Population genetics of the endangered catfish Pseudoplatystoma magdaleniatum (Siluriformes: Pimelodidae) based on species-specific microsatellite loci

Kevin León García-Castro^[1] , José David Rangel-Medrano^[1]  Ricardo Marcel Landínez-García^[1] and Edna Judith Márquez^[1]

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Abstract​

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Introduction​

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The family Pimelodidae (long-whiskered catfishes) comprises 114 species of freshwater fishes widely distributed throughout the Neotropics, including some of the largest and most commercially valuable migratory catfish species (Fricke et al., 2020; Malabarba, Malabarba, 2020). The genus Pseudoplatystoma Bleeker, 1862contains species that inhabits several of the main river basins of South America, can reach more than 1.3 m in length and have a strong commercial interest in fishery due to the high market demand of its meat (Buitrago-Suárez, 2006; Buitrago-Suárez, Burr, 2007; Reis et al., 2016). Pseudoplatystoma magdaleniatum Buitrago-Suárez & Burr, 2007(formerly Pseudoplatystoma fasciatum (Linnaeus, 1766)) is an endemic species, the largest migratory fish from Magdalena River basin (Zapata, Usma, 2013) and the most important fishery resource in Colombia behind Prochilodus magdalenae Steindachner, 1879 (bocachico) (Lasso et al., 2011; Zapata, Usma, 2013; Mojica et al., 2016).

According to the IUCN Red List (Mojica et al., 2016), P. magdaleniatum is currently categorized as endangered (EN) mainly due to heavy fishing pressure and other habitat threats related to livestocking, agriculture, mining and dam construction (Barletta et al., 2010; Angarita et al., 2018; Carranza-Lopez et al., 2019). Moreover, the current fishing regulations are solely based on information about the sexual maturity size and reproductive periods (Mojica et al., 2012; Álvarez-León, 2016) and little is known about the population genetics of this species, although this information is crucial for the development of effective management and preservation policies.

Microsatellites markers have been extensively used to investigate patterns of genetic structure and genetic variability at intra-specific level because of its high polymorphism and reproducibility (Guichoux et al., 2011; Fernandez-Silva et al., 2013). In Pseudoplatystoma, microsatellite locihave been developed for P. corruscans (Spix & Agassiz, 1829), P. punctifer (Castelnau, 1855)and P. reticulatum Eigenmann & Eigenmann, 1889 (Revaldaves et al., 2005; Pereira et al., 2009; Saulo-Machado et al., 2011; Prado et al., 2014), and crossed amplified in congeners to explore their structure and genetic diversity.

Previous population genetics studies in Pseudoplatystoma showed high levels of genetic diversity and significant genetic structure in P. corruscans from Paraguay and Paraná basins (Pereira et al., 2009; Vaini et al., 2016; Prado et al., 2018), and P. reticulatum from Paraguay basin (Abreu et al., 2009; Pereira et al., 2009). This genetic structure is explained by homing behavior (Pereira et al., 2009) or vicariant differentiation due to the presence of geographic accidents (Prado et al., 2018). Additionally, P. punctifer shows low levels of genetic differentiation in Brazilian Amazon (Telles et al., 2014), whereas P. reticulatum exhibits gene flow in Paraguay basin (Prado et al., 2018).

The genetic diversity and structure of P. magdaleniatum based on highly polymorphic lociremain unavailable so far. Thus, this study developed a set of species-specific microsatellite loci using next generation sequencing to evaluate the diversity and population structure of P. magdaleniatum in the Magdalena-Cauca basin. Considering the annual catch decrease, and anthropogenic habitat disturbances (Barletta et al., 2010; Mojica et al., 2012, 2016), the a priori expectation is to find populations with low genetic diversity and evidence of a recent reduction of population size. Additionally, evidence of gene flow is expected in this middle-distance migratory species given the absence of physical barriers throughout the sampling sites. The obtained information is crucial to support the development of proper management policies and provide a benchmark to monitor the genetic variability of this endangered species.

