Biodiversity and Conservation

, Volume 27, Issue 6, pp 1291–1308 | Cite as

The curious case of Bradypus variegatus sloths: populations in threatened habitats are biodiversity components needing protection

  • Sofia Marques Silva
  • José A. Dávila
  • Bryson Voirin
  • Susana Lopes
  • Nuno Ferrand
  • Nadia Moraes-Barros
Original Paper

Abstract

Studying Neotropical wild populations is of particular interest. While this region is facing an escalating habitat degradation, it also has remarkable biodiversity levels, whose origin we are only beginning to understand. A myriad of processes might have had idiosyncratic effects on its numerous species. Within the hottest Neotropical biodiversity hotspot, the Atlantic Forest (AF), species and genetic diversities are organized latitudinally, with decreasing diversity levels southwards. Bradypus variegatus, the brown-throated three-toed sloth, was one of the first species observed to present such pattern. Moreover, within AF, B. variegatus populations seem to be geographically isolated and genetically differentiated. Whether AF B. variegatus isolation, differentiation, and loss of genetic diversity are historical or contemporary (anthropogenic-driven), result from species-specific or general historical events, and if this is of conservation concern remains unclear. Here, we combine micro-evolutionary, multilocus, and high-throughput sequencing approaches to detail the processes responsible for the patterns of genetic diversity on B. variegatus populations in AF, and further understand AF biogeographic history. Few studies made use of similar approaches on Neotropical biodiversity. Our results agree with recent re-interpretations on the AF refugia model and support a species-specific refugium in southern AF, characterized by a metapopulation formation. Finally, we present compelling evidences of the need for conservation actions on AF B. variegatus populations, by comparing genetic diversity levels between populations of different Bradypus species. As far as we know, this is the most comprehensive assessment on Bradypus nuclear DNA diversity.

Keywords

Bradypus pygmaeus Bradypus torquatus Microsatellite loci Next-generation sequencing Nuclear DNA sequences Quaternary climate changes 

Introduction

Biodiversity has been divided into ecosystem, species, and genetic diversities (Heywood and Watson 1995). This empirical, and conceptual division has become more complex and intricate, as the understanding of these components has improved. New elements started to superimpose, such as the functional diversity in an ecosystem (Cernansky 2017), and the population component. Transversal to the several diversity levels, the population component is receiving particular attention in conservation studies (Skrbinšek et al. 2012; Ripperger et al. 2014). Researchers realized that losing a population may be disastrous for the ecosystem at a local scale (McConkey and Drake 2006), and can jeopardize survival of the entire species at a broader extent (Ehrlich and Daily 1993), especially by reducing its evolutionary capacity due to genetic diversity loss (Wright 1977; Lacy 1987). Despite large populations are not usually the focus of conservation actions (Frankham et al. 2004), their study contribute to understand their evolutionary histories, population structure, and highlight geographically restricted portions of genetic diversity, especially in threatened areas (Frankham 2005; Ceballos et al. 2015). For these purposes, evolutionarily significant units and management units have been described (ESUs and MUs, respectively; Esteban et al. 2016; Zenboudji et al. 2016). Despite some debate (Crandall et al. 2000), ESUs constitute isolated populations of a species, with historically distinct evolutionary histories, high genetic differentiation, and complete monophyly (Moritz 1994), whereas MUs are defined as demographically independent populations of a species, with distinct allele frequencies (Moritz 1994, 1999).

Studying populations within lower latitudes is of particular interest for conservation, since the highest levels of biodiversity are recorded in the tropics (Jenkins et al. 2013), coupled with a higher rate of habitat and populations loss, due to a higher degree of threat (Jenkins et al. 2013; Ceballos et al. 2015). Moreover, the available information to conservation actions is still the poorest (Collen et al. 2008). In South America, the Atlantic Forest (AF; Fig. 1a) was highlighted as the foremost Neotropical biodiversity hotspot (Myers et al. 2000). Atlantic Forest houses one of the highest species richness of the world (Jenkins et al. 2013), although it has lost more than 80% of its historical range during the last 500 years (Ribeiro et al. 2009). Most AF wildlife is menaced in some degree, but a more thorough understanding of contemporary effect of habitat loss on its biodiversity is still lacking (Scarano and Ceotto 2015). Understanding both current and past processes behind the patterns of distribution of biodiversity will benefit AF wildlife conservation actions (Richardson and Whittaker 2010).
Fig. 1

a Bradypus variegatus sampling map and geographic nomenclature used throughout this work. Sampled regions/populations: AMZW, western Amazon; AMZE, eastern Amazon; AFNE, north-eastern Atlantic Forest; AFC, central Atlantic Forest; and AFS, southern Atlantic Forest. Brazilian states: AC, Acre; PA, Pará; MA, Maranhão; CE, Ceará; PB, Paraíba; PE, Pernambuco; AL, Alagoas; BA, Bahia; MG, Minas Gerais; SP, São Paulo. b STRUCTURE-like graphic representing B. variegatus population structure in the Atlantic Forest (AF) based on mitochondrial DNA variation (mtDNA; Moraes-Barros et al., 2007). Rectangle areas are proportional to sampling frequency used by Moraes-Barros et al. (2007). Three management units (MUs) were described by the authors. Two MUs presented shared mtDNA haplotypes between AF and Amazonian localities within MA and PA, respectively (Moraes-Barros et al. 2007). c Bayesian cluster analyses of all B. variegatus individual genotypes at five nuclear genes. K = 4 and k = 5 were the most likely number of clusters according to Evanno and likelihood methods, respectively. d Cluster analyses of all B. variegatus individual genotypes at microsatellite loci resulted in k = 2 (Evanno method), k = 5 (likelihood method), and k = 4 (DAPC). Structure within AF is also represented by STRUCTURE-like graphics. e Minimum spanning tree obtained from DAPC of B. variegatus microsatellites. (Color figure online)

