Genetic diversity and aquaculture conservation for a threatened Neotropical catfish

Abstract

Due to the ecological importance of Lophiosilurus alexandri, the present work evaluated its genetic representativeness by comparing wild stocks to broodstocks that were kept at three restocking hatcheries along the São Francisco River. A total of 97 samples were genotyped for newly developed microsatellite markers. Low levels of genetic diversity (average alleles number of 4.2 alleles) were detected in all cases, being more severe in captive groups. Significant pairwise F_ST and D_EST values, Structure, and DAPC analyses showed that wild animals were structured in two groups, and a third group was formed by captive animals, evidencing the need to adopt genetic criteria to retain genetic diversity in the hatcheries. For this reason, three full-sib families were constructed to select the best relatedness estimator for L. alexandri and establish a cut-off value aimed to avoid full-sibling matings in the hatcheries. Two estimators, Wang (R_W) and Lynch & Li (R_LL), were accurate in reflecting the relatedness level for full-sibs in this species. According to them, less than 50% of the potential breeding matings in the three hatcheries are advisable. The innate low diversity of L. alexandri highlights the importance of minimizing inbreeding and retaining genetic diversity towards the species recovery.

Keywords:
Lophiosilurus alexandri; Low genetic diversity; Relatedness estimator; Restocking; Sedentary behavior

Resumo

Devido à importância ecológica de Lophiosilurus alexandri, o presente trabalho avaliou sua representatividade genética, comparando estoques selvagens com plantéis de reprodutores de três larviculturas ao longo do Rio São Francisco. Noventa e sete amostras foram genotipadas com marcadores microssatélites recém-desenvolvidos. Baixos níveis de diversidade genética (número médio de alelos de 4,2) foram detectados em todos os casos, sendo mais severo no cativeiro. Os valores de F_ST e D_EST par a par, as análises do Structure e DAPC mostraram a estruturação dos animais selvagens em dois grupos, e um terceiro formado pelas larviculturas, evidenciando a necessidade de adoção de critérios genéticos para retenção da diversidade genética no cativeiro. Por essa razão, três famílias de irmãos completos foram construídas para selecionar o melhor estimador de parentesco para a espécie e estabelecer os valores mínimos de corte para evitar cruzamentos indesejados. Dois estimadores, Wang (R_W) e Lynch & Li (R_LL), foram eficientes em refletir as relações de parentesco para irmãos completos nessa espécie. Segundo eles, menos de 50% dos potenciais cruzamentos são recomendáveis nas três larviculturas. A baixa diversidade genética inerente ao L. alexandri destaca a importância de minimizar a consanguinidade e evitar perda de diversidade genética, visando a recuperação da espécie.

Palabras-chave:
Baixa diversidade genética; Comportamento sedentário; Estimador de parentesco; Lophiosilurus alexandri; Repovoamento

INTRODUCTION

Brazil is one of most biodiverse countries for freshwater fish species (Collen et al., 2014), but its ichthyofauna has been impacted due to overfishing, riparian forest degradation, siltation, water transposition, and especially, hydroelectric plants. The São Francisco River (SF) is one of the most impacted basins with declines in fish populations, and at least 10% of the river’s more than 200 species are considered threatened (Maneta et al., 2009; Reis et al., 2016; ICMBio, 2018), which has resulted in implementing restocking actions for these species.

One of this species is theLophiosilurus alexandriSteindachner, 1876, an endemic catfish to the SF, which belongs to the order Siluriformes and family Pseudopimelodidae (Carvalho et al., 2016). It is a sedentary, carnivorous, active nocturnally, and exhibits male parental care for the progenies (Sato et al., 2003; Costa et al., 2015).

This species has been subjected to a drastic population reduction in the submiddle and lower stretches of São Francisco River and it has been included into restocking actions for more than two decades (Lopes et al., 2013). Despite the efforts, L. alexandri was included in the Brazilian Red List as Vulnerable, designation for species at high risk of extinction (ICMBio, 2014). Recently, L. alexandriwas also included in the Ministry of Environment’s National Action Plan (PAN São Francisco — Ordinance n.34 of May 27^th, 2015), which aims to conserve threatened species of this river basin.

One neglected aspect by these restocking actions is the genetic representativeness of the broodstocks compared to the wild conspecifics. Keeping sufficient breeders and incubators are usually prohibitive and most of the hatcheries use a limited number of them. Breeders are mostly not renewed and are replaced by their consecutive progeny generations (F₁, F₂, F₃ etc.), therefore using a culture-based restocking model. This kind of strategy favors matings between related individuals and can lead to inbreeding depression, which affects reproductive success, survival, growth, disease resistance, etc and can reduce genetic diversity, which limits the evolutionary potential of the population (Frankham et al., 2010; Keller et al., 2012).

For effective conservation, genetic monitoring of wild populations and captive broodstocks is important to prevent adverse effects on the ichthyofauna (Povh et al., 2008; Schreier et al., 2015). This is particularly important when dealing with species with low genetic diversity, which are more prone to inbreeding and genetic drift (Coleman et al., 2018). Breeding strategies must be adopted to avoid loss of genetic diversity in captive populations of threatened species, such as genetically characterizing the broodstocks (Carvalho et al., 2012), introducing new wild breeders (Whiteley et al., 2015), and slowing down the process of inbreeding by selecting matings based on the degree of relatedness (Wang, 2014).

Microsatellite markers are widely used in population genetic monitoring of species subjected to restocking actions in Brazil, such as for the Pseudoplatystoma corruscans (Spix & Agassiz, 1829) (Dantas et al., 2013), Prochilodus argenteusSpix & Agassiz, 1829 (Coimbra et al., 2017), Steindachneridion parahybae (Steindachner, 1877) (Fonseca et al., 2017), Conorhynchus conirostris (Cuvier, 1829) (Carvalho, Beheregaray, 2018), Salminus franciscanus Lima & Britski, 2007 and Brycon orthotaenia Günther, 1864 (Coimbra et al., 2020). The markers are also used to infer the genetic relatedness owed to recent common ancestry (Wang, 2017). Selection of mating pairs based on molecular relatedness estimates have been used worldwide by hatcheries running conservation programs, such as for the Paralichthys olivaceus(Temminck & Schlegel, 1846), Pagrus major (Temminck & Schlegel, 1843), Verasper moseriJordan & Gilbert, 1898, Onchorhynchus keta (Walbaum, 1792), among others (Fisch et al., 2015).

The use of estimators to determine genetic relatedness between captive-grown breeders could allow selection of those with the lowest degree of genetic similarity, resulting in progenies with greater genetic variability. Genetic relatedness estimators, such as Queller, Goodnight (1989), Ritland (1996), and Lynch, Ritland (1999), based on co-dominant markers, can be used to identify animal relationships. Despite the variety of existing estimators, the best estimator for a certain species can only be found after tests are conducted with the different estimators in groups whose relationships, full-sib or half-sib, are known (Ortega-Villaizan et al., 2011).

For the conservation of L. alexandri, analyses of the genetic diversity remains to be investigated and three hypotheses must be tested: (1) a panmictic population exists along the São Francisco River basin, (2) captive broodstocks of L. alexandri have similar genetic diversity to the wild conspecifics; (3) a relatedness estimator can be used to select the best matings.

In this study, we developed and validated a set of microsatellite markers to evaluate the genetic variability and structure of wild animals and captive broodstocks of L. alexandri. In addition, these markers were also used to identify the best genetic relatedness estimator among L. alexandri full-sib families, towards a conservation aquaculture program.

MATERIAL AND METHODS

Sampling. For the population genetics approach, fin clips from 55 individuals of L. alexandri from three hatcheries along the São Francisco River were collected. Twenty of these were from Paulo Afonso Hatchery of Chesf (São Francisco Hydroelectric Company) in the submiddle stretch of the river, 23 were from the Bebedouro Hatchery of Codevasf (Development Company of São Francisco and Parnaíba Valleys) also in the submiddle stretch of the São Francisco River, and the remaining 12 were from the Itiúba Hatchery of Codevasf in the lower stretch of the river. Forty-two wild animals were sampled, 21 from the submiddle stretch, in the vicinity of Santa Maria da Boa Vista-PE, and 21 from the upper São Francisco River, in the reservoir of Três Marias and downward the reservoir (Fig. 1).

FIGURE 1 |
Map showing the São Francisco River Basin with locations of the three restocking hatcheries (★) of Lophiosilurus alexandri and an experimental laboratory (LAQUA). The wild samples taken in the upper and submiddle stretches are denoted by ⦿.

