Skip to main content
Download PDF
  • Research article
  • Open access
  • Published:

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

BMC Genetics volume 20, Article number: 13 (2019) Cite this article

Abstract

Background

Arapaima 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.

Results

Compared 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.

Conclusions

This 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.

Background

Over the past 10–15 years the detection and analysis of genome-wide single nucleotide polymorphism (SNP) markers have revolutionized genetic investigations of all types of organisms. As elsewhere, within the field of fisheries science, SNPs are becoming the marker of choice for population genetic studies [1, 2], their abundance, distribution and relative ease and accuracy of scoring also proving invaluable in other research areas e.g. elucidating sex determination systems [3,4,5]; constructing genetic linkage maps [6,7,8] and aiding whole genome selection for many farmed species [5, 9]. The bi-allelic nature of SNPs, which allows more confident estimation of allelic frequencies from small sample sizes, and the ability to survey both neutral and non-neutral loci, presents the opportunity to explore different and complementary perspectives cf. microsatellite based population genetic and conservation studies [10,11,12,13,14,15,16,17].

Genotype by sequencing approaches, including restriction-site-associated DNA (RAD) [18] and double digest RAD (ddRAD) [19], provide a scalable, rapid and relatively inexpensive means to simultaneously discover and genotype hundreds to thousands of SNPs in non-model species. These methodologies do, however, require empirical optimization as the number of loci detected is largely dependent on genome size, architecture and base content of the species under study, while the extent of SNP polymorphism can also vary significantly.

The Amazonian pirarucu Arapaima gigas (Schinz, 1822) is the largest freshwater scaled fish in the world with adults reaching up to 250 kg and measuring over 2.5 m in total length [20]. This emblematic air-breather fish is a promising candidate species for aquaculture development and has a valuable market in South America [21]. The natural geographical distribution of A. gigas includes the basins of the Amazon, Tocantins-Araguaia and Essequibo rivers, which cover Brazil, Ecuador, Guyana and Peru. Also, the species has already been introduced into several non-indigenous water systems [22]. Arapaima gigas is a dioecious and iteroparous species with sexual maturity reached after 3 to 5 years of age [23]. Reproduction involves nest building by males and females in the sandy bottom of lentic habitats during the rainy season from November onwards [24, 25]. External fertilisation generally involves a single female, often with contributions from more than one male, a strategy that helps maintain genetic diversity in the species [26]. After spawning, the nest is guarded by both parents until egg hatching, then parental care is provided by the male and a characteristic lateral migration towards flooded food-rich areas ensues [25]. Females normally reproduce multiple times within the reproductive season [23] with a mean fecundity estimated to be c. 11,000 fingerlings counted at the parental care phase (up to 3 months post-hatching) per spawning event, and a balanced sex ratio at hatch [27, 28]. During the dry season, from June onward, water levels in the rivers decrease, marking the end of the parental care [25]. At this stage, adults and fingerlings migrate back to the low lands and disperse in the river canals and floodplain lagoons [25, 29]. Overall, adults are not believed to migrate long distances. They are solitary and well adapted to hypoxic conditions during the drought season [25, 29]. In some regions and years the dry season can be severe, resulting in mass mortalities of A. gigas and rescuing operations are organised for conservation reasons [30].

Given its obligate air-breathing behaviour, A. gigas is an easy target for fishermen and natural populations have been historically depleted or even eradicated close to main cities [31]. It is estimated that populations today represent only 13% of historic levels [32] and since 1986 A. gigas has been included in the CITES red list [22]. While occasionally successful, breeding of A. gigas in captivity is not a routine practice due to complex reproductive traits and dysfunctions in the species (i.e. failure at the final oocyte maturation and ovulation stage, lack of male-female synchronization at spawning), which require further research especially for gender identification and control of spawning [33, 34]. Therefore, fingerlings are valuable in the aquaculture market, and their illegal capture from the wild is a challenge for conservation. Translocations of animals are also a concern as morphotypes (white vs orange fleshed individuals) [31] and potentially novel species have been described, suggesting patterns of allopatric differentiation across different hydrographic basins [35,36,37].

To date, limited numbers of studies have been conducted to characterise the genetic diversity and structure of natural A. gigas populations. These have involved the use of mitochondrial markers (mtDNA), microsatellite or inter-simple sequence repeats (ISSR) markers to study eight populations from the Amazon, Solimões and Tocantins river systems [29, 31, 38, 39], four [30] and five [40] populations from Tocantins and Araguaia and five populations from Essequibo and Branco rivers [37]. Overall, these studies found higher levels of genetic diversity within the large Amazon River basin compared to other systems with the population structure suggesting minimal genetic flow and high genetic differentiation between populations. So far, molecular data has failed to confirm a multispecies scenario for Arapaima, which today is supported only by morphological analyses of very few specimens [35,36,37]. Further genetic research on A. gigas has focused on acquiring tools for gender identification since this species is not sexually dimorphic, a factor that has impeded captive reproduction and aquaculture development. To do so, the species karyotype was characterised (2n = 56) but no apparent sex chromosome dimorphism was observed [41, 42], while later a bulked segregant analysis failed to find sex-related markers [43]. Next generation sequencing (NGS) technologies have not yet been applied to investigate genomic variability and diversity in populations of A. gigas.

In the current study the potential of ddRAD to generate an informative SNP panel for population genetic analyses of the endangered A. gigas was explored. Genetic diversity and structure among five population samples (four wild samples from different river systems, one captive stock) was assessed and compared with published studies using microsatellite and mtDNA markers. The relevance of the results for future conservation and exploitation of this iconic species is discussed.

Results

ddRAD sequencing and degree of polymorphism

The SNP identification from the ddRAD sequencing for 60 individuals of A. gigas is summarised in Fig. 1. In total, 33,932,300 raw reads comprising 16,966,150 paired-end reads were obtained from the MiSeq run. After sample demultiplexing and quality filtering, a total of 29,133,620 reads (85.8%) were kept. Assembling loci (RAD-tags) into the 60 individuals identified 12,378 unique RAD-tags. The numbers of RAD-tags and observed heterozygosity per individual are given in Additional file 1. The panel resolved down to 448 markers containing 1–2 SNPs present in more than 80% of individuals in each of the five populations. A further filtering was used to select only one SNP (the most polymorphic one) when two SNPs were identified in a RAD locus, and also removed the few instances where the same SNP was identified at the 3′ end of paired reads. This reduced the panel used for initial population genetic analyses to 393 SNPs (detailed in Additional file 2). Only 2.3% of A. gigas ddRAD generated RAD loci (135 bases long) were identified to contain SNPs. This was 3–8 times lower than observed for other teleosts studied at the University of Stirling using the same methodology and analysis (Table 1). The levels of polymorphism detected among the A. gigas samples from different locations varied significantly (P < 0.05); higher polymorphic levels were found in the Amazon (3.5%) and Solimões (2.7%) rivers compared to the Araguaia river (1.0%), whilst fish from the Tocantins river and the Captive stock showed intermediate levels (2.0%) (P < 0.05; Table 2).

