The largest fish in the world’s biggest river: Genetic connectivity and conservation of Arapaima gigas in the Amazon and Araguaia-Tocantins drainages

Izeni Pires Farias; Stuart Willis; Adam Leão; Júlia Tovar Verba; Marcelo Crossa; Fausto Foresti; Fabio Porto-Foresti; Iracilda Sampaio; Tomas Hrbek

doi:10.1371/journal.pone.0220882

Abstract

Arapaima, pirarucu or paiche (Arapaima gigas) is one of the largest freshwater fish in the world, and has a long history of commercial exploitation in the Amazon region. To estimate levels of genetic variability and historical and recent connectivity in Arapaima, we examined variation in eleven microsatellite DNA markers in individuals from 22 localities in Brazil, Colombia, and Peru. The results of analysis of molecular variance, Bayesian clustering and discriminant analysis of principal components showed that Arapaima in our samples represents two major populations, one in the Amazonas and one in the Araguaia-Tocantins River basins. The Amazonas population is further structured by isolation-by-distance with the hydrologically largely unconnected Amapá locality representing the eastern-most extreme of this continuum; gene flow predominates at distances of less than 1500 km with localities separated by over 2000 km dominated by genetic drift and effectively forming different populations. We saw no evidence of multiple species of Arapaima in the Amazonas basin, and analysis of pairwise genetic divergence (F_ST) with Mantel tests and correlograms indicated that this largest population exhibits a large-scale pattern of isolation-by-distance, with which results from MIGRATE-N agreed. The degree and significance of genetic divergence indicates that most sampled localities represent demographically independent sub-populations, although we did identify several recent migration events between both proximal and more distant localities. The levels of genetic diversity were heterogeneous across sites, including low genetic diversity, effective population sizes, and evidence of genetic bottlenecks in several places. On average the levels of gene diversity and rarefied allelic richness were higher for localities along the Amazonas mainstem than in the tributaries, despite these being the areas of highest fishing pressure, while the lowest values were found in tributary headwaters, where landscape modification is a significant threat. We recommend that managers consider the regional and local threats to these populations and tailor strategies accordingly, strategies which should ensure the ability of young A. gigas to disperse through floodplain corridors to maintain genetic diversity among otherwise sedentary adult sub-populations.

Citation: Farias IP, Willis S, Leão A, Verba JT, Crossa M, Foresti F, et al. (2019) The largest fish in the world’s biggest river: Genetic connectivity and conservation of Arapaima gigas in the Amazon and Araguaia-Tocantins drainages. PLoS ONE 14(8): e0220882. https://doi.org/10.1371/journal.pone.0220882

Editor: Windsor E. Aguirre, DePaul University, UNITED STATES

Received: January 4, 2019; Accepted: July 25, 2019; Published: August 16, 2019

Copyright: © 2019 Farias et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: Data are available at: (https://github.com/legalLab/publications), in the folder "Farias_et_al_2019". The direct link is: https://github.com/legalLab/publications/tree/master/Farias_et_al_2019.

Funding: This research was supported by the National Council for Scientific and Technological Development of Brazil (Conselho Nacional de Desenvolvimento Científico e Tecnológico) grants: MCT/CNPq/PPG7 557090/2005-9, CNPq/CT-Amazonia 554057/2006-9 and CNPq/CT-Amazonia 575603/2008-9 to IPF, and CNPq/MPA 42/2012 and CNPq 482662/2013-1 to TH, and the State of Amazonas Research Support Foundation (Fundação de Amparo à Pesquisa do Estado do Amazonas) grants: Tematico 899/03 and Universal 062.01341/2018 to IPF. IPF and TH were supported by a Bolsa de Pesquisa scholarship from CNPq during the study. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

Introduction

The Amazon basin suffers from the myth of superabundance, wherein the natural resources present in this region, including its impressive diversity of fishes, are considered inexhaustible by any human demand [1, 2]. This myth derives in part from the huge scale of the Amazon, its high biodiversity, and its low relative human occupational density, since the perceived abundance of a region’s natural resources is directly related to the intensity of exploitation [2]. Indeed this myth of superabundance has been promulgated since the Portuguese colonized the Amazon basin, and particularly during the “boom” of latex rubber export [3]. However, there are well documented cases of drastic reductions in exploited populations, including the black (Melanosuchus niger) and spectacled caimans (Caiman crocodilus) [4, 5], the Amazonian manatee (Trichechus inunguis) [6], numerous species of turtles (Podocnemis spp.) [7], and what may be South America’s largest freshwater fish, the arapaima, pirarucu or paiche (Arapaima gigas Schinz 1822) [8]. Although the iconic arapaima continues to be exploited, it is described on the IUCN Red List as Data Deficient (IUCN, 2017), meaning there is insufficient knowledge of its biology, ecology, and genetics to effectively manage its conservation.

Another prominent uncertainty surrounding the arapaima is the number of species present in this genus and their distribution. In two recent publications, Stewart [9, 10] revised the taxonomy of the genus, revalidated several species and described a new species of Arapaima from the central Amazon [9]. Stewart [9, 10] puts forth an argument that at least six species of Arapaima exist: 1) Arapaima gigas (Schinz, in Cuvier 1822) described from near [Vila] Santarém, Para State, Brazil, only known from the holotype (MNHN A.8837); 2) Arapaima mapae (Valenciennes, in Cuvier and Valenciennes, 1847) described from Lago do Amapá or Lago Grande in Região dos Lagos in Amapá State, Brazil, only known from the holotype (MNHN A.8836); 3) Arapaima arapaima (Valenciennes, in Cuvier and Valenciennes, 1847) described from Guyana (Essequibo basin), only known from the holotype (BMNH 2009.1.19.1) but the holotype is misplaced or lost; 4) Arapaima agassizii (Valenciennes, in Cuvier and Valenciennes, 1847) described from “Brazilian Amazon”, only known from an illustration of the holotype by Spix and Agassiz [11] and the holotype is lost; 5) Arapaima leposoma Steward 2013 described from one specimen collected from the Solimões River shortly upstream of the mouth of the Purus River, Amazonas State, Brazil, only known from the holotype (INPA 16847); and 6) Arapaima sp. incertae sedis, i.e. a species of uncertain taxonomic status, that apparently comprises all other Amazon basin Arapaima specimens deposited in scientific collections.

Günther in 1868 [12] synonymized all the species of arapaima described by Valenciennes, and Spix and Agassiz with Arapaima gigas (Schinz, in Cuvier 1822). In contrast to Günther [12],Stewart [9, 10] concluded that all former species should be considered valid, although they are known only from type specimens, their distributions are unknown, and all Arapaima specimens deposited in scientific collections—specimens sampled throughout the Amazon basin—are Arapaima sp. incertae sedis. Thus, although the specimens analyzed in this study may be Arapaima sp. incertae sedis, until further taxonomic clarification we take the conservative approach and refer to the specimens analyzed in this study as Arapaima gigas.

Arapaima gigas is one of the largest freshwater fishes in South America, competing only with the catfish Brachplatystoma filamentosum for that title, and can reach a length of three meters and weigh more than 200 kg [13, 14]. It is primarily piscivorous [15, 16], and the feeding habits of these large-bodied fishes provide top-down trophic regulation in floodplain ecosystems [17]. Although as adults A. gigas are primarily sedentary fish with low dispersal capability [18], they do make seasonal migrations between permanent wetlands and nearby floodplains (várzea or igapó). During the dry season, A. gigas inhabits permanent wetlands, such as slow-moving rivers and lagoons where adults develop their gonads, engage in courtship, build nests, and reproduce. During the rainy season when water levels rise, A. gigas migrate to the floodplain, where the males provide parental care including mouth brooding, and young exploit the abundant resources of the flooded zones. As the water levels fall, parental care ceases and adult A. gigas migrate back to the permanent wetlands. It is in the permanent wetlands where most fishing occurs, and A. gigas are particularly vulnerable to harpooning as these obligate air breathers surface to refresh their air bladder [19]. Individuals generally become reproductively mature after 3–5 years of age [20] and may live upwards of 15–20 years. Batch fecundity is low, however [21].

Arapaima gigas is native to, and historically common in, the lowland Amazonas basin and the Araguaia-Tocantins basin, the later of which is considered a separate drainage by some authors, although it is connected to the Amazonas by uninterrupted freshwater and exhibits a related biota [22]. In the late 1970’s the species was also unintentionally introduced into the Bolivian Amazon [23]. In a pioneering study of A. gigas population genetics, Hrbek et al. [24] used mitochondrial DNA sequences from 120 individuals from six sites along the main channel of the Amazon basin and one site in the Araguaia-Tocantins basin and observed greater genetic diversity (haplotype diversity) in A. gigas far from large urban centers, where arapaima meat sales and distribution centers are concentrated. These data also suggested that the effective population size of this species had declined along with known decreases in census population density after two centuries of commercial exploitation, and that the Bolivian population was introduced from the Peruvian Amazon. Later, nuclear microsatellite data from the same sites revealed a pattern of isolation-by-distance along the Amazon River main stem [25]. Similar findings were reported by Araripe et al. [26].

In the present study, we expand on previous sampling to include sites throughout the Amazon basin, including the main axis of the Amazon basin and its main tributaries, and in the Araguaia-Tocantins watershed, to examine the distribution of genetic diversity and the pattern and magnitude of population structure. We sought to test if (i) the pattern of isolation-by-distance of A. gigas populations remains throughout the Amazon and Tocantins, (ii) if population genetic structure indicated that some areas exhibited unique genetic variation indicative of historical or sustained divergence, and (iii) whether indications of declines in population genetic diversity were only present near urban centers or were evident throughout the range of this fish.

Material and methods

Ethics statement

Permission to collect samples of was granted by IBAMA Permit Number 11325–1.

Sampling

We analyzed 517 individuals of Arapaima gigas sampled from 19 locations in the Amazon basin and three locations in the Araguaia-Tocantins basin (Fig 1 and Table 1). Samples were from natural populations and were collected by the fishing communities at each location. Tissue samples were preserved in 95% alcohol and deposited in the Coleção de Tecidos da Genética Animal (CTGA) of the Laboratório de Evolução e Genética Animal (LEGAL) at the Universidade Federal do Amazonas (UFAM) in Manaus, Brazil.

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Fig 1. Map of collecting localities of Arapaima gigas.

Pie plots indicate average population ancestry of each of the three main biological clusters detected in STRUCTURE analysis (see Fig 2). Color scheme is same as in Fig 2, K = 3.

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Table 1. Sampling areas of Arapaima gigas analyzed in the present study.

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DNA samples were extracted using the Qiagen extraction kit. Eleven microsatellite loci were amplified following Farias et al. [27] for Arapaima gigas. Loci used in this study were: CTm3, CTm4, CTm5, CTm7, CTm8, CAm2, CAm13, CAm15, CAm16, CAm20 and Cam26. PCR products were generated with labeled primers and visualized on a Megabace 1000 DNA automatic sequencer (GE-Healthcare). Allele sizes were scored against an internal ET-400 ROX size standard. Individuals were genotyped using the Genetic Profiler and Fragment Profiler (GE-Healthcare). MICRO-CHECKER v2.2.3 [28] was used to detect possible errors due to genotyping, null alleles or stutters. The matrix of genotypes is available at https://github.com/legalLab/publications.

Data analysis

Various genetic diversity parameters, including the observed heterozygosity, gene diversity (expected heterozygosity) and the number of alleles per locus was estimated using Arlequin 3.5 [29]. As richness estimates are constrained by sample size [30], we implemented the rarefaction analysis in the program HP-Rare [31] so that the number of alleles and allelic richness estimates could be compared between samples localities. Heterozygosity estimates are less influenced by sample size [32], so no correction was applied. Additionally, we estimated endogamy/inbreeding coefficient (F_IS) within each sampling site using Arlequin 3.5 [29].

To identify major population structuring patterns in the data, we utilized Bayesian clustering of individuals in the program STRUCTURE 2.3.3 [33]. This analysis clusters individual into a pre-defined number of populations (K) that minimize deviations from Hardy-Weinberg predictions and linkage disequilibrium among loci. We performed 10 independent runs for each predetermined number of biological groups (K = 1 to 22; considering that each location could be a different biological group), each run consisting of 1,000,000 MCMC chains after having discarded the first 100,000 chains as burn-in. We used the ‘admixture’ and ‘correlated-allelic-frequencies’ models with and without location information as a prior [34]. The location prior suggests that individuals sampled in the same locality are likely to belong to the same cluster, but it is considered a weak prior, while the admixture model allows individuals to have ancestry from multiple clusters. The optimal number of clusters was inferred based on changes in the precision of clustering with different K (delta K) [35]. While STRUCTURE clusters individuals, SAMOVA 2.0 [36] clusters a priori sampling groups (localities) into a pre-defined number of groups (K) to maximize the genetic variance among groups in a hierarchical AMOVA framework [37]. We clustered localities using 10,000 permutations from 100 starting groupings for K = 1 to 10 (upper value of K guided by STRUCTURE results) both with and without explicit geographical information. We quantified the genetic variance and significance of the STRUCTURE and SAMOVA clusterings using hierarchical AMOVA in Arlequin 3.5 [29] using 10,000 permutations with genetic distance based on allele identity. Finally, we also used a multivariate ordination approach implemented in the Discriminant Analysis of Principal Components (DAPC) [38] using the R package Adegenet 2.1 [39] in R [40]. This procedure uses a discriminant analysis (DA) to maximize the among-group variance in components from a principal components analysis (PCA) of samples assigned to pre-defined groupings (here, sampling localities). This DAPC retained 22 PC axes and 4 discriminant axes.

