfur06, Mfur13, Mfur14, Mfur19, Mfur25 and Mfur26 (Vasconcellos, 2012; Molecular Ecology Resources Primer Development Consortium et al., 2012); and 2) primers for four loci developed for Sciaenops ocellatus (Turner et al., 1998; Saillant et al., 2004) and polymorphic for M. furnieri (D’Anatro et al., 2011): Soc049, Soc050, Soc085 and Soc204.

PCRs were performed with M13 tails with fluorescences 6-FAM, HEX, and NED (Tab. 2), in order to identify different loci during genotyping (Schuelke, 2000; Blacket et al., 2012). The reactions were carried out in a final volume of 20 µL each, containing: 1 x buffer (Buffer), 2.5 mM MgCl₂, 0.2 mM of each dNTP, 0.13 µM of the M13 tailed forward primer, 0.5 µM of the reverse primer, 0.25 µM of M13 tailed fluorescences, 1 U Platinum Taq DNA Polymerase (Invitrogen) and ~50 ng/µL of genomic DNA template, and completed with ultrapure water (Schuelke, 2000; Vasconcellos et al., 2015). Most microsatellite markers were amplified separately (singleplex), and Mfur19 and Mfur25 were amplified simultaneously (multiplex) (Guichoux et al., 2011). Multiplex PCRs were performed with almost the same concentrations, but with 2 x buffer (Markoulatos et al., 2002), primer pairs of both markers, and twice the concentration of fluorescence. For each PCR reaction, a negative control (sample without DNA template) was included, in order to detect possible contamination.

TABLE 1 | Origin and characterization of Umbrina canosai specimens from the Southwestern Atlantic used for molecular analyses, with the region, location, partner institution that provided the specimens, fishing gear and type, sampling period, depth (m), number of individuals (n), sex, total length (mm) and total weight (g) of individuals. Und. = undefined.

Region	Location	Sampling institution	Fishing gear and type	Period	Depth	n	Sex	Total length	Total weight
Southeastern Brazil (SEB)	Niterói (RJ)	Fundação Instituto de Pesca do Estado do Rio de Janeiro (FIPERJ)	Bottom trawl (industrial)	Feb/2019	80–120	31	15 ♀ 16 ♂	282–361	250–542
	Ilhabela (SP)	Instituto de Pesca do Estado de São Paulo (IP/SP)	Gillnets (artisanal)	Sep/2019	16–25	33	15 ♀ 18 ♂	250–303	227–387
Southern Brazil (SB)	Rio Grande (RS)	Universidade Federal do Rio Grande (FURG)	Bottom and midwater trawls and gillnets (industrial)	Aug and Sep/2018	18–105	34	18 ♀ 16 ♂	225–411	144–982
Argentine-Uruguayan Common Fishing Zone (AU)	San Clemente del Tuyú (BsAs)	Universidad Nacional de Mar del Plata (UNMdP)	Bottom trawl (industrial)	Jan/2019	–	15	–	310–405	388–824
	Villa Gesell (BsAs)			Dec/2015	–	4	3 ♀ 1 ♂	340–375	498–640
	Necochea (BsAs)			Apr/2017	–	16	10 ♀ 5 ♂ 1 und.	250–355	238–528

PCRs were performed in a Veriti™ 96-Well thermocycler (Applied Biosystems). Thermocycling conditions for the M. furnieri microsatellites were: initial denaturation at 94°C for 5 min; 30 cycles of denaturation at 94°C for 45 s, primer annealing at 62 °C for 45 s, extension at 72°C for 45 s; followed by eight cycles of denaturation at 94 °C for 45 s, M13 tail annealing at 53°C for 45 s, extension at 72°C for 45 s; and a final extension at 72°C for 30 min (Schuelke, 2000; Vasconcellos et al., 2015). Thermocycling conditions for the S. ocellatus microsatellites were: initial denaturation at 94°C for 3 min; 35 cycles of denaturation at 94°C for 30 s, primer annealing at 60–62°C for 30 s, extension at 72°C for 30 s; followed by eight cycles of denaturation at 94°C for 45 s, M13 tail annealing at 53°C for 45 s, extension at 72°C for 45 s; and a final extension at 72°C for 30 min (Schuelke, 2000; D’Anatro et al., 2011).

To verify amplifications success, PCR products were subjected to electrophoresis with 1.8–2.0% agarose gel stained with GelRed (0.001%), along with a molecular-weight size marker, and visualized with a U.V. transilluminator. The concentrations of PCR products were estimated based on the molecular-weight size marker. Among the tested primers, Mfur13 and Soc049 were eliminated, as they did not amplify the U. canosai samples analyzed in this study. To maximize the number of loci per genotyping reaction, we combined the PCR products marked with different fluorescences in the same reaction (poolplex, Tab. 2) (Guichoux et al., 2011), and genotyping was done in an automated sequencer at Macrogen (South Korea, dna.macrogen.com).

TABLE 2 | Sequences, repetition motifs, annealing temperature, allele size and total number of alleles of the microsatellite primers used for Umbrina canosai, with the M13 tail underlined in the 5’ region of each forward primer. To optimize genotyping, loci were combined in poolplex reactions according to the associated fluorescences. Mfur19 and Mfur25 were amplified in multiplex reactions. Soc050, Soc085 and Soc204 were amplified in the original species without the M13 tail; therefore, original allele sizes are smaller than those in this study.

Poolplex group	Locus	Primer sequences (5’- 3’)	Repetitions	Fluorescence	Annealing temperature		Allele size (basepairs)		Total alleles in U. canosai	Original species
					Original	This study	Original species	U. canosai
1	Mfur02	F: TGTAAAACGACGGCCTTTGAGTTGGATGGATGAGC	(CA)21	HEX	50 °C	62 °C	194–296	218–284	30	Micro­pogonias furnieri (Vasconcellos, 2012; Molecular Ecology Resources Primer Development Consortium et al., 2012)
		R: AGTGAGTTTAGAGGGTCTGC
	Mfur03	F: TGTAAAACGACGGCCATGGAGCGATTAGGATGTGAAT	(CA)19	FAM	50 °C	62 °C	240–336	226–286	26
		R: CTGAAATGACTGAATGAGGCTG
	Mfur06	F: TGTAAAACGACGGCCTTCACGTCTGTAGCTGTGTGTG	(TG)14	NED	50 °C	62 °C	245–267	255–323	30
		R: GGAGACATCTGCCTGTTGAAG
2	Mfur14	F: TGTAAAACGACGGCCTCGTGATGTAGGGTGAAATCAG	(CA)22	FAM	50 °C	62 °C	244–284	238–280	20
		R: TGTGCGAGTGTGAGTTTGCT
	Mfur19a	F: TGTAAAACGACGGCCATTCAGGTTTTGAGTACCG	(TG)20	HEX	50 °C	62 °C	302–332b	278–354	28
		R: TTGAACGACATGGTGCTAGG
	Mfur25a	F: TGTAAAACGACGGCCCATGGCTGTCAAAGAAAAGGA	(TG)22	HEX	50 °C	62 °C	150–220	146–228	34
		R: TGCCTGTAAGCATCAGCAAC
	Mfur26	F: TGTAAAACGACGGCCGAGGATTTTATTGGCTCGCA	(GT)24	NED	60 °C	62 °C	264–326	264–280	8
		R: GCCTGAGACGTGTTGACTGA
3	Soc050	F: TGTAAAACGACGGCCCCCGTGATTTTAGGCTCAGATA	(GT)7	HEX	58 °C	62 °C	183c	195–279	31	Sciaenops ocellatus (Turner et al., 1998; Saillant et al., 2004)
		R: CCTTTAGAGTGCAGTAAGTGATTT
	Soc085	F: TGTAAAACGACGGCCTTTTGGACCTACACTAGAGTAGC	(AC)17	FAM	58 °C	62 °C	80–122c	114–186	35
		R: CGTGGGAGACTAGCGATGTAGAT
	Soc204	F: TGTAAAACGACGGCCACAGCAGTACCTGCCCAAAACTG	(CTG)12	NED	58 °C	60 °C	193c	205–235	11
		R: TCCCCTTCGTCTTCTTCCACTTC

Data analysis. Genotype profiles were read on Peak Scanner™ 1.0 (Applied Biosystems). The true peaks (alleles) were visually differentiated from the stutter peaks and other types of artifacts, following the patterns described in literature (Selkoe, Toonen, 2006, appendix S2; Arif et al., 2010; Guichoux et al.,2011; Flores-Rentería, Krohn, 2013). Based on the peak readings, the distribution of raw genotype size was analyzed in the MsatAllele 1.05 package (Alberto, 2009) in R studio 4.0.2 (R Development Core Team, 2024), where each read mode was established as an allele class (bins). We verified if the allele classes followed the repetition motifs of each microsatellite (di- or trinucleotide), and we manually corrected them according to the read modes when necessary (DeWoody et al., 2006).