Fig. 1
figure 1

Summary of ddRAD sequencing for Arapaima gigas, scheme modified from Brown and collaborators [7]. Workflow of data processing from the obtained raw reads (upper disk) down to the markers used to investigate genomic diversity and structure in populations

Full size image
Table 1 Comparison of basic Stacks [55] statistics for Arapaima gigas versus a range of other fish species that were generated using the same ddRAD methodology and Stacks parameters
Full size table
Table 2 Comparison of ddRAD Stacks statistics for the different populations of Arapaima gigas
Full size table

Analyses of genetic diversity

The 393 loci analysed were all found to be in HWE both within individual population groupings and across the global population sample (P < 0.05; sequential Bonferroni α = 0.05/393 corrected). Analyses of LD indicated significant association between locus 373_82 and 3408_78 in the global dataset after Bonferroni correction (α = 0.05/61463). Therefore, locus 373_82 (lower FST) was removed from further analyses resulting in a final dataset with 392 SNPs. For this dataset (392 SNPs, 60 individuals), the global FST calculated across all loci was 0.389, and individual locus FST values ranged from − 0.04 to 0.97 with their frequency distribution depicted in Fig. 2a. Analysis also detected 57 loci putatively under selection (outlier) (Fig. 2b). Outlier SNPs were kept in the dataset in order to integrate possible adaptive information in further population analysis [44], while their removal did not significantly alter population structure results (data not shown).

Fig. 2
figure 2

Loci analysis for 392 SNPs across 60 individuals analysed from the five populations of Arapaima gigas. a Frequency distribution of FST values. b Detection of loci under selection (outlier) using hierarchical structure model implemented in Arlequin v. 3.5.2.2. Outlier loci are indicated in red (P < 0.01) and blue (P < 0.05) dots

Full size image

All pairwise FST comparisons between population samples were significant (P < 0.05; Bonferroni correction α = 0.05/10; Table 3). Moderate degrees of differentiation were found between the Amazon and Solimões (0.086) populations and between the Captive and Araguaia (0.077) populations. High degrees of differentiation were found between Tocantins and Captive (0.227), Tocantins and Araguaia (0.332), Solimões and Tocantins (0.336), Amazon and Tocantins (0.390), Solimões and Captive (0.423) and Amazon and Captive (0.459) population samples. The highest levels of genetic differentiation were observed between Araguaia and two other samples (Amazon (0.556) and Solimões (0.523)). Table 4 also indicates waterway distances calculated among sampling sites, and Mantel test based on these values indicated that the isolation by distance hypothesis was not supported (P = 0.104; R2 = 0.65).

Table 3 Genetic differentiation (FST) and geographical distance (km) among populations of Arapaima gigas
Full size table
Table 4 Genetic diversity statistics for the five population samples of Arapaima gigas
Full size table

Overall, the population samples from the Amazon basin (Amazon and Solimões) were genetically more diverse than those from the Araguaia and Tocantins rivers in terms of percentage of polymorphic loci, number of private alleles, Shannon’s Information Index (I) and observed heterozygosity (HO) (Table 4). The mean pairwise relatedness (r) between individuals was significantly different from zero for all population samples (P < 0.05), with lower r from the rivers Amazon (0.127) and Solimões (0.125), increased r from Tocantins (0.182) and the r highest from Araguaia (0.323) and the Captive population (0.238) (Fig. 3). Relatedness correlated negatively with HO and with I (R2 = 0.957, P < 0.01, and R2 = 0.956, P < 0.01, respectively), indicating inbreeding as a potential cause for the loss of genetic diversity in the studied populations.

Fig. 3
figure 3

Within population pairwise mean relatedness (r) for Arapaima gigas (n = 12 individuals for each population). Calculations followed method of Lynch and Ritland [62] with confidence intervals of 95% denoted by the U (upper) and L (lower) marks, calculated after 1000 bootstrap resamplings and 1000 permutations in GenAlEx version 6.5

Full size image

Population structure

The number of clusters (K) across the five studied population samples was resolved by the Evanno method, to be two (Fig. 4b). Analysis suggested the Amazon river basin (Amazon and Solimões) and Araguaia river are distinct genetic stocks, and suggested the lower Tocantins river sample is a hybrid zone between these two groupings (Fig. 4a–c). Analyses also showed eight individuals from the Captive population to be genetically similar to the Araguaia population, and four individuals similar to the Tocantins population (Fig. 4a).

Fig. 4
figure 4

Bayesian clustering representation for populations of Arapaima gigas using 392 SNP markers in STRUCTURE v. 2.3.4 [63]. a Analysis of five populations (n = 60 individuals; optimal Evanno’s K = 2). b Graphical representation of optimal number of clusters (K) across the five studied populations determined by Evanno’s method, where highest Delta K indicate the real number of populations [64], indicated by delta K peaking at K = 2. c Geographical representation of structure results for the global population

Full size image

The DAPC analysis identified three groups (K = 3) and agreed with the findings resolved by STRUCTURE. Populations from the Amazon basin (Amazon and Solimões) were clustered together, the genetically “hybrid” area formed by Tocantins was considered as a distinct cluster in DAPC, which also included four Captive individuals. The third cluster grouped together Araguaia and the remaining eight Captive individuals (Additional file 3A). Selection of three clusters was suggested by the Bayesian Inference Criterion (BIC) (decreasing elbow; Additional file 3B), with all individuals being correctly reassigned to their original clusters with 100% membership probability (Additional file 3C).

Discussion

Comparatively few studies, all of limited scope, have been undertaken to characterise genetic variability in the iconic A. gigas [29,30,31, 37,38,39,40]. All have been restricted by the relatively low resolution provided by the genetic markers available for use (i.e. either a single mitochondrial locus, 14 variable microsatellites or dominant AFLP markers). The large 393 SNP set surveyed in the current study provides a potential step change in resolution available to resolve genetic diversity within and among A. gigas populations, allowing a robust genome wide snapshot of variability to be quantified [4, 45]. The screened SNP markers were found to be in HWE with only two loci found to be statistically associated (LD). Further population analyses identified 335 neutral and 57 potential outlier loci. Putative outlier loci were incorporated in the dataset as they can provide valuable information on local adaptation and their inclusion is recommended in analysis of threatened species for aiding define conservation units [44].

The most striking finding of the survey was the extremely low level of genetic variability resolved, both within and between individuals from each of the wild population samples, overall being 3–8 times lower than comparable values for other fish species studied using the same methodology. A low level of detectable variability has also been reported for Northern pike (Esox lucius) [46] where very few SNPs were identified (c. 1% of RAD loci being polymorphic) using a RAD based methodology. Similarly, very low levels of polymorphism in populations of other fish species have been reported elsewhere, e.g. brown trout Salmo trutta [47], the estuarine black bream Acanthopagrus butcheri [48] and the shark Carcharhinus plumbeus [49]. Samples from the Amazon, Solimões and Tocantins rivers were caught by individual fishermen which, given the solitary behaviour of adult A. gigas in the Amazon floodplains, is likely to have minimised sampling bias [29, 31]. However, the sample from the Araguaia river was obtained during a rescue operation, when juvenile fish were trapped in a single small lagoon during the dry season and likely explains the particularly high genetic relatedness (0.323) recorded for this sample. It is likely that the observed lack of genetic variability within the A. gigas populations studied has been influenced by past bottleneck effects, both historic and those more recently documented [22, 32], while genetic drift may have accentuated genetic differences between rivers and regions. The analysis of polymorphism levels in pristine populations from more remote areas could help resolve this issue.