Atlantic forest biodiversity is organized in a latitudinal pattern, i.e. species and genetic diversities are reduced southwards (Silva et al. 2012). Insofar, periods of climate stability and instability were associated with this pattern, but controversial evidences were reported, mostly concerning southern AF (AFS: Fig. 1a; Carnaval and Moritz 2008; Raposo do Amaral et al. 2013; Batalha-Filho et al. 2013a; Ledru et al. 2016; Leite et al. 2016). During hotter and more humid weather conditions, forest expansion was favored, enabling the establishment of biotic connection routes between AF and Amazonian forest (AMZ; Silva et al. 2012 and references therein; Cheng et al. 2013; Batalha-Filho et al. 2013b; Prates et al. 2016). These climate-driven routes were more frequently established in northern and central Brazil, during Plio-Pleistocene (5.3–0.01 mya; Batalha-Filho et al. 2013b). Similarly, within AF range, past climate oscillation likely caused successive and alternated events of forest expansion and retraction (Auler et al. 2004; Pessenda et al. 2004; Wang et al. 2004; Ledru et al. 2009, 2016). However, stable forested areas (or refugia) might have persisted only in northern and central AF (AFNE and AFC, respectively: Fig. 1a). The existence of a southern refugium is more debatable (Fig. 1a; Carnaval and Moritz 2008; Porto et al. 2013; Raposo do Amaral et al. 2013; Leite et al. 2016). Studies on phylogeography and population genetics are contributing to this controversy, and highlighting the importance of idiosyncratic life-history traits on patterns of distribution of biodiversity in AF (Porto et al. 2013; Prates et al. 2016; Thomé et al. 2016).

Bradypus variegatus, the brown-throated three-toed sloth, is a strictly arboreal Neotropical mammal, listed as Least Concern in the IUCN red list of threatened species (Moraes-Barros et al. 2014). The species is one of the most emblematic biological models supporting the AF biodiversity latitudinal pattern (Moraes-Barros et al. 2006, 2007; Martins 2011; Silva et al. 2012). Observed levels of genetic diversity available for AF B variegatus are low, particularly in AFS (Moraes-Barros et al. 2006, 2007). The species is structured in three mitochondrial DNA (mtDNA) phylogroups in the AF, identified as MUs, but show evidences of a Pleistocene connection between the eastern AMZ and northern AF (Fig. 1b; Moraes-Barros et al. 2007; Moraes-Barros and Arteaga 2015). Two hypotheses were postulated to explain the genetic diversity patterns for B. variegatus within AF. The “species-specific hypothesis” suggests that a single lineage of B. variegatus colonized AF around 1.0 mya, probably through northern Brazil, and became isolated from AMZ populations (Moraes-Barros et al. 2006, 2007, 2011; Moraes-Barros and Arteaga 2015). In AF, B. variegatus populations differentiated from each other (Fig. 1b), and putatively suffered bottleneck(s), isolation, and subsequent demographic expansion, following forest retractions and expansions (Moraes-Barros et al. 2006, 2007). Alternatively, but not exclusively, the AF refugia hypothesis emphasizes the role of climate in forest dynamics, therefore predicting that most forest-dependent taxa would have coincident and congruent micro-evolutionary histories (Carnaval and Moritz 2008). This hypothesis was built upon the commonality of genetic patterns described for several AF taxa, for which higher levels of mtDNA diversity in AFC populations corroborated the existence of a Bahia refugium, and mismatch distributions consistent with population expansions supported AFS as a non-refugial area (Carnaval and Moritz 2008). It remains unclear whether the genetic differentiation between AMZ and AF B. variegatus populations is also found at the nuclear level (nDNA). Also, the contribution of contemporary anthropogenic habitat fragmentation for this demographic trend and very low levels of genetic diversity for B. variegatus in AF is unknown. Therefore, the relevance for conservation actions on AF B. variegatus populations is open to investigation.

Two Bradypus species are considered threatened by the IUCN: the Vulnerable maned three-toed sloth, B. torquatus, and the Critically Endangered pygmy three-toed sloth, B. pygmaeus (Chiarello and Moraes-Barros 2014; Voirin et al. 2014). Bradypus torquatus is endemic to the AF, has high population structure, and low levels of mtDNA diversity (Moraes-Barros et al. 2006; Lara-Ruiz et al. 2008). Thus, AF B. variegatus populations and B. torquatus are probably facing similar effect from habitat fragmentation, as both are sympatric in the AF (Hirsch and Chiarello 2011). Bradypus pygmaeus is endemic to Isla Escudo de Veráguas, Panamá, and has a unique population, isolated in the small island (Anderson and Handley 2001; Ruiz-García et al. 2016). Therefore, B. pygmaeus population is expected to present low levels of genetic diversity.

