Skip to main content
NCBI home page
Genetics and Molecular Biology logoLink to Genetics and Molecular Biology
. 2024 Feb 19;47(1):e20230252. doi: 10.1590/1678-4685-GMB-2023-0252

Reduced gene flow and bottleneck in the threatened giant armadillo (Priodontes maximus): implications for its conservation

Nayra T Rodrigues 1, Bruno H Saranholi 1,2, Alexandre R Inforzato 1, Leandro Silveira 3, Arnaud Leonard Jean Desbiez 4,5,6, Pedro M Galetti Jr 1
PMCID: PMC10917080  PMID: 38446984

Abstract

The progressive fragmentation and loss of habitats represent the main threats for endangered species, causing genetic consequences that may have potential implications for a population’s long-term persistence. Large mammals are the most affected species among vertebrates. The giant armadillo Priodontes maximus is a large South American mammal threatened species, showing nocturnal, solitary and fossorial behavior, occurring at low population densities, and its population dynamics are still poorly known. In this study, we carried out the first assessment of genetic variability and population genetic structure of the species, using a panel of 15 polymorphic microsatellites developed by high-throughput genome sequencing. The spatial Bayesian clustering, Fst and Dest results indicated the presence of two genetic clusters (K = 2) in the study area. These results suggest a reduction in gene flow between individuals inhabiting the Brazilian savanna (Cerrado) and the Pantanal wetlands, with the increased human-driven habitat modifications possibly contributing for this scenario. A bottleneck signal was detected in both populations, and a subpopulation structuring in the Cerrado may also be reflecting consequences of the extensive habitat modifications. Findings from this study provide important and useful information for the future maintenance of genetic diversity and long-term conservation of this flagship species.

Keywords: Genetic diversity, habitat fragmentation, Xenarthra, Cingulata, animal Conservation

Introduction

Genetic diversity is a key element for the long-term persistence of a species (Frankel and Soulé, 1981), and its amount and distribution depend on several ecological and evolutionary factors, as well as the deleterious effects of human-driven habitat modifications (Frankel and Soulé, 1981; Frankham et al., 2002). A species with a wide geographic distribution can constitute a large and single panmictic population or different genetically connected populations (e.g., Epps et al., 2007; Marrotte et al., 2017; Saranholi et al., 2022). The connectivity among populations is highly associated to the dispersal capacity of the species and its response to barriers and habitat suitability (Kupfer et al., 2006). In turn, human-driven habitat loss and fragmentation have been threatening biodiversity (Storfer et al., 2010; Ahumada et al., 2011; Gibson et al., 2011; Haddad et al., 2015) by isolating populations and limiting gene flow (Gerlach and Musolf, 2000; Oklander et al., 2010; Haag et al., 2010; Saranholi et al., 2017), as well as causing genetic diversity losses that may threaten long-term population persistence (Frankham et al., 2002; Keyghobadi, 2007).

Several species of mammals show wide geographic distributions in the continents where they occur, and genetic-based studies have been reporting different genetic diversity distribution patterns that can be explained by biogeographic features, dispersal capacity, and habitat adaptation (Clozato et al., 2015). However, due to their typically lower densities, low rates of population growth, and large home range requirements (Crooks et al., 2017), large mammals are the most affected species by habitat loss and fragmentation among vertebrates, resulting in loss of genetic diversity and in the isolation of their populations (reviewed in Lino et al., 2019).

The giant armadillo Priodontes maximus Kerr, 1792 (Mammalia: Cingulata), the largest extant species of armadillo (Emmons and Feer, 1997; Carter et al., 2016; Desbiez et al., 2019a), is part of one of the most ancient lineages of placental mammals, the magna-order Xenarthra (Murphy et al., 2001). This is an ecologically very important species, acting as ecosystem engineers (Leite-Pitman et al., 2004; Desbiez and Kluyber, 2013; Aya-Cuero et al., 2017; Massocato and Desbiez, 2018; Di Blanco et al., 2020; Fontes et al., 2020). It has a distribution that extends over a large area of South America; however, it occurs in discontinuous populations and at low population densities (Cabrera, 1958; Desbiez et al., 2020a; Meritt, 2006).

In the southern part of its distribution in Brazil, the giant armadillo occurs in the Pantanal wetlands and in the Brazilian savanna (Cerrado). Wherever the species occurs, it is naturally rare, and it has become rarer due to the alterations and destruction of its habitat (Marinho-Filho and Medri, 2008; Ferraz et al., 2021), as is the case with the Cerrado domain, which is undergoing extensive modifications, due to the expansion of agriculture and cattle ranching. The giant armadillo presents a low population growth rate, with a litter size of one individual, prolonged parental care, and a three-year interbirth interval (Desbiez et al., 2019b). Furthermore, the age of sexual maturity is estimated to be between six and a half to eight years, with the longest generation time among the Xenarthra (Luba et al., 2020). Therefore, the loss of a single individual can have a significant impact on the population. Despite occurring in anthropized areas, several studies have shown that the giant armadillo depends mainly on native vegetation to survive, especially in its early stages of life (Vynne et al., 2011; Esteves et al., 2018; Desbiez et al., 2020c; Lemos et al., 2020). It is estimated that a population decline of at least 30% has already occurred over the past three generations (Anacleto et al., 2014), mainly due to anthropogenic actions such as habitat loss and fragmentation, hunting, roadkills, and illegal trafficking (Anacleto et al., 2014; Chiarello et al., 2015; Carter et al., 2016; Banhos et al., 2020). Currently, the giant armadillo is categorized as Vulnerable (A2cd) by the Red List of Threatened Species of the International Union for Conservation of Nature and Natural Resources (IUCN; Anacleto et al., 2014) and the Brazilian Institute for Biodiversity Conservation (ICMBio; Chiarello et al., 2015).

Little is known on the population dynamics of these animals (Superina et al., 2014; Carter et al., 2016; Desbiez et al., 2020c , 2021a), and genetic studies have been hindered by the challenge of obtaining biological samples with quality DNA from wild populations (Benirschke and Wurster, 1969; Benirschke et al., 1969; Delsuc et al., 2002, 2003; Redi et al., 2005; Gibb et al., 2016). There is no available information on the population genetics of this species thus far.

Luckily, a partnership with two long-term projects focused on monitoring giant armadillo and roadkills (Giant Armadillo Conservation Program and Anteaters and Highways Project, respectively), as well as a third project focused on ecology in a protected area (Parque Nacional das Emas) allowed us to obtain tissue samples of this rare animal for a very first population genetics study of this threatened species. In turn, microsatellites have been used for genetic studies on large number of metazoans worldwide during the two last decades (reviewed in mammals in Túnez et al., 2021), uncovering a wealth of ecological information concerning mammal species (Broquet et al., 2006; Beja-Pereira et al., 2009; Saranholi et al., 2023). Because microsatellites were absent for the giant armadillo, this study describes an initial panel of 15 microsatellites obtained through next-generation sequencing, which can be very helpful in studies of population genetics, as well as for assessing paternity and kinship.

Considering the aforementioned ecological and behavioral characteristics, we hypothesize that (i) there is a reduced gene flow between the Pantanal wetlands and the Cerrado, resulting in population structuring between these two biomes. On the other hand, considering the extensive human-driven habitat modifications occurring especially in the Cerrado domain, we expect that (ii) gene flow can also be reduced between subpopulations within a biome. Finally, as it is well known that threatened species usually have small (or declining) and fragmented populations, which facilitate the loss of their genetic diversity (Frankham et al., 2002), we predict that (iii) loss of genetic variation may be occurring in giant armadillo, and the long-term persistence of these animals may be threatened. This study represents the first assessment of genetic diversity in populations of P. maximus and used a species-specific panel of microsatellites developed by us, using high throughput sequencing. Besides contributing to increasing the knowledge of the species, it brings new genetic data that may be useful for definitions of conservation strategies for this important endangered species.

Materials and Methods

Ethical statements

Sample collection was performed in compliance with Brazilian legislation, under the SISBIO license number (53798-7) for sample collection permits and for SISGEN genetic material access authorization (A05D558), in addition to being approved by the Ethics Committee on Animal Use of the Federal University of São Carlos (CEUA/UFSCAR - 3597261118).

Study area and samples

A total of 45 giant armadillo tissue samples was collected between 2010 and 2020 in two different Brazilian biomes: Pantanal and Cerrado (Figure 1). In the latter, four blood samples were collected from free-living animals in Parque Nacional das Emas (C-PNE) in the state of Goiás, and eight samples were obtained from roadkills along three highways located in the state of Mato Grosso do Sul - MS-040, MS-355, and BR-262/MS - provided by the Anteaters and Highways Project (C-ATR and P-ATR; https://www.tamanduabandeira.org/). In Pantanal, 32 ear tissue samples (21 from adult and 11 from sub-adult specimens) were obtained from animals monitored at Fazenda Baía das Pedras (P-FBP) located in the Pantanal wetlands, state of Mato Grosso do Sul, by the Giant Armadillo Conservation Program - coordinated by the Wild Animal Conservation Institute (https://www.icasconservation.org.br/projetos/tatucanastra/) - and one roadkill (BR-262/MS). Detailed information related to each sample is available in Table S1.