Population samples from the Amazon and Solimões rivers were more genetically variable (percentage of polymorphic loci, number of private alleles, Shannon’s index and observed heterozygosity) compared to fish from the Araguaia. Interestingly, samples from the river Tocantins and the captive stock showed intermediate diversity levels. These observations confirm and complement previous studies using mtDNA and microsatellite markers which indicated higher diversity in A. gigas sampled from the Amazon and Solimões compared to the river Tocantins [31, 38], and very low levels of genetic diversity in samples from the Araguaia river [30, 40]. The higher genetic diversity in populations from the Amazon basin has previously been suggested to be a consequence of the larger population size in these less urbanised environments. The increased severity of the droughts during the dry season in the Araguaia river [30], may also be a causal factor associated with lower genetic diversity in A. gigas in this drainage.

Importantly, all five population samples surveyed showed significant levels of relatedness (r). Mean population relatedness was negatively correlated with diversity indexes such as observed heterozygosity (HO) and Shannon’s Index, which is indicative of a degree of inbreeding, similar to that reported for mudminnows (Umbra krameri) [50]. An elevated degree of relatedness can be characteristic of populations restocked with related individuals [50]. Populations of A. gigas have been overexploited for more than 200 years, leading to reduced numbers (only 13% of original population estimates), and even extinction in many localities [31, 32]. Restocking with related individuals has been a common practice in some of these areas [32, 51, 52]. However, while high levels of relatedness were observed within each population studied, FST pairwise comparisons showed clear differentiation among populations. This mirrors findings from other genetic studies of A. gigas populations from Amazon, Tocantins and Araguaia rivers [29,30,31, 40], where past bottleneck events were considered to be the main factor underlying current population genetic structure [30, 31]. The relatively sedentary behaviour of A. gigas has been regarded as a major factor contributing to low genetic flow, resulting in local population differentiation [29, 40]. Ecological studies using radio-telemetry to monitor wild individuals also found strong patterns of residency and territoriality in A. gigas [53]. Indeed, a study evaluating the dispersal capacity of A. gigas concluded the existence of high levels of genetic similarity among lakes separated by 25 km, moderate genetic differentiation in sites separated by 100 km and highest genetic differentiation among regions separated by > 1500 km [29], supporting previous ecological observations. In the current study, the high inter-population differentiation together with the lack of support for isolation by distance and the elevated levels of mean population relatedness negatively correlated with diversity (I and HO), corroborate the hypothesis that genetic drift led to a loss of genetic variability and increased differentiation between populations of A. gigas [31]. Analyses of the captive population also illustrated aquaculture playing a role in fish translocation, with the studied broodstock having clearly multiple geographical origins.Translocation of arapaima for aquaculture development has been considered a key issue for the species conservation [22], and the developed SNP panel will greatly assist broodstock identification and pedigree management, prerequisites for the rational maintenance and monitoring of stock genetic diversity.

An initial population genetic study of A. gigas within the Amazon basin, using two discontinuous mitochondrial DNA regions of 1204 base-pairs (bp) (NADH1 segment) and 1143 bp (ATPase segment) revealed minimal evidence of substructuring, which loosely fitted an isolation-by-distance model [31]. A later study, based on seven microsatellite markers and using Bayesian analysis in STRUCTURE detected two distinct clusters, one comprising fish mostly from the lower Tocantins and lower Amazon rivers, the other comprising A. gigas predominantly from the mid- region of the Amazon river [29]. A more recent study of population sampled from the Essequibo river basin using 11 hypervariable microsatellite markers and mtDNA markers (NADH1 segment), identified patterns of allopatric differentiation within the species which led the authors to suggest that sympatric “species” could be present in three of the sampled sites [37]. In support of this finding, it was suggested that Arapaima belongs to a multispecies group based on morphological analyses [35, 36]. The current SNP-based analysis contrasts with these previous findings as it identified significant substructuring both within and between river basins. Though the data did not fit an isolation by distance model, this cannot be ruled out as only a few populations were available for analysis. Despite some high pairwise FST values, > 96% of the RAD loci identified as polymorphic contained only one or two SNPs. This is a much higher proportion than that observed for a range of other fish species using a comparable methodology. A Stacks-based analysis of crypto species would be expected to reveal higher levels of polymorphisms both among and within RADtags. Therefore, the present genetic data do not provide supportive evidence for genetically distinct species within the samples and populations analysed.

Clearly, evidence regarding the overall low levels of variability within populations and significant genetic structuring among populations should guide future management and conservation plans for wild A. gigas. Retaining as much diversity as possible should be a priority, and this can only be realistically achieved through coordinated conservation efforts over a broad geographic scale, and should be informed by regular genetic monitoring. Similarly, the findings of this study have specific implications for the development of commercial A. gigas aquaculture. A concern in all aquaculture enterprises, where high fecundity is the norm, is the potential for deleterious inbreeding. This will require particular awareness and careful genetic management for A. gigas. Also, given the low genetic diversity baseline, the potential for commercial traits gain through selective breeding may be limited. While now being widely employed for improvement of many aquaculture species such as the Nile tilapia (Oreochromis niloticus), the Atlantic salmon (Salmo salar), the rainbow trout (Oncorhynchus mykiss) and several others, reviewed by Yue [9], the application of genomic selection is also likely to be much more challenging for A. gigas, both in terms of marker development and number of pedigrees required to collect relevant data. It may be prudent to consider establishing farm strains from a wide range of wild populations, which would maximise genetic diversity and also act as gene banks for the species.

Conclusions

Though restricted by the unusually low level of polymorphism detected within the arapaima genome, the screening of hundreds of SNPs using ddRAD technology can be considered as a reliable and robust method to determine genetic variability within and between arapaima populations. Furthermore, these genetic markers have a role to play in identifying the origin of animals used in aquaculture, and in informing the maintenance of the genetic diversity of captive reared broodstocks. There is also the opportunity to develop a subset of the most informative SNPs for screening using alternative platforms (qPCR assays; small scale SNP chips), which require less labour intensive and less expensive protocols. Such a panel would probably be better suited than RAD for the much more extensive survey of arapaima populations that is required to better understand and manage the species. As a priority, surveys should focus on the different morphotypes of A. gigas (orange-fleshed and white-fleshed) and the contended species recently described for Arapaima [35, 36] which currently lacks support from molecular data. More detailed genetic studies (i.e. genome scans, QTL analyses, linkage mapping) need much more dense marker panels. While this would be feasible using RAD approaches in arapaima, it is clear that it would require the selection of restriction enzymes that cut Arapaima DNA much more frequently, which would necessitate a 10–50 fold higher sequencing effort than currently used, with associated budget implications.