In this context, our main goals are to further understand isolation and diversification of B. variegatus in AF, and assess the need for conservation actions. Thus, we generate new data on multilocus nDNA sequences and tens of newly described species-specific microsatellites. We assess patterns of genetic diversity and demography at different time-scales by using Bayesian algorithms and coalescence-based clustering analyses. We also compare micro-evolutionary drivers of diversification of B. variegatus in AF to biogeographic patterns already described. With this approach we aim to assess the roles of species-specific and general biogeographic events in the recurrent latitudinal pattern of distribution of AF biodiversity. Finally, to assess the need for conservation actions on AF B. variegatus populations, we test the conservation units previously proposed for B. variegatus (Moraes-Barros et al. 2007), and compare genetic diversity indices between these units and populations from threatened Bradypus Linnaeus, 1758 species. As far as we know, this is the most comprehensive assessment on Bradypus nDNA diversity.

Methods

Sampling

We collected 104 B. variegatus samples from localities within Atlantic Forest (AFNE, AFC and AFS: Fig. 1a), Amazonian forest (AMZE and AMZW: Fig. 1a), and brejos de altitude (small forest enclaves, relicts from past forest expansions; CE: Fig. 1a). AFNE, AFC and AFS partially correspond to management units (MUs) for B. variegatus (Fig. 1a, b; Moraes-Barros et al. 2007), and to putative Pleistocene forest refugia (Carnaval and Moritz 2008; Carnaval et al. 2014). We also sampled 16 B. torquatus from a population in Bahia (BA: Fig. 1a), and 10 B. pygmaeus from its single population in Isla Escudo de Veráguas, Panamá. From these, 33 B. variegatus and 15 B. torquatus samples were previously analyzed for mtDNA (Moraes-Barros et al. 2006, 2007). We collected samples from wild and captive sloths, and carcasses preserved in ethanol (all with known origin), under Brazilian Environment Ministry (19267-3/14597869) and Panamanian licenses (SE/A-21-09 and SEX/A-98-09), and deposited it at LABEC collection (process no. 02000.000019/2003-32; Table S1).

We extracted genomic DNA using a saline protocol, with minor modifications for blood and hair samples (Sambrook et al. 1989). We collected buccal swabs and extracted genomic DNA from them with Oragene Animal Kits (Oragene®), following the manufacturer’s instructions.

Nuclear gene sequences

We designed new primers in mammalian exon conserved regions, so intronic regions could be amplified (Slade et al. 1993), using Primer3 (Rozen and Skaletsky 1999). We also tested already published primers for genes with intraspecific DNA variability in other mammal species (Murphy et al. 1999; Baker 2000). We amplified five fragments of nuclear genes: beta-fibrinogen intron 7 (FGB), recombination activating 2 (RAG2) and beta-globin (HBB) partial genes, hypoxanthine-guanosine phosphoribosyl transferase intron 2 (HPRT), and proteolipid protein 1 (PLP1). We used different primers to obtain amplicons and sequences for all individuals (please see Table S2 for PCR conditions). We purified PCR products with ExoSap GE Healthcare kit, following the manufacturer’s recommendations, and sequenced it with ABI BigDye Terminators kit in an Applied Biosystems 3130xl Genetic Analyzer or an ABI-PRISM 3100 Genetic Analyzer. Both strands were sequenced to improve accuracy of base calling. We visually inspected chromatograms using BioEdit (Hall 1999) and aligned sequences with Clustal Omega (Sievers et al. 2011).

We used PHASE 2.1 (Stephens et al. 2001), implemented in DnaSP 5.0 (Librado and Rozas 2009), to infer the most probable haplotipic phases. Haplotypes with probabilities < 80% were excluded from further analyses. We performed tests for recombination (Hudson and Kaplan 1985) and neutrality deviations (Tajima 1983) also in DnaSP. We used coalescent simulation tests, with 10,000 replicates, to estimate statistical significances (Rozas 2009). We observed one and three indels for FGB and HBB, respectively, in B. variegatus alignments, and one and two indels for HPRT and HBB, respectively, in B. torquatus. These alignment gaps sized one or two nucleotides, and were treated as the fifth character state.

Microsatellite loci

We obtained four polymorphic microsatellites through a partial genomic microsatellite enriched library (Ostrander et al. 1992; Hamilton et al. 1999) and described 52 loci through a high-throughput microsatellite isolation 454 GS-FLX titanium pyrosequencing technique (GenoSat®; Malausa et al. 2011; S3 for detailed information on both methods).