Since the microsatellite markers used in this study were transferred from other sciaenid genera, limitations to amplify some loci were expected (Barbará et al., 2007). Thus, Micro-Checker v. 2.2.3 (Van Oosterhout et al., 2004) was used to inspect the genotypes and estimate the frequencies of null alleles. Loci with high frequencies of null alleles (> 0.10) were excluded from analysis, while loci with moderate frequencies of null alleles (≤ 0.10) had the genotypes adjusted by the Oosterhout method. The input files for each analysis were formatted using Convert v. 1.31 (Glaubitz, 2004) and PGDSpider v. 2.1.1.5 (Lischer, Excoffier, 2012).

The genetic diversity was analyzed in different softwares: allele frequencies, number of alleles (N_A), allelic richness (A_R) and inbreeding coefficient (F_IS) were calculated in FSTAT v. 2.9.4 (Goudet, 2001); observed heterozygosity (H_O) and expected heterozygosity (H_E) were calculated in Arlequin v. 3.5.2.2 (Excoffier, Lischer, 2010). Hardy-Weinberg Equilibrium (HWE) and linkage disequilibrium were tested in GenePop v. 4.7.5 (Raymond, Rousset, 1995) using Markov Chains with 10,000 dememorization steps, 1,000 batches and 10,000 iterations per batch. The False Discovery Rate (FDR) procedure was used to correct the level of significance (α) in multiple tests (Benjamini, Hochberg, 1995; Waples, 2015).

To verify if U. canosai suffered loss of genetic diversity due to recent population decline, three bottleneck methods were applied to each region separately, as well as to the pooled sampling areas. The first method was the Wilcoxon test for heterozygosity excess, performed with 1,000 iterations in Bottleneck v. 1.2.02 (Piry et al., 1999). This test compares the expected heterozygosity under the Hardy-Weinberg Equilibrium (H_E) with the expected heterozygosity under the mutation-drift equilibrium (H_EQ), and a genetic bottleneck is inferred when there is a significant heterozygosity excess, i.e., H_E > H_EQ (Luikart, Cornuet, 1998). H_EQ was calculated under the infinite alleles model, stepwise mutation model, and two-phase model. The two-phase model parameters were chosen following Peery et al. (2012): variance of mutations with more than one repetition σ_g² = 12 (corresponding to mean δ_g = 3.1) and 78% of mutations with one repetition (22% with more than one repetition). The second method was the graphical test of rare allele deficit, also calculated in the Bottleneck software, under the previously described conditions. For stable populations, the allele frequencies present a L-shaped distribution; when there is a bottleneck, rare alleles are lost and the distribution changes its mode (Luikart et al., 1998). The third bottleneck method was the M ratio (Garza, Williamson, 2001), calculated in Arlequin, which is the ratio between the number of alleles (N_A) and the microsatellite size range (r) in terms of number of repetitions. A genetic bottleneck is assumed for populations with M < 0.68 (Garza, Williamson, 2001).

The genetic structure was investigated between pairwise localities by the exact G test of population differentiation in GenePop (with the same Markov Chain parameters described above), and by F_ST indices estimated in Arlequin. Two independent Molecular Analysis of Variance (AMOVA) were also conducted in Arlequin, each with 10,000 permutations, in order to compare: 1) the north stock (SEB) vs. south stock (SB + AU); and 2) among the three regions (SEB, SB, AU). Additionally, Structure v. 2.3.4 (Pritchard et al., 2000) was used to assign individuals with similar genotypes to the most likely population. For this, 1 to 3 populations (K) were simulated, with 20 replicates each, using a Markov Chain with 100,000 burn-in steps, followed by 200,000 simulation steps. The most likely number of K populations was chosen by the probability of individuals belonging to each K population and the largest L(K). The ∆K Evanno method (Evanno et al., 2005) could not be used because only three K populations were simulated.

The statistical power to identify a possible genetic differentiation was assessed by Chi-square and Fisher’s exact tests in Powsim v. 4.1 (Ryman, Palm, 2006), which simulates the Wright-Fisher model for genetic drift. For this, we simulated a genetic differentiation of F_ST = 0.05 (combination of generation time of drift t = 200 with effective population size N_e = 2,000) and F_ST = 0.01 (t = 40; N_e = 2,000) for our sampling design and our allele frequencies. The type I error (α) was also assessed with the same scenario, simulating the base population before drift (t = 0). The Markov Chain parameters used for Powsim were the same for GenePop, running 1,000 replicates.

Migrations among the three regions were estimated in Migrate v. 5.0.4, a software based on the coalescence theory and Bayesian approach (Beerli, 2006). For this, the mean number of migrants per generation (γ) between regions was calculated as γ_ab = Θ_b.M_ab, where Θ represents the mutation-scaled population size and M represents the mutation-scaled migration rate, from region a to b. Migrate combines the genetic data with an arbitrary prior distribution to the parameters Θ and M and generate a posterior distribution (Beerli, 2006; Beerli et al., 2019). The Brownian motion was employed, which is more suitable to microsatellite data. Different runs were performed, considering the default priors of the software, as well as arbitrary priors to Θ and M, using uniform or exponential distributions, with a range 0–20 to simulate a smaller population and a range 0–200 to simulate a larger population (Peter Beerli, 2025, pers. comm.). Better models were choose using the following diagnostics: higher marginal likelihood, calculated by the Bezier-approximated Thermodynamic integration (BTI); and Effective MCMC Sample Size (ESS) >1000 to each posterior parameter (Beerli et al., 2019).

Results​

---

Biological characterization. The genotyped fishes presented total lengths ranging from 225 to 411 mm and total weights ranging from 144 to 982 g (Tab. 1). The age classes varied from 1 to 12 years, representing an age-mixed sample (Kikuchi et al., 2021). Higher gonadosomatic index (GSI) means and the prevalence of mature females were observed in Rio Grande (SB) during August and September, and in Ilhabela during September (SEB), while lower GSI means and the prevalence of spawned females were observed in Niterói (SEB) during February. The GSI pattern observed in this study is in accordance with the reproductive seasonality for the species in Southern Brazil, as previously described by Haimovici, Cousin (1989) (Fig. 2). Therefore, we concluded that U. canosai spawns from late winter to spring in both Southeastern and Southern Brazil.

Molecular marker validation. The ten genotyped loci were polymorphic, totalizing 253 alleles identified (Tab. 2). The highest estimated frequencies of null alleles were found for Mfur26 (mean 0.338), Mfur19 (mean 0.205) and Mfur03 (mean 0.136); therefore, these three loci were excluded from all analyses. The loci Mfur02, Mfur06, Mfur14 and Soc204 presented null alleles for some regions in moderate estimated frequencies (mean ≤ 0.10) and were adjusted in Micro-Checker. The null alleles probably were the cause of two deviations from HWE for Mfur06 in SEB, and for Mfur14 in AU (Tab. 3), as well as of linkage disequilibrium in SEB for the loci pairs Mfur02-Soc204, Mfur14-Soc204, Mfur06-Mfur14 and Mfur02-Mfur14. Finally, Mfur25, Soc050 and Soc085 did not present null alleles, HWE deviations or linkage disequilibrium. Data analysis was performed only with the loci Mfur02, Mfur06, Mfur14, Mfur25, Soc050, Soc085 and Soc204.

FIGURE 2 | Mean gonadosomatic indices (GSI) throughout the months of the year for both sexes of Umbrina canosai (pink for females, black for males). Lines represent previous data from Southern Brazil (Haimovici, Cousin, 1989). Shapes and standard deviation bars represent the data from this study. Squares: Niterói (February: 27 females, 70 males); Triangles: Ilhabela (September: 43 females, 79 males); Circles: Rio Grande (August: 26 females, 20 males; September: 25 females, 14 males).

TABLE 3 | Genetic diversity of Umbrina canosai from the Southwestern Atlantic, evaluated by seven microsatellite loci in three regions: Southeastern Brazil (SEB), Southern Brazil (SB) and Argentine-Uruguayan Common Fishing Zone (AU). For each locus and region, number of genotypes used (N), number of alleles (N_A), allelic richness (A_R), observed heterozygosity (H_O), expected heterozygosity (H_E), probability of conformity to Hardy-Weinberg Equilibrium (HWE) and inbreeding coefficient (F_IS) are shown. Asterisks indicate significant deviations from HWE after the False Discovery Rate procedure. Mfur = Micropogonias furnieri, Soc = Sciaenops ocellatus.