Historical and contemporaneous gene flow and demography

We looked for additional spatial patterns of gene flow by testing for isolation-by-distance through correlation of genetic and geographic distances using the Mantel test [41] implemented in Arlequin 3.5 [29]. Here, geographical distances, in km, followed the course of rivers, and the genetic distance matrix contained Slatkin linear pair-wise F_ST values based on allele identity. The geographical distance between the sampling localities was estimated by tools available on Google Earth, version 4.2 beta 2008 (Google). We also calculated a Mantel correlogram using the vegan package 2.4–5 [42] in R. Distance classes used in the Mantel correlogram ranged from 0 to 5500 km in steps of 500 km, and 5500+ km. This analysis allows for discrimination of migration-dominated and genetic drift-dominated evolutionary processes across spatial scales.

Additionally, we estimated historical and recent gene flow. First, gene flow was estimated by Bayesian analysis using MIGRATE-N version 3.6.11 [43]. Recent dispersal or migration was estimated using, STRUCTURE, and by population assignment in GENODIVE 2b27 [44]. For MIGRATE-N we ran 10 short chains, sampling each chain 10,000 times. We then sampled 500,000 topologies from one long chain, discarding the first 5,000 samples as burn-in. Search of parameter space was improved though adaptive swapping among four heated chains. MIGRATE analyses were repeated three times with random seeds to assess convergence.

We used population assignment in STRUCTURE and GENODIVE v2b27 [44] to identify recent migrants. In STRUCTURE, we specified the population (sample site) origin of each individual, and estimated the probably of assignment to that cluster back two generations (USEPOPINFO = 1, GENSBACK = 2), with three different migration priors: 0.1, 0.05, and 0.01. Convergence of this chain was rapid, so we ran the analysis for 100k generations after equal burn-in, and made three replicate runs. Migrants were identified as individuals with probability less than 0.5 of being from their sampled site. In GENODIVE, we specified an alpha of 0.002 (0.05/22 populations) applied independently to each population, with those exceeding the likelihood ratio threshold identified as migrants. STRUCTURE and GENODIVE both identify recent migrants, but while the GENODIVE analysis has the advantage of providing a formal likelihood ratio comparison, this test effectively assumes that identified individuals are 1^st generation migrants, a constraint that can lead to reduced sensitivity and mis-identification of the source population of 2^nd or 3^rd generation migrants.

In order to assess if the populations of Arapaima have experienced reductions in effective population size we used two moment-based methods implemented in the programs BOTTLENECK [45] and MValue [46], respectively. The program BOTTLENECK identifies populations that have experienced a reduction in effective population size by the presence of heterozygosity excess due to the loss of rare alleles, assuming an approximate infinite alleles model, wherein each mutation creates a new allele. The M-ratio implemented by Mvalue, which considers the range of repeat numbers of microsatellite alleles relative to richness, is considered more sensitive to recent reductions in effective population size, but requires assuming that surveyed microsatellites evolve by quasi-stepwise mutation [46].

The two moment-based methods efficiently detect recent bottlenecks—population reductions with the last few generations; however, populations and species are also subject to historical demographic growth and/or reductions. Therefore we analyzed the data using the coalescent sampler implemented in the program MSVar v1.3 [47, 48]. We ran 10 independent parallel chains sampling every 1,000th proposal, collecting 20,000 proposals in the MCMC chain in each parallel run. Priors for current and historical population size means and variances were set equal, with variances encompassing three orders of magnitude. Prior for mean time of population size change was set at 1,000 generations ago with variance encompassing time range from 1,000,000 to 0 generations. The runs were evaluated for convergence and were pooled to provide an estimate of current and historical effective population size. Convergence was assessed using the Gelman–Rubin criterion [49] and the test of alternative hypotheses (population decline vs. stable population size) was carried out as suggested by Beaumont [47] using Bayes factors. Calculations and plots were performed in the R statistical programming language [40] using the packages CODA [50] and ggplot2 [51].

In addition to these tests, we also estimated the effective population size (Ne) for each population using the LDNe method [52] implemented in the program NeEstimator v2.0 [53], assuming a random mating model and allele frequencies cutoff of 0.02. This analysis, which estimates the number of individuals contributing to the sample based on allelic linkage, assumes that the sample is representative of the age structure of the population, and, when it is not, the Ne estimates are equivalent to the number of breeders that contributed offspring to the generations included the sample [54].

Results

We surveyed genotypes of 11 microsatellite loci in a total of 511 individuals from the mainstem and major tributaries of the Amazon River, the Araguaia-Tocantins River, and Região dos Lagos (Fig 1). MICRO-CHECKER suggested there was no evidence of null alleles in the data. After Bonferroni correction, linkage disequilibrium was observed in 1 to 4% of pairwise comparisons for most loci; however, loci CTm3 and CTm4 had more than 30% probability of being linked. Locus CTm3 was also in H-W disequilibrium in 4 of 22 populations, and so this locus was removed from population structure analyses. Genetic statistics per locus and per sampling locality are shown in S1 Table. Overall, various diversity parameters presented low values for the eastern end of the Amazon Basin (Mexiana and Região dos Lagos), as well as in the middle and upper Purus River (Lábrea, Manuel Urbano), and upper Araguaia River (Ilha do Bananal, APA Meandros do Araguaia). Statistics of genetic diversity by sampling area are listed in Table 2, which shows that the average gene diversity over loci ranged from 0.128 ± 0.111 (APA Meandros do Araguaia) to 0.649 ± 0.342 (Mamirauá). The average number of alleles varied from 6.35 in Mamirauá to 2.17 in Ilha do Bananal (S1 Fig). The inbreeding coefficient, F_IS, ranged from low in Nhamundá (0.00) to high in Ilha do Bananal (0.54) (Table 2); however, only 4 out of the 21 localities were significant (Santa Cruz, Manuel Urbano, and Manacapuru). Proportions of private alleles ranged from 0.02 in Região dos Lagos to 0.67 in Resex Unini, and eight localities presented frequencies of <0.10 of private alleles. Expected heterozygosity (H_E) varied from 0.26 in the specimens collected in the Araguaia-Tocantins drainage (APA Meandros do Araguaia) to 0.66 in the specimens collected near the main channel, at Mamirauá (S2 Fig). Considering only mainstem locations, H_E ranged from 0.50 in the specimens collected in Mexiana to 0.66 in the specimens collected in Mamirauá. Conversely, the specimens collected in the tributaries of the Amazon basin showed H_E of 0.26 in the APA Meandros do Araguaia in the upper Araguaia River, and 0.62 in Tapauá, in the middle Purus River. Five loci were monomorphic for specimens collected at Ilha do Bananal (Araguaia River), two for specimens from the upper Purus River (Manuel Urbano) and one locus in the middle Purus River (Tapauá), Região dos Lagos (Amapá) and APA Meandros do Araguaia.

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Table 2. Genetic characterization of Arapaima gigas sampled from 22 localities in Amazônia.

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Distribution of genetic variability and population differentiation

Evaluation of clustering of individuals with STRUCTURE based on variance in likelihood among runs and across numbers of clusters (delta K) showed that two clusters (K = 2) was optimal, with an additional peaks at K = 3 and K = 6 (Fig 2 and S3 Fig). These clusters corresponded to geography. At K = 2, the clusters indicate the distinctness of fishes from the Araguaia-Tocantins system and its area of influence—and principally those of the upper Araguaia River (APA Meandros do Araguaia, Ilha do Bananal), and the rest of the Amazon basin. At K = 3, Amazonian populations show an east-west structuring gradient. At K = 6, it is also apparent that populations geographically distant from the mainstem of the Amazon River, or not directly connected to it, also show certain degree of reproductive divergence (Fig 2). At K = 6, STRUCTURE results emphasized the distinctness of location in the Purus drainage (Manuel Urbano, Lábrea), lower Madeira (Borba), the Negro (RESEX Unini), upper Tapajós (Jacareacanga) and Amapá (Região dos Lagos). Interestingly, SAMOVA with K = 2 or K = 3, emphasized the same groupings as STRUCTURE (Araguaia, Amapá), which explained 17% of the genetic variance (F_CT = 0.16748) by separating the Araguaia, and 18% (F_CT = 0.17894) by separating both (with p<0.004). However, at K = 4 and K = 5, SAMOVA grouped the Amazon delta (Mexiana) and Negro (Unini) separately, which only provided marginal increases in the genetic variance explained (F_CT = 0.18538 and 0.19299, respectively). Finally, the DAPC showed contiguous overlap among most localities with the exception of the Araguaia and Amapá, and with the Tucuruí locality intermediate between these three groups (Fig 3). The congruency of these analyses with groupings at K = 2 and 3 and incongruence at larger K values indicate the robustness of this population structure.

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Fig 2. Graph of population structure of Arapaima gigas estimated in the program STRUCTURE.

Each individual is represented by a vertical line.

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Fig 3. Results of DAPC analysis showing the scatterplot of the first two principal components based on 11 microsatellite loci of 511 individuals of Arapaima gigas from 22 sampling locations.

The results are plotted showing an ellipse that shows one standard deviation of the variation of each population relative to its centroid. The eastern Amazon and western Amazon labels designate concentration of localities from these geographic regions.

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Correlation between genetic divergence and geographic proximity

Despite forming an evolutionarily coherent population, the genetic structure among localities in the larger, Amazonas basin group was not insignificant, and F_ST values ranged from 0.019 and non-significant (Alto Jurua x Nhamundá) to 0.475 and highly significant (P<0.0001; Lábrea x Região dos Lagos in Amapá) (S2 Table and S4 Fig). A Mantel test indicated that this pattern of genetic divergence was significantly predicted by distance (r = 0.618174, P = 0.0003), suggesting that isolation-by-distance processes structure genetic diversity at the largest scale in the Amazonas group. A Mantel test was similarly positive including all samples (r = 0.673128, P = 0.0001) (Fig 4). Mantel correlograms showed positive spatial autocorrelation in genetic distance among localities up to 1,500 km, while localities separated by more than 2,000 km showed negative or non-significant spatial autocorrelation (Fig 5), indicating that neutral evolutionary processes across populations are dominated by gene flow up to 1,500 km, after which genetic drift plays a larger role between most populations. Importantly, these patterns were true considering all samples or without the Araguaia or Amapá samples.

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Fig 4. Graph of spatial autocorrelation analysis using linear pair-wise F_ST data and the distance between locations in kilometers (following the course of the rivers), all the distance categories showed significant correlations.

The groups West, East and Araguaia refer to pair-wise comparisons between localities within each group, while Between are comparisons between localities of these groups. East and West localities are those that are east and west of the Madeira/Negro divide.

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Fig 5. Graph of Mantel correlograms among localities.

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Analysis of gene flow implemented in the program MIGRATE 3.6.11 [43] revealed a mixed pattern of gene flow among populations. The result indicated bidirectional gene flow between all localities (S2 Table), with all cases presenting more than 1 individual per generation. Although it is clear the reduction in Nm values when the populations of the Araguaia River are included.

Likelihood tests in GENODIVE (Table 3) identified six migrants that were also corroborated by STRUCTURE: three of these with all migration priors, two with the two larger priors, and a single migrant only with the highest prior. Four additional individuals were identified by STRUCTURE with all priors as being 1^st or later generation migrants, and a single individual was identified by GENODIVE as a migrant but was not corroborated in any STRUCTURE run (not shown). Although several of these migrants were from adjacent localities (e.g. Manacapuru and Nhamundá), several also suggested relatively distant dispersals, albeit potentially over several generations.

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Table 3. Results of Likelihood test implemented in GENODIVE.

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Evidence of population size changes

Results for analyses of a recent reduction in effective population size (bottleneck effect) are presented in Table 4. BOTTLENECK analyses detected significant deviations in observed heterozygosity in 12 groups of individuals. Reduction in number of alleles implemented in the program MValue also indicated that 12 Arapaima localities experienced a significant reduction in size (M<0.68), according to Garza & Williamson [46], eight of them from tributaries. Areas which showed significant reduction were not necessarily the same in both analysis. Additionally, estimates of effective number of breeders were relatively low for many sites (Table 4).

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Table 4. Bottleneck metrics for Arapaima gigas by locality.

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In addition to recent population declines, coalescent analyses implemented in the program MSVar [55] indicate long-term decline as well (Figs 6 and 7 and Table 5). Analyses partitioned into two (Araguaia-Tocantins and Amazon) or three (Araguaia-Tocantins, lower Amazon and upper Amazon) groups show the same pattern. Historical population sizes of all groups were approximately equal (Ne 4.71, 95% HPD 3.26–6.15 vs. 4.62, 95% HPD 3.28–6.01 and 4.87, 95% HPD 3.51–6.27; Theta 0.98, 95% HPD 0.23–1.77 vs. 1.13, 95% HPD 0.59–1.65 and 1.43, 95% HPD 0.85–2.01) and began to decline at approximately the same time (4.49, 95% HPD 3.09–5.89 vs. 4.78, 95% HPD 3.39–6.15 and 4.65, 95% HPD 3.28–6.04). However, while populations declines centered on an order of magnitude in the Amazon basin (1.11, 95% HPD 1.62–0.60 and 1.33, 95% HPD 1.91–0.71), they were over two orders of magnitude in the Araguaia-Tocantins basin (2.09, 95% HPD 2.89–1.25).

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Fig 6. Coalescent population size change, Araguaia-Tocantins vs. Amazon.