Methods

Arapaima samples

This study analysed samples from the rivers Amazon (Iquitos, Perú), Solimões (Jarauá, AM, Brazil), Tocantins (Tucuruí, PA, Brazil), Araguaia (Lagoa da Confusão, TO, Brazil), and also a captive broodstock (Taipas, TO, Brazil). Twelve individuals per population / stock were randomly selected from a wider number of sampled specimens, giving a total of 60 individuals (Fig. 5). Samples from Amazon, Solimões and Tocantins rivers were collected from wild captured animals harvested by fisherman during the legal fishing season, samples from these fish having been previously studied using other molecular markers [29, 31]. Animals from Araguaia were opportunistically sampled during a rescue operation where juvenile fish that were trapped in a small lagoon were sampled and later released into an adjacent larger lagoon. The Captive broodstock comprised adults of unknown hydrographic origin, filial generation or degree of relatedness, and were sampled from a farm located in Taipas-TO, Brazil. Fin clips or muscle samples were fixed in 95% ethanol and kept at − 20 °C until DNA extraction at the University of Stirling (Scotland-UK). Table 5 includes information on geographical coordinates and total number of originally sampled fish in each location. All samples were collected in accordance with Brazilian regulations (process number 02001.007554/2005-76 IBAMA/MMA).

Fig. 5
figure 5

Map of Amazon region showing the natural distribution of Arapaima gigas according to Castello and Stewart [22] (by Marta E. Ummus using software ArcGIS). Sampling points include two wild populations from Amazon river Basin (Amazon - 1 and Solimões - 2), two wild populations from Tocantins-Araguaia Basin (Tocantins – 3 and Araguaia - 4), and one captive population (Captive - 5) from Tocantins state, Brazil

Full size image
Table 5 Studied populations of Arapaima gigas
Full size table

DNA extraction

For genomic DNA extraction, a fin clip or muscle sample from each of the 60 individuals was initially incubated for 16 h at 55 °C in a lysis solution containing 200 μL of SSTNE buffer [54], 20 μL 10% SDS, 5 μL proteinase K (10 mg.mL− 1). The temperature was then increased to 70 °C for 15 min to inactivate the proteinase K, before 5 μL RNAse A (2 mg.mL− 1) was added to each sample and further incubated at 37 °C for 1 h. To precipitate proteins, 160 μL 5 M NaCl (0.7 volumes) was then added, mixed and the tubes left on ice for 10 min. This mixture was centrifuged at 22,000 rcf for 10 min to pellet the proteins. A proportion of the supernatant (c. 300 μL) was then transferred to a new microfuge tube containing an equal volume of isopropanol, mixed to precipitate DNA and centrifuged at 22,000 rcf for 1 min to pellet the DNA. The supernatant was carefully removed and the DNA pellet was then washed in 1 mL 70% ethanol for 3 h, after which the 70% ethanol was replaced and the pellet washed for a further 16–20 h. Finally, the samples were centrifuged at 22,000 rcf for 5 min, the ethanol was discarded and the DNA pellet air dried and reconstituted over a 24-h period in 5 mM Tris (pH 8.0). The DNA quality and concentration were evaluated by spectrometry (NanoDrop; Thermo Scientific, USA). Agarose gel electrophoresis (0.8%) was used to check the integrity of the genomic DNA. Finally, DNA concentration prior to library preparation was more accurately quantified by fluorescence assay (Qubit 2.0, Thermo Scientific, USA).

Library preparation and sequencing

A ddRAD library was prepared according to a published method [19] while implementing minor modifications detailed in full in Brown and collaborators [7]. Duplicate restriction digestion reactions were undertaken for each arapaima DNA sample, with different barcoded adapter combinations being used for each restriction digestion. Following sequencing, the duplicate data sets for individuals were combined. Briefly, for each reaction 15 ng DNA was co-digested at 37 °C for 30 min with 0.3 U SbfI (‘rare’ cutter, CCTGCA|GG motif) and 0.3 U SphI (‘common’ cutter, GCATG|C motif) high fidelity restriction enzymes (New England Biolabs; NEB) in a 5 μL reaction volume that included 1× CutSmart™ buffer (NEB). After cooling the reactions to room temperature, 2.5 μL of a premade barcode-adapter mix was added to the digested DNA, and incubated at room temperature for 10 min. This adapter mix comprised individual-specific barcoded combinations of P1 (SbfI-compatible) and P2 (SphI-compatible) adapters at 6 and 72 nM concentrations respectively, in 1× reaction buffer 2 (NEB). Adapters were compatible with Illumina sequencing chemistry. The barcoded adapters were designed such that adapter–genomic DNA ligations did not reconstitute RE sites, while residual RE activity limited concatemerization of genomic fragments during ligation. The adapters included an inline five- or seven-base barcode for sample identification. Ligation was performed over 75 min at 22 °C by addition of a further 2.5 μL of a ligation mix comprising 4 mM rATP (Promega, UK), and 2000 cohesive-end units of T4 ligase (NEB) in 1× CutSmart buffer. The reactions were terminated by addition of 20 μL PB buffer (Minelute PCR Purification Kit; Qiagen UK). Each DNA sample was processed in duplicate (i.e. 120 separate digestion – ligation reactions). All 120 reactions were pooled and column-purified (MinElute PCR Purification Kit) and eluted in 52 μL of EB buffer (Qiagen, UK). The purified pooled sample was separated by agarose gel (1.1%) electrophoresis and fragments ranging from approximately 450 to 650 bp were excised and purified using a MinElute Gel Extraction Kit (Qiagen, UK). The eluted size-selected template DNA (c. 54 μL in EB buffer) was amplified by PCR (11 cycles; 20 separate 20-μL reactions, each with 1.5 μL of pooled template DNA) using Q5 Hot Start High-Fidelity DNA Polymerase (NEB) and Illumina compatible primers. The PCR reactions were combined (400 μL total) and column-purified with the MinElute PCR Purification Kit (Qiagen, UK), then eluted in 50 μL of EB buffer. The library was then re-purified, to ensure no carry over of PCR primer dimer or other low size range amplicons (< 200 bp) using an equal volume of AMPure magnetic beads (Perkin-Elmer, UK) and eluted in 15 μL EB buffer. Finally, the library was sequenced at the Institute of Aquaculture (University of Stirling, Scotland) on a single run on the Illumina MiSeq platform (162 bp paired end reads, 300 cycle kit, v2 chemistry kit; Illumina, Cambridge, UK).

ddRAD genotyping and comparative datasets

After sequencing, reads were de-multiplexed, low quality reads with missing restriction sites and containing ambiguous barcodes were excluded and sequences trimmed to 135 bases long using Stacks version 1.42 [55]. Given the lack of a reference genome for A. gigas, the RAD loci were assembled de novo using the following parameter settings: 4 as the minimum read depth to create a stack (m), 2 as the maximum number of mismatches in an individual (M) and 1 as the maximum mismatch between loci for building the catalogue (n). A robust set of SNPs suitable for population analyses was exported from the catalogue in Genepop format [56], using the ‘populations’ module within Stacks to select only those SNPs that were scored in at least 80% of individuals in each of the five populations and confined to RAD-tags that contained no more than two SNPs.