We checked for genotyping errors running 104 randomizations, and considered a confidence level of 95%, in MICRO-CHECKER 2.2.3 (Van Oosterhout et al. 2004). We tested putative genealogical relationships between sampled individuals in ML-Relate (Kalinowski et al. 2006). Given the low levels of genetic diversity in some B. variegatus populations (Results section), we considered only the highest likelihood values. We used LOSITAN (Beaumont and Nichols 1996; Antão et al. 2008) to detect loci putatively under selection. We used the two-consecutive runs approach, with 105 simulations, and a 99% confidence interval. To perform this analysis, we grouped individuals according to sampling localities, and used only those with more than five samples. Linkage disequilibrium and Hardy–Weinberg equilibrium tests were performed under default settings in Arlequin 3.11 (Excoffier et al. 2005). Finally, to test if genetic diversity patterns were related to geographic distance between individuals, we performed a Mantel test in GenAlEx 6.5 (Peakall and Smouse 2012), with 9999 permutations.

Population structure and differentiation

We assessed population structure in B. variegatus independently for nuclear gene sequences and microsatellites. Nuclear sequences were coded as alleles, to assure loci independence (Pritchard et al. 2010). Populations were not defined a priori. First we used the species full dataset, and then only AF samples were considered. Diversity in microsatellites also allowed investigating intra-population structure.

We ran a Bayesian algorithm implemented in STRUCTURE 2.3.4 (Pritchard et al. 2000). We tested models considering admixture, with allele frequencies correlated under default settings, and no location information. The program was allowed to estimate alpha (relative admixture levels between populations). Each run consisted of 10 iterations at 1 ≤ k ≤ 5, with burnin period length of 105 and 106 MCMC repeats after burnin (Pritchard et al. 2000). We also submitted STRUCTURE results to the Evanno method, because patterns of dispersal among populations might be heterogeneous (Evanno et al. 2005; Earl and vonHoldt 2012). Most likely number of populations in each analysis corresponds to the number of clusters (k) with the highest mean estimate of the log likelihood of the data (likelihood method), or to the highest ΔK (Evanno method).

Using microsatellite data, we also performed a discriminant analysis of principal components (DAPC) using ADEGENET 1.3-4 (Jombart 2008). This analysis has better performance with complex models of population structure (Jombart et al. 2010). The optimal number of clusters is determined by running successive ks with “find.clusters” function. We tested up to 20 clusters. The optimal k value corresponds to the lowest Bayesian information criterion (BIC). The number of retained principal components (PCs) of PCA varied between datasets, from 15 to 25. For the optimal k value, and corresponding number of retained PCs, we implemented “dapc” function, considering the group composition inferred from “find.clusters” analyses (Jombart et al. 2010; Jombart and Collins 2015).

Finally, we performed hierarchical analyses of molecular variance (AMOVA) in Arlequin (Excoffier et al. 2005), to further test each inferred model of population structure.

Past and contemporary demography of B. variegatus

We investigated demographic history for each AF B. variegatus population, inferred from previous analyses. For each nuclear gene sequence dataset and microsatellites, we tested models of demographic and spatial expansion in Arlequin (Rogers 1995; Excoffier et al. 2005). We estimated absolute number of migrants exchanged between two populations (M), pairwise divergence times allowing for unequal population sizes (τ), population sizes (k), and ancestral population sizes (θ0) also in Arlequin (Gaggiotti and Excoffier 2000; Excoffier et al. 2005). Significance levels were obtained with 10,000 bootstrap replicates. There are no mutation rate estimates for the genes used among sloth lineages. Therefore, to obtain time estimates (τ = t2μ; being μ the mutation rate), we considered average mammalian mutation rates for both introns and coding regions, respectively 2.78 × 10−9 and 2.2 × 10−9 per site per year (Li et al. 1996; Kumar and Subramanian 2002), and a lower limit corresponding to half of these estimates, because sloths are slow evolving taxa (Delsuc et al. 2004). Similarly, we used the average mutation rate for microsatellites described for mammals, and half of it (1.0 × 10−4 or 5.0 × 10−5, respectively; Jarne and Lagoda 1996).

We estimated R2 statistics for nuclear gene sequences in DnaSP (Ramos-Onsins and Rozas 2002; Librado and Rozas 2009), and calculated statistical significance by coalescent simulation, as abovementioned. Nuclear genes presented low diversity in AF populations (Results section), preventing the use of multilocus demographic analyses (e.g. Heled and Drummond 2008).

We further used microsatellite data to infer recent demographic fluctuations in AF populations. We ran moment-based methods implemented in Bottleneck 1.2.02 (Cornuet and Luikart 1996) and a long-term Bayesian test, VarEff (Nikolic and Chevalet 2012). Bottleneck tests are particularly appropriate to detect more recent declines in population sizes (Piry et al. 1999). Mohicsshift test is suited to find population reductions that occurred in the last 40–80 generations (Luikart et al. 1998). Given the six-year generation time for B. variegatus (Anderson and Handley 2002), we used this test to detect the effects of current AF fragmentation and habitat reduction over the last 500 years (Ribeiro et al. 2009). We performed both mode-shift and Wilcoxon tests, also implemented in Bottleneck, during 103 iterations, under the two-phase mutation model (TPM), with a variance among multiple steps of 12 or 30 and with a proportion of single-step mutations of 95 or 70%, respectively (Piry et al. 1999; Givens et al. 2007).