Note: all metrics are log10 scale. Theta–historical and current theta (4Neμ); Population size–historical and current effective population size (Ne); Time of change–onset of population size change from historical to current theta; Ratio of sizes–ratio of current to historical theta.

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Fig 7. Coalescent population size change, Araguaia-Tocantins vs. lower Amazon vs. upper Amazon.

Note: all metrics are log10 scale. Theta–historical and current theta (4Neμ); Population size–historical and current effective population size (Ne); Time of change–onset of population size change from historical to current theta; Ratio of sizes–ratio of current to historical theta.

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Table 5. Population decline coalescent metrics for Arapaima gigas by region.

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Discussion

The arapaima is a charismatic fish of special cultural and socioeconomic significance to riverine communities of the Amazon, as well as occupying an apex ecological role in aquatic habitats [15, 16]. As obligate air breathing fish, an adaptation which allows them to exploit hypoxic floodplain environments, Arapaima gigas must regularly surface to renew the air in its highly vascularized swim bladder, but this dynamic also makes them especially vulnerable to human exploitation [56]. Arapaima gigas has been part of the diet of the riparian inhabitants of Amazonia since the early 18^th century [56, 57] and gradually gained significant commercial importance. However, catches began to decline at least as early as the 1960s, and by the 1980s Arapaima gigas was commercially extinct close to major urban centers [56, 58, 59]. In 1975, Arapaima gigas was listed in Appendix II of CITES (Convention on International Trade in Endangered Species) as a species not necessarily under threat of extinction, but for which commercial use must be controlled to avoid utilization incompatible with its survival; yet it is still considered data deficient.

Population structure and isolation-by-distance in a complex river system

One of the most prominent uncertainties surrounding Arapaima is the number of species present in this genus and their distribution. Contrary to the assertions of Stewart [9] [10], we find no evidence for multiple species of Arapaima existing or co-existing in the Amazon basin and satellite river basins. Our sampling does not include, however, samples from the Rupununi, a floodplain in the headwaters of the Essequibo River, and an area of occurrence of Arapaima arapaima (Valenciennes, in Cuvier and Valenciennes, 1847). However, based on extensive ichthyofaunal sharing between the Essequibo and Branco (Amazon) basin—254 freshwater fish species representing ~73.8% of the total species sampled [60], and the mid-Pleistocene separation of these basins [61, 62], we view it unlikely that the population from the Rupununi is not A. gigas.

While we find no evidence for the existence of multiple species of arapaima in the Amazonian ecosystem, we observed population structuring. We found that the greatest structure in our genetic data reflected geographic disjunction of A. gigas in the upper Araguaia-Tocantins—a basin that become effectively isolated from the Amazon basin at the Plio-Pleistocene boundary [63], while the remaining fishes from the Amazonas basin reflected a single, albeit structured population along the east-west axis, with fishes of the Região dos Lagos in Amapá representing eastern-most extreme of this gradient. This was consistent between the STRUCTURE, SAMOVA, and DAPC analyses. While it may be tempting to hypothesize that the Araguaia-Tocantins populations reflect undescribed species, we note that the degree of genetic divergence of these populations (e.g. as measured by F_ST) falls within the range of many widespread but cohesive species e.g. Hey & Pinho [64], and moreover, although contemporary gene flow between Araguaia-Tocantins and Amazon may be restricted, the admixture evident in localities in the eastern Amazon reflects historical gene flow between these areas (Fig 2). Thus, we continue to refer to all individuals as part of a single species (Arapaima gigas Schinz 1822), though we note that our sampling did not include the region from which A. arapaima is described (Guyana).

Within the Amazon—independent whether or not Amapá and/or Araguaia-Tocantins, the two regions not draining directly into the Amazon, were included—there was a strong and significant association between genetic divergence and geographical proximity, indicating that the attenuation of gene flow by distance (isolation-by-distance) is the major process structuring Amazon A. gigas populations on a large geographical scale. This agrees with the results of Hrbek et al. [24, 25] who surveyed additional loci but fewer and more distantly spaced localities. Intriguingly, positive spatial autocorrelation, reflecting the distance across which the homogenizing effects of gene flow are expected to dominate the diversifying effects of genetic drift, was significant up to 1500 km, a notable distance considering that adult A. gigas are known to be largely sedentary, only making small-scale annual migrations to and from the floodplain. We did, however, also discover a number of recent-generation migrants, some between relatively distant localities (Table 3), an inference supported by field and telemetry data [65]. If indeed adults are generally sedentary and show site fidelity, this may suggest an important role for juvenile dispersal as a means of conveying gene flow between sub-populations.

It would be convenient to assume that the weak population structure is the result of contemporary demographic and evolutionary processes as mediated by current landscape structure, but it is well known that the Amazon landscape itself has had a dynamic history. In the area occupied by the larger A. gigas population, the Amazonas River in its current west-to-east format is understood to have only formed around 10–11 million years ago (mya) with the breaching of the “Purus Arch”, a basement arch now largely buried and so named for its location along the Solimões near the Purus River [66, 67]. Prior to this breaching, the “Proto-Solimões” and western Amazon drained northward to the Caribbean and separately from more eastern Atlantic versants [67]. Indeed, previous studies have inferred an effect of the Purus arch not only on species distributions but on contemporary population structuring of widespread species as well. For example, Farias and Hrbek [68], in their analysis of the genus Symphysodon, inferred lineage distributions consistent with the Purus arch. Similarly, Willis et al. [69] discovered that genetic diversity in Cichla monoculus west of the Purus Arch was a subset of that found farther east, consistent with ancient east-to-west colonization. Importantly, Arapaima fossils similar to A. gigas have been discovered in the Miocene age La Venta formation of Colombia (~13 mya) [70], an area that would have been part of the northward-draining “Lago Pebas” system that shortly preceded the current west-to-east arrangement [66, 71]. So, Arapaima may have been present but separated on both sides of the Purus Arch, or colonized the eastern Amazon from the Lago Pebas system in the west. However, we saw no clear indications of diminished genetic diversity in eastern populations (apart from localized depletions discussed below), and plots of F_ST vs. geographic distance were fairly continuous among localities on either side of the Purus Arch (not shown). Thus, if the biogeographic history of Arapaima was significantly influenced by the Purus Arch, gene flow in the intervening period appears to have largely obscured these effects (see also Hrbek et al. [24]).

The degree of population genetic structure in Arapaima gigas, with significant genetic divergence among most localities (S2 Table), is notable for Amazonian fishes, whose continued study has revealed important variance in population structuring patterns. For example, examining the mitochondrial control region at locations on the Amazonas River mainstem, Santos et al. [72] and Farias et al. [73] found high genetic variability for tambaqui (Colossoma macropomum) and relatively low population structuring over vast distances. An analysis of nuclear microsatellites of C. macropomum populations from the Amazon mainstem and its main tributaties by Santos et al. [74] confirmed the mtDNA pattern, however, populations from tributaries and principally those close to headwaters also showed certain degree of differentiation. Similarly, Batista & Alves-Gomes [75], who examined the control region of the catfish Brachyplatystoma rousseauxii, also found high levels of genetic polymorphism and the absence of structuring. Similar patterns have been found with mtDNA or microsatellite markers for Brachyplatystoma platynemum [76], Brycon amazonicus [77], and Prochilodus nigricans [78]. Notably, these species share a migratory or semi-migratory and broadcast-spawning life history, with no parental care. In contrast, the patterns discovered for A. gigas are more similar to other species with sedentary adults, limited batch fecundity, and/or significant parental care, including the cichlids Cichla [79, 80] and Symphysodon [81] and the freshwater ray Paratrygon aierba [82]. Thus, it appears there is a general trend in which genetic variability and the degree of population structure is strongly determined by life history strategy, an observation that may assist in defining effective management strategies in the absence of more detailed information [78][83]. However, we recommend that additional studies to determine the contributions of variation in body size, habitat preferences, and biogeographic history would be prudent [84].

Implications for Arapaima conservation in a threatened river system

The floodplain has been reported as the environment with the highest productivity in the Amazon drainage basin, and is the most common environment of the Solimões-Amazonas axis. The flood-ebb system of the flooded forest, or 'flood pulse', provides exceptional availability of diverse habitats and seasonal resource abundance [85]. However, the floodplain is also the most threatened habitat [86][87]. Arapaima gigas is a floodplain specialist, inhabiting lakes and lagoons that are connected by channels to the riverine network of the Amazonas basin. Although A. gigas is not a “migratory” species, these fishes do make small lateral movements through the network of lakes and channels of the Amazon floodplain, which provide abundant food for growing young [18]. These rich floodplains also provide important corridors for linear migration of individuals along and among river courses [18].

Although, as demonstrated here, while the major population structure of Arapaima gigas are the differences among the Araguaia and Amazon basins and the hydrologically isolated Região dos Lagos (Amapá), the Amazonas population is structured by isolation-by-distance at the largest scale—with the Região dos Lagos representing its eastern-most extreme, we discovered statistically significant genetic divergence (i.e. F_ST) among most localities (S3 Table); in fact, few localities were not significantly divergent, although few F_ST values were greater than 0.2—at mutation-drift equilibrium equivalent to Nm = 1. These results indicate that at the smallest sampled scales, sub-populations of A. gigas are likely to be demographically somewhat independent, and cannot be assumed to compensate for exploitation or replenish one another over fishery-relevant timescales (e.g. Waples [88]). As such, A. gigas at these, and potentially smaller, spatial scales should be considered separate Management Units [89]. This would explain why population trends are different between the Araguaia-Tocantins and Amazon basins, and also some of the differences between the upper and lower Amazon basins.

In this context, the results indicating recent population declines (genetic bottlenecks) compounded onto long-term demographic declines potentially associated with the extent and distribution of Amazonian floodplains [90, 91] and low effective number of breeders is concerning (Table 4), since it appears that demographic recovery will largely depend on local recruitment rather than immigration. Indeed, the majority of localities exhibited effective population sizes well below the “50/500” threshold for limiting inbreeding depression (50) and loss of genetic diversity due to excessive genetic drift (500) [92], guidelines that have even been suggested as needing upward revision [93]. We note, however, that basing management goals from meta-analyses can be risky [94], and species with high parental investment and smaller lifetime fecundity (“K” or “equilibrium” strategists; [95, 96]) often have higher census to effective population size ratios. It is interesting to note recent indications that the socially monogamous A. gigas may regularly engage in polygamous reproduction [97], thereby reducing potential inbreeding and subsequent genetic erosion. Moreover, for species with sedentary adults and potentially significant small-scale spatial genetic (family) structure, it is unclear what an expected effective population size should be at any given spatial scale, and low numbers may be a natural aspect of species that otherwise effectively purge deleterious recessive alleles [98]. However, small populations with low growth rates may nonetheless also experience higher rates of population extirpation in the face of environmental variation [99], and these dynamics may be exacerbated by human exploitation [100]. Given this uncertainty, we suggest that studies examining the minimum viable population size for A. gigas are warranted.

To curb continuing population declines, in 2001 fishing of Arapaima gigas was banned by IBAMA (Brazilian Institute of Environment and Renewable Natural Resources), except in management areas such as the Mamirauá and Piagaçu-Purus Sustainable Development Reserves (RDS) where fishing is controlled, preventing local overexploitation [101, 102]. Additional successes in sustainable exploitation of A. gigas have been reported for community based management initiatives in which individual communities or families restrict access to spatially delimited populations of A. gigas e.g. [103]. Whether these successes can be replicated more widely and ensure long-term viability of A. gigas remains to be seen, especially with regard to evidence of low effective population sizes, genetic bottlenecks, and the potential for erosion of genetic diversity recovered here (Table 4). Whether or not these genetic patterns are the result of documented overexploitation, small populations depend on dispersal and immigration to maintain long-term genetic diversity e.g. [103]. Genetic diversity is the raw material upon which evolution acts, enabling populations to evolve in response to environmental changes, and without which a population may be more susceptible to extirpation or extinction [103, 104]. Here, our observation of recent migrants among populations is important, since it is these individuals that boost genetic variation in populations otherwise limited by local abundance. However, to effectively increase local diversity, these individuals must first successfully disperse. Despite localized sustainable initiatives and a ban on A. gigas fishing in Brazil, illegal fishing continues and creates risk even for dispersers among sustainably-managed areas. Moreover, the floodplain habitats of the Amazon basin through which these dispersers move have been reported as the most threatened in South America due to logging, forest clearing for cattle-ranching, construction of hydroelectric dams, and other disturbances [86]. The long-term viability of A. gigas fisheries will ultimately depend on addressing these significant regional challenges as well.

Some sub-populations of Arapaima gigas also face more localized hurdles to viability. We observed that statistics of genetic diversity were variable across localities, a result also observed by Hrbek et al. [24] with mtDNA. Intriguingly, although localities along the Amazon mainstem (e.g. Santarém, Carreiro da Varzea, Manacapuru, Coari) are those facing the greatest fishing pressure, they are also among the localities with the highest genetic diversity (Table 2), a feature we hypothesize to relate to their positions near the intersections of the river network. Despite their genetic diversity, continued illicit exploitation suggest that the longevity of these populations may depend on management regimes than ensure minimum viable populations. On the other hand, several locations in the upstream portions of tributaries (Eirunepé, Manuel Urbano, Meandros do Araguaia) exhibit lower genetic diversity, a worrisome trend considering that, even though these are the sub-populations that currently face lower fishing pressure, they are increasingly subjected to major habitat changes in Brazil's “arc of deforestation.” As such, these localities may be those for which inbreeding and loss of genetic diversity would be most problematic, and for which immigration may be the most beneficial. Thus, one size does not fit all, and management strategies for the management units will need to be tailored to local challenges.