In order to select the best genetic relatedness estimator, three full-sibling families were constructed: FS1 from Bebedouro hatchery, FS2 from Itiúba hatchery, and FS3 from LAQUA (Laboratório de Aquacultura at the Universidade Federal de Minas Gerais). Each mating pair was tagged with PIT tags and was kept in an individual tank with sand bottom and flowing water. The eggs were collected and transferred to a trough incubator with flowing water, where the fingerlings were maintained until reaching a size of 1 cm. Fifty-three fin clips from the three progenies were collected: 20 samples from FS1, 20 from FS2, and 13 from FS3.

In the genetic diversity analysis, none of the full-sibs was included, only the breeders were used.

One voucher specimen used in this study was deposited in the collection of the Coleção Ictiológica at the Museu de Ciências Naturais da PUC Minas (Voucher number: MCNIP 3217).

DNA extraction. DNA extraction was conducted based on the phenol, chloroform, isoamyl alcohol protocol described by Sambrook et al. (1989). The DNA samples were quantified by fluorimeter (Quantus - Promega), and their integrity and quality were evaluated using 0.8% agarose gel electrophoresis and a NanoVue™ Plus Spectrophotometer (GE Healthcare), respectively.

Microsatellites. Muscle fragments were obtained from a freshly captured L. alexandri. The voucher specimen was fixed in 10 % formalin and later preserved in 70 % ethanol (voucher: LGC6088 at Museu de Ciências Naturais da PUC Minas). Genomic DNA was extracted using a modified salting-out method (Sunnucks, Hales, 1996) and nebulized for 6 min to obtain 200-600bp fragments. Fragments were sequenced using a Nextera kit in a sixth of an Illumina HiSeq 2000 lane.

A set of 178 potentially amplifiable microsatellite loci (PAL) were developed for L. alexandri using the Misa algorithm (Beier et al., 2017) from the scaffold data assembled by SOAP de novo (Li et al., 2008). Primer pairs were designed for each locus using Primer3 (Rozen, Skaletsky, 2000) and tested against the scaffolds using in silico PCR software to verify whether a single product was found. Selected PALs consisted of primers, which produced in silico amplicons with no more than 10 non-ACTG bases. The markers were named with the abbreviation of the species name “Lalex” followed by a number.

From these PALs, 55 loci were initially tested with samples from wild L. alexandri and only those loci that showed polymorphism were subsequently analyzed in captive animals. Four different repeat motifs were tested: 42 dinucleotide motif with eight repeats, one dinucleotide motif locus with nine repeats, ten loci with trinucleotide motifs and six repeats, and two tetranucleotide motif loci with five repeats.

Forward primers received the M13 tail (5’-TGTAAAACGACGGCCAGT-3’) at the 5’ end. PCR amplification followed a modified Schuelke (2000) protocol, with a final volume of 10µl containing 20ng DNA, 10mM Tris-HCl pH 8.4, 50mM KCl, 2.5mM MgCl₂, 2µM forward primer, 8µM reverse primer, 8µM fluorochrome (FAM, NED, PET or VIC), 200µM of each dNTP and 1U Taq DNA polymerase. The PCR amplification conditions were: 94°C for 5 minutes, followed by 30 cycles of 94°C for 30 seconds, 56°C for 45 seconds and 72°C for 45 seconds, followed by eight cycles of 94°C for 30 seconds, 53°C for 45 seconds and 72°C for 45 seconds, plus final extension at 72°C for 10 minutes. Capillary electrophoresis separated PCR products on the ABI 3500 Genetic Analyzer (Applied Biosystems) using GeneScan 600 LIZ v2.0 (Applied Biosystems) as a molecular weight marker.

Genetic data analysis. Polymorphism information content (PIC) was calculated, only for the 42 wild samples, using CERVUS v 3.0.7 software (Kalinowski et al., 2007), and the markers were classified as highly informative for PIC values greater than 0.5, as reasonably informative for values between 0.25 and 0.5, and as slightly informative markers for values less than 0.25 (Botstein et al., 1980). In order to predict the probability of two randomly selected individuals from a population holding the same genotype (Waits et al., 2001), the multi-loci probability of identity (PID) was calculated for all 10 loci and the 42 wild samples, using the GIMLET software (Valière, 2002) and under two relatedness scenarios: ‘‘unrelated’’, which corrects for sampling a small number of individuals, and ‘‘sibs’’, which assumes that the population is composed of siblings.

GENEPOP v 4.0 software (Raymond, Rousset, 1995) was used to obtain the number of alleles (N_a), observed and expected heterozygosity (H_o and H_e, respectively), linkage disequilibrium, allele frequency, and inbreeding coefficient (F_IS). Potential genotyping errors caused by stuttering, alleles dropouts, and null alleles (A_n) occurrence were evaluated by Micro-Checker v. 2.2.3 (Oosterhout et al., 2006). The genetic differentiation was pairwise evaluated between populations by F_ST and the Jost’s estimator (D _EST) (Jost, 2008), implemented in Arlequin v. 3:01 (Excoffier et al., 2005) and GenAlEx 6.51 (Peakall, Smouse, 2012), respectively. The analysis of molecular variance (AMOVA) was calculated in two scenarios: (1) only for the captive groups, and (2) for the wild groups, using Arlequin v. 3:01 (Excoffier et al., 2005). The deviations of the Hardy-Weinberg Equilibrium (HWE) were estimated for all loci and groups using Arlequin v. 3:01 (Excoffier et al., 2005). P-values of multiple tests were corrected by Bonferroni (Rice, 1989).

A Bayesian approach with STRUCTURE v. 2.3.4 was used to investigate the genetic structure of the population using its under admixture model, which assumes that individuals may have different ancestor genotypes with contributions from different populations (Pritchard et al., 2000). The analysis in STRUCTURE v. 2.3.4 was set for a burn-in period of 10,000 interactions, followed by Markov Chain Monte Carlo (MCMC) with 100,000 replicates and four replicates for each hypothetical value of k (k = 1-10).

Structure Harvester v. 0.6.7 software (Earl, vonHoldt, 2012) was used to coordinate the results and assume the most likely number of K (number of clusters - genetic groups), which was estimated through the log-likelihood data (ln (P (X /K )) (Falush et al., 2003) and the best ∆K was based on the rate of change in the log probability of the data (Evanno et al., 2005).

Discriminant Analysis of Principal Components (DAPC) (Jombart et al., 2010) was also used to investigate the population structure and was run in the Adegenet package (Jombart, 2008) in R platform, v. 4.0.2 (R Core Team, 2020). This multivariate analysis is used to identify and describe genetic clusters, while optimizing the variation among them. DAPC does not require populations to be in Hardy-Weinberg Equilibrium or linkage disequilibrium loci (Jombart et al., 2010).

For genetic relatedness analysis, seven microsatellite markers, those that were polymorphic among parental breeders, were used to analyze the parents and progenies from three full-sib families (FS1, FS2 and FS3). The COANCESTRY software (Wang, 2011) was used to generate pairwise relatedness values (dyads) in each full-sib family, according to the five following relatedness estimators: Wang R_W (Wang, 2002), Lynch & Li R_LL (Lynch, 1988; Li et al., 1993), Lynch, Ritland R_LR (1999), Ritland R_xy (1996), and Queller, Goodnight R_QG (1989). In order to choose the best estimator, a mean pairwise relatedness value was calculated for the three full-sib families and compared to the expected reference value for full-siblings (0.5).

The same estimators were applied to the wild samples from the submiddle and upper São Francisco stretches to ensure that estimations were accurate, since an absence of relatedness is expected among wild animals. Further, a minimum value of genetic relatedness of full-sib will be taken as a cut-off value for parentage.

In addition, a simulation of potential breeding pairs within the three hatcheries (Bebedouro, Paulo Afonso and Itiúba) was conducted using the best estimators and their respective cut-off values.

RESULTS

A total of 16,819 tandem repetitive sequences were recovered using the Misa software into contigs assembled by SOAP de novo. After filtering potential amplifiable microsatellite loci, we produced 178 primer pairs for these loci for this catfish (shown in Tab. S1).

Of the 55 microsatellite loci analyzed, 10 showed polymorphisms, 41 were monomorphic, and four did not amplify. The ten polymorphic loci were in HWE, showed PCR amplification consistency, and there was an absence of linkage disequilibrium when genotyped for the wild samples. Polymorphism information content (PIC) values ranged from 0.202 to 0.821 and the number of alleles ranged from two (Lalex5 and Lalex19) to nine (Lalex38) (Tab. 1). The resulting values of multi-loci probability of identity (PID) were 3.4 x 10^-7 and 2.4 x 10^-3 for unrelated and sibs scenarios, respectively.

TABLE 1 |
Characteristics of the ten polymorphic microsatellite markers of Lophiosilurus alexandri. N_a number of alleles; PIC - polymorphism information content; Tm °C melting temperature.