Locus	Index	SEB	SB	AU	All sampling area
Mfur02	N	51	33	34	118
	NA	19	25	25	31
	AR	16.132	22.152	22.378	19.642
	HO	0.8039	0.8788	0.8235	0.8305
	HE	0.9286	0.9552	0.9587	0.9437
	HWE	0.0645	0.4576	0.0248
	FIS	0.135	0.081	0.143
Mfur06	N	34	28	29	91
	NA	23	22	22	30
	AR	21.11	21.198	20.732	22.247
	HO	0.6471	0.9286	0.7931	0.7802
	HE	0.9596	0.9565	0.9552	0.9588
	HWE	*<0.0001	0.4833	0.0117
	FIS	0.329	0.03	0.172
Mfur14	N	47	30	26	103
	NA	17	16	17	20
	AR	14.694	15.386	16.804	15.61
	HO	0.8298	0.7667	0.6923	0.7767
	HE	0.9153	0.9096	0.9163	0.9128
	HWE	0.0081	0.0949	*0.0004
	FIS	0.094	0.159	0.248
Mfur25	N	58	29	26	113
	NA	31	22	19	34
	AR	22.157	20.791	18.728	20.919
	HO	0.9138	0.9655	1.0000	0.9469
	HE	0.9483	0.9480	0.9419	0.9448
	HWE	0.1139	0.4541	0.9138
	FIS	0.037	-0.019	-0.063
Soc050	N	58	27	28	113
	NA	27	21	20	32
	AR	20.56	20.234	18.977	19.898
	HO	0.8448	0.8889	0.8571	0.8584
	HE	0.9267	0.9252	0.8974	0.9181
	HWE	0.0781	0.5793	0.6079
	FIS	0.089	0.04	0.046
Soc085	N	54	25	28	107
	NA	29	22	20	36
	AR	20.154	22	18.782	19.799
	HO	0.8148	0.8800	0.8214	0.8317
	HE	0.9079	0.9200	0.9091	0.9100
	HWE	0.5674	0.5092	0.2398
	FIS	0.103	0.044	0.098
Soc204	N	38	29	28	95
	NA	8	10	6	11
	AR	7.264	9.307	5.679	7.668
	HO	0.6842	0.6207	0.6429	0.6526
	HE	0.7368	0.6461	0.5753	0.6659
	HWE	0.0168	0.1954	0.7719
	FIS	0.072	0.04	-0.12
All loci	N mean	48.6	28.7	28.4	105.7
	NA mean	22.0	19.7	18.4	27.7
	AR mean	17.4387	18.7240	17.4400	17.8676
	HO mean	0.79120	0.84702	0.80434	0.8110
	HE mean	0.90332	0.89438	0.87914	0.8934
	FIS all loci	0.125	0.054	0.086	0.0883

Genetic diversity. The genetic diversity indices for the seven analyzed loci, with 217 alleles in total, are presented in Tab. 3. The indices ranges were: allele number (N_A) from 6 to 31; allelic richness (A_R) from 5.68 to 22.38; observed heterozygosity (H_O) from 0.62 to 1; expected heterozygosity (H_E) from 0.57 to 0.96; and inbreeding coefficient (F_IS) from -0.12 to 0.33. Soc204 was the unique trinucleotide locus analyzed, which showed lower diversity indices in comparison to the dinucleotide loci. Total means for diversity indices were: N_A = 27.7; A_R = 17.867; H_O = 0.811; H_E = 0.893; and F_IS = 0.088.

Genetic bottleneck. The data showed insufficient evidences of genetic bottleneck for U. canosai (Tab. 4). Under the infinite allele mutation model, which is more likely to indicate heterozygosity excess, the Wilcoxon tests were significant when considering only SEB and all sampling areas together (p-values < 0.05). Under the stepwise mutation and two-phase models, which are considered more suitable for microsatellites, all Wilcoxon tests were non-significant. After FDR procedure, all Wilcoxon tests were also non-significant. The allele frequency in the three regions, as well as when considering all sampling areas together, presented a L-shaped distribution, which indicates population stability. All mean M ratios were higher than the critical value M_C = 0.68, indicating absence of bottleneck effects.

Genetic connectivity. Our analyses indicated absence of genetic population structure and high connectivity of U. canosai among the three studied regions. None of the exact G tests between pairs of localities were significant (χ² from 10.96 to 21.67; df = 14; p-values > 0.08), and all F_ST indices were negative and non-significant (Tab. S2). The AMOVAs also showed negative fixation indices (F_CT, F_SC, F_ST) and all variations were attributed to within the regions (SEB, SB, AU), with no variation among regions or among the two stocks (north versus south) (Tab. S3). Since fixation indices are a measure between 0 and 1 (Wright, 1978), negative values were considered as near zero in pairwise tests and AMOVAs.

The Structure software also indicated lack of genetic population structure, with all individuals being equally likely to belong to any simulated population (Fig. 3). The highest L(K) values (mean = -4216.15) were found for K = 1 population. The genetic drift simulated by Powsim considered the allele frequencies, seven loci, three regions and the mean sample size in each region: 48 individuals in SEB, 28 in SB and 28 in AU (Tab. 3). Powsim showed a probability of 100% to detect genetic differentiations in these conditions, indicating a satisfactory statistical power for our analyses. The estimated type I error (α) was 0.042 for Chi-square test and 0.053 for Fisher’s test, which approximate the conventional 0.05 limit for significance testing.

TABLE 4 | Genetic bottleneck methods for Umbrina canosai in the Southwestern Atlantic: Wilcoxon test of heterozygosity excess (p-values), under three mutational models; Garza-Williamson M ratio. Analyses were done separately for each region, and for all sampling area integrated.

Region	Wilcoxon test of heterozygosity excess			M ratio
	Infinite alleles	Two-phase	Stepwise	Mean	Standard deviation
Southeastern Brazil (SEB)	0.0273	0.6562	0.9609	0.7646	0.0757
Southern Brazil (SB)	0.4062	0.8125	0.9609	0.7524	0.1584
Argentine-Uruguayan Common Fishing Zone (AU)	0.1484	0.8125	0.9609	0.7083	0.1378
All sampling area integrated	0.0391	0.9609	0.9805	0.8864	0.0899

FIGURE 3 | Structure assignment probabilities for Umbrina canosai in the Southwestern Atlantic, simulating two populations (K = 2). Each vertical bar represents one individual, and vertical black lines separate the three regions analyzed: 1) Southeastern Brazil (SEB), 2) Southern Brazil, and 3) Argentine-Uruguayan Common Fishing Zone (AU).

A bidirectional gene flow among regions was estimated by Migrate, and the migration magnitude varied according to the arbitrary priors (Tab. S4). Model 0 was not considered, since the default priors of the software are not customized to microsatellite data (Peter Beerli, 2025, pers. comm.). In Models 1 and 2 (smaller population), the mean numbers of migrants per generation (γ) ranged, respectively, from 86.7 to 149.2 (general mean 107.16) and from 153.9 to 207.2 (general mean 179.93). In Model 3 (larger population), γ ranged from 498.1 to 3286.8 (general mean 1630.35). This last model achieved better diagnostics: higher log-marginal-likelihood (Bezier-approximated: -21559.57), and all parameters estimated showed Effective MCMC Sample Size (ESS) > 1000. Likely due to the highly variable dataset, the models did not agree on which region showed the largest migration (Tab. S4).

Discussion​

---

Genetic diversity and bottleneck. All sampled regions showed relatively high genetic diversity for U. canosai, with no evidences of a bottleneck event. The mean heterozygosities for this species were among the highest, considering exploited crustaceans and coastal bony fishes in the study area (Tab. 5). Decades of intensive fishing of the Argentine croaker led to the loss of larger, older female fishes, decrease of spawning potential and changes in populational parameters, such as sex rate, individual growth, and length and age of first maturity (Haimovici et al., 2022). Given this alarming scenery, why is there no evidence of a genetic bottleneck for U. canosai? Two aspects can be discussed: 1) the life history of U. canosai, and 2) the information provided by microsatellites.

Genetic diversity of marine fishes is usually positively correlated with population size (McCusker, Bentzen, 2010), and our data indicated that U. canosai still has a genetically stable population. Therefore, the demographic decline of U. canosai was not severe enough to implicate a genetic bottleneck (Chapman et al., 2011; Pujolar et al., 2011). As thousands of tons of individuals are still landed each year (Haimovici, Cardoso, 2016; Haimovici et al., 2022), the population size is likely still in the order of millions. Moreover, the high genetic connectivity observed in U. canosai could be counteracting genetic bottlenecks, since recovery of genetic diversity after a bottleneck event is faster if migrations occur between areas (Luikart, Cornuet, 1998; Chapman et al., 2011).