Figure 1 - . Geographic distribution of P. maximus and magnified area where sampling was carried out in the Pantanal and Cerrado biomes. P = Pantanal; C = Cerrado; P-ATR = Samples from roadkills in the Pantanal biome; P-FBP = Samples from Fazenda Baía das Pedras; C-ATR = Samples from roadkills in the Cerrado domain; C-PNE = Samples from Parque Nacional das Emas. Sources: P. maximus geographic range from IUNC (Anacleto et al., 2014), and Biome limits obtained from IBGE (2019).

Figure 1 -

Open in a new tab

DNA extraction and microsatellite development

DNA extraction from tissue samples was performed using the Phenol-Chloroform protocol (Sambrook and Russell, 2001). For the isolation of microsatellites, an aliquot of DNA with 468 ng in 15 μL (31.2ng/μL) was used. A single genomic library was prepared according to the standard protocol of the Illumina Nextera DNA Flex kit. Sequencing was performed from both ends (paired-end) using an Illumina HiSeq 2500 system. The search for microsatellites in the genome was performed using the MISA software (https://webblast.ipk-gatersleben.de/misa/), and the primers were designed using Primer3Plus (Untergasser et al., 2007). Considering only microsatellites larger than trinucleotides, 23,389 were found, comprising 5,372 trinucleotides, 15,244 tetranucleotides, 1,821 pentanucleotides, and 952 hexanucleotides.

For the final microsatellite panel, only simple microsatellites showing tetranucleotide motifs were selected for population validation, because they are more unstable than the complex microsatellites, and consequently can lead to a greater number of alleles (Chung et al., 1993; Pépin et al., 1995). We selected those microsatellites with eight or more repeats, favoring the chance that they are more mutable and consequently more polymorphic (Katti et al., 2001). The microsatellites should be in non-coding regions. Microsatellite sequences are deposited in the GenBank (https://www.ncbi.nlm.nih.gov/genbank/, Accession Number OM930795 - OM930809).

Microsatellite amplification and genotyping

For an initial population validation, 30 microsatellite primer pairs were synthesized with the addition of the M13 tail at the 5’ end of one of the primers (forward or reverse), according to Schuelke (2000). PCR amplifications were successful for 15 of the 30 loci tested, with PCR product size ranging from 209 to 278 bp (Table 1). For the amplification reactions, we used 1x GoTaq Buffer solution (Promega), 0.5-4.0 mM MgCl2 (Invitrogen), 0.2 mM of each dNTP, 0.1 μM of the primer with the M13 tail, 0.4 μM of the primer without the M13 tail, 0.4 μM of the M13 primer fluorescently labeled (FAN, PET, VIC, NED), 0.5 unit of GoTaq® DNA Polymerase, and 30 ng of DNA in a final reaction volume of 10 μl. PCR was performed in a Veriti 96 Well Thermal Cycler (Applied Biosystems). The amplification program consisted of initial denaturation at 94 °C for 5 min, followed by 35 cycles of 30 s at 94 °C, 45 s at the annealing temperature (AT, Table 1), 45 s at 72 ºC; 10 additional cycles starting at 94 °C for 30 s, 45 s at 53 °C (primer M13 annealing temperature), 45 s at 72 °C, and a final extension temperature of 72 °C for 20 min.

Table 1 - . Characteristics of the 15 microsatellite loci developed and successfully amplified in Priodontes maximus. Loci names, forward and reverse primer sequences, motif with number of repeats, annealing temperature (AT), PCR product size, and GenBank accession number.

Loci Primer sequence (5’-3’) Motif AT (ºC) Product Size (pb) Accession number
Forward Reverse
Pmax02 CAAGCTCATGATCTGCACATGT AGGATCCCAAGGTAACCTGA (TCTA)14 62.0 258 OM930795
Pmax04 TCTAAGTTGTACATTGGTGTCTGT TCTCCTCCCTCAGCATGACA (TATC)13 64.0 214 OM930796
Pmax05 ACAGTAGGAACATCTTCACGAA GCCCTACCAAAGCCATAATAGC (TATC)12 60.0 245 OM930797
Pmax09 TCCCTGGGAGATACTCAAGGA TCCACTTCCCTGTAGCTTGC (TCTA)12 62.0 240 OM930798
Pmax11 ATCTCTTGTTTCTCTCAGAAGCT TACAACCTGTGACTGCTGCA (TAGA)11 64.0 223 OM930799
Pmax16 ACAATTTAGGACAGAAAAGGACAGA CCCAAATACCCAGATCCTCCA (TAGA)10 62.0 237 OM930800
Pmax17 TCACAGAAATAGAGGGTTCACAGA AGTCAATCTTGCTTGTCTTCCA (TAGA)10 60.0 209 OM930801
Pmax18 ACATCATTCTCCTCCCTGACA TTGTCAGCCCACCCTACTTG (GATA)10 62.0 210 OM930802
Pmax19 TCTGTGTTCTACCAGTCAAGCT TGGTAACTCAATCCAGCAGTTCA (CTAT)10 62.0 221 OM930803
Pmax21 AGTGCTCAAGGAACATGATGT TCGACAGCACTGGTGATACA (TAGA)9 60.0 278 OM930804
Pmax22 ACTTCACCAGCATTCACCAA TGCTCAGCACCATGAAACAA (GATA)9 62.0 256 OM930805
Pmax25 AGTGTTCAAGCAGCATGATGT ACCCTTTATCCAGCTACCCAG (GATA)9 60.0 209 OM930806
Pmax28 ACTCTTTTCTGCACACACTCT GGCAGATAAGTAGCTGAGGCC (ATAG)8 64.0 264 OM930807
Pmax29* GGCTTGCCTCTTAGTCCACA ACCCCAGTCTACCTCCTTCC (TTCA)8 62.0 265 OM930808
Pmax30 GGCAAATTCATGGCAAGACTCT AGAGAAAATGCAGAAAGATCACACT (TATC)8 60.0 248 OM930809
Open in a new tab

* Removed from the subsequent analysis, since this locus was not polymorphic for the individuals analyzed.

Microsatellite amplification was confirmed by 2% agarose gel electrophoresis, and the PCR products were genotyped in an ABI 3730XL automated sequencer (Applied Biosystems). Fragment pattern and size analyses for genotype definition were performed using the microsatellite plugin in the Geneious 6.1.9 software (Kearse et al., 2012).

Population genetic analyses

A panel of 14 polymorphic microsatellite loci was obtained (Table 1) and used for population genetic analyses. Genotypes were analyzed using Micro Checker 2.2.3 (Van Oosterhout et al., 2004) to detect null alleles and other genotyping errors. The existence of different genetic groups within the set of sampled individuals was analyzed through non-spatial and spatial models. A non-spatial Bayesian analysis was performed using the Structure software (Pritchard et al., 2000). We tested values of K ranging from 1 to 5 (number of sampled groups plus one: P-FBP, C-PNE, C-ATR, P-ATR) in 100 independent runs, using 200,000 Markov Chain Monte Carlo (MCMC) iterations, followed by 100,000 burning-in iterations. We performed the analyses using the Admixture Model with correlated allele frequencies. We repeated the analysis for two configurations: no a priori assignment to a given group, and with a priori information about the sampling biome (Cerrado vs. Pantanal; LOCPRIOR configuration); the latter configuration can infer the differences between groups of individuals with low genetic differentiation (Hubisz et al., 2009). To obtain the optimal value of K, we used the log-likelihood LnP (D/K) (Pritchard et al., 2000) and the estimates of Delta-K (Evanno et al., 2005), determined through the online tool Structure Harvester (Earl and Vonholdt, 2012). The consensus individual assignment graph over the 100 independent runs was visualized in Cluster Markov Packager Across K - CLUMPAK (Kopelman et al., 2015). However, the Structure software may fail, in some situations, to detect the real number of clusters, due to its assumptions regarding the population models, so the use of different approaches is recommended (Jombart, 2008; Jombart et al., 2010).

In this way, we also performed populational genetic structure analyses based on spatial models. This approach based on spatial components is recommended to improve the estimates of population structure because it is less affected by isolation by distance (François and Durand, 2010; Perez et al., 2018). We used two spatial genetic structure approaches to evaluate the distribution of genetic variability across space, Geneland (Guillot et al., 2005) and Spatial PCA - sPCA (Jombart, 2008). Both analyses are similar for including individual georeferenced multilocus genotypes to estimate the number of genetic clusters and genetic discontinuity. Geneland uses Bayesian inference and spatial location of samples, which provides further support for cluster analyses, even when cryptic patterns of population structuring occur (McManus et al., 2015), and can be especially useful in the case of sparse sampling (Ball et al., 2010). This analysis was performed using the Geneland 3.1.4 package (Guillot et al., 2005) available in the R software (R Core Team, 2018). Geneland was run assuming an uncorrelated model for allele frequencies. Although the correlated allele frequency model is more powerful in detecting subtle genetic differentiation, it seems more prone to algorithm instabilities, e.g., overestimating K values where isolation by distance occurs (Guillot, 2008). We performed 20 independent runs with K values ranging from 1 to 5, using 1,000,000 MCMC iterations and 1,000 thinning iterations. The final spatial model was run with K values ranging from one to the maximum number of clusters obtained in the initial runs, using 2,000,000 MCMC iterations and 1,000 thinning iterations in 10 independent runs.