The levels of polymorphism within the A. gigas samples were compared to ddRAD data of other teleosts generated from other projects undertaken at the University of Stirling using the same methodology. These species and numbers of samples were: Cyprinus carpio L. (n = 85), Dicentrarchus labrax (L.) (n = 26), Oreochromis niloticus (L.) (n = 6), Clarias anguillaris (L.) (n = 5), Sprattus sprattus (L.) (n = 8), Ctenolabrus rupestris (L.) (n = 20) and Melanogrammus aeglefinus (L.) (n = 16). For each species, the mean (± SD) of the following parameters were calculated: number of unique stacks, number of polymorphic loci and number of SNPs obtained. Non-parametric Kruskal–Wallis one-way ANOVA followed by Dunn’s pairwise post hoc tests were used to compare the ratio between polymorphic loci and unique stacks obtained between species. Statistical analyses were conducted using Minitab version 17.3.1 (Minitab, PA, USA) with significance set at P < 0.05.

Analyses of genetic diversity and structure

Initially, tests for Hardy-Weinberg Equilibrium (HWE) and linkage disequilibrium (LD) were conducted for the SNP loci both within populations and across the global dataset (60 individuals, five populations) using Genepop version 4.6 [56]. To do so, the Markov chain Monte Carlo (MCMC) parameters used were 10,000 dememorizations, 20 batches and 5000 iterations per batch). The software Arlequin version 3.5.2.2 [57] was used to estimate FST values for each locus in the global population, to estimate pairwise genetic differentiation (FST) between populations (10,000 permutations; P < 0.01) and to identify outlier loci (hierarchical island model, 20,000 simulations, 100 demes simulated per group with 10 groups simulated). Sequential Bonferroni [58] corrections for type I errors were applied when multiple tests were performed.

To investigate the hypothesis of isolation by distance, the shortest waterway path among sampling sites (Captive excluded) was measured using Google Earth version 7.1.8 (https://www.google.com/earth). The software GenAlEx version 6.5 [59, 60] was used to perform a Mantel test in which geographical (km) and genetic (FST) distances were correlated. GenAlEx was also used to estimate percentage of polymorphic loci, number of private alleles, Shannon’s Information Index (I), expected (HE) and (HO) observed heterozygosity, and coefficient of inbreeding (FIS) from Weir and Cockerham [61]. In GenAlEx, pairwise individual relatedness was estimated using a method published by Lynch and Ritland [62] to calculate a square matrix of individual pairwise relatedness (r). The average of pairwise values was calculated using the “Pops mean” option, in which significance was tested using 1000 permutations and 1000 bootstrap resamplings to estimate 95% confidence intervals. Pearson Product Moment Correlations was used to correlate levels of relatedness (r) with Shannon’s I and HO using Minitab (P < 0.01).

Population structure was first investigated using a model-based approach, which assumes HWE and linkage equilibrium, implemented in STRUCTURE version 2.3.4 [63]. Initially, estimation of the K-value which maximizes the global likelihood of the dataset (50,000 burn-in; 100,000 MCMC; 10 independent runs per K; ranging K from 1 to 5) was made using an admixture model and frequencies were assumed correlated. Optimal K-value was determined by Evanno’s method [64] using the Best K pipeline of CLUMPAK program [65]. Using the best K-value, a final analysis was conducted with a burn-in of 250,000 using 500,000 MCMC and 10 replications. Results were then averaged and displayed using main pipeline of CLUMPAK. The analysis described above was independently conducted for the global dataset (5 populations), and then for the Amazon river basin (Amazon and Solimões) and the Tocantins-Araguaia river basin (Tocantins, Araguaia and Captive), separately.

Finally, population structure was explored using a discriminant analysis of principal components (DAPC), conducted in R version 3.3.2. using the package Adegenet v. 2.0.1. DAPC analysis is not a model-based approach, and it optimizes the variance between groups while minimizing the differences within clusters. It requires prior identification of a cluster number, which was made using the Bayesian Information Criterion (BIC). The function find.clusters was used to transform original data into principal components (PC), retaining 60 PCs in the analysis. The dapc function performed a discriminant analysis using 20 PCs (> 80% of variance explained) and 5 eigenvalues were retained and examined. The assign.per.pop function was used to evaluate the proportions of successful reassignment of individuals to their original clusters.

Abbreviations

BIC:

Bayesian Inference Criterion

bp:

base pair

CITES:

Convention on International Trade of Endangered Species of Wild Fauna and Flora

DAPC:

Discriminant analysis of principal components

ddRAD:

double digest restriction-site-associated DNA

FIS :

Coefficient of inbreeding

FST :

Fixation index

HE :

Expected heterozygosity

HO :

Observed heterozygosity

HWE:

Hardy-Weinberg Equilibrium

I:

Shannon’s Information Index

ISSR:

Inter simple sequence repeats

LD:

Linkage disequilibrium

MCMC:

Markov chain Monte Carlo

mtDNA:

mitochondrial DNA

NGS:

Next generation sequencing

PC:

Principal components

PCR:

Polymerase chain reaction

qPCR:

quantitative PCR

QTL:

Quantitative trait loci

RAD:

Restriction-site-associated DNA

SNP:

Single nucleotide polymorphism

References

  1. Morin PA, Luikart G, Wayne RK. SNPs in ecology, evolution and conservation. Trends Ecol Evol. 2004;19(4):208–16.

    Article  Google Scholar 

  2. Helyar SJ, Hemmer-Hansen J, Bekkevold D, Taylor MI, Ogden R, Limborg MT, Cariani A, Maes GE, Diopere E, Carvalho GR, et al. Application of SNPs for population genetics of nonmodel organisms: new opportunities and challenges. Mol Ecol Resour. 2011;11(1):123–36.

    Article  PubMed  Google Scholar 

  3. Andrews KR, Good JM, Miller MR, Luikart G, Hohenlohe PA. Harnessing the power of RADseq for ecological and evolutionary genomics. Nat Rev Genet. 2016;17(2):81–92.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Hohenlohe PA, Catchen J, Cresko WA. Population genomic analysis of model and nonmodel organisms using sequenced RAD tags. In: Pompanon F, Bonin A, editors. Data Production and Analysis in Population Genomics: Methods and Protocols, Methods in Molecular Biology, vol. 888. New York: Springer; 2012. p. 235–60.

    Chapter  Google Scholar 

  5. Zhanjiang L. Next generation sequencing and whole genome selection in aquaculture. Iowa: Wiley; 2011.

    Google Scholar 

  6. Palaiokostas C, Bekaert M, Khan MG, Taggart JB, Gharb K, McAndrew BJ, Penman DJ. A novel sex-determining QTL in Nile tilapia (Oreochromis niloticus). BMC Genomics. 2015;16(171):1–10.

    CAS  Google Scholar 

  7. Brown JK, Taggart JB, Bekaert M, Wehner S, Palaiokostas C, Setiawan AN, Symonds JE, Penman DJ. Mapping the sex determination locus in the hāpuku (Polyprion oxygeneios) using ddRAD sequencing. BMC Genomics. 2016;17(448):1–12.