Arapaima from the unique extra-Amazon populations

Although the population in the Amazonas basin sensu stricto contains the majority of individuals and greatest fishing pressure, populations in the Aragauia-Tocantins and Amapá deserve special consideration. The divergence and genetic poverty of the Arapaima gigas in the Araguaia River (APA Meandros do Araguaia and Ilha do Bananal) observed here corroborate the findings of Vitorino et al. [105], who showed low values of genetic diversity and structuring between populations of four localities in Araguaia and Tocantins Rivers, and those of Hrbek et al. [25], who discovered a number of mtDNA haplotypes endemic to the Tocantins basin. The Araguaia-Tocantins River basin is connected to the Amazonas basin by uninterupted freshwater, albeit in the form of small meandering channels, and many researchers do not consider the Araguaia-Tocantins part of the Amazonas basin, since it drains primarily through the “Pará River” to the south of Marajó Island. However, as the presence of A. gigas in both basins implies, they share a close biogeographic history and exhibit similar icthyofaunas [106], along with several other adjacent Atlantic versants (e.g. Araguari, Oiapoque, Parnaiba). However, although numerous species are apparently distributed in both basins, several recent studies have shown that the Araguaia and/or Tocantins exhibit endemic lineages of fishes e.g. [69, 107, 108] and other aquatic organisms, including the Araguaian river dolphin Inia araguaiaensis [109]. The confirmation of an Araguaian population of A. gigas is in keeping with these trends.

The most likely feature promoting divergence of aquatic organisms in the upper Tocantins basin is, ironically, one that no longer exists, the Itaboca waterfalls that were submerged when the Tucuruí hydroelectric dam was constructed. As such, the Tucuruí population of A. gigas, which presented as a genetic intermediate between the Araguaia and eastern Amazon sub-population in STRUCTURE and DAPC analyses, presents something of a conundrum. The sampling locations in the upper Araguaia River (AEP Meandros do Araguaia and Ilha do Bananal), in addition to being about 1,340 and 890 km, respectively, from the reservoir, are also separated by numerous rapids which may limit the gene flow between these areas, implying that the pattern of admixture may be natural. However, the dam itself was also built downstream of the historical barrier (Itaboca), trapping some of the downstream fauna within the flooded region. Thus, the admixture of lower Amazon/lower Tocantins and upper Tocantins lineages may instead be an anthropogenic effect of reservoir construction. Indeed, several other studies have suggested that aquatic organisms of Tucuruí exhibit unique patterns of admixture or hybridization different from that of the lower Tocantins e.g. [69]. In the face of planned and ongoing construction of hydroelectric dams on numerous rivers in Brazil, this trend should serve as a cautionary tale.

The Araguaia-Tocantins basin is considered highly threatened not only by hydroelectric projects in the headwaters of tributary rivers, but it is also among the areas in Brazil with the highest rates of landscape modification for cattle ranching, road construction, and agriculture [22]. The unique population of A. gigas in this region, despite relatively low fishing pressure, should thus be considered highly threatened, especially considering these fishes exhibited the lowest observed levels of genetic diversity. Whether this reduced diversity results from natural (e.g. Pleistocene-age colonizations or bottlenecks) or anthropogenic effects, the ability of this unique population to remain viable in the face of ongoing habitat modifications should be closely monitored.

The population of Arapaima gigas from the Região dos Lagos in Amapá are significant as well. This region, which is technically connected to the Amazonas basin by freshwater from the Amazonas outflow, exhibits an icthyofauna with affinities both for that of the Amazon, as well as those of coastal Guyana drainages to the north [106]. The presence of A. gigas here is thus not a surprise, although, considering the strong and turbulent current that runs along the coast and probably limits dispersal, nor is their distinctness from the other populations. Fortunately, a significant portion of the Região dos Lagos is protected by a state park which limits exploitation, although it remains unknown if A. gigas from the Araguari River are part of this population as well. In either case, habitat degradation from cattle ranching and urban development continues to encroach upon the watersheds were this population is found, and being limited to such a relatively small area, this unique population could easily be placed at risk if current protections were to prove inadequate.

Conclusions

The findings presented here should be seen as an important warning about the fragility of Arapaima gigas populations, given the evidence of reduced genetic capacity and the intersection of threats against them. These data should contribute toward the design of management and conservation programs for this species in the Amazon, Amapá, and Araguaia-Tocantins regions.

Ethics statement

Permits for field collection and molecular analyses were given by IBAMA/CGEN 11325–1. This study involved no experimentation, manipulation or euthanasia of any animals on part of the researchers. We simply collected tissue samples of exemplars fished by local communities for food and as fishes were brought to market. In these instances our institution (UFAM) does not require IACUC approval. Finally, this research could not have been submitted to IACUC for analysis since this committee became operational only after the completion of this study.

Supporting information

S1 Fig. Graphic representation of the average number of alleles per population of Arapaima gigas.

https://doi.org/10.1371/journal.pone.0220882.s001

(EPS)

S2 Fig. Graphic representation of expected heterozygosity (H_E) per population of Arapaima gigas.

https://doi.org/10.1371/journal.pone.0220882.s002

(EPS)

S3 Fig. Absolute values of the 2^nd order rate of change of mean likelihoods of each K.

https://doi.org/10.1371/journal.pone.0220882.s003

(EPS)

S4 Fig. Graphic representation of pairwise F_ST values among groups of individuals of Arapaima gigas.

https://doi.org/10.1371/journal.pone.0220882.s004

(EPS)

S1 Table. Characteristics of the 11 microsatellite loci analyzed for Arapaima gigas considering separately the groups of individuals grouped by collection site.

N_A = Total number of alleles; A_R = Allelic richness; H_O = Observed Heterozygosity; H_E = Expeted Heterozygosity; mono = Monomorphic locus; * significant P value for deviation from HWE after Bonferroni correction (P = 0.00455).

https://doi.org/10.1371/journal.pone.0220882.s005

(DOCX)

S2 Table. Number of migrants per generation Nm = Mθ/2.

Row localities are sending individuals, while column localities are receiving individuals. Locality codes are: 1- Santa Cruz, 2- Puerto Nariño, 3- Carauari, 4- Eirunepé, 5- Mamirauá, 6- Coari, 7- RDS Piagaçu-Purus, 8- Tapauá, 9- Lábrea, 10- Manuel Urbano, 11- Manacapuru, 12- Resex Unini, 13- Careiro da Várzea, 14- Borba, 15- Nhamundá, 16- Santarém, 17- Jacareacanga, 18- Região dos Lagos, 19- Mexiana, 20- Tucuruí, 21- Ilha do Bananal, 22- APA Meandros do Araguaia.

https://doi.org/10.1371/journal.pone.0220882.s006

(XLSX)

S3 Table. Matrix of pairwise F_ST values among localities sampled for Arapaima gigas.

Note: significant differences at p < 0.05 and after Bonferroni correction are in bold.

https://doi.org/10.1371/journal.pone.0220882.s007

(XLSX)

Acknowledgments

We thank all the fishers and former students of the LEGAL lab who over the years have contributed samples to this study. This study is, in part, derived from AL’s Master’s thesis in the Genetics, Conservation and Evolutionary Biology program of INPA/UFAM.