Material and methods

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Sampling. A total of 171 muscle tissues of P. magdaleniatum collected by Integral S.A. between 2011 and 2014 were submitted to genetic analysis (scientific cooperation agreement 19/09/2013, Grant CT–2013–002443, framed under an environmental license number 0155–30/01/2009 from Ministerio de Ambiente, Vivienda y Desarrollo Territorial de Colombia). Sampling corresponded to four sites (Fig. 1) previously described by Landínez-García, Márquez (2016) in the Cauca River: Margento (Section 5) and Punta Cartagena (Section 8), and Magdalena River (Puerto Berrío, Samaná Norte River).

Primers design, genotyping and polymorphism assessment of microsatellite loci. For primer design we used sequence reads previously obtained from a Paired-End genomic library of one specimen of P. magdaleniatum, using the MiSeq Illumina platform (Rangel-Medrano et al., 2016). Bioinformatic procedures including selection of microsatellite regions and primers design as well as assessment of polymorphism of selected loci were conducted following Landínez-García, Márquez (2016).

Loci with tetranucleotide motifs were amplified using fluorescently labeled primers as detailed by Landínez-García, Márquez (2016), with some modifications in the final concentrations of the PCR mix (0.5 pmol/µL of the forward primer and 3% of formamide) and the thermal conditions (31 cycles for cyclic denaturation at 94°C for 30 s and annealing at 57–59°C for 20 s). An additional set of pentanucleotide motifs loci were amplified, following the three primers method (Blacket et al., 2012) and using the amplification conditions reported by Landínez-García, Márquez (2018) with some modifications in the final concentrations: 0.5 pmol/µL of tagged forward primer, 1pmol/μL of reverse primer and, 0.5pmol/μL of fluorescently labelled adapter. Thermal profile consisted of 94°C for 3 min, followed by 45 cycles of 90°C for 20 s and 56°C for 21 s. Finally, a total of 25 microsatellite loci were genotyped as described in Landínez-García, Márquez (2016).

FIGURE 1 | Sampling sites of Pseudoplatystoma magdaleniatum in the Magdalena-Cauca basin.

Genetic diversity. The average number of alleles per locus (Na) and alleles size range were obtained using GenAlEx v6.51b2 (Peakall, Smouse, 2012, 2006). Detection of amplification errors such as null alleles, stutter bands or dropout effects were assessed using Micro-Checker v2.2.3 (Van Oosterhout et al., 2004). The polymorphic information content (PIC) was calculated using CERVUS v3.0.7 (Marshall et al., 1998). Expected (H_E) and observed (H_O) heterozygosity, inbreeding coefficients (F_IS) and departures from Hardy-Weinberg (HWE) and Linkage (LE) equilibria, were calculated using Arlequin v3.5.2.2 (Excoffier, Lischer, 2010). Then, departure from HWE across loci was assessed in the web version of GENEPOP v4.7.5 (Raymond, Rousset, 1995; Rousset, 2008). Additionally, the presence of candidate loci under natural selection was explored using the software BayeScan v2.1 (Foll, Gaggiotti, 2008), setting a prior odd ratio for neutral model of 10:1. Chain parameters included 40 pilot runs of 5,000 iterations each, a sample size of 50,000 and a thinning interval of 10.

Demographic events. Two tests were performed to evaluate recent and drastic changes in the population size, the modified Garza-Williamson index (M-ratio; Garza, Williamson, 2001), calculated in Arlequin v3.5.2.2 (Excoffier, Lischer, 2010); and the one-tailed Wilcoxon signed-rank test according to three mutational models (Cornuet, Luikart, 1996), calculated in BOTTLENECK v1.2.02 (Piry et al., 1999). Sequential Bonferroni correction was performed in multiple comparisons (Holm, 1979; Rice, 1989). Additionally, the effective population size (Ne) was estimated using the single-sample method based on linkage disequilibrium (LD) (Hills, 1981; Waples, 2006), implemented in the software NeEstimator v2.1 (Do et al., 2014) and considering the lowest allele frequency of 0.05. The three tests above were conducted in each deme and taking all sites as a single population. Furthermore, to explore migration events, the software GENECLASS2 (Piry et al., 2004) was used to assess probabilities that each individual is a resident in the sampling site or an immigrant from the other locations. The L_home/L_max ratio was used to detect the first-generation migrants, i.e., the ratio between the likelihood of the individual within its putative location and the highest likelihood among all considered locations. The analysis was based on a Bayesian statistical method (Baudouin, Lebrun, 2001), and simulations implemented the unbiased Monte-Carlo resampling method (Paetkau et al., 2004), using 15 loci, 10,000 individuals and 0.01 as type I error threshold.