    Google Scholar 

  8. Palaiokostas C, Bekaert M, Davie A, Cowan ME, Oral M, Taggart JB, Karim Gharbi K, McAndrew BJ, Penman DJ, Migaud H. Mapping the sex determination locus in the Atlantic halibut (Hippoglossus hippoglossus) using RAD sequencing. BMC Genomics. 2013;14(566):1–12.

    Google Scholar 

  9. Yue GH. Recent advances of genome mapping and marker-assisted selection in aquaculture. Fish Fish. 2014;15:376–96.

    Article  Google Scholar 

  10. Gilbey J, Cauwelier E, Coulson MW, Stradmeyer L, Sampayo JN, Armstrong A, Verspoor E, Corrigan L, Shelley J, Middlemas S. Accuracy of assignment of Atlantic salmon (Salmo salar l.) to rivers and regions in Scotland and Northeast England based on single nucleotide polymorphism (SNP) markers. PLoS One. 2016;11(10):e0164327.

    Article  PubMed  PubMed Central  Google Scholar 

  11. do Prado FD, Vera M, Hermida M, Bouza C, Pardo BG, Vilas R, Blanco A, Fernandez C, Maroso F, Maes GE, et al. Parallel evolution and adaptation to environmental factors in a marine flatfish: Implications for fisheries and aquaculture management of the turbot (Scophthalmus maximus). Evol Appl. 2018;11(8):1322–41.

    Article  PubMed  PubMed Central  Google Scholar 

  12. López ME, Benestan L, Moore JS, Perrier C, Gilbey J, Genova AD, Maass A, Diaz D, Lhorente JP, Correa K, et al. Comparing genomic signatures of domestication in two Atlantic salmon (Salmo salar L) populations with different geographical origins. Evol Appl. 2018; in press.

  13. Laconcha U, Iriondo M, Arrizabalaga H, Manzano C, Markaide P, Montes I, Zarraonaindia I, Velado I, Bilbao E, Goni N, et al. New nuclear SNP markers unravel the genetic structure and effective population size of albacore tuna (Thunnus alalunga). PLoS One. 2015;10(6):e0128247.

    Article  PubMed  PubMed Central  Google Scholar 

  14. Petereit C, Bekkevold D, Nickel S, Dierking J, Hantke H, Hahn A, Reusch T, Puebla O. Population genetic structure after 125 years of stocking in sea trout (Salmo trutta L.). Conserv Genet. 2018;19(5):1123–36.

    Article  CAS  Google Scholar 

  15. Bian L, Su YQ, Gaffney PM. Development of SNP markers for analysis of population structure in white perch (Morone americana) using double digest restriction site-associated DNA sequencing. Conserv Genet Resour. 2016;8:403–6.

    Article  Google Scholar 

  16. Stapley J, Reger J, Feulner PGD, Smadja C, Galindo J, Ekblom R, Bennison C, Ball AD, Beckerman AP, Slate J. Adaptation genomics: the next generation. Trends Ecol Evol. 2010;25(12):705–12.

    Article  PubMed  Google Scholar 

  17. Carreras C, Ordóñez V, Zane L, Kruschel C, Nasto I, Macpherson E, Pascual M. Population genomics of an endemic Mediterranean fish: differentiation by fine scale dispersal and adaptation. Sci Rep. 2017;7(43417):1–12.

    Google Scholar 

  18. Baird NA, Etter PD, Atwood TS, Currey MC, Shiver AL, Lewis ZA, Selker EU, Cresko WA, Johnson EA. Rapid SNP discovery and genetic mapping using sequenced RAD markers. PLoS One. 2008;3(10):e3376.

    Article  PubMed  PubMed Central  Google Scholar 

  19. Peterson BK, Weber JN, Kay EH, Fisher HS, Hoekstra HE. Double digest RADseq: an inexpensive method for SNP discovery and genotyping in model and non-model species. PLoS One. 2012;7(5):1–11.

    Google Scholar 

  20. Nelson JS, Grande TC, Wilson MVH. Fishes of the world. 5th ed. New Jersey: Wiley; 2016.

    Book  Google Scholar 

  21. Oliveira EG, Pinheiro AB, Oliveira VQ, Silva ARM Jr, Moraes MG, Rocha IRCB, Sousa RR, Costa FHF. Effects of stocking density on the performance of juvenile pirarucu (Arapaima gigas) in cages. Aquaculture. 2012;370–71:96–101.

    Article  Google Scholar 

  22. Castello L, Stewart DJ. Assessing CITES non-detriment findings procedures for Arapaima in Brazil. J Appl Ichthyol. 2010;26(1):49–56.

    Article  Google Scholar 

  23. Godinho HP, Santos JE, Formagio PS, Guimarães-Cruz RJ. Gonadal morphology and reproductive traits of the Amazonian fish Arapaima gigas (Schinz, 1822). Acta Zool. 2005;86:289–94.

    Article  Google Scholar 

  24. Castello L. Nesting habitat of Arapaima gigas (Schinz) in Amazonian floodplains. J Fish Biol. 2008;72(6):1520–8.

    Article  Google Scholar 

  25. Castello L. Lateral migration of Arapaima gigas in floodplains of the Amazon. Ecol Freshw Fish. 2008;17(1):38–46.

    Article  Google Scholar 

  26. Farias IP, Leão A, Crossa M, Almeida YS, Honczaryk A, Verba JT, Hrbek T. Evidence of polygamy in the socially monogamous Amazonian fish Arapaima gigas (Schinz, 1822) (Osteoglossiformes, Arapaimidae). Neotrop Ichthyol. 2015;13(1):195–204.

    Article  Google Scholar 

  27. Queiroz HL. Natural history and conservation of Pirarucu, Arapaima gigas, at the Amazonian Várzea: red giants in muddy waters. St Andrews: University of St Andrews; 2000.

    Google Scholar 

  28. Neves AMB. Conhecimento atual sobre o Pirarucu, Arapaima gigas (Cuvier 1817). Bol Mus Para Emílio Goeldi. 1995;11(1):33–56.

    Google Scholar 

  29. Araripe J, Rêgo PS, Queiroz H, Sampaio I, Schneider H. Dispersal capacity and genetic structure of Arapaima gigas on different geographic scales using microsatellite marker. PLoS One. 2013;8(1):1–7.

    Article  Google Scholar 

  30. Vitorino CA, Oliveira RCC, Margarido VP, Venere PC. Genetic diversity of Arapaima gigas (Schinz, 1822) (Osteoglossiformes: Arapaimidae) in the Araguaia-Tocantins basin estimated by ISSR marker. Neotrop Ichthyol. 2015;13(3):557–68.

    Article  Google Scholar 

  31. Hrbek T, Farias IP, Crossa M, Sampaio I, Porto JIR, Meyer A. Population genetic analysis of Arapaima gigas, one of the largest freshwater fishes of the Amazon basin: implications for its conservation. Anim Conserv. 2005;8(3):297–308.