TABLE 5 | Coastal crustaceans and fishes evaluated by microsatellites in the Warm Temperate Southwestern Atlantic Province. Spawning and nursery environments by Castro et al. (2002), Capparelli et al. (2012), Haimovici, Cardoso, 2017. Microsatellite studies: area evaluated, number of loci, mean observed heterozygosity (H_O), mean expected heterozygosity (H_E), F_ST range, references. N = north; SE = southeast; S = south.

Group	Species	Spawning environment	Nursery environment	Area evaluated	Microsatellite loci evaluated	Mean HO	Mean HE	FST range	References
Decapoda, Penaeidae	White shrimp Penaeus schmitti	Shelf	Estuary	SE Brazil (24°S) to S Brazil (27°S)	6	0.8502	0.8633	-0.001 to 0.036	Maggioni et al. (2003)
Decapoda, Portunidae	Blue crab Callinectes sapidus	Shelf and estuary	Estuary	S Brazil (27–33°S)	9	0.5025	0.5466	-0.001 to 0.023	Lacerda et al. (2016)
Teleostei, Mugilidae	Mullet Mugil liza	Shelf	Shelf and estuary	SE Brazil (23°S) to N Argentina (36°S)	9	0.6774	0.6952	0 to 0.018	Mai et al. (2014)
Teleostei, Paralichthyidae	Mud flounder Paralichthys orbignyanus	Shelf	Shelf and estuary	S Brazil (32°S) to N Argentina (41°S)	7	0.866	0.848	0.003 to 0.023	Fainburg et al. (2023)
Teleostei, Sciaenidae	Striped weakfish Cynoscion guatucupa	Shelf	Shelf	N Argentina (36–40°S)	2	0.3990	0.8330	0.007	Sabadin et al. (2010)
	Jamaica weakfish Cynoscion jamaicensis	Shelf	Shelf	SE Brazil (23°S) to S Brazil (33°S)	6	0.3780	0.3908	-0.011 to 0.028	Silva (2015)
	Whitemount croaker Micropogonias furnieri	Shelf	Shelf and estuary	Uruguay and Rio de la Plata (34–35°S)	7 (including 2 non-neutral loci, D’Anatro 2017)	0.6656	0.7464	0.036 to 0.274	D’Anatro et al. (2011)
				SE Brazil (23°S) to Uruguay (34°S)	13	0.7187	0.7671	-0.027 to 0.013	Vasconcellos (2012)
				SE Brazil (23°S) to S Brazil (33°S)	4 (plus 4 introns)	–	–	0 to 0.096	Vasconcellos et al. (2015)
				SE Brazil (22–23°S)	12	0.6805	0.7503	0.012 to 0.101	Franco et al. (2023)
	Argentine croaker Umbrina canosai	Shelf	Shelf	SE Brazil (23°S) to N Argentina (38°30’S)	7	0.8110	0.8934	-0.072 to -0.001 (near zero)	This study

Microsatellites are neutral genetic markers, i.e. they do not directly influence phenotype and fitness (Kirk, Freeland, 2011; Mariani, Bekkevold, 2014). The selective harvesting that reduces larger and more fertile fishes (Haimovici, Cardoso, 2016; Haimovici et al., 2022) can remove important alleles of genes influencing body size, reproduction traits, and adaptation to environmental conditions (Pinsky, Palumbi, 2014). This loss could be reflected by adaptive genetic markers, but not by using neutral microsatellites (Kirk, Freeland, 2011). Similarly, microsatellites did not detect genetic bottleneck in other overexploited bony fishes and elasmobranchs: Micropogonias furnieri and Mugil liza in the Southwestern Atlantic (D’Anatro et al., 2011; Mai et al., 2014), as well as Pristis pectinata and Anguilla anguilla in the North Atlantic (Chapman et al., 2011; Pujolar et al., 2011).

Genetic connectivity. Microsatellite analysis revealed “drift connectivity” for U. canosai, in which the gene flow is enough to maintain similar allele frequences in different regions (Lowe, Allendorf, 2010). This homogeneity may occur because throughout the Warm Temperate Southwestern Atlantic Province there are no obvious geographical barriers with the potential to stop gene flow (Acha et al., 2004; Spalding et al., 2007). Therefore, U. canosai eggs and larvae can be carried both northwards and southwards by marine currents before their larval settlement. Surface drifters have shown that an extension of the Malvinas Current can carry Subantarctic Shelf Water from 33°S in Southern Brazil up to 22°S in the Southeastern region during autumn and winter, mainly due to the action of cold fronts (Fig. 1) (Campos et al., 1996). This current can present linear displacements of 300 to 500 km per month, which corresponds to approximately 10 to 17 km per day, and is capable of carrying planktonic fauna from temperate to tropical zones (Campos et al., 1996; Stevenson et al., 1998). Conversely, the Brazil Current could carry eggs and larvae southwards.

The planktonic larval duration of U. canosai is not reported in literature, but can be inferred from related species. Sciaenid eggs take one day on average to hatch, as is the case of Pogonias cromis (Joseph et al., 1964) and Micropogonias furnieri (Albuquerque et al., 2009). Wild larvae of Micropogonias undulatus take approximately 40–50 days to attain 10 mm of standard length (Cowan, 1988; Nixon, Jones, 1997), which is the approximate settlement size for sciaenids and other coastal bony fishes, when larvae are capable of actively swimming to the bottom (reviewed by Clark et al., 2005; Leis et al., 2006). At 10 mm of total length, U. canosai larvae still have a frontal oblique mouth, and at 22 mm the mouth moves slightly to the inferior position, indicating the transition between the plankton phase and the settlement (Weiss, 1981). Therefore, U. canosai larvae likely remain in the plankton for at least 40 days, assuming that planktonic larval duration is similar in benthic feeding sciaenids from warm temperate waters. Forty days is probably sufficient for some larvae spawned in the southern range of the species to reach and recruit in its northern range or vice-versa. U. canosai spawns during from late winter to spring in Southern Brazil (Haimovici, Cousin, 1989), and the gonadal data observed in this study suggests that the spawning in Southeastern Brazil occurs during the same period (Fig. 2). This overlap can favor the mixture of larvae, contributing to the lack of genetic differentiation between regions.

In terms of migrations, large schools of breeders are known to migrate hundreds to a thousand kilometers between Northern Argentina and South Brazil during the reproductive season, reaching up to the Cape Santa Marta Grande (28°40’S) (Haimovici, Cousin, 1989). It is possible that some move further north to spawn, aided by the extension of the Malvinas Current in winter and early spring (Campos et al., 1996). Individuals can also migrate in the opposite direction in late spring, favored by the Brazil Current. The genetic mixture of U. canosai could be occurring in a “stepping stone” manner, with individuals in Southern Brazil as a midpoint for gradual migrations over generations between Southeastern Brazil and Northern Argentina. Different tagging experiments have shown that coastal bony fishes travel on average tens of kilometers in periods from ~200 days to more than one year. On the other hand, some individuals can move over larger distances, with records of more than a thousand kilometers traveled(Iversen, Tabb, 1962; Murphy et al., 1998; Dunlop et al., 2013).

Genetic patterns in the Warm Temperate Southwestern Atlantic. Populations of crustaceans and coastal bony fishes have been investigated by microsatellites in the Warm Temperate Southwestern Atlantic Province (Tab. 5). Their life cycles include spawning and nursery on the continental shelf (Callinectes sapidus, Cynoscion guatucupa, Cynoscion jamaicensis, U. canosai), use of estuaries as nurseries (C. sapidus, Penaeus schmitti, Micropogonias furnieri, Mugil liza, Paralichthys orbignyanus) or even spawning in estuaries (C. sapidus) (Castro et al., 2002; Capparelli et al., 2012; Haimovici, Cardoso, 2017). Most of them exhibited low genetic structure across the Province (F_ST < 0.05; Tab. 5) and corroborate the general pattern of connectivity for marine fishes with planktonic larvae, which can be dispersed via ocean currents (Cooke et al., 2016).