In turn, the sPCA is a multivariate approach that is free of Hardy-Weinberg equilibrium assumptions, in which the allele frequencies-based principal component score for each individual is multiplied by Moran’s I, a measure of spatial autocorrelation for that individual (Jombart, 2008). sPCA divides spatial autocorrelation into global and local structures, based on whether neighbors are positively or negatively spatially autocorrelated. Local structuring occurs when genetically similar individuals avoid mating with each other, whereas global structuring is expected in genetic clines or spatially distinct genetic groups. sPCA was performed using the adegenet R package (Jombart, 2008) with a distance-based connection network for sampling aggregate patterns. We tested for significant global and local structuring using a MCMC randomization test with 999 permutations (Jombart, 2008).

Following the detection of the genetic clusters, the Wright fixation index (FST) was calculated using Arlequin v.3.5.2.2 (Excoffier and Lischer, 2010). As the traditional FST may present biases when estimated from highly polymorphic markers such as microsatellites (Jost, 2008; Heller and Siegismund, 2009), we also calculated the D differentiation index (Dest) proposed by Jost (2008), using the DEMEtics package (Gerlach et al., 2010), implemented in R (R Core Team, 2018).

We performed a spatial autocorrelation analysis (SAA) in GenAlEx version 6.5 (Peakall and Smouse, 2012), to analyze the effect of isolation by distance (IBD) on population differentiation. The method evaluates the genetic distance between pairs of individuals in distance classes. The statistical significance (p < 0.05) for the spatial autocorrelation coefficients (r) was obtained through 9,999 permutations and 9,999 bootstraps. The distance classes were variable and divided into 2 km, 4 km, 6 km, 8 km, 10 km, 15 km, 25 km, 250 km, 350 km, and 450 km.

Genetic diversity parameters were estimated for the genetic groups found in the population structuring analyses. Deviations from the Hardy-Weinberg equilibrium and linkage disequilibrium between individual pairs of loci were evaluated using GENEPOP (Raymond and Rousset, 1995) with 10,000 repetitions, correcting the p-values according to the Bonferroni procedure (Rice, 1989). The number of alleles (NA), the number of effective alleles (AE), as well as expected (HE) and observed (HO) heterozygosity, were calculated in GenAlex v6.5 (Peakall and Smouse, 2012). By using FSTAT 2.9.3.2 (Goudet, 2001), we calculated the inbreeding coefficient (FIS), and p-values for excess and deficit of heterozygotes and allelic richness (RA). The polymorphic information content (PIC) for each locus was evaluated using the CERVUS software (Kalinowski et al., 2007). Effective population size (NE) was estimated based on linkage disequilibrium (Waples, 2006) using NeEstimator v2.1 (Do et al., 2014) assuming minimum allele frequency of rare alleles equal to 0.05 and 0.01.

To detect genetic evidence of population decline, we used the Bottleneck software (Cornuet and Luikart, 1996; Piry et al., 1999). The values were obtained by simulations under three mutation models: the infinite allele model (IAM), stepwise mutation model (SMM), and two-phase mutation (TPM), accepting 70% and 90% of the stepwise mutation (Piry et al., 1999), using 1,000 iterations. The Wilcoxon test was applied to determine the statistical significance of the results (p<0.05), which is appropriate for analyses with less than 20 loci (Piry et al., 1999). Populations exhibiting significant excess of heterozygotes would be considered to have experienced a recent genetic bottleneck. To exclude any sample size bias, we used HybridLab 1.0 (Nielsen et al., 2006) to have the same number of individuals for each genetic group from simulated individuals using the allele frequencies of the base population, and then we ran Bottleneck with the same parameters as described above.

Results

Of the 30 microsatellite primer pairs synthesized for validation, 15 loci were successfully amplified (Table 1), and 14 loci showed polymorphism among the giant armadillo individuals studied (Table 2), resulting in a very informative microsatellite-panel (PIC >0.5; Table 2). Since the locus Pmax29 was not polymorphic, the subsequent genetic analyses were performed using a set of the 14 polymorphic microsatellite loci.

Table 2 - . Parameters of genetic diversity for each genetic cluster, based on 14 microsatellite loci. N = number of individuals analyzed; NA = number of alleles per locus; RA = allele richness; AE = effective number of alleles; PIC = polymorphic information content; HWE = p-value of Fisher's exact test for adherence to Hardy-Weinberg equilibrium (α = 0.05); HO = observed heterozygosity; HE = expected heterozygosity; FIS = inbreeding coefficient. Significance levels of FIS values for p < 0.00357; PS = smaller FIS values; PL = larger FIS values.

Loci N NA RA AE PIC HWE HO HE FIS PS PL
Cerrado Cluster (N=12) Pmax02 12 3 2.990 2.380 0.502 0.911 0.667 0.580 -0.107 0.410 0.839
Pmax04 11 5 4.818 4.246 0.724 0.791 0.727 0.764 0.096 0.732 0.489
Pmax05 12 3 3.000 2.796 0.57 0.187 0.833 0.642 -0.257 0.335 0.828
Pmax09 12 4 3.740 2.526 0.541 0.818 0.583 0.604 0.078 0.689 0.567
Pmax11 11 4 3.974 3.227 0.633 0.669 0.818 0.690 -0.139 0.210 0.921
Pmax16 10 4 3.900 3.279 0.633 0.212 0.500 0.695 0.328 0.964 0.125
Pmax17 12 3 2.990 2.133 0.468 0.187 0.417 0.531 0.257 0.982 0.089
Pmax18 12 2 2.000 2.000 0.375 0.248 0.333 0.500 0.371 0.992 0.192
Pmax19 9 4 4.000 2.282 0.512 0.725 0.778 0.562 -0.333 0.092 1.000
Pmax21 11 3 3.000 2.988 0.591 0.580 0.545 0.665 0.226 0.950 0.142
Pmax22 12 4 3.748 2.420 0.535 0.410 0.833 0.587 -0.384 0.025 1.000
Pmax25 12 3 3.000 2.642 0.55 0.860 0.583 0.622 0.105 0.703 0.532
Pmax28 12 4 3.686 1.704 0.386 0.970 0.500 0.413 -0.168 0.317 1.000
Pmax30 11 4 3.971 2.444 0.547 0.692 0.636 0.591 -0.029 0.682 0.635
Mean - 3.571 3.487 2.648 0.540 - 0.625 0.603 - - -
Total - 50 - - - - - - 0.044 0.575 0.428
Pantanal Cluster (N=33) Pmax02 33 3 2.968 2.380 0.510 0.957 0.576 0.589 0.023 0.596 0.532
Pmax04 32 7 5.618 4.808 0.762 0.034 0.781 0.805 0.029 0.735 0.428
Pmax05 33 4 3.450 2.489 0.540 0.671 0.545 0.607 0.104 0.864 0.321
Pmax09 33 3 2.808 2.310 0.471 1.000 0.576 0.576 0.000 0.610 0.557
Pmax11 33 5 3.701 2.789 0.573 1.000 0.697 0.651 -0.071 0.353 0.789
Pmax16 32 6 5.377 4.491 0.743 0.552 0.813 0.790 -0.029 0.489 0.685
Pmax17 32 3 2.636 2.190 0.438 0.611 0.625 0.552 -0.134 0.289 0.828
Pmax18 33 4 3.241 2.565 0.539 0.240 0.667 0.620 -0.077 0.375 0.742
Pmax19 33 6 5.036 2.892 0.624 0.787 0.788 0.664 -0.189 0.035 0.992
Pmax21 33 3 2.991 2.767 0.562 0.267 0.576 0.648 0.114 0.857 0.246
Pmax22 33 4 3.050 1.659 0.364 0.196 0.394 0.403 0.023 0.725 0.489
Pmax25 32 3 2.994 2.602 0.546 0.721 0.719 0.625 -0.152 0.185 0.896
Pmax28 33 5 3.740 2.979 0.603 0.091 0.606 0.675 0.103 0.878 0.246
Pmax30 33 4 3.256 2.461 0.532 0.732 0.606 0.603 -0.005 0.557 0.628
Mean - 5.452 3.633 2.813 0.557 - 0.640 0.629 - - -
Total - 60 - - - - - - -0.018 0.296 0.707
Open in a new tab

Population Genetic Structuring

The non-spatial Structure clustering analysis based on the LnP value indicated K = 1 as the most likely K for both the no LOCPRIOR and LOCPRIOR models. In contrast, the results yielded by the method proposed by Evanno et al. (2005) indicated K = 2 as the most probable value for the no LOCPRIOR (Figure S1) and LOCPRIOR (Figure S2). However, this method does not allow for the calculation of Delta K when K = 1. In the individual assignment graph, the individuals have similar probabilities of belonging to both clusters, suggesting that no population structuring could be detected by these methods (Figure S3).