    Article  Google Scholar 

  32. Castello L, Stewart DJ, Arantes CC. Modeling population dynamics and conservation of arapaima in the Amazon. Rev Fish Biol Fish. 2011;21(3):623–40.

    Article  Google Scholar 

  33. Núñez J, Chu-Koo F, Berland M, Arévalo L, Ribeyro O, Duponchelle F, Renno J. Reproductive success and fry production of the paiche or pirarucu, Arapaima gigas (Schinz), in the region of Iquitos, Perú. Aquac Res. 2011;42(6):815–22.

    Article  Google Scholar 

  34. Sevilhano T, Carvalho RF, Oliveira NAJ, Oliveira JE, Maltarollo VG, Trossini G, Garcez R, Bartolini P. Molecular cloning and characterization of pirarucu (Arapaima gigas) follicle-stimulating hormone and luteinizing hormone β-subunit cDNAs. PLoS One. 2017;12(8):e0183545.

    Article  PubMed  PubMed Central  Google Scholar 

  35. Stewart DJ. Re-description of Arapaima agassizii (Valenciennes), a rare fish from Brazil (Osteoglossomorpha: Osteoglossidae). Copeia. 2013;2013(1):38–51.

    Article  Google Scholar 

  36. Stewart DJ. A new species of Arapaima (Osteoglossomorpha: Osteoglossidae) from the Solimões River, Amazonas state, Brazil. Copeia. 2013;2013(3):470–6.

    Article  Google Scholar 

  37. Watson LC, Stewart DJ, Kretzer AM. Genetic diversity and population structure of the threatened Giant Arapaima in southwestern Guyana: implications for their conservation. Copeia. 2016;104(4):864–72.

    Article  Google Scholar 

  38. Hrbek T, Crossa M, Farias IP. Conservation strategies for Arapaima gigas (Schinz, 1822) and the Amazonian várzea ecosystem. Braz J Biol. 2007;67(4):909–17.

    Article  CAS  PubMed  Google Scholar 

  39. Hamoy IG, Santos EJM, Santos SEB. Rapid and inexpensive analysis of genetic variability in Arapaima gigas by PCR multiplex panel of eight microsatellites. Genet Mol Res. 2008;7(1):29–32.

    Article  CAS  PubMed  Google Scholar 

  40. 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(159):1–10.

    Google Scholar 

  41. Marques DK, Venere PC, Galetti PM Jr. Chromosomal characterization of the bonytongue Arapaima gigas (Osteoglossiformes: Arapaimidae). Neotrop Ichthyol. 2006;4(2):215–8.

    Article  Google Scholar 

  42. Rosa R, Rubert M, Caetano-Filho M, Giuliano-Caetano L. Conserved cytogenetic features in the Amazonian Arapaima, Arapaima gigas (Schinz 1822) from Jamari River, Rondônia–Brazil. Open Biol. 2009;2:91–4.

    Article  Google Scholar 

  43. Almeida IG, Ianella P, Faria MT, Paiva SR, Caetano AR. Bulked segregant analysis of the pirarucu (Arapaima gigas) genome for identification of sex-specific molecular markers. Genet Mol Res. 2013;12(4):6299–308.

    Article  CAS  PubMed  Google Scholar 

  44. Moore JS, Bourret V, Dionne M, Bradbury I, O’ Reilly P, Kent M, Chaput G, Bernatchez L. Conservation genomics of anadromous Atlantic Salmon across its north american range: outlier loci identify the same patterns of population structure as neutral loci. Mol Ecol. 2014;23(23):5680–97.

    Article  CAS  PubMed  Google Scholar 

  45. Jeffries DL, Copp GH, Handley LL, Olsén KH, Sayer CD, Hänfling B. Comparing RADseq and microsatellites to infer complex phylogeographic patterns, an empirical perspective in the crucian carp, Carassius carassius, L. Mol Ecol. 2016;25(13):2997–3018.

    Article  PubMed  Google Scholar 

  46. Rondeau EB, Minkley DR, Leong JS, Messmer AM, Jantzen JR, von Schalburg KR, Lemon C, Bird NH, Koop BF. The genome and linkage map of the northern pike (Esox lucius): conserved Synteny revealed between the salmonid sister group and the Neoteleostei. PLoS One. 2014;9(7):e102089.

    Article  PubMed  PubMed Central  Google Scholar 

  47. Prodöhl PA, Walker AF, Hynes R, Taggart JB, Ferguson A. Genetically monomorphic brown trout (Salmo trutta L.) populations, as revealed by mitochondrial DNA, multilocus and single-locus minisatellite (VNTR) analyses. Heredity (Edinb). 1997;79:208–13.

    Article  Google Scholar 

  48. Yap ES, Spencer PBS, Chaplin JA, Potter IC. The estuarine teleost, Acanthopagrus butcheri (Sparidae), shows low levels of polymorphism at five microsatellite loci. Mol Ecol. 2003;9(12):2224–5.

    Article  Google Scholar 

  49. Heist EJ, Gold JR. Microsatellite DNA variation in sandbar sharks (Carcharhinus plumbeus) from the Gulf of Mexico and mid-Atlantic bight. Copeia. 1999;1:182–6.

    Article  Google Scholar 

  50. Takács P, Erős T, Specziár A, Sály P, Vitál Z, Ferincz A, Molnár T, Szabolcsi Z, Bíró P, Csoma E. Population genetic patterns of threatened European Mudminnow (Umbra krameri Walbaum, 1792) in a fragmented landscape: implications for conservation management. PLoS One. 2015;10(9):e0138640.

    Article  PubMed  PubMed Central  Google Scholar 

  51. Garcia-Dávila C, Castro-Ruiz C, Chota-Macuyama W, Biffi C, Deza S, Bazan R, Garcia J, Rebaza M, Rebaza C, Chavez C, et al. Caracterización genética de ejemplares de paiche Arapaima gigas (Cuvier, 1829) utilizados en el repoblamiento del lago Imiria (cuenca del río Ucayali). Folia Amaz. 2011;20(1–2):67–75.

    Article  Google Scholar 

  52. Castello L, Arantes CC, Mcgrath DG, Stewart DJ, Sousa FS. Understanding fishing-induced extinctions in the Amazon. Aquat Conserv. 2015;25(5):587–98.

    Article  Google Scholar 

  53. Núñez J, Duponchelle F, Cotrina-Doria M, Renno JF, Chavez-Veintimilla C, Rebaza C, Deza S, Dávila CG, Chu-Koo F, Tello S, et al. Movement patterns and home range of wild and re-stocked Arapaima gigas (Schinz, 1822) monitored by radio-telemetry in Lake Imiria, Peru. J Appl Ichthyol. 2015;31(4):10–8.

    Article  Google Scholar 

  54. Blanquer A. Phylogeographie intraspecifique d’un poisson marin, le flet Platichthys flesus L. (Heterosomata). Polymorphisme des marqueurs nucleaires et mitochondriaux. Montpellier: University of Montpellier; 1990.

    Google Scholar 

  55. Catchen J, Hohenlohe P, Bassham S, Amores A, Cresko W. Stacks: an analysis tool set for population genomics. Mol Ecol Notes. 2013;22(11):3124–40.