Nonetheless, some studies reported moderate to high genetic structure to M. furnieri along the Province (F_ST > 0.05; Tab. 5). Three aspects can explain this genetic contrast between M. furnieri (structured) and U. canosai (high connectivity). First, U. canosai was evaluated here only by neutral microsatellites, while some studies evaluated M. furnieri using microsatellites jointly to intron length polymorphisms (Vasconcellos et al., 2015) or linked to non-neutral regions (D’Anatro et al., 2011; D’Anatro, 2017), which probably influenced F_ST values. Second, a study sampled M. furnieri along the Rio de Janeiro coast and included an area north of Cape Frio (23°S), considering individuals outside of the Warm Temperate Southwestern Atlantic Province (Franco et al., 2023). Third, both sciaenid species have distinct life cycles, and this likely drives their population genetics. M. furnieri uses estuaries and lagoons as nursery ground; this strategy, associated with semi-closed environments, can favor a partial genetic isolation, as observed along the Uruguay coast (D’Anatro et al., 2011). On the other hand, U. canosai completes the entire life cycle in the continental shelf. In the ocean, F_ST values accumulate slowly along vast distances. At least 5,000 km would be necessary for a fish of median size to achieve high genetic structures between populations (F_ST > 0.15) (Cooke et al., 2016). Since the Warm Temperate Southwestern Atlantic Province has approximately 2,500 km in a straight line, this is not likely to occur.

Implications for conservation and management. The conservation status of U. canosai was categorized in 2019 as “Least Concern” by the International Union for Conservation of Nature (IUCN), since the global declines were not considered to result in the endangerment of the species(Haimovici et al., 2021). Despite its overfishing, U. canosai could be resilient due to its biological characteristics: relatively small-body, high fecundity, long spawning season, flexible and early age maturity, and flexible and fast individual growth (Haimovici, Cousin, 1989; Haimovici et al., 2022). These characteristics contrast to those of more vulnerable marine fishes, which are usually large-bodied and have late age at maturity (Reynolds et al., 2005; Olden et al., 2007). Genetic data can provide information on the risks of local and global extinctions (Collen et al., 2016) and the microsatellite data corroborated the low risk of extinction reported by IUCN (Haimovici et al., 2021), as well as the resilience capacity observed for the species (Haimovici et al., 2022).

In fisheries management, a holistic approach to fish stock identification is desirable (Begg, Waldman, 1999; Cadrin et al., 2014). For U. canosai, previous studies on the parasitic assembly, growth and otolith shape identified two demographically distinct stocks: one in Southeastern Brazil, and another from Southern Brazil to Northern Argentina (Canel et al., 2019; Kikuchi et al., 2021). These environment-based techniques can be complemented by the inheritance-based microsatellites, which showed high connectivity between stocks. In this scenario, the removal of individuals from a stock can be recovered by immigrants from the neighboring stock, a phenomenon known as “rescue effect” (Benestan, 2019). Conversely, overexploitation can cause the gradual depletion of both stocks. Therefore, a collaborative management by Brazil, Uruguay, and Argentina is necessary for the U. canosai conservation, given that it is a migratory, transboundary and genetically connected marine fish (Defeo et al., 2025).

Caution is needed considering the limitations of a standard panel of microsatellites (Mariani, Bekkevold, 2014; Benestan, 2019). Measures should be considered to prevent the risk of economic collapse for Argentine croaker, i.e. when stocks decline and no longer contribute to the fisheries production (Haimovici, Cardoso, 2016; Haimovici et al., 2022). Moreover, the Southwestern Atlantic is considered an ocean-warming hotspot. In this sense, climatic changes can drive new impacts on U. canosai not considered in this study, such as the reduction of larval connectivity caused by tropicalization and alterations in current routes (Defeo et al., 2025).

Finally, additional studies with non-neutral/adaptive genetic markers and genomic methods, such as single nucleotide polymorphisms (SNPs) (Benestan, 2019), should be conducted to further enlighten the resilience and population genetics of U. canosai, and increase the resolution for stock delimitations.

Acknowledgments​

---

We are grateful to Maria Cristina Flores, Sarah Teodoro, Luciana Medeiros, Roberta Barutot and Lúcia Nunes for their essential support in laboratory analysis and genotyping. In the same manner, we thank Peter Beerli for his valuable assistance in migration analyses; Luis Gustavo Cardoso, José Muelbert, Luís Sampaio for their contributions to the discussion; Lucas Rodrigues for elaborating Fig. 2; Eidi Kikuchi for providing the fish age data. We acknowledge all partners in fish sampling: Francyne Vieira and Fernanda Gonçalves from FIPERJ for Niterói samples; Laura Villwock and Acácio Tomas from IP/SP for Ilhabela samples; Marcio Freire, Camila Bosenbecker and Felippe Abbatepaulo from FURG for Rio Grande samples; Juan Timi and Delfina Canel from UNMdP for samples of Buenos Aires Province. This study was started from DMV’s M.Sc. dissertation in Biological Oceanography under the supervision of MH and MCP (PPGOB/FURG; ppgocbio.furg.br).

References​

---

Acha EM, Mianzan HW, Guerrero RA, Favero M, Bava J. Marine fronts at the continental shelves of austral South America: Physical and ecological processes. J Mar Syst. 2004; 44(1–2):83–105. https://doi.org/10.1016/j.jmarsys.2003.09.005

Alberto F. MsatAllele-1.0: an R package to visualize the binning of microsatellite alleles. J Hered. 2009; 100(3):394–97. https://doi.org/10.1093/jhered/esn110

Albuquerque CQ, Muelbert JH, Sampaio LAN. Early developmental aspects and validation of daily growth increments in otoliths of Micropogonias furnieri (Pisces, Sciaenidae) larvae reared in laboratory. Panam J Aquat. 2009; 4(3):259–66.

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

Almeida R, Mateus CS, Alves MJ, Marques JP, Pereira J, Prista N et al. Evidence of genetic segregation among meagre (Argyrosomus regius) Atlantic spawning areas. J Mar Sci Eng. 2022; 10(12):1843. https://doi.org/10.3390/jmse10121843

Alvarenga M, D’Elia AKP, Rocha G, Arantes CA, Henning F, Vasconcelos ATR et al. Mitochondrial genome structure and composition in 70 fishes: a key resource for fisheries management in the South Atlantic. BMC Genomics. 2024; 25:215. https://doi.org/10.1186/s12864-024-10035-5

Arif IA, Khan HA, Shobrak M, Al Homaidan AA, Al Sadoon M, Al Farhan AH et al. Interpretation of electrophoretograms of seven microsatellite loci to determine the genetic diversity of the Arabian Oryx. Genet Mol Res. 2010; 9:259–65.

Barbará T, Palma-Silva C, Paggi GM, Bered F, Fay MF, Lexer C. Cross-species transfer of nuclear microsatellite markers: potential and limitations. Mol Ecol. 2007; 16(18):3759–67. https://doi.org/10.1111/j.1365-294X.2007.03439.x

Beerli P. Comparison of Bayesian and maximum-likelihood inference of population genetic parameters. Bioinformatics. 2006; 22(3):341–45. https://doi.org/10.1093/bioinformatics/bti803

Beerli P, Mashayekhi S, Sadeghi M, Khodaei M, Shaw K. Population genetic inference with MIGRATE. Curr Protoc Bioinformatics. 2019; 68(1):e87. https://doi.org/10.1002/cpbi.87

Begg GA, Waldman JR. An holistic approach to stock identification. Fish Res. 1999; 43(1–3):35–44. https://doi.org/10.1016/S0165-7836(99)00065-X

Benestan L. Population genomics applied to fishery management and conservation. In: Oleksiak M, Rajora O, editors. Population genomics: marine organisms. Cham: Springer; 2019. p.399–421.

Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc Ser B Methodol. 1995; 57(1):289–300. https://doi.org/10.1111/j.2517-6161.1995.tb02031.x

Blacket MJ, Robin C, Good RT, Lee SF, Miller AD. Universal primers for fluorescent labelling of PCR fragments – an efficient and cost-effective approach to genotyping by fluorescence. Mol Ecol Resour. 2012; 12(3):456–63. https://doi.org/10.1111/j.1755-0998.2011.03104.x

Cadrin SX, Karr LA, Mariani S. Stock identification methods: applications in fishery science. Cambridge: Academic Press; 2014.

Campos EJD, Ikeda Y, Castro BM, Gaeta SA, Lorenzzetti JA, Stevenson MR. Experiment studies circulation in the Western South Atlantic. Eos Trans AGU. 1996; 77:253–59. https://doi.org/10.1029/96EO00177

Canel D, Levy E, Soares IA, Braicovich PE, Haimovici M, Luque JL et al. Stocks and migrations of the demersal fish Umbrina canosai (Sciaenidae) endemic from the subtropical and temperate Southwestern Atlantic revealed by its parasites. Fish Res. 2019; 214:10–18. https://doi.org/10.1016/j.fishres.2019.02.001

Capparelli MV, Kasten P, Castilho AL, Costa RC. Ecological distribution of the shrimp Litopenaeus schmitti (Burkenroad, 1936) (Decapoda, Penaeoidea) in Ubatuba Bay, São Paulo, Brazil. Invertebr Reprod Dev. 2012; 56(3):173–79. https://doi.org/10.1080/07924259.2011.587272

Castro PMG, Cergole MC, Carneiro MH, Mucinhato CMD, Servo GJM. Crescimento, mortalidade e taxa de explotação do goete Cynoscion jamaicensis (Perciformes: Sciaenidae), na região sudeste/sul do Brasil. Bol Inst Pesca. 2002; 28(2):141–53.