References

1. Araújo-Lima CARM, Goulding M. Os Frutos do Tambaqui. Ecologia, Conservação e Cultivo na Amazônia. Belem, Brazil: Sociedade Civil Mamirauá - MCT—CNPq; 1998.
2. Goulding M, Smith NJH, Mahar DJ. Floods of Fortune: Ecology and Economy along the Amazon. New York, NY: Columbia University Press; 2000.
3. Prang G. A caboclo society in the middle Rio Negro basin: ecology, economy, and history of an ornamental fishery in the state of Amazonas, Brazil. Wayne State University. 2001.
4. Smith NJH. Caimans, capybaras, otters, manatees, and man in Amazônia. Biol Conserv. 1980;19: 177–187.
- View Article
- Google Scholar
5. Medem F. Los Crocodylia de Sur America. Vol. 2. Bogota, Colombia: Ed. Carrera; 1983.
6. Best RC. Apparent dry-season fasting in Amazonian manatees (Mammalia: Sirenia). Biotropica. 1983;15: 76–78.
- View Article
- Google Scholar
7. Best RC. The aquatic mammals and reptiles of the Amazon. In: Sioli H, editor. The Amazon Limnology and Landscape Ecology of a Mighty Tropical River and its Basins. Dodechet, The Netherlands: Dr. Junk W Publisher; 1984. pp. 371–412.
8. Goulding M. Fishes and the Forest: Explorations in Amazonian Natural History. Los Angeles, CA: University of California press; 1980.
9. Stewart DJ. A new species of Arapaima (Osteoglossomorpha: Osteoglossidae) from the Solimões River, Amazonas State, Brazil. Copeia. 2013;2013: 470–476.
- View Article
- Google Scholar
10. Stewart DJ. Re-description of Arapaima agassizii (Valenciennes), a rare fish from Brazil (Osteoglossomorpha: Osteoglossidae). Copeia. 2013;2013: 38–51.
- View Article
- Google Scholar
11. Spix JB von, Agassiz L. Selecta genera et species piscium Brasiliensium: Quos in itinere per Brasiliam annis MDCCCXVII-MDCCCXX jussu et auspiciis Maximiliani Josephi I. Munich, Germany: Bavariae regisaugustissimi peracto collegit et pingendos curavit Dr. J. B. de Spix; 1829.
12. Günther A. Catalogue of the fishes in the British Museum, Volume 7. London, England: The British Museum (Natural History); 1868.
13. Saint-Paul U. Potential for aquaculture of South American freshwater fishes: a review. Aquaculture. 1986;54: 205–240.
- View Article
- Google Scholar
14. Nelson JS. Fishes of the World, 3rd edition. New York, NY: John Wiley and Sons, Inc.; 1994.
15. Fontenele O. Contribuição para o conhecimento da biologia do pirarucu, Arapaima gigas (Cuvier), em cativeiro (Actinopterygii, Osteoglossidae). Rev Bras Biol. 1948;8: 445–459.
- View Article
- Google Scholar
16. Menezes RS. Notas biológicas e econômicas sobre o pirarucu Arapaima gigas (Cuvier) (Actinopterygii, Arapaimidae). Serviço Informação Agrícola / Minist da Agric Ser Estud técnicos. 1951;3: 9–39.
- View Article
- Google Scholar
17. Carvalho F, Power M, Forsberg BR, Castello L, Martins EG, Freitas CEDC. Trophic ecology of Arapaima sp. in a ria lake-river-floodplain transition zone of the Amazon. Ecol Freshw Fish. 2018;27: 237–246.
- View Article
- Google Scholar
18. Castello L. Lateral migration of Arapaima gigas in floodplains of the Amazon. Ecol Freshw Fish. 2008;17: 38–46.
- View Article
- Google Scholar
19. Fernandes MN, da Cruz AL, da Costa OTF, Perry SF. Morphometric partitioning of the respiratory surface area and diffusion capacity of the gills and swim bladder in juvenile Amazonian air-breathing fish, Arapaima gigas. Micron. 2012;43: 961–70. pmid:22512942
- View Article
- PubMed/NCBI
- Google Scholar
20. Arantes CC, Castello L, Stewart DJ, Certa M, Queiroz HL de. Population density, growth and reproduction of arapaima in an Amazonian river-floodplain. Ecol Freshw Fish. 2010;19: 455–465.
- View Article
- Google Scholar
21. Queiroz HL de. Natural history and conservation of pirarucu Arapaima gigas, at the Amazonian Várzea: Red giants in muddy waters. St. Andrews, England: University of St. Andrews; 2000. p. 222.
22. Goulding M, Barthem RB, Ferreira EJG. The Smithsonian Atlas of the Amazon. Washington, DC: Smithsonian Institution Press; 2003.
23. Miranda-Chumacero G, Wallace R, Calderón H, Calderón G, Willink P, Guerrero M, et al. Distribution of arapaima (Arapaima gigas) (Pisces: Arapaimatidae) in Bolivia: implications in the control and management of a non-native population. BioInvasions Rec. 2012;1: 129–138.
- View Article
- Google Scholar
24. 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: 297–308.
- View Article
- Google Scholar
25. Hrbek T, Crossa M, Farias IP. Conservation strategies for Arapaima gigas (Schinz, 1822) and the Amazonian várzea ecosystem. Brazilian J Biol. 2007;67: 909–917.
- View Article
- Google Scholar
26. Araripe J, do Rêgo PS, Queiroz HL de, Sampaio I, Schneider H. Dispersal capacity and genetic structure of Arapaima gigas on different geographic scales using microsatellite markers. PLoS One. 2013;8: e54470. pmid:23372730
- View Article
- PubMed/NCBI
- Google Scholar
27. Farias IP, Hrbek T, Brinkmann H, Sampaio I, Meyer A. Characterization and isolation of DNA microsatellite primers for Arapaima gigas, an economically important but severely over-exploited fish species of the Amazon basin. Mol Ecol Notes. 2003;3: 128–130.
- View Article
- Google Scholar
28. Van Oosterhout C, Hutchinson WF, Wills DPM, Shipley P. MICRO-CHECKER: software for identifying and correcting genotyping errors in microsatellite data. Mol Ecol Notes. 2004;4: 535–538.
- View Article
- Google Scholar
29. 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: 564–567. pmid:21565059
- View Article
- PubMed/NCBI
- Google Scholar
30. Leberg PL. Estimating allelic richness: Effects of sample size and bottlenecks. Mol Ecol. 2002;11: 2445–2449. pmid:12406254
- View Article
- PubMed/NCBI
- Google Scholar
31. Kalinowski ST. Hp-Rare 1.0: a computer program for performing rarefaction on measures of allelic richness. Mol Ecol Notes. 2005;5: 187–189.
- View Article
- Google Scholar
32. Nei M, Roychoudhury AK. Sampling variances of heterozygosity and genetic distance. Genetics. 1974;76: 379–390. Available: https://www.ncbi.nlm.nih.gov/pubmed/4822472 pmid:4822472
- View Article
- PubMed/NCBI
- Google Scholar
33. Pritchard JK, Stephens M, Donnelly P. Inference of population structure using multilocus genotype data. Genetics. 2000;155: 945–959. Available: https://www.ncbi.nlm.nih.gov/pubmed/10835412 pmid:10835412
- View Article
- PubMed/NCBI
- Google Scholar
34. Hubisz MJ, Falush D, Stephens M, Pritchard JK. Inferring weak population structure with the assistance of sample group information. Mol Ecol Resour. 2009;9: 1322–1332. pmid:21564903
- View Article
- PubMed/NCBI
- Google Scholar
35. Evanno G, Regnaut S, Goudet J. Detecting the number of clusters of individuals using the software STRUCTURE: a simulation study. Mol Ecol. 2005;14: 2611–2620. pmid:15969739
- View Article
- PubMed/NCBI
- Google Scholar
36. Dupanloup I, Schneider S, Excoffier L. A simulated annealing approach to define the genetic structure of populations. Mol Ecol. 2002;11: 2571–2581. pmid:12453240
- View Article
- PubMed/NCBI
- Google Scholar
37. Excoffier L, Smouse PE, Quattro JM. Analysis of molecular variance inferred from metric distances among DNA haplotypes: Application to human mitochondrial DNA restriction data. Genetics. 1992;131: 479–491. Available: https://www.ncbi.nlm.nih.gov/pubmed/1644282 pmid:1644282
- View Article
- PubMed/NCBI
- Google Scholar
38. 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: 94. pmid:20950446
- View Article
- PubMed/NCBI
- Google Scholar
39. Jombart T. adegenet: a R package for the multivariate analysis of genetic markers. Bioinformatics. 2008;24: 1403–1405. pmid:18397895
- View Article
- PubMed/NCBI
- Google Scholar
40. R Core Team. R: A Language and Environment for Statistical Computing [Internet]. Vienna, Austria: R Foundation for Statistical Computing; 2018. Available: http://www.r-project.org
41. Mantel N. The detection of disease clustering and a generalized regression approach. Cancer Res. 1967;27: 209–220. Available: https://www.ncbi.nlm.nih.gov/pubmed/6018555 pmid:6018555
- View Article
- PubMed/NCBI
- Google Scholar
42. Oksanen J, Blanchet FG, Friendly M, Kindt R, Legendre P, McGlinn D, et al. vegan: Community Ecology Package [Internet]. 2018. Available: https://cran.r-project.org/package = vegan
- View Article
- Google Scholar
43. Beerli P, Felsenstein J. Maximum likelihood estimation of a migration matrix and effective population sizes in n subpopulations by using a coalescent approach. Proc Natl Acad Sci U S A. 2001;98: 4563–4568. pmid:11287657
- View Article
- PubMed/NCBI
- Google Scholar
44. Meirmans PG, Van Tienderen PH. GENOTYPE and GENODIVE: Two programs for the analysis of genetic diversity of asexual organisms. Mol Ecol Notes. 2004;4: 792–794.
- View Article
- Google Scholar
45. Piry S, Luikart G, Cornuet J-M. BOTTLENECK: a computer program for detecting recent reductions in the effective size using allele frequency data. J Hered. 1999;90: 502–503.
- View Article
- Google Scholar
46. Garza JC, Williamson EG. Detection of reduction in population size using data from microsatellite loci. Mol Ecol. 2001;10: 305–318. pmid:11298947
- View Article
- PubMed/NCBI
- Google Scholar
47. Beaumont MA. Detecting population expansion and decline using microsatellites. Genetics. 1999;153: 2013–2029. Available: https://www.ncbi.nlm.nih.gov/pubmed/10581303 pmid:10581303
- View Article
- PubMed/NCBI
- Google Scholar
48. Storz JF, Beaumont MA. Testing for genetic evidence of population expansion and contraction: an empirical analysis of microsatellite DNA variation using a hierarchical Bayesian model. Evolution (N Y). 2002;56: 154–166.
- View Article
- Google Scholar
49. Gelman A, Rubin DB. Inference from iterative simulation using multiple sequences. Stat Sci. 1992;7: 457–472.
- View Article
- Google Scholar
50. Plummer M, Best N, Cowles K, Vines K. CODA: Convergence diagnosis and output analysis for MCMC. R News. 2006;6: 7–11. Available: https://journal.r-project.org/archive/
- View Article
- Google Scholar
51. Wickham H. ggplot2: Elegant Graphics for Data Analysis [Internet]. Springer-Verlag New York; 2009. Available: http://ggplot2.org
52. Waples RS, Do C. Ldne: a program for estimating effective population size from data on linkage disequilibrium. Mol Ecol Resour. 2008;8: 753–6. pmid:21585883
- View Article
- PubMed/NCBI
- Google Scholar
53. Do C, Waples RS, Peel D, Macbeth GM, Tillett BJ, Ovenden JR. NeEstimator v2: re-implementation of software for the estimation of contemporary effective population size (Ne) from genetic data. Mol Ecol Resour. 2014;14: 209–214. pmid:23992227
- View Article
- PubMed/NCBI
- Google Scholar
54. Palstra FP, Fraser DJ. Effective/census population size ratio estimation: a compendium and appraisal. Ecol Evol. 2012;2: 2357–2365. pmid:23139893
- View Article
- PubMed/NCBI
- Google Scholar
55. Storz JF, Beaumont MA, Alberts SC. Genetic evidence for long-term population decline in a savannah-dwelling primate: inferences from a hierarchical Bayesian model. Mol Biol Evol. 2002;19: 1981–1990. pmid:12411607
- View Article
- PubMed/NCBI
- Google Scholar
56. Veríssimo J. A Pesca na Amazônia. Rio de Janeiro, Brazil: Livraria Clássica, de Alves & C.; 1895.
57. Kurlansky M. Cod: A Biography of the Fish that Changed the World. London, England: Jonathan Cape; 1998.
58. Goulding M. Ecologia de Pesca do Rio Madeira. Manaus, Brazil: INPA; 1979.
59. Crossa M. NM, Petrere M Jr. Morphometric relationships and indirect determination of the length frequency structure of the pirarucu, Arapaima gigas (Cuvier), in Brazilian Amazonia. Fish Manag Ecol. 1999;6: 233–240.
- View Article
- Google Scholar
60. Souza LS de, Armbruster JW, Werneke DC. The influence of the Rupununi portal on distribution of freshwater fish in the Rupununi district, Guyana. Cybium. 2012;36: 31–43.
- View Article
- Google Scholar
61. Schaefer CEGR, Vale Júnior JF. Mudanças climáticas e evolução da paisagem em Roraima: uma resenha do Cretáceo ao recente. In: Barbosa RI, Ferreira EJG, Castellón EG, editors. Homem, Ambiente e Ecologia no Estado de Roraima. Manaus, AM: Instituto Nacional de Pesquisas da Amazônia (INPA); 1997. pp. 231–265.
62. Cremon ÉH, Rossetti D de F, Sawakuchi A de O, Cohen MCL. The role of tectonics and climate in the late Quaternary evolution of a northern Amazonian River. Geomorphology. Elsevier B.V.; 2016;271: 22–39.
- View Article
- Google Scholar
63. Rossetti D de F, Valeriano M de M. Evolution of the lowest Amazon basin modeled from the integration of geological and SRTM topographic data. Catena. 2007;70: 253–265.
- View Article
- Google Scholar
64. Hey J, Pinho C. Population genetics and objectivity in species diagnosis. Evolution (N Y). 2012;66: 1413–1429. pmid:22519781
- View Article
- PubMed/NCBI
- Google Scholar
65. Campos‐Silva JV, Hawes JE, Peres CA. Population recovery, seasonal site fidelity, and daily activity of pirarucu (Arapaima spp.) in an Amazonian floodplain mosaic. Freshw Biol. 2019;64: 1255–1264.
- View Article
- Google Scholar
66. Bemerguy RL, Costa JBS. Considerações sobre a evolução do sistema de drenagem da Amazônia e sua relação com o arcabouço tectônico-estrutural. Bol do Mus Para Emílio Goeldi, Série Ciências da Terra. 1991;3: 75–97.
- View Article
- Google Scholar
67. Rossetti D de F, Cohen MCL, Tatumi SH, Sawakuchi A de O, Cremon ÉH, Mittani JCR, et al. Mid-Late Pleistocene OSL chronology in western Amazonia and implications for the transcontinental Amazon pathway. Sediment Geol. Elsevier B.V.; 2015;330: 1–15.
- View Article
- Google Scholar
68. Farias IP, Hrbek T. Patterns of diversification in the discus fishes (Symphysodon spp. Cichlidae) of the Amazon basin. Mol Phylogenet Evol. 2008;49: 32–43. pmid:18762435
- View Article
- PubMed/NCBI
- Google Scholar
69. Willis SC, Macrander J, Farias IP, Ortí G. Simultaneous delimitation of species and quantification of interspecific hybridization in Amazonian peacock cichlids (genus Cichla) using multi-locus data. BMC Evol Biol. 2012;12: 96. pmid:22727018
- View Article
- PubMed/NCBI
- Google Scholar
70. Lundberg JG, Chernoff B. A Miocene fossil of the Amazonian fish Arapaima (Teleostei: Arapaimidae) from the Magdalena River region of Colombia: biogeographic and evolutionary implications. Biotropica. 1992;24: 2–14.
- View Article
- Google Scholar
71. Hoorn C, Wesselingh FP, ter Steege H, Bermudez MA, Mora A, Sevink J, et al. Amazonia through time: Andean uplift, climate change, landscape evolution, and biodiversity. Science (80-). 2010;330: 927–931. pmid:21071659
- View Article
- PubMed/NCBI
- Google Scholar
72. Santos M da CF, Ruffino ML, Farias IP. High levels of genetic variability and panmixia of the tambaqui Colossoma macropomum (Cuvier, 1818) in the main channel of the Amazon River. J Fish Biol. 2007;71A: 33–44.
- View Article
- Google Scholar
73. Farias IP, Torrico JP, García-Dávila C, Santos M da CF, Hrbek T, Renno J-F. Are rapids a barrier for floodplain fishes of the Amazon basin? A demographic study of the keystone floodplain species Colossoma macropomum (Teleostei: Characiformes). Mol Phylogenet Evol. 2010;56: 1129–1135. pmid:20362063
- View Article
- PubMed/NCBI
- Google Scholar
74. Santos M da CF, Hrbek T, Farias IP. Multiloci approach to understand the historical and contemporary genetic connectivity of the keystone floodplain species Colossoma macropomum (Teleostei: Characiformes) in the Amazon basin. Front Genet. 2018;9: 263. pmid:30154822
- View Article
- PubMed/NCBI
- Google Scholar
75. Batista J da S, Alves-Gomes JA. Phylogeography of Brachyplatystoma rousseauxii (Siluriformes—Pimelodidae) in the Amazon Basin offers preliminary evidence for the first case of “homing” for an Amazonian migratory catfish. Genet Mol Res. 2006;5: 723–740. pmid:17183483
- View Article
- PubMed/NCBI
- Google Scholar
76. Ochoa LE, Pereira LHG, Costa-Silva GJ, Roxo FF, Batista J da S, Formiga K, et al. Genetic structure and historical diversification of catfish Brachyplatystoma platynemum (Siluriformes: Pimelodidae) in the Amazon basin with implications for its conservation. Ecol Evol. 2015;5: 2005–2020. pmid:26045952
- View Article
- PubMed/NCBI
- Google Scholar
77. Oliveira RC de, Santos M da CF, Bernardino G, Hrbek T, Farias IP. From river to farm: an evaluation of genetic diversity in wild and aquaculture stocks of Brycon amazonicus (Spix & Agassiz, 1829), Characidae, Bryconinae. Hydrobiologia. 2018;805: 75–88.
- View Article
- Google Scholar
78. Machado VN, Willis SC, Hrbek T, Farias IP. Population genetic structure of the Amazonian black flannelmouth characin (Characiformes, Prochilodontidae: Prochilodus nigricans Spix & Agassiz, 1829): contemporary and historical gene flow of a migratory and abundant fishery species. Environ Biol Fishes. 2017;100: 1–16.
- View Article
- Google Scholar
79. Willis SC, Winemiller KO, Montaña CG, Macrander J, Reiss P, Farias IP, et al. Population genetics of the speckled peacock bass (Cichla temensis), South America’s most important inland sport fishery. Conserv Genet. 2015;16: 1345–1357.
- View Article
- Google Scholar
80. Willis SC. One species or four? Yes! …and, no. Or, arbitrary assignment of lineages to species obscures the diversification processes of Neotropical fishes. PLoS One. 2017;12: e0172349. pmid:28235096
- View Article
- PubMed/NCBI
- Google Scholar
81. Amado MV, Hrbek T, Farias IP. A molecular perspective on systematics, taxonomy and classification Amazonian discus fishes of the genus Symphysodon. Int J Evol Biol. 2011;2011: 360654. pmid:21811676
- View Article
- PubMed/NCBI
- Google Scholar
82. Frederico RG, Farias IP, Araújo MLG, Charvet-Almeida P, Alves-Gomes JA. Phylogeography and conservation genetics of the Amazonian freshwater stingray Paratrygon aiereba Müller & Henle, 1841 (Chondrichthyes: Potamotrygonidae). Neotrop Ichthyol. 2012;10: 71–80.
- View Article
- Google Scholar
83. Winemiller KO. Life history strategies, population regulation, and implications for fisheries management. Can J Fish Aquat Sci. 2005;62: 872–885.
- View Article
- Google Scholar
84. Meirmans PG, Goudet J, Gaggiotti OE. Ecology and life history affect different aspects of the population structure of 27 high-alpine plants. Mol Ecol. 2011;20: 3144–3155. pmid:21689194
- View Article
- PubMed/NCBI
- Google Scholar
85. Lowe-McConnell RH. Ecological aspects of seasonality in fishes of tropical waters. Symp Zool Soc London. 1979;44: 219–241.
- View Article
- Google Scholar
86. Castello L, McGrath DG, Hess LL, Coe MT, Lefebvre PA, Petry P, et al. The vulnerability of Amazon freshwater ecosystems. Conserv Lett. 2013;6: 217–229.
- View Article
- Google Scholar
87. Castello L, Macedo MN. Large-scale degradation of Amazonian freshwater ecosystems. Glob Chang Biol. 2016;22: 990–1007. pmid:26700407
- View Article
- PubMed/NCBI
- Google Scholar
88. Waples RS. Evolutionary significant units, distinct population segments, and the Endangered Species Act: reply to Pennock and Dimmick. Conserv Biol. 1998;12: 718–721.
- View Article
- Google Scholar
89. Palsbøll PJ, Bérubé M, Allendorf FW. Identification of management units using population genetic data. Trends Ecol Evol. 2007;22: 11–16. pmid:16982114
- View Article
- PubMed/NCBI
- Google Scholar
90. Behling H. Holocene environmental dynamics in coastal, eastern and central Amazonia and the role of the Atlantic sea-level change. Geogr Helv Swiss J Geogr. 2011;66: 208–216.
- View Article
- Google Scholar
91. Rossetti D de F, Gribel R, Toledo PM de, Tatumi SH, Yee M, Tudela DRG, et al. Unfolding long-term Late Pleistocene–Holocene disturbances of forest communities in the southwestern Amazonian lowlands. Ecosphere. 2018;9: e02457.
- View Article
- Google Scholar
92. Franklin IR. Evolutionary change in small populations. In: Soule ME, Wilcox BA, editors. Conservation Biology: An Evolutionary-Ecological Perspective. Sunderland, MA, USA: Sinauer Associates, Inc.; 1980. pp. 135–140.
93. Frankham R, Bradshaw CJA, Brook BW. Genetics in conservation management: Revised recommendations for the 50/500 rules, Red List criteria and population viability analyses. Biol Conserv. Elsevier Ltd; 2014;170: 56–63.
- View Article
- Google Scholar
94. Jamieson IG, Allendorf FW. How does the 50/500 rule apply to MVPs? Trends Ecol Evol. Elsevier Ltd; 2012;27: 580–584. pmid:22868005
- View Article
- PubMed/NCBI
- Google Scholar
95. Pianka ER. On r- and K-selection. Am Nat. 1970;104: 592–597.
- View Article
- Google Scholar
96. Winemiller KO, Rose KA. Patterns of life-history diversification in North American fishes: implications for population regulation. Can J Fish Aquat Sci. 1992;49: 2196–2218.
- View Article
- Google Scholar
97. Farias IP, Leão AS de A, Almeida YS, Verba JT, Crossa M, Honczaryk A, et al. Evidence of polygamy in the socially monogamous Amazonian fish Arapaima gigas (Cuvier, 1817). Neotrop Ichthyol. 2015;13: 195–204.
- View Article
- Google Scholar
98. García-Dorado A. On the consequences of ignoring purging on genetic recommendations for minimum viable population rules. Heredity (Edinb). 2015;115: 185–187. pmid:25873145
- View Article
- PubMed/NCBI
- Google Scholar
99. Frankham R. Genetics and extinction. Biol Conserv. 2005;126: 131–140.
- View Article
- Google Scholar
100. Purcell SW, Polidoro BA, Hamel J-F, Gamboa RU, Mercier A. The cost of being valuable: predictors of extinction risk in marine invertebrates exploited as luxury seafood. Proc R Soc B Biol Sci. 2014;281: 20133296–20133296. pmid:24598425
- View Article
- PubMed/NCBI
- Google Scholar
101. Queiroz HL de, Crampton WGR. Estratégias para Manejo de Recursos Pesqueiros em Mamirauá. Brasilia, Brazil: Sociedade Civil Mamirauá, MCT—CNPq; 1999. p. 208.
102. Castello L. A method to count pirarucu Arapaima gigas: fishers, assessment, and management. North Am J Fish Manag. 2004;24: 379–389.
- View Article
- Google Scholar
103. Campos-Silva JV, Peres CA. Community-based management induces rapid recovery of a high-value tropical freshwater fishery. Sci Rep. Nature Publishing Group; 2016;6: 34745. pmid:27731319
- View Article
- PubMed/NCBI
- Google Scholar
104. Reed DH, Frankham R. Correlation between fitness and genetic diversity. Conserv Biol. 2003;17: 230–237.
- View Article
- Google Scholar
105. 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.
- View Article
- Google Scholar
106. Jégu M, Keith P. Lower Oyapock River as northern limit for the Western Amazon fish fauna or only a stage in its northward progression. C R Biol. 1999;322: 1133–1143.
- View Article
- Google Scholar
107. Willis SC, López-Fernández H, Montaña CG, Farias IP, Ortí G. Species-level phylogeny of “Satan’s perches” based on discordant gene trees (Teleostei: Cichlidae: Satanoperca Günther 1862). Mol Phylogenet Evol. 2012;63: 798–808. pmid:22406409
- View Article
- PubMed/NCBI
- Google Scholar
108. Amaral CRL, Brito PM, Silva DA, Carvalho EF. A new cryptic species of South American freshwater pufferfish of the genus Colomesus (Tetraodontidae), based on both morphology and DNA data. PLoS One. 2013;8: e74397. pmid:24040239
- View Article
- PubMed/NCBI
- Google Scholar
109. Hrbek T, da Silva VMF, Dutra N, Gravena W, Martin AR, Farias IP. A new species of river dolphin from Brazil or: how little do we know our biodiversity. PLoS One. 2014;9: e0083623. pmid:24465386
- View Article
- PubMed/NCBI
- Google Scholar