Locus	Primer sequences (5' - 3')	Motif	Allele size (bp)	Na	PIC	Tm °C
Lalex1	F: GGACGTCATACCTTCTCTGTCC	(AT)8	119 - 125	3	0.469	60
	R: CTGAACACACTCAGAGGAAAGC
Lalex5	F: CCGTAACAGGCTCGGTTTT	(CT)8	135 - 139	2	0.202	60
	R: AGAGACAGGTCTGGGGAACA
Lalex9	F: CTATTACGGCTGTTTCCTTGG	(CT)8	132 - 146	5	0.675	60
	R: CTCATCTTGAGGAAACGTTGG
Lalex15	F: CACTTCATGGTCTGGCTTTG	(AC)8	146 - 164	5	0.604	60
	R: CACTGAAAGCACTATTGACAGCA
Lalex19	F: GAAATGAAACAGGCCAGGAA	(TC)8	194 - 200	2	0.293	60
	R: TGTAGAAGAGTAGACCACGAACTGA
Lalex25	F: AGACATGTTGCTGCCCTCCA	(TG)8	184 - 286	4	0.279	60
	R: CTATAGAAACAGAAGCGCTAA
Lalex30	F: CACCGATTCAGAAACTGAAGG	(CG)8	177 - 203	6	0.668	60
	R: ATGCAGAAAAATCGCAAACA
Lalex37	F: TGGGTTAAACGCAAAACAATC	(CAG)6	259 - 265	3	0.368	62
	R: GAGACAGACACACTGGACTTTGA
Lalex38	F: GCGAAAATCGCAGGTTTCT	(AT)9	277 - 295	9	0.821	62
	R: ACGCATATTCACGCACAAGA
Lalex58	F: CAGAACCGTCCTGTTCAACC	(TTC)6	282 - 288	3	0.444	60
	R: CCAACACCGTCACTCCTAACT

Genetic variability of the 10 microsatellite loci for each of the five sampling sites is described in Tab. 2. A total of 49 alleles were found, 37 of which were present in the upper SF samples, 32 in the submiddle SF, 24 in Bebedouro, 27 in Paulo Afonso, and 23 in the Itiúba samples. The average number of alleles varied between 2.30 and 3.70 per sampling group. Thirteen alleles were private, seven of which belonged to the wild samples from the upper SF, two from wild samples from the submiddle SF, two from Bebedouro, and one each from Paulo Afonso and Itiúba hatcheries. Null alleles were found in Lalex25, Lalex30, and Lalex38 but not systematically in all samples. No evidence was found of either the misidentification of stutters as alleles or dropouts.

The average observed and expected heterozygosities (H_o and H_e), respectively ranged from 0.350 to 0.525 and from 0.393 to 0.481, respectively. Hardy-Weinberg (HWE) equilibrium deviations were significant after Bonferroni correction for Lalex1 (Bebedouro and upper SF), Lalex9 (upper SF), Lalex25 (upper SF), Lalex30 (Bebedouro), Lalex38 (submiddle SF), but none of the loci deviated from HWE in the wild samples. Among the 50 estimates (10 loci x 5 sampling sites) of HWE deviation, only six were significant.

TABLE 2 |
Genetic diversity for wild (upper and submiddle stretches of the São Francisco River) and captive (Bebedouro, Paulo Afonso, and Itiúba) Lophiosilurus alexandri samples. N = number of individuals, N_a = alleles number, A_n = null alleles, H_e = expected heterozygosity, H_o = observed heterozygosity, F_IS = inbreeding coefficient, HWE = Hardy-Weinberg equilibrium deviation, ns = not significant, * p <0.05, adjusted by Bonferroni correction, K = 50.

Samples	Lalex1	Lalex5	Lalex9	Lalex15	Lalex19	Lalex25	Lalex30	Lalex37	Lalex38	Lalex58	Average
Bebedouro
N	23	23	23	23	23	23	23	23	23	23	23
Na	3	1	2	1	2	3	5	2	3	2	2.40
An	No	No	No	No	No	No	No	No	No	No	-
He	0.598	0	0.322	0	0.476	0.544	0.653	0.414	0.603	0.322	0.393
Ho	0.913	0	0.391	0	0.391	0.565	0.609	0.565	0.522	0.391	0.435
FIS	-0.545	1	-0.222	1	0.182	-0.040	0.070	-0.375	0.137	-0.222	0.098
HWE	*	ns	ns	ns	ns	ns	*	ns	ns	ns	-
Paulo Afonso
N	19	20	20	20	20	20	20	20	18	18	19.5
Na	3	2	5	1	2	4	3	2	3	2	2.70
An	No	No	No	No	No	No	Yes	No	Yes	No	-
He	0.679	0.050	0.660	0	0.262	0.664	0.549	0.512	0.541	0.413	0.433
Ho	0.684	0.050	0.550	0	0.300	0.550	0.300	0.550	0.278	0.444	0.371
FIS	-0.009	0	0.171	1	-0.152	0.176	0.460	-0.077	0.494	-0.079	0.198
HWE	ns	ns	ns	ns	ns	ns	ns	ns	ns	ns	-
Itiúba
N	12	12	12	12	12	12	12	12	9	11	12
Na	3	2	2	2	2	3	2	2	3	2	2.30
An	No	No	No	No	No	No	No	No	No	No	-
He	0.627	0.083	0.344	0.228	0.228	0.685	0.228	0.522	0.569	0.416	0.393
Ho	0.917	0.083	0.250	0.250	0.250	0.417	0.083	0.667	0.222	0.364	0.350
FIS	-0.494	0	0.283	-0.100	-0.100	0.402	0.645	-0.294	0.624	0.130	0.110
HWE	ns	ns	ns	ns	ns	ns	ns	ns	ns	ns
Wild - submiddle SF
N	19	21	21	21	21	21	21	21	19	21	20.6
Na	3	2	3	2	2	2	4	2	9	3	3.20
An	No	No	No	No	No	No	No	No	Yes	No	-
He	0.605	0.251	0.361	0.136	0.438	0.251	0.696	0.438	0.886	0.473	0.453
Ho	0.947	0.286	0.286	0.143	0.238	0.286	0.619	0.524	0.368	0.381	0.408
FIS	-0.592	-0.143	0.213	-0.053	0.462	-0.143	0.113	-0.202	0.591	0.198	0.044
HWE	ns	ns	ns	ns	ns	ns	ns	ns	*	ns	-
Wild - upper SF
N	20	21	21	21	20	21	20	20	21	21	20.6
Na	3	2	3	3	2	4	5	3	9	3	3.70
An	No	No	No	No	No	Yes	Yes	No	No	No	-
He	0.537	0.215	0.553	0.535	0.262	0.336	0.747	0.415	0.784	0.424	0.481
Ho	1.000	0.238	0.952	0.810	0.300	0.095	0.500	0.400	0.619	0.333	0.525
FIS	-0.905	-0.111	-0.754	-0.532	-0.152	0.721	0.337	0.038	0.215	0.218	-0.092
HWE	*	ns	*	ns	ns	*	ns	ns	ns	ns	-

Regarding the inbreeding coefficient (F_IS), mean values were all positive, ranging between 0.044 and 0.198, except for the upper SF that was -0.092. Overall F_ST among the hatcheries was around 0.10 and the pairwise estimates between them ranged from 0.03 to 0.16 (p < 0.05). The F_ST between wild groups (submiddle and upper SF) was 0.25. The pairwise F_ST and D _EST values between the two hatcheries that restock the submiddle (Paulo Afonso and Bebedouro) and their wild conspecifics (submiddle SF) varied between 0.30 and 0.34 and 0.32 and 0.33, respectively. Overall, D _EST among the hatcheries was around 0.07, and the pairwise between them ranged from 0.016 to 0.10 (p < 0.05) (Tab. 3). The AMOVA results for captive samples showed that most of the variance (86.6%) found could be explained by differences within the hatcheries and not among them (10.5%) (p < 0.05). For the wild samples, 78.7% of the variance could be explained by differences within groups and 27.4% for differences between them.

TABLE 3 |
Estimated pairwise F_ST (below diagonal) and pairwise D _EST (above diagonal) values for captive and wild samples of Lophiosilurus alexandri.

	Bebedouro	Paulo Afonso	Itiúba	Wild – submiddle SF	Wild – upper SF
Bebedouro	-	0.102	0.100	0.335	0.352
Paulo Afonso	0.150*	-	0.016	0.321	0.263
Itiúba	0.159*	0.036	-	0.313	0.302
Wild - submiddle SF	0.343*	0.305*	0.319*	-	0.238
Wild - upper SF	0.340*	0.252*	0.297*	0.250*	-

• F_ST: * Significance level p < 0.05, adjusted by Bonferroni correction.
  D_EST: Significant values at p < 0.05, based on 999 permutations using GenAlEx version 6.501 software, are highlighted in bold.