Chapman DD, Simpfendorfer CA, Wiley TR, Poulakis GR, Curtis C, Tringali M et al. Genetic diversity despite population collapse in a critically endangered marine fish: the smalltooth sawfish (Pristis pectinata). J Hered. 2011; 102(6):643–52. https://doi.org/10.1093/jhered/esr098

Clark DL, Leis JM, Hay AC, Trnski T. Swimming ontogeny of larvae of four temperate marine fishes. Mar Ecol Prog Ser. 2005; 292:287–300. https://doi.org/10.3354/meps292287

Collen B, Dulvy NK, Gaston KJ, Gärdenfors U, Keith DA, Punt AE et al. Clarifying misconceptions of extinction risk assessment with the IUCN Red List. Biol Lett. 2016; 12(4):20150843. http://doi.org/10.1098/rsbl.2015.0843

Cooke GM, Schlub TE, Sherwin WB, Ord TJ. Understanding the spatial scale of genetic connectivity at sea: unique insights from a land fish and a meta-analysis. PLoS ONE. 2016; 11(5):e0150991. https://doi.org/10.1371/journal.pone.0150991

Cousseau MB, Perrotta R. Peces marinos de Argentina: biología, distribución, pesca. Mar del Plata: INIDEP; 2013.

Cowan Jr JH. Age and growth of Atlantic croaker, Micropogonias undulatus, larvae collected in the coastal waters of the northern Gulf of Mexico as determined by increments in saccular otoliths. Bull Mar Sci. 1988; 42(3):349–57.

D’Anatro A. Correlation Between Environmental Factors, a Life History Trait, Phenotypic Cohesion, and Gene Flow Levels in Natural Populations of Micropogonias furnieri: Is Salinity the Main Factor Driving Divergence? Estuar Coast. 2017; 40:1717–31. https://doi.org/10.1007/s12237-017-0234-1

D’Anatro A, Pereira AN, Lessa EP. Genetic structure of the white croaker, Micropogonias furnieri Desmarest 1823 (Perciformes: Sciaenidae) along Uruguayan coasts: Contrasting marine, estuarine, and lacustrine populations. Environ Biol Fish. 2011; 91:407–20. https://doi.org/10.1007/s10641-011-9799-x

Defeo O, Franco BC, Piola AR, Buratti CC, Cardoso LG, Cortés F et al. Facing oceanographic, fisheries, and governance hotspots: scientific evidence and policy implications from the southwest South Atlantic Ocean. Mar Policy. 2025; 173:106584. https://doi.org/10.1016/j.marpol.2024.106584

DeWoody J, Nason JD, Hipkins VD. Mitigating scoring errors in microsatellite data from wild populations. Mol Ecol Notes. 2006; 6(4):951–57. https://doi.org/10.1111/j.1471-8286.2006.01449.x

Dunlop SW, Mann BQ, van der Elst RP. A review of the Oceanographic Research Institute’s Cooperative Fish Tagging Project: 27 years down the line. African J Mar Sci. 2013; 35:209–21. https://doi.org/10.2989/1814232X.2013.769909

Evanno G, Regnaut S, Goudet J. Detecting the number of clusters of individuals using the software STRUCTURE: a simulation study. Mol Ecol. 2005; 14(8):2611–20. https://doi.org/10.1111/j.1365-294X.2005.02553.x

Excoffier L, Lischer HEL. Arlequin suite ver 3.5: a new series of program stopper form 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

Fainburg L, Sabadin D, Díaz de Astarloa JM, Fernández Iriarte P. Population structure of mud flounder Paralichthys orbignyanus from the south-western Atlantic Ocean. J Fish Biol. 2023; 102(2):455–64. https://doi.org/10.1111/jfb.15271

Flores-Rentería L, Krohn A. Scoring microsatellite loci. In: Kantartzi S, editor. Microsatellites. Methods Mol Biol. Totowa NJ: Humana Press; 2013. p.319–36.

Franco TP, Vilasboa A, Araújo FG, Gama JM, Correia AT. Identifying whitemouth croaker (Micropogonias furnieri) populations along the Rio de Janeiro coast, Brazil, through microsatellite and otolith analyses. Biology. 2023; 12(3):360. https://doi.org/10.3390/biology12030360

Fundação Instituto de Pesca do Estado do Rio de Janeiro (FIPERJ). Relatório anual 2015 [Internet]. Niterói; 2016. Available from: https://www.rj.gov.br/fiperj/node/747

FURG/SEMA/MPA. Boletim da Pesca Industrial Marinha no Rio Grande do Sul – 2025 [Internet]. Rio Grande: Laboratório de Dinâmica Populacional Pesqueira, Instituto de Oceanografia, FURG; 2026. Available from: https://demersais.furg.br/

Garza JC, Williamson EG. Detection of reduction in population size using data from microsatellite loci. Mol Ecol. 2001; 10(2):305–18. https://doi.org/10.1046/j.1365-294x.2001.01190.x

Glaubitz JC. CONVERT: a user-friendly program to reformat diploid genotypic data for commonly used population genetic software packages. Mol Ecol Notes. 2004; 4(2):309–10. https://doi.org/10.1111/j.1471-8286.2004.00597.x

Gonzalez Alberdi P, Nani A. Contribuición al conocimiento de la biología del pargo blanco Umbrina canosai, de la region de Mar del Plata. CARPAS – Docum. Téc. Num. 1967; 10:1–36.

Goudet J. FSTAT, a program to estimate and test gene diversities and fixation indices, version 2.9.3. 2001. Available from: https://www2.unil.ch/popgen/softwares/fstat.htm

Guichoux E, Lagache L, Wagner S, Chaumeil P, Léger P, Lepais O et al. Current trends in microsatellite genotyping. Mol Ecol Resour. 2011; 11(4):591–11. https://doi.org/10.1111/j.1755-0998.2011.03014.x

Haimovici M. A prospecção pesqueira e abundância de estoques marinhos no Brasil nas décadas de 1960 a 1990: levantamento de dados e avaliação crítica. Programa Revizee: levantamento de dados pretéritos. Brasília: Ministério do Meio Ambiente; 2007.

Haimovici M, Absalonsen L, Velasco G, Miranda LW. Diagnóstico do estoque e orientações para o ordenamento da pesca de Umbrina canosai (Berg, 1895). In: Rossi-Wongtschowski CLDB, Ávila-da-Silva AO, Cergole MC, editors. Análise das principais pescarias comerciais da região Sudeste-Sul do Brasil: dinâmica populacional das espécies em explotação – II. São Paulo: Universidade Federal de São Paulo; 2006. p.77–85.

Haimovici M, Cardoso LG. Colapso do estoque de Umbrina canosai do sul do Brasil devido à introdução do arrasto-de-meia-água. Bol Inst Pesca. 2016; 42(1):258–67. https://doi.org/10.20950/1678-2305.2016v42n1p258

Haimovici M, Cardoso LG. Long-term changes in the fisheries in the Patos Lagoon estuary and adjacent coastal waters in Southern Brazil. Mar Biol Res. 2017; 13:135–50. https://doi.org/10.1080/17451000.2016.1228978

Haimovici M, Cousin JCB. Reproductive biology of the castanha Umbrina canosai (Pisces, Scianidae) in Southern Brazil. Rev Bras Biol. 1989; 49:523–37.

Haimovici M, Kikuchi E, Cardoso LG. Changes in the population structure and life history associated with long-term intense fishing of the Argentinian croaker Umbrina canosai in southern Brazil. Aquat Living Resour. 2022; 35:12. https://doi.org/10.1051/alr/2022012

Haimovici M, Martins AS, Costa PAS, Braga AC, Mincarone MM. Distribuição e abundância do nécton demersal da plataforma e talude continental da Bacia de Campos. In: Curbelo-Fernandez MP, Braga, AC, editors. Comunidades demersais e bioconstrutores: caracterização ambiental regional da Bacia de Campos, Atlântico Sudoeste. Rio de Janeiro: Elsevier; 2017. p.111–38.

Haimovici M, Martins AS, Vieira PC. Distribuição e abundância de teleósteos demersais sobre a plataforma continental do sul do Brasil. Rev Bras Biol. 1996; 56:27–50.