Genetic structure analysis. GenAlEx v6.51b2 (Peakall, Smouse, 2006, 2012) was used to perform an analysis of molecular variance (AMOVA) and to calculate the standardized statistics F’_ST (Meirmans, 2006) and Jost´s D_EST (Jost, 2008).Additionally, a Bayesian clustering analysis was carried out on Structure v2.3.4 (Pritchard et al., 2000), using 500,000 Markov chain Monte Carlo, 50,000 iterations as burn-in and considering LOCPRIOR, admixture and correlated allele frequencies as a priori models. Analysis was conducted setting 20 runs using a range of K from one to seven. The web-based software StructureSelector (Li, Liu, 2018) was used to determine the most likely value of K based on Puechmaille estimators (2016), and the Ln Pr(X|K) (Pritchard et al., 2000) and ∆K (Evanno et al., 2005) statistics. Furthermore, the membership coefficient matrix (Q-matrix) was plotted using the program Clumpak (Kopelman et al., 2015), integrated in StructureSelector (Li, Liu, 2018). Finally, a discriminant analysis of principal components (DAPC) was obtained using the R package Adegenet (Jombart, 2008), with 30 principal components retained (67.7% of total variance).

Results​

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Twenty-five microsatellites loci with tetra- (21) and pentanucleotide (4) motifs were developed for P. magdaleniatum (Tab. 1) and no amplification errors were detected by Micro-Checker v2.2.3. Allelic size ranged from 97 to 318 bp and PIC values were above 0.500 (average: 0.756). A total of nine loci were excluded from further analysis because showed low levels of polymorphism (Psm29), inconsistent amplification (> 30% of missing data after genotyping; Psm20, Psm10, Psm17 and, Psm23) or remain to be evaluated in a greater sample due to limited amounts or degraded DNA (Psm42, Psm43, Psm45, Psm50, Psm09). Therefore, a total of 15 loci microsatellite were employed in the final analyses in 147 individuals of P. magdaleniatum from four sampling sites.

TABLE 1 | Characteristics of 25 microsatellite loci developed for Pseudoplatystoma magdaleniatum. F: forward; R: reverse; a–d: tails A–D (Blacket et al., 2012); Rm: repeat motif; Ra: Allelic size range (bp); Na: number of alleles; H_O: observed heterozygosity; H_E: expected heterozygosity; P: p-value for Hardy-Weinberg equilibrium test (values in bold denote statistical significance at P < 0.05); PIC: Polymorphic Information Content.