The structure analysis was done in two scenarios, firstly for all groups (captive and wild) and secondly only for the wild samples, in both cases the highest likelihood of K values suggested two genetic clusters (K = 2). All suggested clusters were present in the five sampling sites, however, at different proportions. For the first scenario, cluster 1 (red) was predominant among wild animals and was present in more than 96% of the individuals from the submiddle and upper SF. For captive samples, cluster 2 (green) was more abundant and was present in more than 98% of the samples (Fig. 2A). For the second scenario, there was a distinction between samples from the submiddle and upper SF. Cluster 2 (green) was predominant in the submiddle samples (98.6%) and cluster 1 (red) was primary presented in the upper SF samples (Fig. 2B).

FIGURE 2 |
Attributions of Lophiosilurus alexandri genotypes from A. captive broodstocks (Bebedouro, Paulo Afonso, and Itiúba) and wild samples taken from two stretches of the São Francisco River (upper and submiddle) and B. only for the wild samples. Each vertical bar represents a different individual and the length is proportional to the inferred group, cluster 1 (red) and cluster 2 (green). Respective estimated K value through log-likelihood. Y axis for delta (K) values and X axis for different K values tested.

Results of DAPC identified the presence of five clusters (k = 5), with an overlap of cluster clusters 1, 3, and 5 by captive animals. DAPC showed a clear separation of the three groups: upper SF, submiddle SF, and captive samples (Fig. 3).

FIGURE 3 |
Scatterplot of the Discriminant Analysis of Principal Components (DAPC) of Lophiosilurus alexandri genotypes from captive broodstocks (Bebedouro, Paulo Afonso, and Itiúba (clusters 1, 3, and 5) and wild samples taken from two stretches of the São Francisco River (upper (cluster 2) and submiddle (cluster 4). Clusters are shown by different colors and inertia ellipses, while dots represent individuals. Eigenvalues of the analysis are displayed in inset.

Regarding the genetic relatedness, the R_LR, R_xy and R_QG estimators showed negative mean pairwise relatedness values for all three full-sib families and were discarded. However, R_wand R_LL gave positive average values between 0.51 and 0.64 for the three full-sib families and negative average values for the wild samples (Tab. 4).

TABLE 4 |
Genetic relatedness estimators for three full-sibling families (Bebedouro (FS1), Itiúba (FS2), and LAQUA (FS3) and for wild animals of Lophiosilurus alexandri from the upper and submiddle São Francisco River.

Sampling sites	Indexes (mean values ± standard deviation)
	Wang	Lynch & Li	Lync & Rd	Ritland	Queller & Gt
FS1	0.58 ± 0.03	0.54 ± 0.04	-0.05 ± 0.17	-0.02 ± 0.04	-0.15 ± 0.34
FS2	0.64 ± 0.03	0.60 ± 0.04	-0.05 ± 0.26	-0.02 ± 0.03	-0.09 ± 0.29
FS3	0.54 ± 0.07	0.51 ± 0.09	-0.08 ± 0.35	-0.05 ± 0.07	-0.07 ± 0.41
Wild – submiddle SF	-0.001 ± 0.10	-0.04 ± 0.11	-0.05 ± 0.08	-0.04 ± 0.08	-0.04 ± 0.10
Wild – upper SF	-0.06 ± 0.14	-0.03 ± 0.16	-0.05 ± 0.07	-0.05 ± 0.06	-0.05 ± 0.13

The distribution of pairwise relatedness in intervals for the three full-sib families and wild samples under the R_wand R_LL estimators is represented in Fig. 4. The extremes of distribution pairwise values into full-sibs were 0.12 and 1 for the R_W estimator and 0.03 and 1 for R_LL, which the minimum extremes were used as cut-off values for full-sib relatedness. The distribution of pairwise relatedness values for the wild samples showed that there were an average of 46.4% positive pairwise values for the upper SF, while for the submiddle SF there were 45.2%.

FIGURE 4 |
Relatedness estimators Wang (R_W) and Lynch & Li (R_LL) calculated for A. the three full-sib families (■ FS1, ■ FS2 and ■ FS3) and B. for the wild samples ( ■ upper and ■ submiddle São Francisco River stretches) ofLophiosilurus alexandri.

R_W and R_LL were chosen to conduct a simulation of breeding pairs in each hatchery (Bebedouro, Paulo Afonso and Itiúba), using 0.12 and 0.03 as cut-off values, respectively. For R_W, the Bebedouro hatchery (BEB) indicated 58.46% (76/130) advisable matings, while in the Paulo Afonso (PA) hatchery there were 64% (64/100) advisable matings, and in Itiúba (IT), there were 69.45% (25/36) advisable ones. Yet for R_LL, Bebedouro showed 37.7% (49/130), Paulo Afonso 53% (53/100) and Itiúba 58.34% (21/36) advisable matings (Fig. 5).

FIGURE 5 |
Candidates breeding pairs in the three hatcheries, Bebedouro (BEB), Paulo Afonso (PA) and Itiúba (IT), assessed by Coancestry (Lynch & Li estimator).

DISCUSSION

Microsatellites. This is the first study that developed and used microsatellite markers to examine the genetic diversity of the threatened Neotropical catfish L. alexandri. According to Botstein et al. (1980) classification of PIC values, of the ten loci genotyped of the wild samples from the submiddle and upper São Francisco (mean PIC: 0.482), four were highly informative (Lalex1, Lalex9, Lalex15, and Lalex30), five were reasonably informative (Lalex1, Lalex19, Lalex25, Lalex37, and Lalex58), and one was slightly informative (Lalex5). Similarly, Hughes et al. (2015) were able to determine the structuring and a low genetic diversity in the Australian lungfish Neoceratodus forsteri (Krefft, 1870) after the authors used a set of 11 microsatellite markers in which two were highly informative, six were reasonably informative, and three were slightly informative (mean PIC: 0.345). In the present study, although not all loci were highly informative under PIC classification, the multi-loci probability of identity (PID) values were below the threshold value for codominant markers 0.01 (Waits et al., 2001), which is necessary to accurately distinguish individual genotypes, even in the sibs scenario. This indicates that the novel microsatellite set described herein is effective in assessing genetic diversity, population structure, and relatedness in the L. alexandri.

Genetic diversity. A low number of alleles (two to nine), with an average of 4.2 alleles, and expected heterozygosity (H_e) (0.393 to 0.481) were found. These values were well below those reported by Martinez et al. (2018), which found an average number of alleles per locus of 14.81 for freshwater fish species.

The low diversity found in L. alexandri is not common in other Siluriformes. Shibatta et al. (2013) described 11 microsatellite loci for Microglanis cottoides(Boulenger, 1891) (Siluriformes, Pseudopimelodidae) with high levels of variability (number of alleles ranging from 2 to 20, mean of 8.3 alleles per locus, and H_e from 0.119 to 0.931). For Zungaro jahu(Ihering, 1898) (Siluriformes, Pimelodidae), another migratory catfish, the number of alleles ranged from three to 10, with an average number of 6.7 alleles per locus, and H_e between 0.3298 and 0.8670 (Carrillo-Avila et al., 2009). Similarly, in a study with Pseudoplatystoma corruscans(Siluriformes, Pimelodidae) six microsatellites showed an average of 7.8 alleles, H_o between 0.6082 and 0.7171 and a H_e between 0.7143 and 0.7810 (Dantas et al., 2013). Saulo-Machado et al. (2011) found polymorphism in all 15 analyzed loci for Pseudoplatystoma punctifer(Castelnau, 1855) (Siluriformes, Pimelodidae), a migratory Amazon catfish (Buitrago-Suárez, Burr, 2007), with the number of alleles ranging from 2 to 18 and H_e from 0.025 to 0.931.

It is possible that the low number of polymorphic loci found in L. alexandri might be related to its sedentary behavior. The Australian lungfish Neoceratodus forsteri(Sarcopterygii, Neoceratodontidae), which also has sedentary habits and parental care (Pusey et al., 2004; James et al., 2010), show similar values of genetic diversity in terms of average number of alleles and heterozygosities (Hughes et al., 2015). Other Brazilian freshwater fish species with limited displacement and parental care to whom low genetic diversity has been reported include the Arapaima gigas, with an average of 3.57 alleles per locus, ranging from 1 to 5 and H_e from 0.026 to 0.504 (Vitorino et al., 2017), and the Geophagus brasiliensis (Quoy & Gaimard, 1824) which showed an average of 5.33 alleles per locus and H_e from 0.534 to 0.706 (Ferreira et al., 2015). Also, studies with the Neotropical catfish species Neoplecostomus yapoZawadzki, Pavanelli & Langeani, 2008 (Philippsen et al., 2009) and Hypostomus ancistroides (Ihering, 1911) (Sofia et al., 2008) using other markers related the low genetic diversity to the sedentary habits of these species.