VarEff estimates less contemporary demographic changes. It simultaneously estimates time since the demographic event, past effective population sizes, and the most probable number of past demographic events (Chevalet and Nikolic 2010; Nikolic and Chevalet 2012). To do so, we used wide, but realistic priors (Heller et al. 2008), since little information is available for B. variegatus (Hayssen 2010). We estimated time since the origin of populations considering pairwise time of population differentiation estimated from Arlequin (Gaggiotti and Excoffier 2000). As above mentioned, we considered mammalian average mutation rates. VarEff estimates current effective population size, and we used this value to set the corresponding prior, nevertheless a large variance was assumed (Nikolic and Chevalet 2012). After exploratory runs, we considered 106 iterations were enough to obtain robust estimates. We ran six models for each population, considering TPM mutation model with 95% of single step mutation, two mutation rates, and different putative numbers (two, four and six) of past demographic events.

Comparative nuclear genetic diversity in Bradypus sloths

We used Arlequin (Excoffier et al. 2005) to estimate genetic differentiation and diversity indices for the inferred B. variegatus populations. These were estimated independently for each nuclear gene sequences datasets, and then averaged. For microsatellites, we estimated mean number of alleles (A), and average observed (Ho) and expected heterozygosities (He) for each population, considering only polymorphic loci in the population under analysis(Excoffier et al. 2005). Diversity indices were also estimated for B. pygmaeus and B. torquatus datasets. We performed pairwise comparisons of diversity estimates in all populations using Mann–Whitney two-tailed test in R 3.1.0 (R Core Team 2016).

Results

Molecular markers

Nuclear genes contained 3208, 3568, and 4249 bp for Bradypus variegatus, B. torquatus, and B. pygmaeus analyses, respectively. We have deposited all newly described sequences at GenBank database (MG523426–MG523858). Detailed number of individuals sequenced for each nuclear gene and population, and respective summary statistics are depicted in Table S4. We attributed the few departures from neutrality to population structure or low historical genetic diversity.

We successfully amplified 56 microsatellites from B. variegatus. From these, we cross-amplified 51 for B. pygmaeus, and zero for B. torquatus. All genotypes were deposited at Dryad Data Repository. Overall, only two loci were monomorphic (TT47 and TT92), four presented unexpected allele sizes in several populations (TT36, TT41, TT57, and TT111; Supporting Information S3), and three (TT25, TT100, and TT110) were possibly under selection, so we excluded these markers from further analyses. We did not find evidences for sex-linked loci. We detected null alleles in B. variegatus dataset, but assumed this results from population structure and demographic history, because we performed several PCR replicates, including simple PCRs. Nine B. variegatus samples had too missing data and six individuals could be related, so we excluded them. No locus consistently deviated from Hardy–Weinberg equilibrium and we did not detect consistent departures from linkage disequilibrium.

Population structure and differentiation

Population structure estimated from nuclear gene sequences for all samples of B. variegatus varied between four and five clusters (Evanno and likelihood methods, respectively; Fig. 1c). We recovered a similar structure within AF from models including samples only from this biome (data not shown). AMOVA performed with k = 4 and k = 5 were statistically significant for all parameters (p < 0.001), but mean variation among populations was higher at k = 4 (66.00% ± 12.25) than at k = 5 (60.77% ± 15.28), indicating that k = 4 represented better the population structure (AMZW, AMZECE, AFNE, AFC + AFS: Fig. 1c). Most genes (except PLP) supported genetic population differentiation, as corresponding pairwise population differentiation indices were statistically significant, either considering four or five populations. Mean Fst values estimated from nuclear genes are depicted in Table S5.

Population structure described from microsatellites varied between k = 2, 4, and 5 (Fig. 1d–e; Evanno method, discriminant analysis of principal components DAPC, and likelihood method, respectively). DAPC “find.clusters” function retained 50 PCs of PCA, represented in three discriminant functions, which explained 94.7% of the total genetic variability. AMOVA, performed for each model, retained more variation at the individual level. Variation among populations was higher for k = 5 (Table S6), and Fst values were statistically significant for this model (p < 0.003; Table S5). Therefore, we consider five populations best describe current population structure (AMZW, AMZECE, AFNE, AFC, and AFS in Fig. 1d). Using only AF samples, we achieved a similar population structure, within this biome. Both likelihood method and DAPC supported a k = 3 (AFNE, AFC and AFS; data not shown). For DAPC, 93.0% of the genetic variation was explained by the retained 40 PCs of PCA, classified by two discriminant functions. Evanno method supported k = 4, evidencing further structure within AFC, which was partially corroborated by DAPC analyses restricted to this population (Fig. 1d), and differentiation index (Fst = 0.14; p < 0.001). Although DAPC found similar support for further population structure in AFC, k = 3, and likelihood and Evanno methods support k = 4, both models were excluded, because individuals were randomly distributed in space, and presented highly admixed individuals, respectively.

Similarly, likelihood and Evanno methods, but not DAPC, supported three clusters within AFNE (Fig. 1d), in which significant pairwise differentiation was found (0.093 ≤ Fst ≤ 0.24; p < 0.01). While within AFS population, only DAPC supported further population structure, separating a more continental group AFS1 from another one following the coastline and Tietê river AFS2 (Figs. 1d and S8; Fst = 0.098; p < 0.001). Lastly, genetic diversity was not randomly distributed in space, as we found a positive correlation between genetic and geographic distances for AF samples (R2 = 0.368, p = 0.01).