On the other hand, the spatial Bayesian analysis performed in Geneland (Guillot et al., 2005) identified the presence of two genetic clusters, separating the sampled individual groups from Pantanal (P-FBP + P-ATR) and Cerrado (C-PNE + C-ATR) with posterior probabilities of 90% (Figure 2). It is worth noting that the same pattern was observed in the sPCA (Figure 3), which indicates that most variation occurs in the global structure (Figure 3a), resulting in two genetic clusters (Figure 3b). The presence of these two genetic clusters was also confirmed by the significant results for Wright’s fixation index (FST = 0.0253, p = 0.0021) and Jost differentiation index (Dest = 0.03830, p = 0.006).

Figure 2 - . Graph with the most probable number of clusters (K=2) provided by the Geneland package (a) and posterior probability maps for spatial assignment of Priodontes maximus (b). Spatial grouping suggests two distinct genetic clusters throughout the geographic area surveyed. The dashed line corresponds to the approximated boundary between the Pantanal and Cerrado biomes. Black dots represent the locations of the individuals. Higher probabilities of belonging to the cluster are represented by the colors yellow and white. P = Pantanal; C = Cerrado; P-ATR = Samples from roadkills in the Pantanal; P-FBP = Samples from Fazenda Baía das Pedras; C-ATR = Samples from roadkills in the Cerrado; C-PNE = Samples from Parque Nacional das Emas.

Figure 2 -

Open in a new tab

Figure 3 - . Genetic structure assessed by sPCA of Priodontes maximus. (a) and (c) Barplot showing that spatially meaningful genetic variance in the dataset is contained in the first three positive axes of the global structure (indicated by the blue bars), while, comparatively, very little variance is present in local structure (indicated by the grey bars) for all individuals and only for Cerrado, respectively. (b) and (d) The results show the first Principal Component (PC) and the respective mappings of cluster membership (colors represent the membership probability for two clusters) for all individuals and only for Cerrado, respectively.

Figure 3 -

Open in a new tab

Spatial autocorrelation analysis (SAA) showed a significant positive correlation only for the first (0-2 km; r = 0.114 with p = 0.004) and second (2-4 km; r = 0.054 with p = 0.011) distance classes, indicating that individuals within both classes are more genetically similar than individuals that are more spatially distant. The x-intercept of r was between 6 and 8 km (Figure 4). Indeed, a subpopulation structure was detected by sPCA analysis within the Cerrado cluster, in agreement with the findings indicating that spatially closer individuals are genetically more similar (Figure 3d).

Figure 4 - . Spatial autocorrelation for Priodontes maximus. The graphs show the genetic correlation coefficient (r) as a function of the geographic distance between the defined spatial distance classes. Red dashed lines represent the upper (U) and lower (L) bounds of the null hypothesis (no spatial structure) based on 10,000 random permutations. Error bars represent 95% confidence intervals on r based on 1,000 bootstraps.

Figure 4 -

Open in a new tab

Genetic diversity

No evidence of null alleles was found, and no locus is either in linkage disequilibrium (LD) after the Bonferroni correction (p = 0.0035) or outside the expected values of Hardy-Weinberg equilibrium (p > 0.0035) for both populations identified in the spatial analysis (Pantanal and Cerrado). It is worth mentioning that significant linkage disequilibrium was observed between two loci (Pmax22 and Pmax25) after the Bonferroni correction (p = 0.00357) when all the samples were treated as a single population.

The mean observed and expected heterozygosity values did not differ significantly between the Cerrado (Ho = 0.625, He = 0.603) and Pantanal (Ho = 0.640, He = 0.629) populations (Table 2). No significant FIS values were found for both populations. The mean allelic richness (RACerrado= 3.487; RAPantanal= 3.633) and numbers of effective alleles (AECerrado= 2.648; AEPantanal = 2.813) were similar between both populations. The average PIC values (> 0.50) showed that the set of microsatellite loci used was highly informative.

The effective population size (NE), calculated using all individuals (45), ranged from 42.1 (95% CI = 28.8 - 67.9) to 59.3 (95% CI = 38.4 - 109.7), assuming minimum frequency of rare alleles as 0.05 and 0.01, respectively.

The Wilcoxon significance test found a significant excess of heterozygotes in the Cerrado (p < 0.05) for n = 12 individuals (collected data) using the IAM and TPM accepting both 70% and 90% of SMM. When using simulated genotypes (n = 33), a significant excess of heterozygotes was found using all three mutation models tested (Table 3). In turn, in the Pantanal (n=33) we found a significant excess of heterozygotes (p < 0.05) using IAM, TPM with 70% and 90% of SMM, and SMM (Table 3).

Table 3 - . P-values for excess and/or deficit of heterozygotes generated by the Wilcoxon test. IAM = infinite allele mutation model; SMM = stepwise mutation model; TPM = two-phase mutation model, accepting 70% and 90% of the stepwise mutation model. Results for n = 12 (collected data) and n = 33 (simulated genotypes) in Cerrado and n = 33 in Pantanal (collected data). * significant p-values p < 0.05.

12 33
IAM TPM SMM IAM TPM SMM
70% 90% 70% 90%
Cerrado 0.00031* 0.00671* 0.03381* 0.06763 0.00003* 0.00021* 0.00131* 0.01227*
Pantanal - - - - 0.00006* 0.00076* 0.02472* 0.12061
Open in a new tab

Discussion

Results from this study indicate a probable gene flow restriction between Pantanal and Cerrado populations, and a potential loss of genetic variation for giant armadillos within the study area, corroborating our initial predictions. The 14 microsatellite loci developed in this study were successfully amplified and showed a very informative mean PIC value, according to the classical work of Botstein et al. (1980), which makes them very useful for population genetic analyses. Samples for which a few loci (1 to 4) were not successfully amplified turned out to be from roadkills. It is notoriously difficult to obtain a large amount of high-quality DNA in such cases, due to post-mortem DNA degradation under high environmental temperatures and UV radiation (Rodríguez-Castro et al., 2017; Amarilla-Stevens et al., 2023), which are typical in the region studied.

Spatial Bayesian analysis (K=2) suggests a reduction in gene flow between giant armadillos inhabiting the Pantanal and the Cerrado. With high assignment values, this analysis allocated all individuals sampled in the Cerrado domain to a single population, separated from those allocated in Pantanal. The presence of two genetic clusters (Pantanal and Cerrado) was reinforced by the FST and Dest results. To some extent, these results are expected, considering the already established fact that the giant armadillo occurs at low densities, and in discontinuous populations (Cabrera, 1958; Meritt, 2006; Desbiez et al., 2020a, 2020c). The detection of linkage disequilibrium between two loci (Pmax22 and Pmax25) when all samples were analyzed as a single population corroborates these findings, and is consistent with the Admixture linkage disequilibrium (ALD) that arises when two separate populations are mixed (Pfaff et al., 2001). The disagreement between the optimal K-values found by the non-spatial (Structure) and spatial (Geneland and sPCA) analyses may be due to the assumptions made in the population models used in the former which, in some cases, may fail to detect the actual number of clusters (Jombart, 2008; Jombart et al., 2010); for example, in cases where populations have lower levels of genetic divergence, with FST values < 0.03 (Latch et al., 2006; Waples and Gaggiotti, 2006), as observed in our study between the Pantanal and Cerrado populations. Additionally, where cryptic patterns of population structuring may occur, the spatial model used by both Geneland and sPCA offers greater support to clustering analyses (e.g., McManus et al., 2015), and these analyses are also particularly useful in situations where sampling is sparse (Ball et al., 2010), as is the case of our study. Further studies with a larger and spatially wider sampling set - as well as using a SNP panel largely distributed in the genome - are encouraged for confirming the population genetic structuring observed.

This scenario of genetic population differentiation between Cerrado and Pantanal may have a historical explanation, considering the different characteristics of the biomes (vegetation, climate, altitude), as well the discontinuity already reported between giant armadillo populations (Cabrera, 1958; Meritt, 2006; Desbiez et al., 2020a,c). However, the increased environmental degradation already reported in the surveyed region (Ferraz et al., 2021) can also be a contributing factor, as it severely limits the movement of individuals between Cerrado and Pantanal. The conversion of land within Cerrado into agricultural and pasture areas is primarily responsible for the loss of permeability and functional connectivity (Sugai et al., 2014), and the large reduction and fragmentation of viable habitats for the giant armadillo in this domain may promote the further isolation of its populations (Ferraz et al., 2021).

The significant positive correlation found for distances of up to 4 km obtained by the SAA analysis indicates that individuals spatially closer are more genetically similar than those who are spatially more distant. These findings suggest an isolation by distance model at least for short distances, which could be modified by a break in the typical IBD clinal allele frequency pattern (Ruiz-Gonzalez et al., 2015) at longer distances. A quite similar situation was reported in tapir along an Atlantic Forest corridor (Saranholi et al., 2022). The authors found a significant spatial autocorrelation at short distances, suggesting that gene flow in tapir was mostly IBD-regulated, but a break in the clinal IBD pattern resulted in population structuring likely induced by gene flow barriers promoted by human-driven landscape modifications (Saranholi et al., 2022). It is worth noting that the geographical distance between the Cerrado and Pantanal sampling sites (C-ATR x P-FBP and C-PNE x P-FBP) being shorter than the distance between the two Cerrado sampling sites (C-ATR and C-PNE), reinforce that a break in a potential clinal IBD pattern may be promoting population structuring between Pantanal and Cerrado. In addition, the x-intercept at 8 km in the SSA analysis indicates a tendency to a limited range of dispersion (Janecka et al., 2017; Maciel et al., 2019) of giant armadillo in the studied area, corroborating the subpopulation structuring observed in the Cerrado. However, considering that a median home range of 25 km2 (or 2500 ha) for adult giant armadillos has been reported for the individuals in Pantanal (Desbiez et al., 2020a), this small dispersion range (8 km) suggested by the spatial autocorrelation analysis still could benefit from a wider sampling, for a more precise decision.