Locus	Primer sequence (5´–3´)	Rm	Ra	Na	HO	HE	P	PIC
Psm19	F:GCTGATTTCTTGGTTTGCTTGG	ATCT	181–217	10	0.782	0.826	0.072	0.863
	R:ATATGGGGCGAGAGGAAAGC
Psm06	F:AGCTTACTCATGTGCGTGGG	ATCT	207–259	14	0.827	0.881	0.118	0.855
	R:TCTAAATTTGGCCAGTAGGGG
Psm17	F:GAGGCTCTCGAACGAACTGC	AAAC	259–295	10	0.832	0.867	0.672	0.846
	R:TCGCGGGAACCAAAGACC
Psm11	F:TGATGTGGAAAACTGGAGAGC	ATCT	132–192	14	0.822	0.868	0.433	0.844
	R:TGTCGTGTGACTGCGATCC
Psm03	F:AAGTGCAAAGTTAAAGACACCACC	AAAG	144–196	12	0.777	0.854	0.052	0.843
	R:TCAATATTGTGTATCATTACACCGC
Psm09	F:ATGGTTTGAACCAAAAGCGG	TCTG	246–318	17	0.800	0.848	0.158	0.842
	R:TTTGTTTCGGCTTATTCGGG
Psm14	F:GACAAGCAGAGTGACACAGGC	ATCT	106–154	12	0.810	0.863	0.096	0.831
	R:AAATGGAGACAGGTGTACAGGG
Psm04	F:CGATGTACAGTCTTTTGTGGAGC	ATCT	111–191	20	0.847	0.872	0.055	0.828
	R:GCTGTAAGTAAACAATTTCCCTTGG
Psm24	F:TGATGTGGGAAGTTGAAGAAGC	ATCT	199–243	12	0.909	0.879	0.004	0.826
	R:GTGAATATGGTGAGTGCCGC
Psm02	F:CCATCTTGTGTTGCAATGGG	ATCT	156–212	15	0.860	0.827	0.392	0.825
	R:AGCTTGGTGAATGTTGTGGG
Psm10	F:AATTAGTGTGTTGCTTGCCAGG	TCTG	140–196	13	0.708	0.823	0.016	0.825
	R:GTGCCAAGGCTTTGTCTTCC
Psm20	F:TTCTCTGATGCCTCTGCTCG	AACT	236–276	11	0.846	0.870	0.566	0.823
	R:TTTTAAGCCTGCACACACTTTAGC
Psm01	F:CCCACTCACCTACCGAAACC	ATCT	97–145	12	0.901	0.875	0.251	0.890
	R:TAGATGGACAGGTGAACGGG
Psm18	F:TGGCTTAGCGAATCACATGC	AAAG	199–235	10	0.851	0.817	0.619	0.840
	R:GTGTTGTGTTTCATTGGGGC
Psm22	F:TGAAACTGATAGGGCAACAGC	ATGG	165–209	12	0.782	0.845	0.037	0.795
	R:TGTGTCTGGAATAGAATCTGGACC
Psm21	F:TCAGCTCCACTGATCTACTGACG	ATCT	217–269	12	0.729	0.675	0.972	0.769
	R:GGCAGGTGTGTTGATTGGG
Psm23	F:CTGGGGCTATATGGTCTGGC	AAAG	273–309	12	0.826	0.813	0.228	0.745
	R:TCTTTGCTGATTTACTTCTTTGGC
Psm25	F:TCTGGTCACGAACATCCTGC	ATGG	120–144	8	0.689	0.691	0.863	0.740
	R:TGCAGTGAGGAGAAGGATTGG
Psm42b	F:CAAAATACTGCAAACAAAACTGAGC	AAAAC	216–275	11	0.727	0.732	0.682	0.730
	R:CTTATATCGCACAGCGCAGG
Psm16	F:TGTAGCCTCACCTCTGCTGC	AAAG	166–190	7	0.563	0.586	0.127	0.652
	R:TGTCCCAGTTCCCTGTGAGG
Psm50a	F:GCGGTCTCTCTTCATAGGGG	AAGAG	272–307	9	0.664	0.678	0.108	0.638
	R:ACGGAGGCCATTATGAGTCG
Psm45c	F:GTGCTGTTCTGCATTTTCCG	AATAG	287–312	6	0.694	0.667	0.955	0.680
	R:CAGATGGTGACAGAGGCAGG
Psm43d	F:AACCTTTGGCAGGGTTTAAGG	AAAAG	128–158	7	0.281	0.608	0.000	0.563
	R:CACCCACATTGAGAACTGGC
Psm26	F:AGCTTGCTAGCAGTTGTGCC	ATCT	169–201	8	0.594	0.554	0.747	0.554
	R:TCACGTCTGTTGTTCATTCCC
Psm29	F:CCGATGACGTAACAATTTACCC	ATCT	190–194	2	0.551	0.493	0.150	0.473
	R:CACGTCTAGAGACACCAAATACTGG

 

Genetic diversity. The numbers of alleles per locus were similar in all sites (Na: 9–10) and values of H_O and H_E ranged from 0.762 to 0.798, and 0.770 to 0.791, respectively (Tab. 2). The inbreeding coefficient values (F_IS) were non-significant and no departure of HWE was detected in the samples studied (Tab. 2). Additionally, no departure of LE and no evidence of natural selection in any locus were found (posterior probability: 0.061–0.193, i.e., Bayes Factor favoring neutral model).