Past and contemporary demography of B. variegatus

Divergence time (τ) and population size (k and θ0) estimates varied considerably between nuclear genes, but were not statistically significant, although convergence was reached (Table S9). However, a decreasing trend in all demographic estimates is evident and congruent with an increase in geographic distance, from AMZW to AMZE + CE, AFNE, AFC, and AFS (Tables S7 and S9).

We only detected changes in population size in AFNE, in which signs of heterozygosity excess were found, indicating a recent bottleneck, under TPM model with higher variance among multiple steps and lower probability of single-step mutations (p = 0.0225). Similarly, VarEff detected a demographic decrease trend starting around 0.018 mya (3000 generations ago; Fig. 2a). Bradypus variegatus AFC and AFS populations seem to have had constant effective population sizes (Fig. 2b, c). Other models of demographic and spatial expansion were not statistically significant.
Fig. 2

Bayesian population demographic analyses for Bradypus variegatus microsatellite data. a North-eastern, b central and c south-eastern Atlantic Forest populations. Results from models considering a mutation rate of 5.0 × 10−5 and six demographic events are depicted, but similar results were obtained with other models. Upper row: posterior distribution of effective size across generation time. Coverage > 99.47%. Lower row: estimates of effective size, at present (black), 500 years ago (≈83 generations; blue) and 0.018 million years ago (≈3000 generations; red). (Color figure online)

Comparative nuclear genetic diversity in Bradypus sloths

Diversity indices estimated for nuclear genes and microsatellites showed similar trends (Table S4 and Fig. 3, respectively). Higher gene (H) and nucleotide diversities (π) estimated for B. variegatus AMZW population were significantly different from those estimated for AF populations of that species. We also found a significant difference comparing H from B. variegatus AMZW population with B. torquatus and B. pygmaeus populations (p < 0.04). The same was observed for π for B. variegatus AMZW population and B. pygmaeus (p < 0.02), and π for B. torquatus comparing to B. variegatus AFS and B. pygmaeus populations (p < 0.04). We found few other statistically significant differences, but without any underlying pattern (data not shown).
Fig. 3

Mean microsatellite diversity indices for Bradypus variegatus, and B. pygmaeus populations. Mean observed (filled square) and expected (filled circle) heterozygosities, mean number of alleles (filled triangle), and inbreeding coefficients (Fis) (asterisk) are depicted. Arrows indicate statistically significant Fis indices (p < 0.008). AMZW western Amazon, AMZECE eastern Amazon and state of Ceará, AFNE north-eastern Atlantic Forest, AFC central Atlantic Forest, AFS southern Atlantic Forest

The number of polymorphic microsatellites and private alleles were higher for AMZW (47 and 66, respectively) and AMZECE populations (46 and 21), intermediate for AFNE (42 and 19) and AFC populations (45 and 13), and lower for AFS population (35 and 2). Twenty-two loci were polymorphic for B. pygmaeus (supplementary information S3). Observed and expected heterozygosities (Ho and He), and in some cases the number of alleles (A), for both AMZ populations were statistically different from estimates for all B. variegatus AF populations and B. pygmaeus (p < 0.05). Noteworthy, B. pygmaeus had higher Ho and He then B. variegatus AFS population, but only Ho was significantly different (p < 0.01; Fig. 3). Finally, we obtained statistically significant inbreeding coefficients for AFNE and AFC populations (Fis = 0.124, p < 0.001 and Fis = 0.094, p < 0.001, respectively; Fig. 3).

Discussion

Past connection routes, current isolation, and identification of evolutionary significant units

Nuclear DNA (nDNA) diversity for Bradypus variegatus confirms a past connection route between AF and AMZ through northern and northeastern Brazil (Moraes-Barros et al. 2007; Moraes-Barros and Arteaga 2015). Likely, this was a climate-driven route, established during Plio-Pleistocene, shared with numerous vertebrates, such as birds and reptiles (Silva et al. 2012 and references therein; Batalha-Filho et al. 2013b; Prates et al. 2016). We hypothesize this was the route used by B. variegatus to colonize AF about 1.0 mya (Moraes-Barros and Arteaga 2015). Our data indicate that, long after AF colonization, AFNE population suffered a long-duration bottleneck (18 kya), coinciding with a period of drier climate (16–39 kya; Auler et al. 2004; Wang et al. 2004). These climate conditions led to a shift in forest composition in AFNE (Monta de et al. 2014), or contributed to a reduction in northeastern forested habitats (Ledru et al. 2016), either wise interrupting the connection AF/AMZ, and enabling AF populations to become isolated. Thus, B. variegatus AF and AMZ populations are recently and significantly diverged lineages (see also Moraes-Barros et al. 2006, 2007 for mtDNA evidences), and AF populations can be considered a distinct evolutionarily significant unit (ESU, Moritz 1994). The existence of other ESUs in South America, and even a new taxonomic rearrangement for B. variegatus from Central America remains to be tested (Moraes-Barros et al. 2007; Ruiz-García et al. 2016).