The genetic diversity represented by allelic richness was very similar in both populations studied. It is important to highlight that the mean allelic richness observed here (RACerrado = 3.487; RAPantanal = 3.633) for the giant armadillo was lower than that found for other, non-threatened Xernathrans, such as Chaetophractus vellerosus (RA = 15; Nardelli et al., 2016) and Dasypus novencinctus (RA = 12.6; Arteaga et al., 2012). Endangered species that have suffered a reduction in their population size have likely lost many of their alleles, so most of them have lower allele richness than related non-endangered species (Frankham et al., 2002), and this may well be the case observed here in the giant armadillo. The number of effective alleles was also low in both populations (AECerrado = 2.648; AEPantanal = 2.813), indicating that the number of alleles that actually contribute to genetic diversity is lower than the number of total alleles found. This result can stem from the small effective population size, which prevents the retention of all alleles at high frequencies in both populations (Kimura and Crow, 1964). This is what is expected for a highly endangered species.

Our results suggest that there has been a recent bottleneck in giant armadillo. Although all components of genetic diversity are affected by a reduction in population size, bottlenecks have a greater immediate effect on allele number than on heterozygosity, causing heterozygosity excess at selectively neutral loci (Nei et al., 1975; Allendorf, 1986; Spencer et al., 2000). Large losses of heterozygosity are more likely if the bottleneck lasts for several generations, or if the recovery of the population after the bottleneck is slow (Leberg, 1992). Since the excess of heterozygotes observed when the population undergoes a recent reduction can be detected during 0.25 to 2.5 x 2Ne generations, our results demonstrate that the species suffered a population reduction more ancient than the recent three-generations reduction suggested by Anacleto et al. (2014). In fact, the population reduction persists to current generations, as a result of targeted hunting, road collisions, as well as the continued loss and fragmentation of the habitats where the species occurs (Chiarello et al., 2015; Alho et al., 2019; Banhos et al., 2020; Ferraz et al., 2021).

Certain consequences of reduced population may not be observed until several generations after the bottleneck (Price and Hadfield, 2014). Long generation times and lifespans can function as intrinsic buffers against loss of genetic diversity (Hailer et al., 2006), resulting in delayed detection of genetic diversity loss. The giant armadillo is suspected to have a natural life expectancy of more than 20 years (Desbiez et al., 2020b , 2021b), a generation time of 8 years (Luba et al., 2020) and a low population growth rate, with a litter size of one and extended parental care (Desbiez et al., 2019b). These biological characteristics may explain the putative slow reduction in the heterozygosity found here.

Despite occurring in anthropized areas, the giant armadillo survives by feeding mainly on native vegetation (Vynne et al., 2011; Esteves et al., 2018; Desbiez et al., 2020c; Lemos et al., 2020; Ferraz et al., 2021), and this aspect is crucial for the conservation of the species. However, our results suggest that the increased human-driven habitat modification, particularly in the Cerrado domain, may have genetically impacted the giant armadillo, leading to the reduced gene flow observed between Pantanal and Cerrado, and to the bottleneck detected in both populations. The subpopulation structuring detected in the Cerrado, increasing the level of discontinuity between populations, gives credit to the suggestion that genetic consequences of habitat modifications can already be felt, and threaten local populations of giant armadillos. The bottlenecks and reduction in gene flow may be acting in synergy to decrease both genetic diversity and population capability to persist. The expansion of fully protected areas, creation of corridors, road passages, and other conservation actions would be recommended, and could be crucial for mitigating the endangerment and boosting species persistence not only for the giant armadillo, but other local species as well. Given the current conservation status of the giant armadillo, it is imperative that its genetic diversity and population structure should be assessed throughout the species distribution, so that effective conservation actions may be planned and brought to fruition, in order to ensure its long-term viability.

Acknowledgements

This study is part of the Giant Armadillo Conservation Program, which benefited from multiple grants, mostly from zoos in North America and Europe, previously mentioned in this work. We thank team members of both the Giant Armadillo Conservation Program and Anteaters and Highways for sample collection, in particular Gabriel Massocato, Danilo Kluyber and Debora Yogui. NTR thanks Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brazil (CAPES, Finance Code 001). PMGJ thanks to Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq, 303524/2019-7). BHS received scholarship from Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP, 2022/01741-3).

Supplementary Material.

The following online material is available for this article:

Table S1 -. Location of samples used in the study.
1415-4757-GMB-47-1-e20230252-s1.pdf (143.9KB, pdf)
Figure S1 -. Delta K (a) and L(K) (b) values without prior information (no LOCPRIOR) obtained from the Bayesian clustering analysis performed in Structure.
1415-4757-GMB-47-1-e20230252-s2.pdf (70.2KB, pdf)
Figure S2 -. Delta K (a) and L(K) (b) values with prior information (LOCPRIOR) obtained from the Bayesian clustering analysis performed in Structure.
1415-4757-GMB-47-1-e20230252-s3.pdf (90.4KB, pdf)
Figure S3 -. Results of the Bayesian clustering analysis performed in Structure, showing the percentage of membership of each individual to each cluster.
1415-4757-GMB-47-1-e20230252-s4.pdf (166.4KB, pdf)