Haimovici M, Reis EG. Determinação de idade e crescimento da Castanha Umbrina canosai, (Pisces, Sciaenidae) do Sul do Brasil. Atlântica. 1984; 7:25–46.

Haimovici M, Ruarte C, Rico R. Umbrina canosai. The IUCN Red List of Threatened Species 2021: e.T195077A49223922 [Internet]. Cambridge; 2021. Available from: https://dx.doi.org/10.2305/IUCN.UK.2021-1.RLTS.T195077A49223922.en

Haimovici M, Teixeira RL, Arruda C. Alimentação da castanha Umbrina canosai (Pisces: Scienidae) no sul do Brasil. Rev Bras Biol. 1989; 49(2):511–22.

Haimovici M, Wongtschowski CLDBR, Bernardes RA, Fischer LG, Vooren CM, Santos RA et al. Prospecção pesqueira de espécies demersais com rede de arrasto-de-fundo na região Sudeste-Sul do Brasil. Série Documentos REVIZEE – Score Sul. São Paulo: IO-USP; 2008.

Hauser L, Adcock GJ, Smith PJ, Ramírez JHB, Carvalho GR. Loss of microsatellite diversity and low effective population size in an overexploited population of New Zealand snapper (Pagrus auratus). PNAS. 2002; 99(18):11742–47. https://doi.org/10.1073/pnas.172242899

Instituto de Pesca (IP) do Estado de São Paulo. Programa de monitoramento da pesca marinha e estuarina do Estado de São Paulo [Internet]. São Paulo; 2026. Available from: www.propesq.pesca.sp.gov.br

Iversen ES, Tabb DC. Subpopulations based on growth and tagging studies of spotted seatrout, Cynoscion nebulosus, in Florida. Copeia. 1962; 1962(3):544–48. https://doi.org/10.2307/1441176

Joseph EB, Massmann WH, Norcross JJ. The pelagic eggs and early larval stages of the black drum from Chesapeake Bay. Copeia. 1964; 1964(2):425–34. http://doi.org/10.2307/1441037

Kikuchi E, Cardoso LG, Canel D, Timi JT, Haimovici M. Using growth rates and otolith shape to identify the population structure of Umbrina canosai (Sciaenidae) from the Southwestern Atlantic. Mar Biol Res. 2021; 17(3):272–85. https://doi.org/10.1080/17451000.2021.1938131

Kirk H, Freeland JR. Applications and implications of neutral versus non-neutral markers in molecular ecology. Int J Mol Sci. 2011; 12(6):3966–88. https://doi.org/10.3390/ijms12063966

Lacerda ALF, Kersanach R, Cortinhas MCS, Prata PFS, Dumont LFC, Proietti MC et al. High connectivity among blue crab (Callinectes sapidus) populations in the Western South Atlantic. PLoS ONE. 2016; 11(4):e0153124. https://doi.org/10.1371/journal.pone.0153124

Leis JM, Hay AC, Trnski T. In situ ontogeny of behaviour in pelagic larvae of three temperate, marine, demersal fishes. Mar Biol. 2006; 148:655–69. https://doi.org/10.1007/s00227-005-0108-0

Lischer HE, Excoffier L. PGDSpider: an automated data conversion tool for connecting population genetics and genomics programs. Bioinformatics. 2012; 28(2):298–99. https://doi.org/10.1093/bioinformatics/btr642

Lowe WH, Allendorf FW. What can genetics tell us about population connectivity? Mol Ecol. 2010; 19(23):3038–51. https://doi.org/10.1111/j.1365-294X.2010.04688.x

Luikart G, Allendorf FW, Cornuet J-M, Sherwin WB. Distortion of allele frequency distributions provides a test for recent population bottlenecks. J Hered. 1998; 89(3):238–47. https://doi.org/10.1093/jhered/89.3.238

Luikart G, Cornuet J-M. Empirical evaluation of a test for identifying recently bottlenecked populations from allele frequency data. Conserv Biol. 1998; 12(1):228–37. https://doi.org/10.1111/j.1523-1739.1998.96388.x

Maggioni R, Rogers AD, Maclean N. Population structure of Litopenaeus schmitti (Decapoda: Penaeidae) from the Brazilian coast identified using six polymorphic microsatellite loci. Mol Ecol. 2003; 12(12):3213–17. https://doi.org/10.1046/j.1365-294X.2003.01987.x

Mai ACG, Miño CI, Marins LFF, Monteiro-Neto C, Miranda L, Schwingel PR et al. Microsatellite variation and genetic structuring in Mugil liza (Teleostei: Mugilidae) populations from Argentina and Brazil. Estuar Coast Shelf Sci. 2014; 149:80–86. https://doi.org/10.1016/j.ecss.2014.07.013

Mariani S, Bekkevold D. The nuclear genome: neutral and adaptive markers in fisheries science. In: Cadrin SX, Karr LA, Mariani S, editors. Stock identification methods: applications in fishery science. Cambridge: Academic Press; 2014. p.297–327.

Markoulatos P, Siafakas N, Moncany M. Multiplex polymerase chain reaction: a practical approach. J Clin Lab Anal. 2002; 16(1):47–51. https://doi.org/10.1002/jcla.2058

Matano RP, Palma ED, Piola AR. The influence of the Brazil and Malvinas Currents on the Southwestern Atlantic Shelf circulation. Ocean Sci. 2010; 6:983–95. https://doi.org/10.5194/os-6-983-2010

McCusker MR, Bentzen P. Positive relationships between genetic diversity and abundance in fishes. Mol Ecol. 2010; 19(22):4852–62. https://doi.org/10.1111/j.1365-294X.2010.04822.x

Militelli MI, Macchi GJ, Rodrigues KA. Comparative reproductive biology of Sciaenidae family species in the Río de la Plata and Buenos Aires Coastal Zone, Argentina. J Mar Biol Assoc UK. 2013; 93(2):413–23. https://doi.org/10.1017/S0025315412001488

Molecular Ecology Resources Primer Development Consortium, Arias MC, Arnoux E, Bell JJ, Bernadou A, Bino G et al. Permanent genetic resources added to Molecular Ecology Resources Database 1 December 2011 – 31 January 2012. Mol Ecol Resour. 2012; 12(3):570–72. https://doi.org/10.1111/j.1755-0998.2012.03133.x

Murphy MD, Adams DH, Tremain DM, Winner BL. Direct validation of ages determined for adult black drum, Pogonias cromis, in east-central Florida, with notes on black drum migration. Fish Bull. 1998; 96:382–87.

Navarro G, Rozycki V, Monsalvo M, Puljak GM. Estadísticas de la pesca marina en Argentina. Evolución de los desembarques 2015-2020 [Internet]. Buenos Aires; 2022. Available from: https://www.magyp.gob.ar/sitio/areas/pesca_maritima/desembarques/

Nixon SW, Jones CM. Age and growth of larval and juvenile Atlantic croaker, Micropogonias undulatus, from the Middle Atlantic Bight and estuarine waters of Virginia. Fish Bull. 1997; 95:773–84.

Olden JD, Hogan ZS, Zanden MJV. Small fish, big fish, red fish, blue fish: size-biased extinction risk of the world’s freshwater and marine fishes. Glob Ecol Biogeogr. 2007; 16(6):694–701. https://doi.org/10.1111/j.1466-8238.2007.00337.x

Peery MZ, Kirby R, Reid BN, Stoelting R, Doucet-Bëer E, Robinson S et al. Reliability of genetic bottleneck tests for detecting recent population declines. Mol Ecol. 2012; 21(14):3403–18. https://doi.org/10.1111/j.1365-294X.2012.05635.x

Pinsky ML, Palumbi SR. Meta-analysis reveals lower genetic diversity in overfished populations. Mol Ecol. 2014; 23(1):29–39. https://doi.org/10.1111/mec.12509

Piola AR, Campos EJD, Möller Jr. OO, Charo M, Martinez C. Subtropical shelf front off eastern South America. J Geophys Res Ocean. 2000; 105(C3):6565–78. https://doi.org/10.1029/1999JC000300

Piry S, Luikart G, Cornuet J-M. BOTTLENECK: a computer program for detecting recent reductions in the effective population size using allele frequency data. J Hered. 1999; 90(4):502–503. https://doi.org/10.1093/jhered/90.4.502

Pritchard JK, Stephens M, Donnelly P. Inference of population structure using multilocus genotype data. Genetics. 2000; 155(2):945–59. https://doi.org/10.1093/genetics/155.2.945

Pujolar JM, Bevacqua D, Capoccioni F, Ciccotti E, De Leo GA, Zane L. No apparent genetic bottleneck in the demographically declining European eel using molecular genetics and forward-time simulations. Conserv Genet. 2011; 12:813–25. https://doi.org/10.1007/s10592-011-0188-y