    Article  Google Scholar 

  56. Rousset F. GENEPOP’007: a complete re-implementation of the GENEPOP software for windows and Linux. Mol Ecol Resour. 2008;8:103–6.

    Article  PubMed  Google Scholar 

  57. Excoffier L, Lischer HEL. Arlequin suite ver 3.5: a new series of programs to perform population genetics analyses under Linux and windows. Mol Ecol Resour. 2010;10:564–7.

    Article  PubMed  Google Scholar 

  58. Rice WR. Analyzing tables of statistical tests. Evolution. 1989;43(1):223–5.

    Article  PubMed  Google Scholar 

  59. Peakall R, Smouse PE. GENALEX 6: genetic analysis in excel. Population genetic software for teaching and research. Mol Ecol Notes. 2006;6:288–95.

    Article  Google Scholar 

  60. Peakall R, Smouse PE. GenAlEx 6.5: genetic analysis in excel. Population genetic software for teaching and research—an update. Bioinformatics. 2012;28(19):2537–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Weir BS, Cockerham CC. Estimating F-statistics for the analysis of population structure. Evolution. 1984;38(6):1358–70.

    CAS  PubMed  Google Scholar 

  62. Lynch M, Ritland K. Estimation of pairwise relatedness with molecular markers. Genetics. 1999;152:1753–66.

    CAS  PubMed  PubMed Central  Google Scholar 

  63. Pritchard JK, Stephens M, Donnelly P. Inference of population structure using multilocus genotype data. Genetics. 2000;155:945–59.

    CAS  PubMed  PubMed Central  Google Scholar 

  64. Evanno G, Regnaut S, Goudet J. Detecting the number of clusters of individuals using the software STRUCTURE: a simulation study. Mol Ecol. 2005;14:2611–20.

    Article  CAS  PubMed  Google Scholar 

  65. Kopelman NM, Mayzel J, Jakobsson M, Rosenberg NA, Mayrose I. CLUMPAK: a program for identifying clustering modes and packaging population structure inferences across K. Mol Ecol Resour. 2015;15(5):1179–91.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Palaiokostas C, Bekaert M, Taggart JB, Gharbi K, McAndrew B, Chatain B, Penman DJ, Vandeputte M. A new SNP-based vision of the genetics of sex determination in European sea bass (Dicentrarchus labrax). Genet Sel Evol. 2015;47(68):1–10.

    Google Scholar 

  67. Jansson E, Taggart JB, Mortensen S, Wehner S, Kvamme BO, Dahle G, Glover KA. Development of SNP and microsatellite markers for goldsinny wrasse (Ctenolabrus rupestris) from ddRAD sequencing data. Conserv Genet Resour. 2016;8(3):201–6.

    Article  Google Scholar 

  68. Jombart T. adegenet: a R package for the multivariate analysis of genetic markers. Bioinformatics. 2008;24(11):1403–5.

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

Authors thank Marta E. Ummus for elaborating a map in ArcGIS.

Funding

This project was supported by the Ministry of Agriculture, Livestock, and Food Supply through CNPq (Grants. N. 457465/2012-3 and 434400/2016-5) and by the Brazilian Micro and Small Business Support Service SEBRAE (FAPTO Grant N. 2538/2012) to Eduardo Varela. The Brazilian Agricultural Research Corporation (EMBRAPA) provided support in the form of salaries to LT and EV, but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. The specific roles of these authors are articulated in the ‘Authors’ contributions’ section.

Availability of data and materials

Illumina raw sequence reads used in this work are available for download from the European Nucleotide Archive under project no. PRJEB26083.

Author information

Authors and Affiliations

  1. EMBRAPA Pesca e Aquicultura, Palmas, TO, CEP 77008-900, Brazil

    Lucas Simon Torati & Eduardo Sousa Varela

  2. Institute of Aquaculture, University of Stirling, Stirling, FK9 4LA, Scotland, UK

    Lucas Simon Torati, John Bernard Taggart & Hervé Migaud

  3. Instituto de Estudos Costeiros, Campus de Bragança, Universidade Federal do Pará, Bragança, PA, CEP 68600-000, Brazil

    Juliana Araripe

  4. Max Planck Institute of Psychiatry, Kraepelinstr. 2-10, 80804, Munich, Germany

    Stefanie Wehner

Authors
  1. Lucas Simon Torati
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. John Bernard Taggart
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Eduardo Sousa Varela
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Juliana Araripe
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Stefanie Wehner
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Hervé Migaud
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Experimental concept and design sample selection: LT, HM, EV, JA. Tissue collection: LT, HM, EV, JA. ddRAD library construction, MiSeq sequencing, genomic data processing: JBT, SW, LT. Manuscript writing and figures: LT. Manuscript review: all authors contributed to and approved the final manuscript.

Corresponding author

Correspondence to Lucas Simon Torati.

Ethics declarations

Ethics approval and consent to participate

All samples were collected in accordance with Brazilian regulations (process number 02001.007554/2005-76 IBAMA/MMA), have approval from the National System for the Management of Genetic Heritage and Associated Traditional Knowledge – SISGen (AA4F2B0) and no ethics approval was necessary to conduct this study.

Consent for publication

Not applicable.

Competing interests

LT and EV are employed by EMBRAPA, a Brazilian agricultural research corporation. There are no patents, products in development or marketed products to declare. This does not alter the authors’ adherence to all the Biological Conservation policies on sharing data and materials. The authors declare that they have no competing interests.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Additional files

Additional file 1:

General information and summary data from the ddRAD study of Arapaima gigas: a) the combinatorial inline barcodes used for each individual - DNA samples were processed in duplicate, with a different barcode combination used for each RE digestion - ligation reaction, thus each individual is represented by two sets of identifiers; b) basic RAD locus statistics generated by STACKS for each of the 60 samples; c) mean observed heterozygosity within the 392 loci surveyed for each individual. (XLSX 14 kb)

Additional file 2:

Sequence information and associated basic genetic statistics for each of the 393 SNPs used in this study. The position of assayed SNP is indicated by red font within each 135 base RADTag sequence, together with basic genetic statistics (computed with GenAlEx) for the combined sample set (n = 60). (XLSX 60 kb)

Additional file 3:

A. Discriminant analysis of principal components (DAPC) using 392 SNP markers in Adegenet v. 2.0.1 [68] for the five Arapaima gigas populations sampled (n = 60 individuals). B. Selection of number of clusters was based on Bayesian Inference Criterion (BIC), which indicates 3 clusters for data summarization (elbow drop). C. Membership probabilities (red = 1, white = 0) for individuals into clusters, blue crosses indicate the prior cluster provided into DAPC. (TIF 2254 kb)

Rights and permissions

Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Torati, L.S., Taggart, J.B., Varela, E.S. et al. Genetic diversity and structure in Arapaima gigas populations from Amazon and Araguaia-Tocantins river basins. BMC Genet 20, 13 (2019). https://doi.org/10.1186/s12863-018-0711-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12863-018-0711-y

Keywords

BMC Genomic Data

ISSN: 2730-6844

Contact us