References

  1. Ahumada JA, Silva CEF, Gajapersad K, Hallam C, Hurtado J, Martin E, McWilliam A, Mugerwa B, O’Brien T, Rovero F, et al. Community structure and diversity of tropical forest mammals: Data from a global camera trap network. Philos Trans R Soc B: Biol Sci. 2011;366:2703–2711. doi: 10.1098/rstb.2011.0115. [DOI] [PMC free article] [PubMed] [Google Scholar]
  2. Allendorf FW. Genetic drift and the loss of alleles versus heterozygosity. Zoo Biol. 1986;5:181–190. [Google Scholar]
  3. Alho CJ, Mamede SB, Benites M, Andrade BS, Sepúlveda JJ. Ameaças à biodiversidade do pantanal brasileiro pelo uso e ocupação da terra. Ambient Soc. 2019;22:e01891 [Google Scholar]
  4. Amarilla-Stevens HN, Stevens RD, Phillips CD, Bradley RD. Temporal rate of postmortem DNA degradation in archived tissue samples: Evidence from liver and muscle. J Mammal. 2023;104:194–202. [Google Scholar]
  5. Arteaga MC, Piñero D, Eguiarte LE, Gasca J, Medellín RA. Genetic structure and diversity of the nine-banded armadillo in Mexico. J Mammal. 2012;93:547–559. [Google Scholar]
  6. Aya-Cuero C, Rodríguez-Bolaños A, Superina M. Population density, activity patterns, and ecological importance of giant armadillos (Priodontes maximus) in Colombia. J Mammal. 2017;98:770–778. [Google Scholar]
  7. Ball MC, Finnegan L, Manseau M, Wilson P. Integrating multiple analytical approaches to spatially delineate and characterize genetic population structure: An application to boreal caribou (Rangifer tarandus caribou) in central Canada. Conserv Genet. 2010;11:2131–2143. [Google Scholar]
  8. Banhos A, Fontes BL, Yogui DR, Alves MH, Ardente NC, Valls R, Barreto LM, Damásio L, Ferreguetti AC, Carvalho AS, et al. Highways are a threat for giant armadillos that underpasses can mitigate. Biotropica. 2020;52:421–426. [Google Scholar]
  9. Beja-Pereira A, Oliveira R, Alves PC, Schwartz MK, Luikart G. Advancing ecological understandings through technological transformations in noninvasive genetics. Mol Ecol Resour. 2009;9:1279–1301. doi: 10.1111/j.1755-0998.2009.02699.x. [DOI] [PubMed] [Google Scholar]
  10. Benirschke K, Wurster DH. The chromosomes of the giant armadillo, Priodontes giganteus Geoffroy. Acta Zool Pathol Ant. 1969;49:125–130. [PubMed] [Google Scholar]
  11. Benirschke K, Low RJ, Ferm VH. In: Comparative mammalian cytogenetic. Benirschke K, editor. Springer-Verlag; New York: 1969. Cytogenetic studies of some armadillos; pp. 330–345. [Google Scholar]
  12. Botstein D, White RL, Skolnick M, Davis RW. Construction of a genetic linkage map in man using restriction fragment length polymorphisms. Am J Hum Genet. 1980;32:314–331. [PMC free article] [PubMed] [Google Scholar]
  13. Broquet T, Johnson CA, Petit E, Thompson I, Burel F, Fryxell JM. Dispersal and genetic structure in the American marten, Martes americana. Mol Ecol. 2006;15:1689–1697. doi: 10.1111/j.1365-294X.2006.02878.x. [DOI] [PubMed] [Google Scholar]
  14. Cabrera A. Catálogo de los mamíferos de América del Sur. Rev Mus Argent Cienc Nat Bernardino Rivadavia. 1958;4:1–308. [Google Scholar]
  15. Carter TS, Superina M, Leslie DM. Priodontes maximus (Cingulata: Chlamyphoridae) Mamm Species. 2016;48:21–34. [Google Scholar]
  16. Chung MY, Ranum LP, Duvick LA, Servadio A, Zoghbi HY, Orr HT. Evidence for a mechanism predisposing to intergenerational CAG repeat instability in spinocerebellar ataxia type I. Nat Genet. 1993;5:254–258. doi: 10.1038/ng1193-254. [DOI] [PubMed] [Google Scholar]
  17. Clozato CL, Mazzoni CJ, Moraes‐Barros N, Morgante JS, Sommer S. Spatial pattern of adaptive and neutral genetic diversity across different biomes in the lesser anteater (Tamandua tetradactyla) Ecol Evol. 2015;5:4932–4948. doi: 10.1002/ece3.1656. [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Cornuet JM, Luikart G. Description and power analysis of two tests for detecting recent population bottlenecks from allele frequency data. Genetics. 1996;144:2001–2014. doi: 10.1093/genetics/144.4.2001. [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Crooks KR, Burdett CL, Theobald DM, King SR, Di Marco M, Rondinini C, Boitani L. Quantification of habitat fragmentation reveals extinction risk in terrestrial mammals. Proc Natl Acad Sci U S A. 2017;114:7635–7640. doi: 10.1073/pnas.1705769114. [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. Delsuc F, Scally M, Madsen O, Stanhope MJ, De Jong WW, Catzeflis FM, Springer M, Douzery EJ. Molecular phylogeny of living xenarthrans and the impact of character and taxon sampling on the placental tree rooting. Mol Biol Evol. 2002;19:1656–1671. doi: 10.1093/oxfordjournals.molbev.a003989. [DOI] [PubMed] [Google Scholar]
  21. Delsuc F, Stanhope MJ, Douzery EJP. Molecular systematics of armadillos (Xenarthra, Dasypodidae): Contribution of maximum likelihood and Bayesian analyses of mitochondrial and nuclear genes. Mol Phylogenet Evol. 2003;28:261–275. doi: 10.1016/s1055-7903(03)00111-8. [DOI] [PubMed] [Google Scholar]
  22. Desbiez ALJ, Kluyber D. The role of giant armadillos (Priodontes maximus) as physical ecosystem engineers. Biotropica. 2013;45:537–540. [Google Scholar]
  23. Desbiez ALJ, Massocato GF, Kluyber D, Luba CN, Attias N. How giant are giant armadillos? The morphometry of giant armadillos (Priodontes maximus Kerr, 1792) in the Pantanal of Brazil. Mam Biol. 2019;95:9–14. [Google Scholar]
  24. Desbiez ALJ, Massocato GF, Kluyber D. Insights in giant armadillo (Priodontes maximus Kerr, 1792) reproduction. Mammalia. 2019;84:283–293. [Google Scholar]
  25. Desbiez ALJ, Massocato GF, Kluyber D, Luba CN, Attias N. Spatial ecology of the giant armadillo (Priodontes maximus) in Midwestern Brazil. J Mammal. 2020;101:151–163. [Google Scholar]
  26. Desbiez ALJ, Kluyber D, Massocato GF, Oliveira-Santos LGR, Attias N. Life stage, sex, and behavior shape habitat selection and influence conservation strategies for a threatened fossorial mammal. Hystrix. 2020;31:123–129. [Google Scholar]
  27. Desbiez ALJ, Massocato GF, Attias N, Cove M. Comparing density estimates from a short-term camera trap survey with a long-term telemetry study of giant armadillos (Priodontes maximus) Mastozool Neotrop. 2020;27:241–246. [Google Scholar]
  28. Desbiez ALJ, Kluyber D, Massocato GF, Attias N. Methods for the characterization of activity patterns in elusive species: The giant armadillo in the Brazilian Pantanal. J Zool. 2021;315:301–312. [Google Scholar]
  29. Desbiez ALJ, Larsend D, Massocatoa GF, Attias N, Kluyber D, Rumiz DI. First estimates of potential lifespan of giant armadillo (Priodontes maximus) in the wild. Edentata. 2021;22:9–15. [Google Scholar]
  30. Di Blanco YE, Desbiez ALJ, di Francescantonio D, Di Bitetti MS. Excavations of Giant Armadillos alter environmental conditions and provide new resources for a range of species. J Zool. 2020;311:227–238. [Google Scholar]
  31. 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. doi: 10.1111/1755-0998.12157. [DOI] [PubMed] [Google Scholar]
  32. Earl DA, Vonholdt BM. Structure Harvester: A website and program for visualizing STRUCTURE output and implementing the Evanno method. Conserv Genet Resour. 2012;4:359–361. [Google Scholar]
  33. Emmons LH, Feer F. Neotropical rainforest mammals: A field guide. 2. University of Chicago Press; Chicago: 1997. 396 [Google Scholar]
  34. Epps CW, Wehausen JD, Bleich VC, Torres SG, Brashares JS. Optimizing dispersal and corridor models using landscape genetics. J Appl Ecol. 2007;44:714–724. [Google Scholar]
  35. Esteves CF, Homem DH, Bernardo R, Lima EF. Notes on giant armadillo Priodontes maximus (Cingulata: Chlamyphoridae) distribution and ecology in Eucalyptus plantation landscapes in eastern Mato Grosso do Sul State, Brazil. Edentata. 2018;19:47–56. [Google Scholar]
  36. 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. doi: 10.1111/j.1365-294X.2005.02553.x. [DOI] [PubMed] [Google Scholar]
  37. 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. doi: 10.1111/j.1755-0998.2010.02847.x. [DOI] [PubMed] [Google Scholar]
  38. Ferraz KMPMB, de Oliveira BG, Attias N, Desbiez ALJ. Species distribution model reveals only highly fragmented suitable patches remaining for giant armadillo in the Brazilian Cerrado. Perspect Ecol Conserv. 2021;19:43–52. [Google Scholar]
  39. Fontes BL, Desbiez ALJ, Massocato GF, Srbek-Araujo AC, Sanaiotti TM, Bergallo HG, Ferreguetti AC, Noia CHR, Schettino VR, Valls R, et al. The local extinction of one of the greatest terrestrial ecosystem engieneers, the giant armadillo (Priodontes maximus) in one of its last refuges in the Atlantic Forest will be felt by a large vertebrate community. Glob Ecol Conserv. 2020;24:e01357 [Google Scholar]