Despite the low number of alleles detected in the present study, a decrease (20.3%) in the average number of alleles present in the captive animals, when compared to the wild conspecifics from the submiddle, could be observed. Coimbra et al. (2017) found a significant loss (from 30 to 50%) in the average number of allelic richness between wild and hatchery samples of Prochilodus argenteus. Similarly, Iwersen et al. (2019) and Matsumoto, Hilsdorf (2009) analyzed the genetic diversity in Prochilodus lineatus(Valenciennes, 1837) and Brycon insignis Steindachner, 1877, respectively, and observed a reduction of almost 30% in the number of alleles between wild and hatchery broodstocks. A review conducted by Araki, Schmid (2010) in nearly 50 restocking actions worldwide reported that the decreased number of alleles found in captive breeders compared to those wild conspecifics was the most common feature.

Genetic diversity is used as an essential criterion for evolutionary potential. As long as the population retains genetic diversity, there will be evolutionary adaptation to deal with a variable environment (Frankham et al., 2010; Allendorf et al., 2012). Restocking processes based on culture-based methods, lead to mating closely related individuals and to inbreeding (Keller et al., 2012). The three hatcheries of L. alexandri showed high mean inbreeding (F_IS) values between 0.098-0.198, while the wild upper sampling site presented a negative number (-0.092). The submiddle sampling site, in turn, presented a positive value (0.044). Also, private alleles were more abundant in the upper than in the submiddle stretch. The submiddle stretch of the São Francisco River has in one extreme the reservoir of Sobradinho, and at the other end the reservoirs of Itaparica, Moxotó and Paulo Afonso Complex (I, II, III and IV), with no perennial rivers (ANA, 2003), which makes it the most fragmented stretch of the river. In this cascade of reservoirs, Sobradinho (the second largest lake in South America) is the first one in this region to retain the highest rate of nutrients and sediments, thus creating important modifications on primary productivity downstream, with severe implications for fish communities (Santos et al., 2020). Furthermore, the submiddle stretch is affected by long periods of droughts (Stolf et al., 2012), contrasted with the upper region, which has 48 perennial tributaries (ANA, 2003). The wild samples from the upper stretch were mostly collected in the dendritic reservoir of Tres Marias, which despite being a reservoir, is rich in nutrients due to marginal pond grasses that become submerged during the rainy season and decompose, providing food for the fish community (Sato, Sampaio, 2005). Therefore, the two wild sampling sites (upper and submiddle São Francisco) constitute contrasting areas in terms of nutrient and sediment input that might explain differences in genetic diversity level. Hence, it is possible to consider that there are at least two units of conservation in this river, one in the upper and another in the submiddle stretch. Additional samples taken in the middle and lower stretches may reveal other units along a gradient system.

The continuous restocking of L. alexandri in the submiddle stretch with high levels of inbreeding may reduce population persistence and make the species even more vulnerable. The innate low genetic diversity detected in the L. alexandri makes the challenge of restocking actions even more difficult.

In order to avoid the loss of alleles and maximize genetic diversity, it is of utmost importance that restocking actions use an abundant number of breeders (fully genotyped) and that matings be selected under a relatedness cut-off value.

Genetic structure. The genetic difference found between wild animals from the upper and the submiddle SF might be related to the sedentary behavior and parental care of this species. Sedentary behavior and short displacements have been pointed to as limiting factors for gene flow, resulting in population structure in many freshwater species, such as Arapaima gigas(Schinz, 1822) (Torati et al., 2019), Geophagus brasiliensis(Ferreira et al., 2015), Ichthyoelephas longirostris(Steindachner, 1879) (Landínez-García, Márquez, 2016), and Mogurnda adspersa(Castelnau, 1878) (Hughes et al., 2012). Lophiosilurus alexandri inhabits sandy bottoms where it buries itself waiting for its prey (Costa et al., 2015), rarely showing displacement. The sedentary behavior and the distance of nearly 1,500 km between the upper and submiddle sampling sites in the São Francisco River might also have acted to favor a genetic structuring, limiting gene flow.

The genetic structure emphasizes the necessity of having distinct broodstocks for each stretch of the river in conservation aquaculture programs and that the exchange of breeders between hatcheries from different stretches should be avoided.

Considering the levels of genetic differentiation (Tab. 3, Fig. 3), in terms of inbreeding and number of alleles found between hatcheries (Paulo Afonso and Bebedouro) that restock the submiddle SF and the corresponding wild sample, the use of these captive breeders should not be recommended for conservation purposes. The continuing use of these broodstocks for restocking can cause genetic erosion of wild stocks and decrease the adaptive genetic variation and individual fitness, thus limiting the evolutionary potential of the population (Bijlsma, Loeschcke, 2012; Carvalho et al., 2012). Therefore, we suggest replacing the existing captive broodstock with wild animals (fully genotyped), as well as adopting a capture-based restocking method to guarantee the maintenance of genetic diversity and support long-term conservation of the species, or alternatively selecting breeding pairs under a relatedness cut-off value.

Relatedness. The developed microsatellites were also useful in identifying the best genetic relatedness estimators (R_W and R_LL) in three full-sib families. Considering the distribution of pairwise relatedness of the three full-sib families, it was possible to determine that 0.12 and 0.03 are the cut-off values for full-sib relatedness, according to R_W and R_LL, respectively. Considering the innate low diversity of the species, R_LL should be used for selecting matings in captive systems. The practice of using relatedness estimators has been successfully applied in other culture-based restocking programs, such as for the coho salmon (Conrad et al., 2013) and sockeye salmon (Kozfkay et al., 2008).

The distribution of pairwise relatedness in wild samples indicated that there is a considerable number of pairwise combinations with some degree of relatedness even in the natural environment. It is possible to speculate that the life strategy (sedentary habit with parental care) of L. alexandri might interfere in the genetic relatedness and matings between related individuals that would happen more frequently than expected by chance.

The availability of L. alexandri in the wild is still incipient even after two decades of restocking actions for the species. Reports related to the monitoring of artisanal fishing in the submiddle and lower São Francisco River from June 2017 to April 2018 revealed an average monthly yield of 52.69 kg of L. alexandri, compared to 783.5 kg for Prochilodus argenteus and 2,861.3 kg for Myleus micans(Lütken, 1875), other species that are equally subjected to restocking actions (Chesf, 2017, 2018).

Based on the low genetic diversity recovered from wild and hatchery broodstocks, we recommend that greater efforts should be taken to minimize the genetic loss in captivity and further genetic erosion of wild populations in restocking actions. Towards a conservation aquaculture program for L. alexandri, the implementation of the “culture-based restocking” method requires a strategy based on the use of a minimum number of animals to form the founding broodstock, the physical tagging of breeders, the genetic monitoring of breeders and progenies (prior to being released) and the adoption of a genetic relatedness estimator.

ACKNOWLEDGEMENTS

This work was funded by Grupo Boticário de Proteção à Natureza (1076_20162), Conselho Nacional de Desenvolvimento Científico e Tecnológico - CNPq (grant 407789/ 2016-2), Fundação de Amparo à Pesquisa do Estado do Rio de Janeiro (FAPERJ, CNE 202.780/2018) and Fundação de Amparo a Pesquisa de Minas Gerais (FAPEMIG). A scholarship was provided by Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) to the first author (grant 1694483). We thank CNPq for providing a research fellowship to Ronald Kennedy Luz (grant 308547/2018-7), Daniel Cardoso de Carvalho (grant 306155/2018-4), and Maria Raquel Moura Coimbra (grant 304518/2016-6). The authors are grateful to Miguel Arcanjo dos Santos Neto from Paulo Afonso Hatchery of Chesf and to Mateus Felix from Itiúba Hatchery of Codevasf for providing biological material.