[ref1] 1. Araújo-Lima CARM, Goulding M. Os Frutos do Tambaqui. Ecologia, Conservação e Cultivo na Amazônia. Belem, Brazil: Sociedade Civil Mamirauá - MCT—CNPq; 1998.

[ref2] 2. Goulding M, Smith NJH, Mahar DJ. Floods of Fortune: Ecology and Economy along the Amazon. New York, NY: Columbia University Press; 2000.

[ref3] 3. Prang G. A caboclo society in the middle Rio Negro basin: ecology, economy, and history of an ornamental fishery in the state of Amazonas, Brazil. Wayne State University. 2001.

[ref4] 4. Smith NJH. Caimans, capybaras, otters, manatees, and man in Amazônia. Biol Conserv. 1980;19: 177–187.
View Article
Google Scholar

[5] View Article

[6] Google Scholar

[ref5] 5. Medem F. Los Crocodylia de Sur America. Vol. 2. Bogota, Colombia: Ed. Carrera; 1983.

[ref6] 6. Best RC. Apparent dry-season fasting in Amazonian manatees (Mammalia: Sirenia). Biotropica. 1983;15: 76–78.
View Article
Google Scholar

[9] View Article

[10] Google Scholar

[ref7] 7. Best RC. The aquatic mammals and reptiles of the Amazon. In: Sioli H, editor. The Amazon Limnology and Landscape Ecology of a Mighty Tropical River and its Basins. Dodechet, The Netherlands: Dr. Junk W Publisher; 1984. pp. 371–412.

[ref8] 8. Goulding M. Fishes and the Forest: Explorations in Amazonian Natural History. Los Angeles, CA: University of California press; 1980.

[ref9] 9. Stewart DJ. A new species of Arapaima (Osteoglossomorpha: Osteoglossidae) from the Solimões River, Amazonas State, Brazil. Copeia. 2013;2013: 470–476.
View Article
Google Scholar

[14] View Article

[15] Google Scholar

[ref10] 10. Stewart DJ. Re-description of Arapaima agassizii (Valenciennes), a rare fish from Brazil (Osteoglossomorpha: Osteoglossidae). Copeia. 2013;2013: 38–51.
View Article
Google Scholar

[17] View Article

[18] Google Scholar

[ref11] 11. Spix JB von, Agassiz L. Selecta genera et species piscium Brasiliensium: Quos in itinere per Brasiliam annis MDCCCXVII-MDCCCXX jussu et auspiciis Maximiliani Josephi I. Munich, Germany: Bavariae regisaugustissimi peracto collegit et pingendos curavit Dr. J. B. de Spix; 1829.

[ref12] 12. Günther A. Catalogue of the fishes in the British Museum, Volume 7. London, England: The British Museum (Natural History); 1868.

[ref13] 13. Saint-Paul U. Potential for aquaculture of South American freshwater fishes: a review. Aquaculture. 1986;54: 205–240.
View Article
Google Scholar

[22] View Article

[23] Google Scholar

[ref14] 14. Nelson JS. Fishes of the World, 3rd edition. New York, NY: John Wiley and Sons, Inc.; 1994.

[ref15] 15. Fontenele O. Contribuição para o conhecimento da biologia do pirarucu, Arapaima gigas (Cuvier), em cativeiro (Actinopterygii, Osteoglossidae). Rev Bras Biol. 1948;8: 445–459.
View Article
Google Scholar

[26] View Article

[27] Google Scholar

[ref16] 16. Menezes RS. Notas biológicas e econômicas sobre o pirarucu Arapaima gigas (Cuvier) (Actinopterygii, Arapaimidae). Serviço Informação Agrícola / Minist da Agric Ser Estud técnicos. 1951;3: 9–39.
View Article
Google Scholar

[29] View Article

[30] Google Scholar

[ref17] 17. Carvalho F, Power M, Forsberg BR, Castello L, Martins EG, Freitas CEDC. Trophic ecology of Arapaima sp. in a ria lake-river-floodplain transition zone of the Amazon. Ecol Freshw Fish. 2018;27: 237–246.
View Article
Google Scholar

[32] View Article

[33] Google Scholar

[ref18] 18. Castello L. Lateral migration of Arapaima gigas in floodplains of the Amazon. Ecol Freshw Fish. 2008;17: 38–46.
View Article
Google Scholar

[35] View Article

[36] Google Scholar

[ref19] 19. Fernandes MN, da Cruz AL, da Costa OTF, Perry SF. Morphometric partitioning of the respiratory surface area and diffusion capacity of the gills and swim bladder in juvenile Amazonian air-breathing fish, Arapaima gigas. Micron. 2012;43: 961–70. pmid:22512942
View Article
PubMed/NCBI
Google Scholar

[38] View Article

[39] PubMed/NCBI

[40] Google Scholar

[ref20] 20. Arantes CC, Castello L, Stewart DJ, Certa M, Queiroz HL de. Population density, growth and reproduction of arapaima in an Amazonian river-floodplain. Ecol Freshw Fish. 2010;19: 455–465.
View Article
Google Scholar

[42] View Article

[43] Google Scholar

[ref21] 21. Queiroz HL de. Natural history and conservation of pirarucu Arapaima gigas, at the Amazonian Várzea: Red giants in muddy waters. St. Andrews, England: University of St. Andrews; 2000. p. 222.

[ref22] 22. Goulding M, Barthem RB, Ferreira EJG. The Smithsonian Atlas of the Amazon. Washington, DC: Smithsonian Institution Press; 2003.