  40. François O, Durand E. Spatially explicit Bayesian clustering models in population genetics. Mol Ecol Resour. 2010;10:773–784. doi: 10.1111/j.1755-0998.2010.02868.x. [DOI] [PubMed] [Google Scholar]
  41. Frankel OH, Soulé ME. Conservation and evolution. Cambridge University Press; Cambridge: 1981. 327 [Google Scholar]
  42. Frankham R, Ballou JD, Briscoe DA. Introduction to conservation genetics. Cambridge University Press; Cambridge: 2002. 617 [Google Scholar]
  43. Gerlach G, Musolf K. Fragmentation of landscape as a cause for genetic subdivision in bank voles. Conserv Biol. 2000;14:1066–1074. [Google Scholar]
  44. Gerlach G, Jueterbock A, Kraemer P, Deppermann J, Harmand P. Calculations of population differentiation based on G(ST) and D: Forget G(ST) but not all of statistics! Mol Ecol. 2010;19:3845–3852. doi: 10.1111/j.1365-294X.2010.04784.x. [DOI] [PubMed] [Google Scholar]
  45. Gibb GC, Condamine FL, Kuch M, Enk J, Moraes-Barros N, Superina M, Poinar HN, Delsuc F. Shotgun mitogenomics provides a reference phylogenetic framework and timescale for living xenarthrans. Mol Biol Evol. 2016;33:621–642. doi: 10.1093/molbev/msv250. [DOI] [PMC free article] [PubMed] [Google Scholar]
  46. Gibson L, Lee TM, Koh LP, Brook BW, Gardner TA, Barlow J, Peres CA, Bradshaw CJA, Laurance WF, Lovejoy TE, et al. Primary forests are irreplaceable for sustaining tropical biodiversity. Nature. 2011;478:378–381. doi: 10.1038/nature10425. [DOI] [PubMed] [Google Scholar]
  47. Guillot G, Mortier F, Estoup A. GENELAND: A computer package for landscape genetics. Mol Ecol Notes. 2005;5:712–715. [Google Scholar]
  48. Guillot G. Inference of structure in subdivided populations at low levels of genetic differentiation. The correlated allele frequencies model revisited. Bioinformatics. 2008;24:2222–2228. doi: 10.1093/bioinformatics/btn419. [DOI] [PubMed] [Google Scholar]
  49. Haag T, Santos AS, Sana DA, Morato RG, Cullen L, Crawshaw PG, De Angelo C, Di Bitetti MS, Salzano FM, Eizirik E. The effect of habitat fragmentation on the genetic structure of a top predator: Loss of diversity and high differentiation among remnant populations of Atlantic Forest jaguars (Panthera onca) Mol Ecol. 2010;19:4906–4921. doi: 10.1111/j.1365-294X.2010.04856.x. [DOI] [PubMed] [Google Scholar]
  50. Haddad NM, Brudvig LA, Clobert J, Davies KF, Gonzalez A, Holt RD, Lovejoy TE, Sexton JO, Austin MP, Collins CD, et al. Habitat fragmentation and its lasting impact on Earth’s ecosystems. Sci Adv. 2015;1:e1500052. doi: 10.1126/sciadv.1500052. [DOI] [PMC free article] [PubMed] [Google Scholar]
  51. Hailer F, Helander B, Folkestad AO, Ganusevich SA, Garstad S, Hauff P, Koren C, Nygård T, Volke V, Vilà C, et al. Bottlenecked but long-lived: high genetic diversity retained in white-tailed eagles upon recovery from population decline. Biol Lett. 2006;2:316–319. doi: 10.1098/rsbl.2006.0453. [DOI] [PMC free article] [PubMed] [Google Scholar]
  52. Heller R, Siegismund HRS. Relationship between three measures of genetic differentiation GST, DEST and G’ST: How wrong have we been? Mol Ecol. 2009;18:2080–2083. doi: 10.1111/j.1365-294x.2009.04185.x. [DOI] [PubMed] [Google Scholar]
  53. 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. doi: 10.1111/j.1755-0998.2009.02591.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  54. Janecka JE, Zhang Y, Li D, Munkhtsog B, Bayaraa M, Galsandorj N, Wangchuk TR, Karmacharya D, Li J, Lu Z, et al. Range-wide snow leopard phylogeography supports three subspecies. J Hered. 2017;108:597–607. doi: 10.1093/jhered/esx044. [DOI] [PubMed] [Google Scholar]
  55. Jombart T. Adegenet: A R package for the multivariate analysis of genetic markers. Bioinformatics. 2008;24:1403–1405. doi: 10.1093/bioinformatics/btn129. [DOI] [PubMed] [Google Scholar]
  56. Jombart T, Devillard S, Balloux F. Discriminant analysis of principal components: A new method for the analysis of genetically structured populations. BMC Genetics. 2010;11:94. doi: 10.1186/1471-2156-11-94. [DOI] [PMC free article] [PubMed] [Google Scholar]
  57. Jost L. GST and its relatives do not measure differentiation. Mol Ecol. 2008;17:4015–4026. doi: 10.1111/j.1365-294x.2008.03887.x. [DOI] [PubMed] [Google Scholar]
  58. Kalinowski ST, Taper ML, Marshall TC. Revising how the computer program CERVUS accommodates genotyping error increases success in paternity assignment. Mol Ecol. 2007;16:1099–1106. doi: 10.1111/j.1365-294X.2007.03089.x. [DOI] [PubMed] [Google Scholar]
  59. Katti MV, Ranjekar P, Gupta VS. Differential distribution of simple sequence repeats in eukaryotic genome sequences. Mol Biol Evol. 2001;18:1161–1167. doi: 10.1093/oxfordjournals.molbev.a003903. [DOI] [PubMed] [Google Scholar]
  60. Kearse M, Moir R, Wilson A, Stones-Havas S, Cheung M, Sturrock S, Buxton S, Cooper A, Markowitz S, Duran C, et al. Geneious Basic: An integrated and extendable desktop software platform for the organization and analysis of sequence data. Bioinformatics. 2012;28:1647–1649. doi: 10.1093/bioinformatics/bts199. [DOI] [PMC free article] [PubMed] [Google Scholar]
  61. Keyghobadi N. The genetic implications of habitat fragmentation for animals. Can J Zool. 2007;85:1049–1064. [Google Scholar]
  62. Kimura M, Crow JF. The number of alleles that can be maintained in a finite population. Genetics. 1964;49:725–738. doi: 10.1093/genetics/49.4.725. [DOI] [PMC free article] [PubMed] [Google Scholar]
  63. Kopelman NM, Mayzel J, Jakobsson M, Rosenberg NA, Mayrose I. Clumpak: A program for identifying clustering modes and packaging population structure inferences across K. Mol Ecol Resour. 2015;15:1179–1191. doi: 10.1111/1755-0998.12387. [DOI] [PMC free article] [PubMed] [Google Scholar]
  64. Kupfer JA, Malanson GP, Franklin SB. Not seeing the ocean for the islands: The mediating influence of matrix‐based processes on forest fragmentation effects. Glob Ecol Biogeogr. 2006;15:8–20. [Google Scholar]
  65. Latch EK, Dharmarajan G, Glaubitz JC, Rhodes OE. Relative performance of Bayesian clustering software for inferring population substructure and individual assignment at low levels of population differentiation. Conserv Genet. 2006;7:295–302. [Google Scholar]
  66. Leberg PL. Effects of population bottlenecks on genetic diversify as measured by allozyme electrophoresis. Evol. 1992;46:477–494. doi: 10.1111/j.1558-5646.1992.tb02053.x. [DOI] [PubMed] [Google Scholar]
  67. Leite-Pitman R, Powell G, Cruz D, Escobedo M, Escobar K, Vilca V, Mendoza A. Habitat use and activity of the giant armadillo (Priodontes maximus): Preliminary data from southeastern Peru. Society for Conservation Biology Meeting; New York: 2004. [Google Scholar]
  68. Lemos F, Costa A, Azevedo F, Fragoso C, Freitas-Junior M, Rocha E. Surveying in highly-modified landscapes to document the occurrence of threatened species: A study of the giant armadillo Priodontes maximus in central Brazil. Oryx. 2020;54:133–139. [Google Scholar]
  69. Lino A, Fonseca C, Rojas D, Fischer E, Pereira MJR. A meta-analysis of the effects of habitat loss and fragmentation on genetic diversity in mammals. Mamm Biol. 2019;94:69–76. [Google Scholar]
  70. Luba CN, Kluyber D, Massocato GF, Attias N, Fromme L, Rodrigues ALR, Ferreira AMR, Desbiez ALJ. Size mattters: Penis size, sexual maturity and their consequences for giant armadillo conservation planning. Mamm Biol. 2020;100:621–630. [Google Scholar]
  71. Maciel GF, Rufo DA, Keuroghlian A, Russo AC, Brandt NM, Vieira NF, Nóbrega BM, Nava A, Nardi MS, Jácomo ATA, et al. Genetic diversity and population structure of white-lipped peccaries (Tayassu pecari) in the Pantanal, Cerrado and Atlantic Forest from Brazil. Mamm Biol. 2019;95:85–92. [Google Scholar]
  72. Marinho-Filho J, Medri IM. In: Livro vermelho da fauna brasileira ameaçada de extinção. Machado ABM, Drummond GM, Paglia AP, editors. MMA; Brasília: 2008. Priodontes maximus (Kerr, 1792) pp. 707–709. [Google Scholar]
  73. Marrotte RR, Bowman J, Brown MGC, Cordes C, Morris KY, Prentice MB, Wilson PJ. Multi-species genetic connectivity in a terrestrial habitat network. Mov Ecol. 2017;5:21. doi: 10.1186/s40462-017-0112-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
  74. Massocato GF, Desbiez ALJ. Presença e importância do tatu-canastra, Priodontes maximus (Kerr, 1792), na maior área protegida do leste do Estado de Mato Grosso do Sul, Brasil. Edentata. 2018;18:26–33. [Google Scholar]
  75. McManus JS, Dalton DL, Kotzé A, Smuts B, Dickman A, Marshal JP, Keith M. Gene flow and population structure of a solitary top carnivore in a human-dominated landscape. Ecol Evol. 2015;5:335–344. doi: 10.1002/ece3.1322. [DOI] [PMC free article] [PubMed] [Google Scholar]