Niche modeling corroborates that open vegetation biomes in central Brazil isolated AF populations from all other B. variegatus populations (Phillips et al. 2006). Isolation of AF populations is intensified by anthropogenic deforestation affecting both AF and AMZ (Ribeiro et al. 2009; Fundação SOS Mata Atlântica and INPE 2011; Gomez et al. 2015), but the effects of deforestation on AF are particularly concerning, given the biome smaller range (Ribeiro et al. 2009; Jenkins et al. 2013; Pimm et al. 2014) and greater heterogeneity (Silva et al. 2012).

Micro-evolutionary patterns and processes within the Atlantic Forest

No signs of population size changes for AFS B. variegatus population, and similar levels of nDNA diversity for AFS and AFC populations question the AF refugia model as postulated by Carnaval and Moritz (2008). Either the expansion event detected with mtDNA data for AFS population (Carnaval and Moritz 2008) resulted from stochasticity inherent to the genetic evolution of the locus; or detection of population size changes at nuclear markers was hindered by the low genetic variability for AFS population as a result of bottleneck/isolation/expansion process; or a AFS refugium with unstable forest composition was maintained (Carnaval et al. 2014; Ledru et al. 2016). As explained next, we support the last two possibilities combined.

First, decreasing genetic indices from AMZ to AFNE, and then towards AFS (pairwise ancestral population sizes, number of migrants, and nDNA diversity; but see also Moraes-Barros et al. 2006 for mtDNA diversity estimates) support a one dimensional stepping-stone-like model of colonization of AF, with only a few individuals colonizing the next area (Kimura and Weiss 1964; Wade and McCauley 1988), in a successive chain of bottleneck events with a north to south direction. Similar founder effects, in a similar time period, were recently detected in Anolis Daudin, 1802 species (Prates et al. 2016).

Second, genetic diversity described for slow evolving molecular markers (nuclear genes), which in average reflect older evolutionary processes, suggest less population structure, than do molecular markers with higher evolutionary rates (mtDNA and microsatellites; Jarne and Lagoda 1996; Li et al. 1996; Kumar and Subramanian 2002). While diversity at nuclear genes clustered AFC and AFS populations together, mtDNA and microsatellites supported differentiation of these populations (this study; Moraes-Barros et al. 2006, 2007). Thus, nDNA diversity confirms an increase in differentiation and isolation of B. variegatus populations in the AF over time.

Third, if B. variegatus first colonized AF around 1.0 mya (Moraes-Barros and Arteaga 2015) through AFNE (this study), AFS was colonized last, and so the time elapsed since the origin of AFS population might have not been enough for this population to have recovered its genetic diversity. Sloths are slow evolving mammals (Delsuc et al. 2004; Moraes-Barros et al. 2011), so we cannot exclude this possibility. Yet, as further discussed in the next section, levels of genetic diversity for AFS population are significantly lower than most B. variegatus populations analyzed. Therefore, we consider more likely that unstable forest and habitat patchiness, caused by local climate oscillation (Carnaval et al. 2014; Ledru et al. 2016), contributed to maintain a metapopulation structure. Metapopulation dynamics can uphold population genetic diversity at lower values (Gilpin 1991).

For medium-sized mammals such as sloths, a metapopulation system might be evidenced by the presence of locally discrete breeding subpopulations, and signs of distinct growth rate among subpopulations (Elmhagen and Angerbjörn 2003). Our data supports both. We identify two significantly differentiated subpopulations within AFS population (AFS1 and AFS2), which might correspond to locally distinct breeding units. Additionally, heterogeneous Quaternary climatic changes in AFS (Auler et al. 2004; Pessenda et al. 2004; Wang et al. 2004; Ledru et al. 2009, 2016) could have originated enough environmental instability and asynchronic habitat patchiness (i.e. consecutive gains and losses of different forest patches) in a time scale compatible with B. variegatus longevity, low migration ability and low mutation rates (Sunquist and Montgomery 1973; Delsuc et al. 2004; Moraes-Barros et al. 2011), which would have been sufficient to influence differently the growth rates of each subpopulation. Dynamic forest availability allowing the maintenance of a metapopulation, for instance in micro-refugia within southern AF highlands (Carnaval et al. 2014; Montade et al. 2014), rather than the previous AF refugia model (which suggested no forest refugium within AFS; Carnaval and Moritz 2008), seems a compatible scenario. Nevertheless, complementarity or exclusion between models needs to be further tested in this and other species.

Historical metapopulation dynamics were never proposed as a process to explain patterns of genetic diversity for AF mammals. Yet, this hypothesis complements and further confirms the complexity suggested by other models for AF biogeographic history (Carnaval and Moritz 2008; Batalha-Filho et al. 2012; Silva et al. 2012; Raposo do Amaral et al. 2013), particularly those evoking AF populations demographic fluctuations as a main driver of diversification (Cabanne et al. 2008; Fitzpatrick et al. 2009; Raposo do Amaral et al. 2013).