Demographic events. Both modified Garza-Williamson index (M-ratio: 0.20–0.22) and significant results of the Wilcoxon signed-rank test for IAM model in each location and pooled samples (Tab. 3) indicate a recent reduction in the population size, contrasting the results from SMM and TPM. Since few loci follow the strict SMM, IAM or TPM are considered the most likely mutational models in microsatellites (Piry et al., 1999). Additionally, the effective population size showed low values in Margento and Punta Cartagena contrasting to that observed in the Magdalena River and in the overall estimation (Tab. 3). On the other hand, four of 147 individuals were detected as potential immigrants in their sampling sites and were assigned to the most likely location of origin. Specifically, one individual from Margento (P = 0.005) and one from Punta Cartagena (P = 0.001) were assigned to Samaná Norte, whereas one individual from Puerto Berrío was assigned to Punta Cartagena (P = 0.004), and one from Samaná Norte was assigned to Margento (P = 0.008).

TABLE 2 | Genetic diversity of Pseudoplatystoma magdaleniatum from four sampling sites in the Magdalena-Cauca basin (Colombia) using 15 microsatellite loci. N: sample size; Na: average number of alleles per locus; H_O: observed heterozygosity; H_E: expected heterozygosity; F_IS: inbreeding coefficient; P: p-value for Hardy-Weinberg equilibrium test.

Site	N	Na	Ho	HE	FIS	P
Margento	30	8.933	0.798	0.777	-0.014	0.880
Punta Cartagena	32	9.133	0.762	0.770	0.011	0.262
Puerto Berrío	57	10.000	0.773	0.791	0.010	0.103
Samaná Norte	28	9.000	0.798	0.778	-0.048	0.080

 

TABLE 3 | Bottleneck test and effective population size (Ne) estimation in four sampling sites of the Magdalena-Cauca basin. P-values from one tailed Wilcoxon signed-rank test under infinite alleles model (IAM), stepwise mutation model (SMM) and two-phase mutation model (TPM). M-ratio: modified index of Garza-Williamson. Values in bold denote statistical significance after correction for multiple comparisons.

Site	IAM	SMM	TPM	M-ratio	Ne
Margento	0.008	0.489	0.524	0.20	171.4
Punta Cartagena	0.012	0.064	0.359	0.21	187.9
Puerto Berrío	0.000	0.252	0.083	0.22	Infinite
Samaná Norte	0.000	0.330	0.188	0.20	6317.9
Overall	0.000	0.995	0.104	0.21	1748.7

 

FIGURE 2 | Results of Structure (A, B) and Discriminant analysis of principal components (C) for Pseudoplatystoma magdaleniatum. A: K = 1; B: K = 2; M: Margento, PC: Punta Cartagena, PB: Puerto Berrío, SN: Samaná Norte.

TABLE 4 | Pairwise comparisons of genetic structure estimators. Non-significant values above and below diagonal show Jost´s D_EST and F´_ST statistics, respectively.

Site	Margento	Punta Cartagena	Puerto Berrío	Samaná Norte
Margento	–	0.005	0.005	-0.011
Punta Cartagena	0.022	–	0.006	-0.017
Puerto Berrío	0.016	0.003	–	-0.016
Samaná Norte	-0.002	-0.008	-0.016	–

 

Population structure. Although ∆K suggested that K = 2, every individual had similar membership probability to each cluster, which is concordant with both the maximum log likelihood and the MedMeaK and MedMedK statistics for K = 1 (Figs. 2A–B). Likewise, the results from DAPC (Fig. 2C), AMOVA (F´_{ST (3, 293)} = 0.003, P = 0.352), F’_ST and D_EST statistics showed genetic similarities among sites (Tab. 4). Additionally, AMOVA showed that 92% of genetic differences was explained by variance within individuals.