[ref23] 23. Miranda-Chumacero G, Wallace R, Calderón H, Calderón G, Willink P, Guerrero M, et al. Distribution of arapaima (Arapaima gigas) (Pisces: Arapaimatidae) in Bolivia: implications in the control and management of a non-native population. BioInvasions Rec. 2012;1: 129–138.
View Article
Google Scholar

[47] View Article

[48] Google Scholar

[ref24] 24. 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: 297–308.
View Article
Google Scholar

[50] View Article

[51] Google Scholar

[ref25] 25. Hrbek T, Crossa M, Farias IP. Conservation strategies for Arapaima gigas (Schinz, 1822) and the Amazonian várzea ecosystem. Brazilian J Biol. 2007;67: 909–917.
View Article
Google Scholar

[53] View Article

[54] Google Scholar

[ref26] 26. Araripe J, do Rêgo PS, Queiroz HL de, Sampaio I, Schneider H. Dispersal capacity and genetic structure of Arapaima gigas on different geographic scales using microsatellite markers. PLoS One. 2013;8: e54470. pmid:23372730
View Article
PubMed/NCBI
Google Scholar

[56] View Article

[57] PubMed/NCBI

[58] Google Scholar

[ref27] 27. Farias IP, Hrbek T, Brinkmann H, Sampaio I, Meyer A. Characterization and isolation of DNA microsatellite primers for Arapaima gigas, an economically important but severely over-exploited fish species of the Amazon basin. Mol Ecol Notes. 2003;3: 128–130.
View Article
Google Scholar

[60] View Article

[61] Google Scholar

[ref28] 28. Van Oosterhout C, Hutchinson WF, Wills DPM, Shipley P. MICRO-CHECKER: software for identifying and correcting genotyping errors in microsatellite data. Mol Ecol Notes. 2004;4: 535–538.
View Article
Google Scholar

[63] View Article

[64] Google Scholar

[ref29] 29. 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: 564–567. pmid:21565059
View Article
PubMed/NCBI
Google Scholar

[66] View Article

[67] PubMed/NCBI

[68] Google Scholar

[ref30] 30. Leberg PL. Estimating allelic richness: Effects of sample size and bottlenecks. Mol Ecol. 2002;11: 2445–2449. pmid:12406254
View Article
PubMed/NCBI
Google Scholar

[70] View Article

[71] PubMed/NCBI

[72] Google Scholar

[ref31] 31. Kalinowski ST. Hp-Rare 1.0: a computer program for performing rarefaction on measures of allelic richness. Mol Ecol Notes. 2005;5: 187–189.
View Article
Google Scholar

[74] View Article

[75] Google Scholar

[ref32] 32. Nei M, Roychoudhury AK. Sampling variances of heterozygosity and genetic distance. Genetics. 1974;76: 379–390. Available: https://www.ncbi.nlm.nih.gov/pubmed/4822472 pmid:4822472
View Article
PubMed/NCBI
Google Scholar

[77] View Article

[78] PubMed/NCBI

[79] Google Scholar

[ref33] 33. Pritchard JK, Stephens M, Donnelly P. Inference of population structure using multilocus genotype data. Genetics. 2000;155: 945–959. Available: https://www.ncbi.nlm.nih.gov/pubmed/10835412 pmid:10835412
View Article
PubMed/NCBI
Google Scholar

[81] View Article

[82] PubMed/NCBI

[83] Google Scholar

[ref34] 34. Hubisz MJ, Falush D, Stephens M, Pritchard JK. Inferring weak population structure with the assistance of sample group information. Mol Ecol Resour. 2009;9: 1322–1332. pmid:21564903
View Article
PubMed/NCBI
Google Scholar

[85] View Article

[86] PubMed/NCBI

[87] Google Scholar

[ref35] 35. Evanno G, Regnaut S, Goudet J. Detecting the number of clusters of individuals using the software STRUCTURE: a simulation study. Mol Ecol. 2005;14: 2611–2620. pmid:15969739
View Article
PubMed/NCBI
Google Scholar

[89] View Article

[90] PubMed/NCBI

[91] Google Scholar

[ref36] 36. Dupanloup I, Schneider S, Excoffier L. A simulated annealing approach to define the genetic structure of populations. Mol Ecol. 2002;11: 2571–2581. pmid:12453240
View Article
PubMed/NCBI
Google Scholar

[93] View Article

[94] PubMed/NCBI

[95] Google Scholar

[ref37] 37. Excoffier L, Smouse PE, Quattro JM. Analysis of molecular variance inferred from metric distances among DNA haplotypes: Application to human mitochondrial DNA restriction data. Genetics. 1992;131: 479–491. Available: https://www.ncbi.nlm.nih.gov/pubmed/1644282 pmid:1644282
View Article
PubMed/NCBI
Google Scholar

[97] View Article

[98] PubMed/NCBI

[99] Google Scholar

[ref38] 38. 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: 94. pmid:20950446
View Article
PubMed/NCBI
Google Scholar

[101] View Article

[102] PubMed/NCBI

[103] Google Scholar

[ref39] 39. Jombart T. adegenet: a R package for the multivariate analysis of genetic markers. Bioinformatics. 2008;24: 1403–1405. pmid:18397895
View Article
PubMed/NCBI
Google Scholar

[105] View Article

[106] PubMed/NCBI

[107] Google Scholar

[ref40] 40. R Core Team. R: A Language and Environment for Statistical Computing [Internet]. Vienna, Austria: R Foundation for Statistical Computing; 2018. Available: http://www.r-project.org

[ref41] 41. Mantel N. The detection of disease clustering and a generalized regression approach. Cancer Res. 1967;27: 209–220. Available: https://www.ncbi.nlm.nih.gov/pubmed/6018555 pmid:6018555
View Article
PubMed/NCBI
Google Scholar

[110] View Article

[111] PubMed/NCBI

[112] Google Scholar

[ref42] 42. Oksanen J, Blanchet FG, Friendly M, Kindt R, Legendre P, McGlinn D, et al. vegan: Community Ecology Package [Internet]. 2018. Available: https://cran.r-project.org/package = vegan
View Article
Google Scholar

[114] View Article

[115] Google Scholar

[ref43] 43. Beerli P, Felsenstein J. Maximum likelihood estimation of a migration matrix and effective population sizes in n subpopulations by using a coalescent approach. Proc Natl Acad Sci U S A. 2001;98: 4563–4568. pmid:11287657
View Article
PubMed/NCBI
Google Scholar

[117] View Article

[118] PubMed/NCBI

[119] Google Scholar

[ref44] 44. Meirmans PG, Van Tienderen PH. GENOTYPE and GENODIVE: Two programs for the analysis of genetic diversity of asexual organisms. Mol Ecol Notes. 2004;4: 792–794.
View Article
Google Scholar

[121] View Article

[122] Google Scholar

[ref45] 45. Piry S, Luikart G, Cornuet J-M. BOTTLENECK: a computer program for detecting recent reductions in the effective size using allele frequency data. J Hered. 1999;90: 502–503.
View Article
Google Scholar

[124] View Article

[125] Google Scholar

[ref46] 46. Garza JC, Williamson EG. Detection of reduction in population size using data from microsatellite loci. Mol Ecol. 2001;10: 305–318. pmid:11298947
View Article
PubMed/NCBI
Google Scholar

[127] View Article

[128] PubMed/NCBI

[129] Google Scholar

[ref47] 47. Beaumont MA. Detecting population expansion and decline using microsatellites. Genetics. 1999;153: 2013–2029. Available: https://www.ncbi.nlm.nih.gov/pubmed/10581303 pmid:10581303
View Article
PubMed/NCBI
Google Scholar

[131] View Article

[132] PubMed/NCBI

[133] Google Scholar

[ref48] 48. Storz JF, Beaumont MA. Testing for genetic evidence of population expansion and contraction: an empirical analysis of microsatellite DNA variation using a hierarchical Bayesian model. Evolution (N Y). 2002;56: 154–166.
View Article
Google Scholar

[135] View Article

[136] Google Scholar

[ref49] 49. Gelman A, Rubin DB. Inference from iterative simulation using multiple sequences. Stat Sci. 1992;7: 457–472.
View Article
Google Scholar

[138] View Article

[139] Google Scholar

[ref50] 50. Plummer M, Best N, Cowles K, Vines K. CODA: Convergence diagnosis and output analysis for MCMC. R News. 2006;6: 7–11. Available: https://journal.r-project.org/archive/
View Article
Google Scholar

[141] View Article

[142] Google Scholar

[ref51] 51. Wickham H. ggplot2: Elegant Graphics for Data Analysis [Internet]. Springer-Verlag New York; 2009. Available: http://ggplot2.org

[ref52] 52. Waples RS, Do C. Ldne: a program for estimating effective population size from data on linkage disequilibrium. Mol Ecol Resour. 2008;8: 753–6. pmid:21585883
View Article
PubMed/NCBI
Google Scholar

[145] View Article

[146] PubMed/NCBI

[147] Google Scholar

[ref53] 53. Do C, Waples RS, Peel D, Macbeth GM, Tillett BJ, Ovenden JR. NeEstimator v2: re-implementation of software for the estimation of contemporary effective population size (Ne) from genetic data. Mol Ecol Resour. 2014;14: 209–214. pmid:23992227
View Article
PubMed/NCBI
Google Scholar

[149] View Article

[150] PubMed/NCBI

[151] Google Scholar

[ref54] 54. Palstra FP, Fraser DJ. Effective/census population size ratio estimation: a compendium and appraisal. Ecol Evol. 2012;2: 2357–2365. pmid:23139893
View Article
PubMed/NCBI
Google Scholar

[153] View Article

[154] PubMed/NCBI

[155] Google Scholar

[ref55] 55. Storz JF, Beaumont MA, Alberts SC. Genetic evidence for long-term population decline in a savannah-dwelling primate: inferences from a hierarchical Bayesian model. Mol Biol Evol. 2002;19: 1981–1990. pmid:12411607
View Article
PubMed/NCBI
Google Scholar

[157] View Article

[158] PubMed/NCBI

[159] Google Scholar

[ref56] 56. Veríssimo J. A Pesca na Amazônia. Rio de Janeiro, Brazil: Livraria Clássica, de Alves & C.; 1895.

[ref57] 57. Kurlansky M. Cod: A Biography of the Fish that Changed the World. London, England: Jonathan Cape; 1998.

[ref58] 58. Goulding M. Ecologia de Pesca do Rio Madeira. Manaus, Brazil: INPA; 1979.

[ref59] 59. Crossa M. NM, Petrere M Jr. Morphometric relationships and indirect determination of the length frequency structure of the pirarucu, Arapaima gigas (Cuvier), in Brazilian Amazonia. Fish Manag Ecol. 1999;6: 233–240.
View Article
Google Scholar

[164] View Article

[165] Google Scholar

[ref60] 60. Souza LS de, Armbruster JW, Werneke DC. The influence of the Rupununi portal on distribution of freshwater fish in the Rupununi district, Guyana. Cybium. 2012;36: 31–43.
View Article
Google Scholar

[167] View Article

[168] Google Scholar

[ref61] 61. Schaefer CEGR, Vale Júnior JF. Mudanças climáticas e evolução da paisagem em Roraima: uma resenha do Cretáceo ao recente. In: Barbosa RI, Ferreira EJG, Castellón EG, editors. Homem, Ambiente e Ecologia no Estado de Roraima. Manaus, AM: Instituto Nacional de Pesquisas da Amazônia (INPA); 1997. pp. 231–265.

[ref62] 62. Cremon ÉH, Rossetti D de F, Sawakuchi A de O, Cohen MCL. The role of tectonics and climate in the late Quaternary evolution of a northern Amazonian River. Geomorphology. Elsevier B.V.; 2016;271: 22–39.
View Article
Google Scholar

[171] View Article

[172] Google Scholar

[ref63] 63. Rossetti D de F, Valeriano M de M. Evolution of the lowest Amazon basin modeled from the integration of geological and SRTM topographic data. Catena. 2007;70: 253–265.
View Article
Google Scholar

[174] View Article

[175] Google Scholar

[ref64] 64. Hey J, Pinho C. Population genetics and objectivity in species diagnosis. Evolution (N Y). 2012;66: 1413–1429. pmid:22519781
View Article
PubMed/NCBI
Google Scholar

[177] View Article

[178] PubMed/NCBI

[179] Google Scholar

[ref65] 65. Campos‐Silva JV, Hawes JE, Peres CA. Population recovery, seasonal site fidelity, and daily activity of pirarucu (Arapaima spp.) in an Amazonian floodplain mosaic. Freshw Biol. 2019;64: 1255–1264.
View Article
Google Scholar

[181] View Article

[182] Google Scholar

[ref66] 66. Bemerguy RL, Costa JBS. Considerações sobre a evolução do sistema de drenagem da Amazônia e sua relação com o arcabouço tectônico-estrutural. Bol do Mus Para Emílio Goeldi, Série Ciências da Terra. 1991;3: 75–97.
View Article
Google Scholar

[184] View Article

[185] Google Scholar

[ref67] 67. Rossetti D de F, Cohen MCL, Tatumi SH, Sawakuchi A de O, Cremon ÉH, Mittani JCR, et al. Mid-Late Pleistocene OSL chronology in western Amazonia and implications for the transcontinental Amazon pathway. Sediment Geol. Elsevier B.V.; 2015;330: 1–15.
View Article
Google Scholar

[187] View Article

[188] Google Scholar

[ref68] 68. Farias IP, Hrbek T. Patterns of diversification in the discus fishes (Symphysodon spp. Cichlidae) of the Amazon basin. Mol Phylogenet Evol. 2008;49: 32–43. pmid:18762435
View Article
PubMed/NCBI
Google Scholar

[190] View Article

[191] PubMed/NCBI

[192] Google Scholar

[ref69] 69. Willis SC, Macrander J, Farias IP, Ortí G. Simultaneous delimitation of species and quantification of interspecific hybridization in Amazonian peacock cichlids (genus Cichla) using multi-locus data. BMC Evol Biol. 2012;12: 96. pmid:22727018
View Article
PubMed/NCBI
Google Scholar

[194] View Article

[195] PubMed/NCBI

[196] Google Scholar

[ref70] 70. Lundberg JG, Chernoff B. A Miocene fossil of the Amazonian fish Arapaima (Teleostei: Arapaimidae) from the Magdalena River region of Colombia: biogeographic and evolutionary implications. Biotropica. 1992;24: 2–14.
View Article
Google Scholar

[198] View Article

[199] Google Scholar

[ref71] 71. Hoorn C, Wesselingh FP, ter Steege H, Bermudez MA, Mora A, Sevink J, et al. Amazonia through time: Andean uplift, climate change, landscape evolution, and biodiversity. Science (80-). 2010;330: 927–931. pmid:21071659
View Article
PubMed/NCBI
Google Scholar

[201] View Article

[202] PubMed/NCBI

[203] Google Scholar

[ref72] 72. Santos M da CF, Ruffino ML, Farias IP. High levels of genetic variability and panmixia of the tambaqui Colossoma macropomum (Cuvier, 1818) in the main channel of the Amazon River. J Fish Biol. 2007;71A: 33–44.
View Article
Google Scholar