REFERENCES

• Allendorf FW, Luikart GH, Aitken SN. Conservation and the genetics of populations. Oxford: Wiley Blackwell Publishing; 2012.
• ANA. Integrated Management of land based activities in the São Francisco Basin Project - Subsidies for the Formulation of Operational Policies for the Great Reservoirs in the São Francisco River Basin - Executive Summary of the Final Report [Internet]. Brasília; 2003. Available from: https://iwlearn.net/resolveuid/ea2e8752b68ec3ff4355084927ac34a3
  » https://iwlearn.net/resolveuid/ea2e8752b68ec3ff4355084927ac34a3
• Araki H, Schmid C. Is hatchery stocking a help or harm? Evidence, limitations and future directions in ecological and genetic surveys. Aquaculture. 2010; 308:2-11. https://doi.org/10.1016/j.aquaculture.2010.05.036
  » https://doi.org/10.1016/j.aquaculture.2010.05.036
• Beier S, Thiel T, Münch T, Scholz U, Maschier M. MISA-web: a web server for microsatellite prediction. Bioinformatics. 2017; 33(16):2583-85. https://doi.org/10.1093/bioinformatics/btx198
  » https://doi.org/10.1093/bioinformatics/btx198
• Bijlsma R, Loeschcke V. Genetic erosion impedes adaptive responses to stressful environments. Evol Appl. 2012; 5(2):117-29. https://doi.org/10.1111/j.1752-4571.2011.00214.x
  » https://doi.org/10.1111/j.1752-4571.2011.00214.x
• Botstein D, White RL, Skolnick M, Davis RW. Construction of genetic linkage map in man using restriction fragment length polymorphisms. Am J Hum Genet. 1980; 32(3):314-31.
• Buitrago-Suárez UA, Burr BM. Taxonomy of the catfish genus Pseudoplatystoma Bleeker (Siluriformes: Pimelodidae) with recognition of eight species. Zootaxa. 2007; 1512(1):1-38. http://dx.doi.org/10.11646/zootaxa.1512.1.1
  » http://dx.doi.org/10.11646/zootaxa.1512.1.1
• Carrillo-Avila M, Resende EK, Marques DKS, Galetti PM. Isolation and characterization of polymorphic microsatellites in the threatened catfish Jaú, Zungaro jahu (Siluriformes, Pimelodidae). Conserv Genet. 2009; 10(5):1597-99. https://doi.org/10.1007/s10592-008-9802-z
  » https://doi.org/10.1007/s10592-008-9802-z
• Carvalho DC, Beheregaray LB. Conservation genetics of the threatened catfish Conorhynchos conirostris (Siluriformes: incertae sedis), an evolutionary relict endemic to the São Francisco River Basin, Brazil. Conserv Genet. 2018; 1:1-8. http://doi.org/10.1007/s10592-018-1090-7
  » http://doi.org/10.1007/s10592-018-1090-7
• Carvalho DC, Oliveira DA, Beheregaray LB, Torres RA. Hidden genetic diversity and distinct evolutionarily significant units in a commercially important Neotropical apex predator, the catfish Pseudoplatystoma corruscans Conserv Genet. 2012; 13(6):1671-75. https://doi.org/10.1007/s10592-012-0402-6
  » https://doi.org/10.1007/s10592-012-0402-6
• Carvalho DC, Perini VR, Bastos AS, Costa IR, Luz RK, Furtado C et al. The complete mitochondrial genome of the threatened neotropical catfish Lophiosilurus alexandri (Siluriformes: Pseudopimelodidae) and phylogenomic analysis show monophyly of Pimelodoidea. Genet Mol Biol. 2016; 39(4):674-77. https://doi.org/10.1590/1678-4685-gmb-2016-0007
  » https://doi.org/10.1590/1678-4685-gmb-2016-0007
• Coimbra MR, Dantas HL, Luna MM, Lima MA, Sales M, da Silva BC et al. High gene flow in two migratory Neotropical fish species, Salminus franciscanus and Brycon orthotaenia, and implications for conservation aquaculture. Aquat Conserv. 2020; 30(6):1063-73. https://doi.org/10.1002/aqc.3336
  » https://doi.org/10.1002/aqc.3336
• Coimbra MRM, Lima APS, Oliveira KKC, Severi W. Microsatellite assessment of the genetic diversity in indigenous populations of curimba (Prochilodus argenteus) in the São Francisco river (Brazil). Conserv Genet. 2017; 18(4):965-75. https://doi.org/10.1007/s10592-017-0947-5
  » https://doi.org/10.1007/s10592-017-0947-5
• Coleman RA, Gauffre B, Pavlova A, Berehegaray LB, Kearns J, Lyon J et al. Artificial barriers prevent genetic recovery of small isolated populations of a low-mobility freshwater fish. Heredity. 2018; 120:515-32. https://doi.org/10.1038/s41437-017-0008-3
  » https://doi.org/10.1038/s41437-017-0008-3
• Collen B, Whitton F, Dyer EE, Baillie JEM, Cumberlidge N, Darwall WRT et al. Global patterns of freshwater species diversity, threat and endemism. Global Ecol Biogeogr. 2014; 23(1):40-51. https://doi.org/10.1111/geb.12096
  » https://doi.org/10.1111/geb.12096
• Companhia Hidrelétrica do São Francisco (Chesf). Subprograma de Monitoramento da Pesca Artesanal, Relatório 01-11 [Internet]. Porto Alegre; 2017. Available from: http://www.chesf.gov.br/sustentabilidade/Pages/MeioAmbiente/Monitoramento-do-Rio-Sao-Francisco.aspx
  » http://www.chesf.gov.br/sustentabilidade/Pages/MeioAmbiente/Monitoramento-do-Rio-Sao-Francisco.aspx
• Companhia Hidrelétrica do São Francisco (Chesf). Subprograma de Monitoramento da Pesca Artesanal, Relatório 01-11 [Internet]. Porto Alegre; 2018. Available from: http://www.chesf.gov.br/sustentabilidade/Pages/MeioAmbiente/Monitoramento-do-Rio-Sao-Francisco.aspx
  » http://www.chesf.gov.br/sustentabilidade/Pages/MeioAmbiente/Monitoramento-do-Rio-Sao-Francisco.aspx
• Conrad JL, Gilbert-Horvath EA, Garza JC. Genetic and phenotypic effects on reproductive outcomes for captively-reared coho salmon, Oncorhynchus kisutch Aquaculture. 2013; 404:95-104. https://doi.org/10.1016/j.aquaculture.2013.04.013
  » https://doi.org/10.1016/j.aquaculture.2013.04.013
• Costa DC, Silva WS, Melillo-Filho R, Filho KCM, Santos JCE, Luz RK. Capture, adaptation and artificial control of reproduction of Lophiosilurus alexandri: A carnivorous freshwater species. Anim Reprod Sci. 2015; 159:148-54. https://doi.org/10.1016/j.anireprosci.2015.06.009
  » https://doi.org/10.1016/j.anireprosci.2015.06.009
• Dantas HL, Santos Neto MA, Oliveira KKC, Severi W, Diniz FM, Coimbra MRM. Genetic diversity of captive and wild threatened catfish Pseudoplatystoma corruscans in the Sao Francisco River. Rev Fish Sci. 2013; 21(3-4):237-46. https://doi.org/10.1080/10641262.2013.800787
  » https://doi.org/10.1080/10641262.2013.800787
• Earl DA, vonHoldt BM. STRUCTURE HARVESTER: a website and program for visualizing STRUCTURE output and implementing the Evanno method. Conserv Genet Res. 2012; 4(2):359-61. https://doi.org/10.1007/s12686-011-9548-7
  » https://doi.org/10.1007/s12686-011-9548-7
• Evanno G, Regnault 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
  » https://doi.org/10.1111/j.1365-294X.2005.02553.x
• Excoffier L, Laval G, Schneider S. Arlequin ver. 3.0: An integrated software package for population genetics data analysis. Evol Bioinform. 2005; 1:47-50. https://doi.org/10.1177/117693430500100003
  » https://doi.org/10.1177/117693430500100003
• Falush D, Stephens M, Pritchard JK. Inference of population structure using multilocus genotype data: linked loci and correlated allele frequencies. Genetics. 2003; 164(4):1567-87. http://doi.org/10.3410/f.1015548.197423
  » http://doi.org/10.3410/f.1015548.197423
• Ferreira GG, Galindo BA, Frantine-Silva W, Almeida FS, Sofia SH. Genetic structure of a Neotropical sedentary fish revealed by AFLP, microsatellite and mtDNA markers: a case study. Conserv Genet. 2015; 16(1):151-66. https://doi.org/10.1007/s10592-014-0648-2
  » https://doi.org/10.1007/s10592-014-0648-2
• Fisch KM, Kozfkay CC, Ivy JA, Ryder OA, Waples RS. Fish hatchery genetic management techniques: integrating theory with implementation. N Am J Aquacult. 2015; 77(3):343-57. https://doi.org/10.1080/15222055.2014.999846
  » https://doi.org/10.1080/15222055.2014.999846