Discussion​

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This study tested three hypotheses related to the population genetics of P. magdaleniatum. The a priori expectations were that this species exhibits: (1) low levels of genetic diversity, (2) evidence of recent reductions of population size, likely resulting from overfishing and other anthropic threats, and (3) gene flow given its middle migratory range and the absence of physical barriers among sampling locations. To test these hypotheses, this study developed 25 microsatellite loci containing tetra- and pentanucleotide motifs to avoid genotyping errors that may rise during allelic assignment (Dewoody et al., 2006). Selected loci for population genetics analysis showed high polymorphism (mean total Na: 12) with PIC values ranging from 0.554–0.863, which indicates that these markers are informative (Botstein et al., 1980) and suitable for further population genetic studies in P. magdaleniatum.

Results obtained in this study did not support the hypothesis of low genetic diversity in samples of P. magdaleniatum as we found high number of alleles per locus, and high values of observed and expected heterozygosities. Given the difference in the motif sizes of the loci used, average values of alleles per locus may not be strictly comparable with those found in congeners. However, mean values of expected heterozygosity were similar to the highest value for P. corruscans (H_E: 0.3213–0.9424) (Revaldaves et al., 2005; Pereira et al., 2009; Prado et al., 2018) and P. reticulatum (H_E: 0.056–0.885) from Paraguay River and Upper Paraná basin (Abreu et al., 2009; Prado et al., 2014) and for P. punctifer (H_E: 0.553–0.839) from Madeira and Purús rivers (Telles et al., 2014). Finally, values of expected heterozygosity were higher than the average value reported for Neotropical Siluriformes (H_E: 0.609±0.210) using microsatellite loci (see Hilsdorf, Hallerman, 2017).

This work shows evidence of a recent genetic bottleneck in P. magdaleniatum, supporting the hypothesis of a reduced population size, consistent with reports of landings decline of this species in the Magdalena basin (Mojica et al., 2012). Despite the high genetic diversity found in P. magdaleniatum, the low values of M-ratio may indicate that number of alleles of this species were higher in the past (Garza, Williamson, 2001) and, therefore, suggest a recent loss of allelic diversity as consequence of a drastic reduction in the population size. Additionally, the expected heterosigosity (H_E) exceeded the simulated heterozygosity using the current number of alleles under mutation-drift equilibrium (H_eq), as was evidenced under the Infinite Allele Model (Cornuet, Luikart, 1996). Other studies in large Neotropical Siluriformes as P. corruscans, P. reticulatum and Conorhynchos conirostris (Valenciennes, 1840) also found evidence of bottleneck and high genetic diversity using microsatellites in populations that inhabit drastically affected areas by human activities as river damming and pollution (Carvalho, Beheregaray, 2018; Prado et al., 2018).

Following Frankham et al. (2014), the effective population sizes of P. magdaleniatum (< 500) in the Cauca river sites (Margento and Punta Cartagena) indicate that this species exhibits limitations in its long-term evolutionary potential. This outcome is in line with drastic reduction in the population size of P. magdaleniatum found in this study. Despite the bottlenecked populations, the estimated high effective population sizes (infinite, 6317.9) show that P. magdaleniatum does not exhibit limitations in its long-term evolutionary potential in the Magdalena River (Puerto Berrío and Samaná Norte). However, these discrepancies may be a consequence of the estimation method bias although the factors that influence it remain unclear (Waples, England, 2011; Waples et al., 2014; Wang et al., 2016). Thus, we suggest using these values exclusively as a reference for further studies using a greater number of samples and markers, which might improve the confidence of this method (Hill, 1981; Pudovkin et al., 1996; Waples, Do, 2010).