[205] View Article

[206] Google Scholar

[ref73] 73. Farias IP, Torrico JP, García-Dávila C, Santos M da CF, Hrbek T, Renno J-F. Are rapids a barrier for floodplain fishes of the Amazon basin? A demographic study of the keystone floodplain species Colossoma macropomum (Teleostei: Characiformes). Mol Phylogenet Evol. 2010;56: 1129–1135. pmid:20362063
View Article
PubMed/NCBI
Google Scholar

[208] View Article

[209] PubMed/NCBI

[210] Google Scholar

[ref74] 74. Santos M da CF, Hrbek T, Farias IP. Multiloci approach to understand the historical and contemporary genetic connectivity of the keystone floodplain species Colossoma macropomum (Teleostei: Characiformes) in the Amazon basin. Front Genet. 2018;9: 263. pmid:30154822
View Article
PubMed/NCBI
Google Scholar

[212] View Article

[213] PubMed/NCBI

[214] Google Scholar

[ref75] 75. Batista J da S, Alves-Gomes JA. Phylogeography of Brachyplatystoma rousseauxii (Siluriformes—Pimelodidae) in the Amazon Basin offers preliminary evidence for the first case of “homing” for an Amazonian migratory catfish. Genet Mol Res. 2006;5: 723–740. pmid:17183483
View Article
PubMed/NCBI
Google Scholar

[216] View Article

[217] PubMed/NCBI

[218] Google Scholar

[ref76] 76. Ochoa LE, Pereira LHG, Costa-Silva GJ, Roxo FF, Batista J da S, Formiga K, et al. Genetic structure and historical diversification of catfish Brachyplatystoma platynemum (Siluriformes: Pimelodidae) in the Amazon basin with implications for its conservation. Ecol Evol. 2015;5: 2005–2020. pmid:26045952
View Article
PubMed/NCBI
Google Scholar

[220] View Article

[221] PubMed/NCBI

[222] Google Scholar

[ref77] 77. Oliveira RC de, Santos M da CF, Bernardino G, Hrbek T, Farias IP. From river to farm: an evaluation of genetic diversity in wild and aquaculture stocks of Brycon amazonicus (Spix & Agassiz, 1829), Characidae, Bryconinae. Hydrobiologia. 2018;805: 75–88.
View Article
Google Scholar

[224] View Article

[225] Google Scholar

[ref78] 78. Machado VN, Willis SC, Hrbek T, Farias IP. Population genetic structure of the Amazonian black flannelmouth characin (Characiformes, Prochilodontidae: Prochilodus nigricans Spix & Agassiz, 1829): contemporary and historical gene flow of a migratory and abundant fishery species. Environ Biol Fishes. 2017;100: 1–16.
View Article
Google Scholar

[227] View Article

[228] Google Scholar

[ref79] 79. Willis SC, Winemiller KO, Montaña CG, Macrander J, Reiss P, Farias IP, et al. Population genetics of the speckled peacock bass (Cichla temensis), South America’s most important inland sport fishery. Conserv Genet. 2015;16: 1345–1357.
View Article
Google Scholar

[230] View Article

[231] Google Scholar

[ref80] 80. Willis SC. One species or four? Yes! …and, no. Or, arbitrary assignment of lineages to species obscures the diversification processes of Neotropical fishes. PLoS One. 2017;12: e0172349. pmid:28235096
View Article
PubMed/NCBI
Google Scholar

[233] View Article

[234] PubMed/NCBI

[235] Google Scholar

[ref81] 81. Amado MV, Hrbek T, Farias IP. A molecular perspective on systematics, taxonomy and classification Amazonian discus fishes of the genus Symphysodon. Int J Evol Biol. 2011;2011: 360654. pmid:21811676
View Article
PubMed/NCBI
Google Scholar

[237] View Article

[238] PubMed/NCBI

[239] Google Scholar

[ref82] 82. Frederico RG, Farias IP, Araújo MLG, Charvet-Almeida P, Alves-Gomes JA. Phylogeography and conservation genetics of the Amazonian freshwater stingray Paratrygon aiereba Müller & Henle, 1841 (Chondrichthyes: Potamotrygonidae). Neotrop Ichthyol. 2012;10: 71–80.
View Article
Google Scholar

[241] View Article

[242] Google Scholar

[ref83] 83. Winemiller KO. Life history strategies, population regulation, and implications for fisheries management. Can J Fish Aquat Sci. 2005;62: 872–885.
View Article
Google Scholar

[244] View Article

[245] Google Scholar

[ref84] 84. Meirmans PG, Goudet J, Gaggiotti OE. Ecology and life history affect different aspects of the population structure of 27 high-alpine plants. Mol Ecol. 2011;20: 3144–3155. pmid:21689194
View Article
PubMed/NCBI
Google Scholar

[247] View Article

[248] PubMed/NCBI

[249] Google Scholar

[ref85] 85. Lowe-McConnell RH. Ecological aspects of seasonality in fishes of tropical waters. Symp Zool Soc London. 1979;44: 219–241.
View Article
Google Scholar

[251] View Article

[252] Google Scholar

[ref86] 86. Castello L, McGrath DG, Hess LL, Coe MT, Lefebvre PA, Petry P, et al. The vulnerability of Amazon freshwater ecosystems. Conserv Lett. 2013;6: 217–229.
View Article
Google Scholar

[254] View Article

[255] Google Scholar

[ref87] 87. Castello L, Macedo MN. Large-scale degradation of Amazonian freshwater ecosystems. Glob Chang Biol. 2016;22: 990–1007. pmid:26700407
View Article
PubMed/NCBI
Google Scholar

[257] View Article

[258] PubMed/NCBI

[259] Google Scholar

[ref88] 88. Waples RS. Evolutionary significant units, distinct population segments, and the Endangered Species Act: reply to Pennock and Dimmick. Conserv Biol. 1998;12: 718–721.
View Article
Google Scholar

[261] View Article

[262] Google Scholar

[ref89] 89. Palsbøll PJ, Bérubé M, Allendorf FW. Identification of management units using population genetic data. Trends Ecol Evol. 2007;22: 11–16. pmid:16982114
View Article
PubMed/NCBI
Google Scholar

[264] View Article

[265] PubMed/NCBI

[266] Google Scholar

[ref90] 90. Behling H. Holocene environmental dynamics in coastal, eastern and central Amazonia and the role of the Atlantic sea-level change. Geogr Helv Swiss J Geogr. 2011;66: 208–216.
View Article
Google Scholar

[268] View Article

[269] Google Scholar

[ref91] 91. Rossetti D de F, Gribel R, Toledo PM de, Tatumi SH, Yee M, Tudela DRG, et al. Unfolding long-term Late Pleistocene–Holocene disturbances of forest communities in the southwestern Amazonian lowlands. Ecosphere. 2018;9: e02457.
View Article
Google Scholar

[271] View Article

[272] Google Scholar

[ref92] 92. Franklin IR. Evolutionary change in small populations. In: Soule ME, Wilcox BA, editors. Conservation Biology: An Evolutionary-Ecological Perspective. Sunderland, MA, USA: Sinauer Associates, Inc.; 1980. pp. 135–140.

[ref93] 93. Frankham R, Bradshaw CJA, Brook BW. Genetics in conservation management: Revised recommendations for the 50/500 rules, Red List criteria and population viability analyses. Biol Conserv. Elsevier Ltd; 2014;170: 56–63.
View Article
Google Scholar

[275] View Article

[276] Google Scholar

[ref94] 94. Jamieson IG, Allendorf FW. How does the 50/500 rule apply to MVPs? Trends Ecol Evol. Elsevier Ltd; 2012;27: 580–584. pmid:22868005
View Article
PubMed/NCBI
Google Scholar

[278] View Article

[279] PubMed/NCBI

[280] Google Scholar

[ref95] 95. Pianka ER. On r- and K-selection. Am Nat. 1970;104: 592–597.
View Article
Google Scholar

[282] View Article

[283] Google Scholar

[ref96] 96. Winemiller KO, Rose KA. Patterns of life-history diversification in North American fishes: implications for population regulation. Can J Fish Aquat Sci. 1992;49: 2196–2218.
View Article
Google Scholar

[285] View Article

[286] Google Scholar

[ref97] 97. Farias IP, Leão AS de A, Almeida YS, Verba JT, Crossa M, Honczaryk A, et al. Evidence of polygamy in the socially monogamous Amazonian fish Arapaima gigas (Cuvier, 1817). Neotrop Ichthyol. 2015;13: 195–204.
View Article
Google Scholar

[288] View Article

[289] Google Scholar

[ref98] 98. García-Dorado A. On the consequences of ignoring purging on genetic recommendations for minimum viable population rules. Heredity (Edinb). 2015;115: 185–187. pmid:25873145
View Article
PubMed/NCBI
Google Scholar

[291] View Article

[292] PubMed/NCBI

[293] Google Scholar

[ref99] 99. Frankham R. Genetics and extinction. Biol Conserv. 2005;126: 131–140.
View Article
Google Scholar

[295] View Article

[296] Google Scholar

[ref100] 100. Purcell SW, Polidoro BA, Hamel J-F, Gamboa RU, Mercier A. The cost of being valuable: predictors of extinction risk in marine invertebrates exploited as luxury seafood. Proc R Soc B Biol Sci. 2014;281: 20133296–20133296. pmid:24598425
View Article
PubMed/NCBI
Google Scholar

[298] View Article

[299] PubMed/NCBI

[300] Google Scholar

[ref101] 101. Queiroz HL de, Crampton WGR. Estratégias para Manejo de Recursos Pesqueiros em Mamirauá. Brasilia, Brazil: Sociedade Civil Mamirauá, MCT—CNPq; 1999. p. 208.

[ref102] 102. Castello L. A method to count pirarucu Arapaima gigas: fishers, assessment, and management. North Am J Fish Manag. 2004;24: 379–389.
View Article
Google Scholar

[303] View Article

[304] Google Scholar

[ref103] 103. Campos-Silva JV, Peres CA. Community-based management induces rapid recovery of a high-value tropical freshwater fishery. Sci Rep. Nature Publishing Group; 2016;6: 34745. pmid:27731319
View Article
PubMed/NCBI
Google Scholar

[306] View Article

[307] PubMed/NCBI

[308] Google Scholar

[ref104] 104. Reed DH, Frankham R. Correlation between fitness and genetic diversity. Conserv Biol. 2003;17: 230–237.
View Article
Google Scholar

[310] View Article

[311] Google Scholar

[ref105] 105. 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.
View Article
Google Scholar

[313] View Article

[314] Google Scholar

[ref106] 106. Jégu M, Keith P. Lower Oyapock River as northern limit for the Western Amazon fish fauna or only a stage in its northward progression. C R Biol. 1999;322: 1133–1143.
View Article
Google Scholar

[316] View Article

[317] Google Scholar

[ref107] 107. Willis SC, López-Fernández H, Montaña CG, Farias IP, Ortí G. Species-level phylogeny of “Satan’s perches” based on discordant gene trees (Teleostei: Cichlidae: Satanoperca Günther 1862). Mol Phylogenet Evol. 2012;63: 798–808. pmid:22406409
View Article
PubMed/NCBI
Google Scholar

[319] View Article

[320] PubMed/NCBI

[321] Google Scholar

[ref108] 108. Amaral CRL, Brito PM, Silva DA, Carvalho EF. A new cryptic species of South American freshwater pufferfish of the genus Colomesus (Tetraodontidae), based on both morphology and DNA data. PLoS One. 2013;8: e74397. pmid:24040239
View Article
PubMed/NCBI
Google Scholar

[323] View Article

[324] PubMed/NCBI

[325] Google Scholar

[ref109] 109. Hrbek T, da Silva VMF, Dutra N, Gravena W, Martin AR, Farias IP. A new species of river dolphin from Brazil or: how little do we know our biodiversity. PLoS One. 2014;9: e0083623. pmid:24465386
View Article
PubMed/NCBI
Google Scholar

[327] View Article

[328] PubMed/NCBI

[329] Google Scholar

Figures

Abstract

Introduction

Material and methods

Ethics statement

Sampling

Data analysis

Historical and contemporaneous gene flow and demography

Results

Distribution of genetic variability and population differentiation

Correlation between genetic divergence and geographic proximity

Evidence of population size changes

Discussion

Population structure and isolation-by-distance in a complex river system

Implications for Arapaima conservation in a threatened river system

Arapaima from the unique extra-Amazon populations

Conclusions

Ethics statement

Supporting information

S1 Fig. Graphic representation of the average number of alleles per population of Arapaima gigas.

S2 Fig. Graphic representation of expected heterozygosity (HE) per population of Arapaima gigas.

S3 Fig. Absolute values of the 2nd order rate of change of mean likelihoods of each K.

S4 Fig. Graphic representation of pairwise FST values among groups of individuals of Arapaima gigas.

S1 Table. Characteristics of the 11 microsatellite loci analyzed for Arapaima gigas considering separately the groups of individuals grouped by collection site.

S2 Table. Number of migrants per generation Nm = Mθ/2.

S3 Table. Matrix of pairwise FST values among localities sampled for Arapaima gigas.

Acknowledgments

References

S2 Fig. Graphic representation of expected heterozygosity (H_E) per population of Arapaima gigas.

S3 Fig. Absolute values of the 2^nd order rate of change of mean likelihoods of each K.

S4 Fig. Graphic representation of pairwise F_ST values among groups of individuals of Arapaima gigas.

S3 Table. Matrix of pairwise F_ST values among localities sampled for Arapaima gigas.