Our results support that older biogeographic events, such as AF/AMZ connectivity, affected coincidently different vertebrates, but different processes are needed to explain recent diversification and demographic events in AF (Silva et al. 2012). This implies that recent AF biogeographic history must be told considering species idiosyncrasies, such as life-history traits and ecological requirements, rather than just searching for a common pattern and general hypotheses (Porto et al. 2013). Also, this search should consider variables locally, since AFNE and AFS regions seem to have been influenced differently such variables (i.e. climatic oscillation, forest connectivity, and habitat availability; Silva et al. 2012).

Conservation of populations as biodiversity components

Nuclear DNA data confirms the three B. variegatus MUs in AF, AFNE, AFC and AFS, respectively (Moraes-Barros et al. 2007), and supports their independence from AMZ populations. The need for conservation actions goes beyond the fact that these MUs correspond to three different main biogeographic components of AF (Silva et al. 2012); B. variegatus is the first example, among AF mammals, of a severe historical genetic diversity loss. Contemporary AF fragmentation has been highlighted as driver of genetic diversity loss in felid populations (jaguar, Panthera onca Haag et al. 2010; puma, Puma concolor Miotto et al. 2012), but even those populations had higher levels of microsatellite diversity than all AF B. variegatus populations. Interestingly, genetic diversity estimates for AFC and, particularly, AFS populations were similar or even lower than those reported for the populations here analyzed of the Vulnerable B. torquatus, and Critically Endangered B. pygmaeus. This trend was also observed for B. torquatus mtDNA (Moraes-Barros et al. 2006, 2007).

Additionally, although inbreeding indices were smaller than 0.20, Fis values were statistically significant for AFNE and AFC MUs. Whether true inbreeding is occurring, or if these estimates are just a consequence of the low genetic variability, requires further study. This is particularly relevant since AF range has decreased around 80% in the past 500 years, and continues to be deforested (Ribeiro et al. 2009; Fundação SOS Mata Atlântica and INPE 2011).

Habitat fragmentation can increase population structure to prejudicial levels (Miotto et al. 2012), and populations of B. variegatus in the AF are already highly structured. More than two decades ago, Emmons (1990) advised that B. variegatus should have threatened status in areas where its habitat is endangered, such as in the AF. Furthermore, similar levels of population isolation, structure, and genetic diversity in AF populations of B. torquatus and B. variegatus (this study; Moraes-Barros et al. 2006; Lara-Ruiz et al. 2008; Ruiz-García et al. 2016) highlight the need for immediate conservation measures, before a more thorough evaluation is possible (Brito 2004).

Natural histories of many Neotropical species remain largely unknown, including widespread and common species (e.g. southern tamandua Tamandua tetradactyla Hayssen 2011; several birds Kerr et al. 2009). Likely, during the next few years, many other poorly-studied Neotropical taxa will be distributed as genetically structured populations, some even isolated, and facing considerable habitat loss and fragmentation (Jenkins et al. 2013; Batalha-Filho and Miyaki 2016; Thomé et al. 2016). The evidences presented and our study support that threat levels, within the Neotropics, should be evaluated at the population level.

Notes

Acknowledgements

Authors acknowledge J. Morgante for all his support, IREC, CTM and LABEC staffs for technical support, and those who helped with sampling: Parque Ambiental Chico Mendes (J. Guimarães and Sr. Josué), LNN-UFPA (P. Sousa); Zoo-CE (Vets. Lucio and Leandro), CETAS-CE (A. Klefasz), CETAS-PB (E. Victor), CETAS-AL (M. Belluci), CETAS-BA (M.C. Pires), UFPE (J.A. Feijó, D.A. Moraes and J.E. Garcia), Retiro Ecológico (R. Siqueira and Sr. Lenilson), UFC (Felipe), UFPB (F. Barros, M. Lima and U. Gonçalves), MHN UFAL (J. Luiz), Instituto Maracajá (S. Chinem and M. Motta), DEPAVE-SP (J. Summa and M.E. Summa), A. Oliveira, C. Clozato, and many others without whom would not be possible to have such sampling effort. We acknowledge the anonymous researchers for very helpful comments on an earlier version on the manuscript. S.M.S. had an FCT PhD Grant (SFRH/BD/40638/2007), and a PNPD/CAPES fellowship at PPGZOO MPEG/UFPA. NM-B was supported by Capes, EU’s Seventh Framework Programme (No 286431) and NORTE-01-0145-FEDER-000007.

Supplementary material

10531_2017_1493_MOESM1_ESM.pdf (299 kb)
Supplementary material 1 (PDF 298 kb)

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Copyright information

© Springer Science+Business Media B.V., part of Springer Nature 2017

Authors and Affiliations

  1. 1.LABEC, Departamento de Genética e Biologia Evolutiva, Instituto de BiociênciasUniversidade de São PauloSão PauloBrazil
  2. 2.CIBIO/InBioUniversidade do PortoVairãoPortugal
  3. 3.Department of ZoologyMPEGBelémBrazil
  4. 4.IREC, CSIC-UCLM-JCCMCiudad RealSpain
  5. 5.Max-Planck Institute for OrnithologySeewiesenGermany
  6. 6.California Academy of SciencesSan FranciscoUSA

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