This study supports the hypothesis of gene flow in P. magdaleniatum in the Magdalena-Cauca basin, consistent with the migration range of this species and the absence of physical barriers among sampling sites. Although P. magdaleniatum is cataloged as a medium distance migratory species (100–500 km Zapata, Usma, 2013), the most distant sampling sites of this study are separated by at least 680 km, which might suggest that longitudinal migration of P. magdaleniatum could be longer than the currently reported for this species. This idea agrees with the analysis of detection of potential migrants between Margento and Samaná Norte, although this hypothesis must be further tested by mark-recapture studies (Lucas, Baras, 2000). In addition, the high fertility (Arce Hernández, 2008; Jiménez-Segura et al., 2009) coupled with dynamic hydrological interactions among rivers, tributaries and floodplains (Jiménez-Segura et al., 2010) may facilitate larval dispersal and eventually connect distant populations within the Magdalena-Cauca basin. Furthermore, as previously reported in Characiformes (Landínez-García, Márquez, 2018), the observed gene flow in P. magdaleniatum may also be a consequence of extreme floods resulting from La Niña event between 2010–2011 (IDEAM – Instituto de Hidrología Meteorología y Estudios Ambientales, 2014) in the Magdalena-Cauca basin. Further investigations in a broader geographic scale are required to have a wider perspective on the genetic structure of this emblematic species.

In conclusion, P. magdaleniatum behaves as a single panmictic population with a high genetic diversity that has experienced recent reduction in its population size. This provides a baseline for future studies aiming to monitor the genetic variability of this species throughout the Magdalena-Cauca basin. Additionally, in the face of the current anthropic threats, both demographic projection analyses and genetic information must be incorporated in conservation and management policies that ensure long-term preservation of this important fishery resource.

Acknowledgments​

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This work was funded by Universidad Nacional de Colombia Sede Medellín and Empresas Públicas de Medellín, Grant CT–2013–002443–R1 “Variación genotípica y fenotípica de poblaciones de especies reófilas presentes en el área de influencia del proyecto hidroeléctrico Ituango”; Grant CT–2019–000661 “Variabilidad genética de un banco de peces de los sectores medio y bajo del Río Cauca”. The authors thank the Centro Nacional de Secuenciación Genómica, Universidad de Antioquia (Medellín, Colombia) for assistance in bioinformatics analysis.

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Authors

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Kevin León García-Castro^[1] , José David Rangel-Medrano^[1]  Ricardo Marcel Landínez-García^[1] and Edna Judith Márquez^[1]

^[1]    Facultad de Ciencias, Escuela de Biociencias, Laboratorio de Biología Molecular y Celular, Universidad Nacional de Colombia Sede Medellín. Carrera 65 No 59A – 110 Bloque 19 A Laboratorio 310, 050034 Medellín, Colombia. (KLGC) klgarciac@unal.edu.co; (JDRM) jdrangelm@unal.edu.co; (RMLG) rmlandin@unal.edu.co; (EJM) ejmarque@unal.edu.co (corresponding author).

Authors Contribution

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Kevin León García-Castro: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Software, Validation, Visualization, Writing-original draft, Writing-review and editing.

José David Rangel-Medrano: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Validation, Visualization, Writing-original draft, Writing-review and editing.

Ricardo Marcel Landínez-García: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Validation, Visualization, Writing-original draft, Writing-review and editing.

Edna Judith Márquez: Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing-original draft, Writing-review and editing.

Ethical Statement​

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Environmental license number 0155–30/01/2009 from Ministerio de Ambiente, Vivienda y Desarrollo Territorial de Colombia.

Competing Interests

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The authors declare no competing interests.

How to cite this article

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García-Castro KL, Rangel-Medrano JD, Landínez-García RM, Márquez EJ. Population genetics of the endangered catfish Pseudoplatystoma magdaleniatum (Siluriformes: Pimelodidae) based on species-specific microsatellite loci. Neotrop Ichthyol. 2021; 19(1):e200120. https://doi.org/10.1590/1982-0224-2020-0120

Copyright​

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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

© 2021 The Authors.

Diversity and Distributions Published by SBI

Submitted October 26, 2020

Accepted February 17, 2021
by Claudio Oliveira

Epub March 31, 2021