• Fonseca FS, Domingues RR, Hallerman EM, Hilsdorf AW. Genetic diversity of an imperiled Neotropical catfish and recommendations for its restoration. Fronti Genetics. 2017; 8:196. https://doi.org/10.3389/fgene.2017.00196
  » https://doi.org/10.3389/fgene.2017.00196
• Frankham R, Ballou JD, Briscoe DA. Introduction to Conservation Genetics. Cambridge: Cambridge University Press; 2010. https://doi.org/10.1017/CBO9780511809002
  » https://doi.org/10.1017/CBO9780511809002
• Hughes JM, Real KM, Marshall JC, Schmidt DJ. Extreme genetic structure in a small-bodied freshwater fish, the purple spotted gudgeon, Mogurnda adspersa (Eleotridae). Plos One. 2012; 7(7):e40546. https://doi.org/10.1371/journal.pone.0040546
  » https://doi.org/10.1371/journal.pone.0040546
• Hughes JM, Schmidt DJ, Huey JA, Real KM, Espinoza T, McDougall A et al. Extremely low microsatellite diversity but distinct population structure in a long-lived threatened species, the Australian lungfish Neoceratodus forsteri (Dipnoi). Plos One. 2015; 10(4):e0121858. https://doi.org/10.1371/journal.pone.0121858
  » https://doi.org/10.1371/journal.pone.0121858
• Instituto Chico Mendes de Conservação da Biodiversidade (ICMBio). Espécies Ameaçadas - Lista 2014 [Internet]. Brasília; 2014. Available from: https://www.icmbio.gov.br/portal/faunabrasileira/lista-de-especies/6358-especie-6358
  » https://www.icmbio.gov.br/portal/faunabrasileira/lista-de-especies/6358-especie-6358
• Instituto Chico Mendes de Conservação da Biodiversidade (ICMBio). Livro Vermelho da Fauna Brasileira Ameaçada de Extinção: Volume VI - Peixes. Brasília: ICMBio/MMA; 2018.
• Iwersen LHL, Melo CMR, Lazoski C, Zaniboni-Filho E, Ribolli J. Genetic implications of restocking programs on wild populations of streaked prochilodProchilodus lineatus Bol Inst Pesca. 2019; 45(3):e497. https://doi.org/10.20950/1678-2305.2019.45.3.497
  » https://doi.org/10.20950/1678-2305.2019.45.3.497
• James KM, Fallon SJ, McDougall A, Espinoza T, Broadfoot C. Assessing the potential for radiocarbon dating the scales of Australian lungfish (Neoceratodus forsteri). Radiocarbon. 2010; 52(3):1084-89. https://doi.org/10.1017/S0033822200046166
  » https://doi.org/10.1017/S0033822200046166
• Jombart T. Adegenet: a R package for the multivariate analysis of genetic markers. Bioinformatics. 2008; 24:1403-05. https://doi.org/10.1093/bioinformatics/btn129
  » https://doi.org/10.1093/bioinformatics/btn129
• Jombart T, Devillard S, Balloux F. Discriminant analysis of principal components: a new method for the analysis of genetically structured populations. BMC Genet. 2010; 11(1):94. http://doi.org/10.1186/1471-2156-11-94
  » http://doi.org/10.1186/1471-2156-11-94
• Jost L. GST and its relatives do not measure differentiation. Mol Ecol. 2008; 17(18):4015-26. https://doi.org/10.1111/j.1365-294X.2008.03887.x
  » https://doi.org/10.1111/j.1365-294X.2008.03887.x
• Kalinowski ST, Taper ML, Marshall TC. Revising how the computer program CERVUS accommodates genotyping error increases success in paternity assignment. Mol Ecol. 2007; 16(5):1099-106. https://doi.org/10.1111/j.1365-294X.2007.03089.x
  » https://doi.org/10.1111/j.1365-294X.2007.03089.x
• Keller LF, Biebach I, Ewing SR, Hoeck PE. The genetics of reintroductions: inbreeding and genetic drift. In: Ewen JG, Armstrong DP, Parker KA, Seddon PJ, editors. Reintroduction biology: integrating science and management. New Jersey: Blackwell Publishing Ltd; 2012. p.360-94. https://doi.org/10.1002/9781444355833.ch11
  » https://doi.org/10.1002/9781444355833.ch11
• Kozfkay CC, Campbell MR, Heindel JA, Baker DJ, Kline P, Powell MS et al. A genetic evaluation of relatedness for broodstock management of captive, endangered Snake River sockeye salmon, Oncorhynchus nerka Conserv Genet. 2008; 9(6):1421. https://doi.org/10.1007/s10592-007-9466-0
  » https://doi.org/10.1007/s10592-007-9466-0
• Landínez-García RM, Márquez EJ. Development and characterization of 24 polymorphic microsatellite loci for the freshwater fish Ichthyoelephas longirostris (Characiformes: Prochilodontidae). PeerJ. 2016; 4:e2419. https://doi.org/10.7717/peerj.2419
  » https://doi.org/10.7717/peerj.2419
• Li R, Li Y, Kristiansen K, Wang J. SOAP: short oligonucleotide alignment program. Bioinformatics. 2008; 24(5):713-14. https://doi.org/10.1093/bioinformatics/btn025
  » https://doi.org/10.1093/bioinformatics/btn025
• Li CC, Weeks DE, Chakravarti A. Similarity of DNA fingerprints due to chance and relatedness. Hum Hered. 1993; 43(1):45-52. https://doi.org/10.1159/000154113
  » https://doi.org/10.1159/000154113
• Lopes JP, Franca FL, Santos Neto MA. O domínio na produção de alevinos de pacamã: Propagação na Chesf permite repovoamento no rio São Francisco. Panorama da Aquicultura. 2013; 23:24-29. Available from: https://panoramadaaquicultura.com.br/o-dominio-na-producao-de-alevinos-de-pacama/
  » https://panoramadaaquicultura.com.br/o-dominio-na-producao-de-alevinos-de-pacama/
• Lynch M. Estimation of relatedness by DNA fingerprinting. Mol Biol Evol. 1998; 5(5):584-99. https://doi.org/10.1093/oxfordjournals.molbev.a040518
  » https://doi.org/10.1093/oxfordjournals.molbev.a040518
• Lynch M, Ritland Kvvvv . Estimation of Pairwise Relatedness with Molecular Markers. Genetics. 1999; 152(4):1753-66. https://www.genetics.org/content/152/4/1753
  » https://www.genetics.org/content/152/4/1753
• Maneta MP, Torres M, Wallender WW, Vosti S, Kirby M, Bassoi LH et al. Water demand and flows in the São Francisco River Basin (Brazil) with increased irrigation. Agric Water Manag. 2009; 96(8):1191-200. https://doi.org/10.1016/j.agwat.2009.03.008
  » https://doi.org/10.1016/j.agwat.2009.03.008
• Martinez AS, Willoughby JR, Christie MR. Genetic diversity in fishes is influenced by habitat type and life-history variation. Ecol Evol. 2018; 8(23):12022-031. https://doi.org/10.1002/ece3.4661
  » https://doi.org/10.1002/ece3.4661
• Matsumoto CK, Hilsdorf AWS. Microsatellite variation and population genetic structure of a neotropical endangered Bryconinae species Brycon insignisSteindachner, 1877: implications for its conservation and sustainable management. Neotrop Ichthyol. 2009; 7(3):395-402. https://doi.org/10.1590/S1679-62252009000300006
  » https://doi.org/10.1590/S1679-62252009000300006
• Oosterhout CV, Weetman D, Hutchinson WF. Estimation and adjustment of microsatellite null alleles in nonequilibrium populations. Mol Ecol Notes. 2006; 6(1):255-56. https://doi.org/10.1111/j.1471-8286.2005.01082.x
  » https://doi.org/10.1111/j.1471-8286.2005.01082.x
• Ortega-Villaizan MM, Noguchi D, Taniguchi N. Minimization of genetic diversity loss of endangered fish species captive broodstocks by means of minimal kinship selective crossbreeding. Aquaculture. 2011; 318(1-2):239-43. https://doi.org/10.1016/j.aquaculture.2011.04.047
  » https://doi.org/10.1016/j.aquaculture.2011.04.047
• Peakall R, Smouse PE. GenAlEx 6.5: genetic analysis in Excel. Population genetic software for teaching and research - an update. Bioinformatics. 2012; 28(19):2537-39. https://doi.org/10.1093/bioinformatics/bts460
  » https://doi.org/10.1093/bioinformatics/bts460
• Philippsen JS, Renesto E, Geahl AM, Artoni RF, Shibatta OA, Zawadzki CH. Genetic variability in four samples of Neoplecostomus yapo (Teleostei: Loricariidae) from the Paranapanema basin, Brazil. Neotrop Ichthyol. 2009; 7(1):25-30. https://doi.org/10.1590/S1679-62252009000100004
  » https://doi.org/10.1590/S1679-62252009000100004