QGIS Development Team. QGIS Geographic Information System. Open Source Geospatial Foundation Project. 2024. Available from: http://qgis.osgeo.org

R Development Core Team. R: A language and environment for statistical computing. Vienna: R Foundation for Statistical Computing; 2024. 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

Reynolds JD, Dulvy NK, Goodwin NB, Hutchings JA. Biology of extinction risk in marine fishes. Proc R Soc B Biol Sci. 2005; 272(1579):2337–44. https://doi.org/10.1098/rspb.2005.3281

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

Sabadin DE, González-Castro M, Iudica C, Díaz de Astarloa JM, Fernández-Iriarte PJ. Morphometric and genetic assessment of the Cynoscion guatucupa population structure from Buenos Aires coast, Argentine Sea. Rev Biol Mar Oceanogr. 2010; 45(3):513–17. https://doi.org/10.4067/S0718-19572010000300016

Saillant E, Cizdziel K, O’Malley KG, Turner TF, Pruett CL, Gold JR. Microsatellite markers for red drum, Sciaenops ocellatus. Gulf Mex Sci. 2004; 22(1):101–07. https://doi.org/10.18785/goms.2201.10

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

Selkoe KA, Toonen RJ. Microsatellites for ecologists: a practical guide to using and evaluating microsatellite markers. Ecol Lett. 2006; 9(5):615–29. https://doi.org/10.1111/j.1461-0248.2006.00889.x

Silva BA. Filogeografia e estrutura populacional da pescada-goete Cynoscion jamaicensis (Sciaenidae – Perciformes) do Atlântico Sul ocidental utilizando abordagem multilocus. [Master Dissertation]. Belém: Universidade Federal do Pará; 2015.

Spalding MD, Fox HE, Allen GR, Davidson N, Ferdaña 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

Stevenson MR, Dias-Brito D, Stech JL, Kampel M. How do cold water biota arrive in a tropical bay near Rio de Janeiro, Brazil? Cont Shelf Res. 1998; 18(13):1595–612. https://doi.org/10.1016/S0278-4343(98)00029-6

Tellechea JS, Fine ML, Norbis W. Passive acoustic monitoring, development of disturbance calls and differentiation of disturbance and advertisement calls in the Argentine croaker Umbrina canosai (Sciaenidae). J Fish Biol. 2017; 90(4):1631–43. https://doi.org/10.1111/jfb.13257

Turner TF, Richardson LR, Gold JR. Polymorphic microsatellite DNA markers in red drum (Sciaenops ocellatus). Mol Ecol. 1998; 7(12):1771–73. https://doi.org/10.1046/j.1365-294x.1998.00501.x

Uruguay. Dirección Nacional de Recursos Acuáticos (DINARA). Boletín Estadístico Pesquero 2018 [Internet]. Montevideo; 2019. Available from: https://www.gub.uy/ministerio-ganaderia-agricultura-pesca/politicas-y-gestion/boletin-estadistico-pesquero

Van Oosterhout C, Hutchinson WF, Wills DPM, Shipley P. MICRO-CHECKER: software for identifying and correcting genotyping errors in microsatellite data. Mol Ecol Notes. 2004; 4(3):535–38. https://doi.org/10.1111/j.1471-8286.2004.00684.x

Vasconcellos AV. Diferenciação molecular de estoques de corvinas (Micropogonias furnieri) na costa Atlântica da América do Sul. [PhD Thesis]. Rio de Janeiro: Universidade Federal do Rio de Janeiro; 2012.

Vasconcellos AV, Lima D, Bonhomme F, Vianna M, Solé-Cava AM. Genetic population structure of the commercially most important demersal fish in the Southwest Atlantic: the whitemouth croaker (Micropogonias furnieri). Fish Res. 2015; 167:333–37. https://doi.org/10.1016/j.fishres.2015.03.008

Waples RS. Testing for Hardy-Weinberg proportions: have we lost the plot? J Hered. 2015; 106(1):1–19. https://doi.org/10.1093/jhered/esu062

Weiss G. Ictioplancton del estuário de Lagoa dos Patos, Brasil. [PhD Thesis]. La Plata: Universidad Nacional de La Plata; 1981. Available from: https://doi.org/10.35537/10915/5041

Wright S. Evolution and genetics of population: the theory of gene frequencies. London: University of Chicago Press; 1978.

Yu Q, Liu S, Zhu Q, Chen R, Hu W, Xu D. Genetic variability of mass-selected and wild populations of yellow drum (Nibea albiflora) revealed using microsatellites. Fishes. 2024; 9(1):25. https://doi.org/10.3390/fishes9010025

Zaneti-Prado EM. Bionomia e ciclo de vida de Umbrina canosai Berg, 1895. Bol Inst Oceanogr. 1979; 28(1):119–65. https://doi.org/10.1590/S0373-55241979000100003

Zaneti-Prado EM, Vazzoler AEAM. Umbrina canosai: Alguns aspectos do ciclo de vida na região da plataforma continental brasileira entre Cabo Frio (23°S) e Torres (29º21’S). Brasília: resumos da 28ª Reunião Anual da Sociedade Brasileira para o Progresso da Ciência; 1976. p.224.

Authors

---

Davi M. Vasconcellos^1,2,3 , Ana Luzia Lacerda^2,4, Maíra Carneiro Proietti^1,2,5 and Manuel Haimovici^1,2

^[1]    Programa de Pós-Graduação em Oceanografia Biológica, Universidade Federal do Rio Grande (FURG), Av. Itália, km 8, 96203 900, Rio Grande, RS, Brazil. (DMV) davi.vasconcellos.m@gmail.com (corresponding author), (MCP) mairaproietti@gmail.com, (MH) manuelhaimovici@gmail.com.

^[2]    Instituto de Oceanografia, Universidade Federal do Rio Grande (FURG), Av. Itália, km 8, 96203-900, Rio Grande, RS, Brazil. (ALL) ana.de_figueiredo_lacerda@univ-lr.fr.

^[3]    Secretaria de Município do Meio Ambiente e Mudanças Climáticas (SMMA), Rua Capitão-Tenente Heitor Perdigão, 55, 96200 580, Rio Grande, RS, Brazil.

^[4]    Littoral, Environnement et Sociétés (LIENSs), UMR 7266, CNRS, La Rochelle Université, La Rochelle, France.

^[5]    The Ocean Cleanup, Coolsingel 6, 3011 AD, Rotterdam, The Netherlands.

Authors’ Contribution

---

Davi M. Vasconcellos: Data curation, Formal analysis, Investigation, Methodology, Software, Writing-original draft, Writing-review and editing.

Ana Luzia Lacerda: Formal analysis, Software, Validation, Writing-review and editing.

Maíra Carneiro Proietti: Conceptualization, Funding acquisition, Methodology, Resources, Supervision, Writing-review and editing.

Manuel Haimovici: Conceptualization, Funding acquisition, Project administration, Resources, Supervision, Writing-review and editing.

Ethical Statement​

---

All animals used in this study were obtained by commercial fishery; therefore, no ethics approval was needed.

Competing Interests

---

The author declares no competing interests.

Data availability statement

---

The microsatellite data are available in the Figshare repository at https://doi.org/10.6084/m9.figshare.30615713. The DMV’s M.Sc. dissertation (Portuguese and English) is available at https://argo.furg.br/?BDTD12916 and includes a guide in Portuguese to interpret genotype profiles.

AI statement

---

The authors did not use any AI-assisted technologies in the creation of this manuscript or its figures.

Funding

---

Conselho Nacional de Pesquisa e Desenvolvimento (CNPq) provided a Scholarship to DMV (GM 131640/2018–6), a Research Fellowship to MCP (PQ 312470/2018–5), and a Research Senior to MH (PQ-Sr 301785/2026-0). Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) provided financial support through the Programa de Excelência Acadêmica – PROEX.

Supplementary Material

---

Supplementary material SUP

Peer Review

---

Peer Review File

How to cite this article

---

Vasconcellos DM, Lacerda AL, Proietti MC, Haimovici M. Resilience despite overfishing? Microsatellites reveal high genetic diversity and connectivity for Umbrina canosai (Acanthuriformes: Sciaenidae) in the Southwestern Atlantic. Neotrop Ichthyol. 2026; 24(3):e250106. https://doi.org/10.1590/1982-0224-2025-0106

Copyright​

---

This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

Distributed under

Creative Commons CC-BY 4.0

© 2025 The Authors.

Diversity and Distributions Published by SBI

Accepted June 14, 2026

Submitted November 11, 2025

Epub October 9, 2026