Genetic diversity and structure in Arapaima gigas populations from Amazon and Araguaia-Tocantins river basins

Abstract

BackgroundArapaima gigas (Schinz, 1822) is the largest freshwater scaled fish in the world, and an emerging species for tropical aquaculture development. Conservation of the species, and the expansion of aquaculture requires the development of genetic tools to study polymorphism, differentiation, and stock structure. This study aimed to investigate genomic polymorphism through ddRAD sequencing, in order to identify a panel of single nucleotide polymorphisms (SNPs) and to simultaneously assess genetic diversity and structure in wild (from rivers Amazon, Solimões, Tocantins and Araguaia) and captive populations.ResultsCompared to many other teleosts, the degree of polymorphism in A. gigas was low with only 2.3% of identified RAD-tags (135 bases long) containing SNPs. A panel of 393 informative SNPs was identified and screened across the five populations. Higher genetic diversity indices (number of polymorphic loci and private alleles, Shannon’s Index and HO) were found in populations from the Amazon and Solimões, intermediate levels in Tocantins and Captive, and very low levels in the Araguaia population. These results likely reflect larger population sizes from less urbanized environments in the Amazon basin compared to Araguaia. Populations were significantly differentiated with pairwise FST values ranging from 0.086 (Amazon × Solimões) to 0.556 (Amazon × Araguaia). Mean pairwise relatedness among individuals was significant in all populations (P < 0.01), reflecting a degree of inbreeding possibly due to severe depletion of natural stocks, the species sedentary behaviour and possible sampling biases. Although Mantel test was not significant (P = 0.104; R2 = 0.65), Bayesian analysis in STRUCTURE and discriminant analysis of principal components (DAPC) showed populations of Amazon and Solimões to be genetically differentiated from Araguaia, with Tocantins comprising individuals from both identified stocks.ConclusionsThis relatively rapid genotyping by sequencing approach proved to be successful in delineating arapaima stocks. The approach and / or SNP panels identified should prove valuable for more detailed genetic studies of arapaima populations, including the elucidation of the genetic status of described discrete morphotypes and aid in delivery of conservation programs to maintain genetic diversity in reservoirs across the Amazon region.Electronic supplementary materialThe online version of this article (10.1186/s12863-018-0711-y) contains supplementary material, which is available to authorized users.

Genetic diversity analysis of a Flax (Linum usitatissimum L.) global collection

“…The negative correlation between diversity indices (H and I) and relatedness indicates that inbreeding and genetic drift play a signi cant role in reducing genetic variability in the studied population which results in increased differentiation among sub-populations. Similar phenomenon was also found in Arapaima gigas species(50).The success of any breeding program usually depends on the right choice of parental groups at the inception. The NDSU ax breeding program is comparatively old.…”

Historical demography and climate driven distributional changes in a widespread Neotropical freshwater species with high economic importance

“…The climatic niche for A. gigas was estimated from 85 wild occurrences (Fig. A) based on our field collections (7 points), in previous published works (Hrbek et al , 11 points; Torati et al , 4 points; Vitorino et al , 4 points; Vitorino et al , 1 point) and 57 available points in the Global Biodiversity Information Facility (GBIF) that were manually checked to avoid inconsistencies. We performed a combination of nine distribution algorithms with biomod2 (Thuiller et al ), including generalized linear models (GLM; McCullagh and Nelder ), multivariate adaptive regression splines (MARS; Friedman ), classification tree analysis (CTA; Breiman et al ), mixture discriminant analysis (MDA; Hastie et al ), artificial neural networks (ANN; Ripley ), generalised boosted models (GBM; Ridgeway ), random forests (Breiman ), surface range envelop (SRE; Busby ) and Maximum Entropy (Maxent; Phillips et al ).…”

“…Therefore, some authors accept as much as five species for Arapaima (Castello et al ). However, recent publications that analyzed the genetic diversity using molecular markers, considered Arapaima as a monotypic genus (Hrbek et al , , Farias et al , Torati et al ). Here, taking into account the morphological classification performed in the museum after the voucher deposit, we decided to consider the specimens in all our analyses as monotypic ( A. gigas ), but discussing our results considering the potential for cryptic species to occur.…”

“…Previous studies on the population genetic diversity of A. gigas with molecular markers generally pointed to the absence of genetic structuring in the Am and lower To‐Ar basins, and a more pronounced structuring in the upper To‐Ar. These previous analyses, however, focused on few mtDNA sequences (Hrbek et al ), nuclear markers associated with repetitive regions (Hrbek et al , Vitorino et al , , Farias et al ), and randomly distributed genotypes along the genome (Torati et al ). Here, we sampled specimens in the field and deposited vouchers for both wild populations and fish farms, obtaining SNP markers with DArTSeq (Kilian et al ).…”

Characterizing the genetic structure of introduced Nile tilapia (Oreochromis niloticus) strains in Tanzania using double digest RAD sequencing

“…Both RAD and ddRAD-seq have been widely applied in aquaculture breeding and genetics studies (Robledo et al 2018). In particular, ddRAD-seq has been applied for genotyping large multiplexed datasets (e.g., Maroso et al 2018), construction of genetic linkage maps (e.g., Recknagel et al 2013;Oral et al 2017), analyzing life history traits (e.g., Pukk 2016), mapping sex determining loci (e.g., Palaiokostas et al 2015;Brown et al 2016), genomic predictions and genome-wide association studies (e.g., Barria et al 2018), assessing genetic diversity (e.g., Hosoya et al 2018;Tony et al 2015;Siccha-Ramirez et al 2018;Torati et al 2019), phylogeography (e.g., Stobie et al 2018, and species identification in tilapias (Syaifudin et al 2019).…”