• Povh JA, Ribeiro RP, Sirol RN, Streit Jr DP, Lopera-Barrero NM, Vargas L et al. Diversidade genética de pacu do rio Paranapanema e do estoque de um programa de repovoamento. Pesqu Agropecu Bras. 2008; 43(2):201-06. https://doi.org/10.1590/S0100-204X2008000200007
  » https://doi.org/10.1590/S0100-204X2008000200007
• Pritchard JK, Stephens P, Donnelly P. Inference of population structure using multilocus genotype data. Genetics. 2000; 155(2):945-59.
• Pusey BJ, Kennard MJ, Arthington AH. Freshwater Fishes of North-Eastern Australia. Victoria: CSIRO; 2004.
• Queller DC, Goodnight KF. Estimating relatedness using genetic markers. Evolution. 1989; 43(2):258-75. https://doi.org/10.1111/j.1558-5646.1989.tb04226.x
  » https://doi.org/10.1111/j.1558-5646.1989.tb04226.x
• R Development Core Team. R: a language and environment for statistical computing [Computer software manual - Internet]. Vienna: R Foundation for Statistical Computing; 2020. Available from: https://www.r-project.org/
  » 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
  » https://doi.org/10.1093/oxfordjournals.jhered.a111573
• Reis RE, Albert JS, Di Dario F, Mincarone MM, Petry P, Rocha LA. Fish biodiversity and conservation in South America. J Fish Biol. 2016; 89(1):12-47. https://doi.org/10.1111/jfb.13016
  » https://doi.org/10.1111/jfb.13016
• Rice WR. Analyzing tables of statistical tests. Evolution. 1989; 43(1):223-25. https://doi.org/10.1111/j.1558-5646.1989.tb04220.x
  » https://doi.org/10.1111/j.1558-5646.1989.tb04220.x
• Ritland K. Estimators for pairwise relatedness and individual inbreeding coefficients. Genet Res. 1996; 67(2):175-85. https://doi.org/10.1017/S0016672300033620
  » https://doi.org/10.1017/S0016672300033620
• Rozen S, Skaletsky H. Primer3 on the WWW for general users and for biologist programmers. In: Misener S, Krawetz S, editors. A Bioinformatics methods and protocols. Totowa: Humana Press; 2000. p.365-86.
• Sambrook J, Fritsch EFE, Maniatis T. Molecular Cloning: A Laboratory Manual. Cold Spring Harbor: Cold Spring Harbor Laboratory Press; 1989.
• Santos NCL, Dias RM, Alves DC, Melo BAR, Ganassin MJM, Gomes LC et al. Trophic and limnological changes in highly fragmented rivers predict the decreasing abundance of detritivorous fish. Ecol Indic. 2020; 110:105933. https://doi.org/10.1016/j.ecolind.2019.105933
  » https://doi.org/10.1016/j.ecolind.2019.105933
• Sato Y, Fenerich-Verani N, Nuñer APON, Godinho HP, Verani JR. Padrões reprodutivos de peixes da bacia do São Francisco. In: Godinho HP, Godinho AL, editors. Águas e peixes e pescadores do São Francisco das Minas Gerais. Belo Horizonte: PUC Minas; 2003. p.229-74.
• Sato Y, Sampaio EV. Ictiofauna na região do Alto São Francisco, com ênfase no Reservatório de Três Marias, Minas Gerais. In: Nogueira MG, Henry R, Jorcin A, editors. Ecologia de Reservatórios: Impactos potenciais, ações de manejo e sistemas em cascata. São Carlos: RiMa; 2005. p.251-74.
• Saulo-Machado AC, Formiga KM, Ortiz MF, Sousa ACB, Alves-Gomes JA, Batista JS. Polymorphic microsatellite DNA markers for the Amazonian catfish Pseudoplatystoma punctifer (Siluriformes: Pimelodidae). Conserv Genet Resour. 2011; 3(2):307-10. https://doi.org/10.1007/s12686-010-9349-4
  » https://doi.org/10.1007/s12686-010-9349-4
• Schreier A, Stephenson S, Rust P, Young S. The case of the endangered Kootenai River white sturgeon (Acipenser transmontanus) highlights the importance of post-release genetic monitoring in captive and supportive breeding programs. Biol Conserv. 2015; 192:74-81. https://doi.org/10.1016/j.biocon.2015.09.011
  » https://doi.org/10.1016/j.biocon.2015.09.011
• Schuelke M. An economic method for the fluorescent labeling of PCR fragments. Nat Biotechnol. 2000; 18:233-34. https://doi.org/10.1038/72708
  » https://doi.org/10.1038/72708
• Shibatta LS, Ferreira DG, Oliveira C, Almeida FS, Shibatta OA, Sofia AH. Development and characterization of microsatellite loci of Microglanis cottoides (Siluriformes: Pseudopimelodidae) and cross-species amplification. Neotrop Ichthyol. 2013; 11(3):581-85. https://doi.org/10.1590/S1679-62252013000300011
  » https://doi.org/10.1590/S1679-62252013000300011
• Sofia SH, Galindo BA, Paula FM, Sodre LMK, Martinez CBR. Genetic diversity of Hypostomus ancistroides (Teleostei, Loricariidae) from an urban stream. Genet Mol Biol. 2008; 31:317-23 https://doi.org/10.1590/S1415-47572008000200027
• Stolf R, Piedade SMDS, Silva JRD, Silva LC, Maniero MA. Water transfer from São Francisco river to semiarid northeast of Brazil: technical data, environmental impacts, survey of opinion about the amount to be transferred. Eng Agric. 2012; 32(6):998-1010. https://doi.org/10.1590/S0100-69162012000600001
  » https://doi.org/10.1590/S0100-69162012000600001
• Sunnucks P, Hales D. Numerous transposed sequences of mitochondrial cytochrome oxidase I-II in aphids of the genus Sitobion (Hemiptera: Aphididae). Mol Biol Evol. 1996; 13(3):510-24. https://doi.org/10.1093/oxfordjournals.molbev.a025612
  » https://doi.org/10.1093/oxfordjournals.molbev.a025612
• Torati LS, Taggart JB, Varela ES, Araripe J, Wehner S, Migaud H. Genetic diversity and structure in Arapaima gigas populations from Amazon and Araguaia-Tocantins river basins. BMC Genet. 2019; 20(1):13. https://doi.org/10.1186/s12863-018-0711-y
  » https://doi.org/10.1186/s12863-018-0711-y
• Valière N. GIMLET: a computer program for analysing genetic individual identification data. Mol Ecol Notes. 2002; 2(3):377-79. https://doi.org/10.1046/j.1471-8286.2002.00228.x-i2
  » https://doi.org/10.1046/j.1471-8286.2002.00228.x-i2
• Vitorino CA, Nogueira F, Souza IL, Araripe J, Venere PC. Low Genetic Diversity and Structuring of the Arapaima (Osteoglossiformes, Arapaimidae) Population of the Araguaia-Tocantins Basin. Front Genet. 2017; 8:1-10. https://doi.org/10.3389/fgene.2017.00159
  » https://doi.org/10.3389/fgene.2017.00159
• Waits LP, Luikart G, Taberlet P. Estimating the probability of identity among genotypes in natural populations: cautions and guidelines. Mol Ecol. 2001; 10(1):249-56. https://doi.org/10.1046/j.1365-294x.2001.01185.x
  » https://doi.org/10.1046/j.1365-294x.2001.01185.x
• Wang J. An estimator for pairwise relatedness using molecular markers. Genetics. 2002; 160(3):1203-15.
• Wang J. COANCESTRY: a program for simulating, estimating and analyzing relatedness and inbreeding coefficients. Mol Ecol Resour. 2011; 11(1):141-45. https://doi.org/10.1111/j.1755-0998.2010.02885.x
  » https://doi.org/10.1111/j.1755-0998.2010.02885.x
• Wang J. Marker-based estimates of relatedness and inbreeding coefficients: an assessment of current methods. J Evol Biol. 2014; 27(3):518-30. https://doi.org/10.1111/jeb.12315
  » https://doi.org/10.1111/jeb.12315
• Wang J. Estimating pairwise relatedness in a small sample of individuals. Heredity. 2017; 119(5):302-13. https://doi.org/10.1038/hdy.2017.52
  » https://doi.org/10.1038/hdy.2017.52
• Whiteley AR, Fitzpatrick SW, Funk WC, Tallmon DA. Genetic rescue to the rescue. Trends Ecol Evol. 2015; 30(1):42-49. https://doi.org/10.1016/j.tree.2014.10.009
  » https://doi.org/10.1016/j.tree.2014.10.009

ADDITIONAL NOTES

• HOW TO CITE THIS ARTICLE

  Farias RS, Silva BCNR, Nascimento WVG, Silva GC, Luz RK, Prosdocimi F, Figueiredo RACR, Carvalho DC, Coimbra MRM. Genetic diversity and aquaculture conservation for a threatened Neotropical catfish. Neotrop Ichthyol. 2020; 18(3):e200028. https://doi.org/10.1590/1982-0224-2020-0028

Edited by

Alexandre Hilsdorf

Publication Dates

• Publication in this collection
  09 Oct 2020
• Date of issue
  2020

History

• Received
  20 Apr 2020
• Accepted
  14 Aug 2020