  76. Meritt DAJ. Research questions on the behavior and ecology of the giant armadillo (Priodontes maximus) Edentata. 2006;7:30–33. [Google Scholar]
  77. Murphy WJ, Eizirik E, Johnson WE, Zhang YP, Ryder OA, O’Brien SJ. Molecular phylogenetics and the origins of placental mammals. Nature. 2001;409:614–618. doi: 10.1038/35054550. [DOI] [PubMed] [Google Scholar]
  78. Nardelli M, Ibáñez EA, Dobler D, Justy F, Delsuc F, Abba AM, Cassini MH, Túnez JI. Genetic structuring in a relictual population of screaming hairy armadillo (Chaetophractus vellerosus) in Argentina revealed by a set of novel microsatellite loci. Genetica. 2016;144:469–476. doi: 10.1007/s10709-016-9915-0. [DOI] [PubMed] [Google Scholar]
  79. Nei M, Maruyama T, Chakraborty R. The bottleneck effect and genetic variability in populations. Evolution. 1975;29:1–10. doi: 10.1111/j.1558-5646.1975.tb00807.x. [DOI] [PubMed] [Google Scholar]
  80. Nielsen AEG, Bach LA, Kotlicki P. HYBRIDLAB ver. 1.0: A program for generating simulated hybrids from population samples. Mol Ecol Notes. 2006;6:971–973. [Google Scholar]
  81. Oklander LI, Kowalewski MM, Corach D. Genetic consequences of habitat fragmentation in black-and-gold howler (Alouatta caraya) populations from northern Argentina. Int J Primatol. 2010;31:813–832. [Google Scholar]
  82. Peakall R, Smouse PE. GENALEX 6: Genetic analysis in Excel. Population genetic software for teaching and research. Mol Ecol Notes. 2012;6:288–295. doi: 10.1093/bioinformatics/bts460. [DOI] [PMC free article] [PubMed] [Google Scholar]
  83. Pépin L, Amigues Y, Lépingle A, Berthier JL, Bensaid A, Vaiman D. Sequence conservation of microsatellites between Bos Taurus (cattle), Capra hircus (goat) and related species. Examples of use in parentage testing and phylogeny analysis. Heredity. 1995;74:53–61. doi: 10.1038/hdy.1995.7. [DOI] [PubMed] [Google Scholar]
  84. Perez MF, Franco FF, Bombonato JR, Bonatelli IA, Khan G, Romeiro-Brito M, Fegies AC, Ribeiro PM, Silva GAR, Moraes EM. Assessing population structure in the face of isolation by distance: Are we neglecting the problem? Divers Distrib. 2018;24:1883–1889. [Google Scholar]
  85. Pfaff CL, Parra EJ, Bonilla C, Hiester K, McKeigue PM, Kamboh MI, Hutchinson RG, Ferrell RE, Boerwinkle E, Shriver MD. Population structure in admixed populations: Effect of admixture dynamics on the pattern of linkage disequilibrium. Am J Hum Genet. 2001;68:198–207. doi: 10.1086/316935. [DOI] [PMC free article] [PubMed] [Google Scholar]
  86. Piry S, Luikart G, Cornuet JM. Bottleneck: A computer program for detecting recent reductions in the effective population size using allele frequency data. J Hered. 1999;90:502–503. [Google Scholar]
  87. Price MR, Hadfield MG. Population genetics and the effects of a severe bottleneck in an ex situ population of critically endangered Hawaiian tree snails. PLoS One. 2014;9:e114377. doi: 10.1371/journal.pone.0114377. [DOI] [PMC free article] [PubMed] [Google Scholar]
  88. Pritchard JK, Stephens M, Donnelly P. Inference of population structure using multilocus genotype data. Genetics. 2000;155:945–959. doi: 10.1093/genetics/155.2.945. [DOI] [PMC free article] [PubMed] [Google Scholar]
  89. Raymond M, Rousset F. Genepop (version-1.2) - Population genetics software for exact tests and ecumenicism. J Hered. 1995;86:248–249. [Google Scholar]
  90. Redi CA, Zacharias H, Merani S, Oliveira-Miranda M, Aguilera M, Zuccotti M, Garagna S, Capanna E. Genome sizes in Afrotheria, Xenarthra, Euarchontoglines and Laurasiatheria. J Hered. 2005;96:485–493. doi: 10.1093/jhered/esi080. [DOI] [PubMed] [Google Scholar]
  91. Rice WR. Analyzing tables of statistical tests. Evolution. 1989;43:223–225. doi: 10.1111/j.1558-5646.1989.tb04220.x. [DOI] [PubMed] [Google Scholar]
  92. Rodríguez-Castro KG, Ciocheti G, Ribeiro JW, Ribeiro MC, Galetti PM. Using DNA barcode to relate landscape attributes to small vertebrate roadkill. Biodivers Conserv. 2017;26:1161–1178. [Google Scholar]
  93. Ruiz-Gonzalez A, Cushman SA, Madeira MJ, Randi E, Gómez-Moliner BJ. Isolation by distance, resistance and/or clusters? Lessons learned from a forest-dwelling carnivore inhabiting a heterogeneous landscape. Mol Ecol. 2015;24:5110–5129. doi: 10.1111/mec.13392. [DOI] [PubMed] [Google Scholar]
  94. Sambrook J, Russell DW. Molecular cloning: A laboratory manual. 3. Cold Spring Harbor Laboratory Press; New York: 2001. [Google Scholar]
  95. Saranholi BH, Chávez-Congrains K, Galetti PM. Evidence of recent fine-scale population structuring in South American Puma concolor. Diversity. 2017;9:44 [Google Scholar]
  96. Saranholi BH, Sanches A, Moreira-Ramírez JF, da Silva Carvalho C, Galetti M, Galetti PM., Jr Long-term persistence of the large mammal lowland tapir is at risk in the largest Atlantic forest corridor. Perspect Ecol Conserv. 2022;20:263–271. [Google Scholar]
  97. Saranholi BH, Gestich CC, de Oliveira ME. Molecular ecology in neotropical mammals: Key aspects for conservation. In: Galetti PM Jr , editor. Conservation genetics in the Neotropics. Springer; Cham: 2023. pp. 411–437. [Google Scholar]
  98. Schuelke M. An economic method for the fluorescent labeling of PCR fragments. Nat Biotechnol. 2000;18:233–234. doi: 10.1038/72708. [DOI] [PubMed] [Google Scholar]
  99. Spencer CC, Neigel JE, Leberg PL. Experimental evaluation of the usefulness of microsatellite DNA for detecting demographic bottlenecks. Mol Ecol. 2000;9:1517–1528. doi: 10.1046/j.1365-294x.2000.01031.x. [DOI] [PubMed] [Google Scholar]
  100. Storfer A, Murphy MA, Spear SF, Holderegger R, Waits LP. Landscape genetics: Where are we now? Mol Ecol. 2010;19:3496–3514. doi: 10.1111/j.1365-294X.2010.04691.x. [DOI] [PubMed] [Google Scholar]
  101. Sugai LSM, Costa-Pereira R, Ochoa-Quintero JM, Torrecilha S, Ericksson A, Nunes AP, Keuroghlian A, Araujo AC, Paranhos-Filho AC, Desbiez ALJ, et al. Anais 5th Simpósio de Geotecnologias no Pantanal Campo Grande. MS: 2014. Incorporating biodiversity expert knowledge in landscape conservation planning: A case study involving the Pantanal; pp. 542–553. [Google Scholar]
  102. Superina M, Pagnutti N, Abba AM. What do we know about armadillos? An analysis of four centuries of knowledge about a group of South American mammals, with emphasis on their conservation. Mamm Rev. 2014;44:69–80. [Google Scholar]
  103. Túnez JI, Ibañez EA, Nardelli M, Peralta DM, Byrne MS. In: Molecular ecology and conservation genetics of neotropical mammals. Nardelli M, Túnez JI, editors. Springer; Cham: 2021. The use of molecular markers in neotropical mammal conservation; pp. 35–62. [Google Scholar]
  104. Untergasser A, Nijveen H, Rao X, Bisseling T, Geurts R, Leunissen JA. Primer3Plus, an enhanced web interface to Primer3. Nucleic Acids Res. 2007;35:W71–W74. doi: 10.1093/nar/gkm306. [DOI] [PMC free article] [PubMed] [Google Scholar]
  105. Van Oosterhout C, Hutchinson WF, Wills DP, Shipley P. MICRO‐CHECKER: Software for identifying and correcting genotyping errors in microsatellite data. Mol Ecol Notes. 2004;4:535–538. [Google Scholar]
  106. Vynne C, Keim JL, Machado RB, Marinho-Filho J, Silveira L, Groom MJ, Wasser SK. Resource selection and its implications for wide-ranging mammals of the Brazilian Cerrado. PLoS One. 2011;6:e28939. doi: 10.1371/journal.pone.0028939. [DOI] [PMC free article] [PubMed] [Google Scholar]
  107. Waples RS. A bias correction for estimates of effective population size based on linkage disequilibrium at unlinked gene loci. Conserv Genet. 2006;7:167–184. [Google Scholar]
  108. Waples RS, Gaggiotti OE. What is a population? An empirical evaluation of some genetic methods for identifying the number of gene pools and their degree of connectivity. Mol Ecol. 2006;15:1419–1439. doi: 10.1111/j.1365-294X.2006.02890.x. [DOI] [PubMed] [Google Scholar]

Internet Resources

  1. Anacleto TCS, Miranda F, Medri I, Cuellar E, Abba AM, Superina M. The IUCN Red List of Threatened Species. 2014. [24 August 2021]. Priodontes maximus; e.T18144A47442343. [DOI] [Google Scholar]
  2. Chiarello AG, Röhe F, Miranda FR, Mourão GDM, Silva KD, Vaz SM, Anacleto TDS. Avaliação do Risco de Extinção de Priodontes maximus (Kerr, 1792) no Brasil. Processo de avaliação do risco de extinção da fauna brasileira. 2015. [7 July 2021]. ICMBio. https://www.icmbio.gov.br/portal/faunabrasileira/lista-de-especies/7093-priodontes-maximus . [Google Scholar]
  3. Goudet J. [7 July 2021];FSTAT, a program to estimate and test gene diversities and fixation indices. 2001 version 2.9. 3. http://www2.unil.ch/popgen/softwares/fstat.htm .
  4. IBGE - Instituto Brasileiro de Geografia e Estatística . Mapas de biomas do Brasil. 2019. [8 November 2023]. Escala 1:250.000. https://www.ibge.gov.br/geociencias/informacoes-ambientais/vegetacao/15842-biomas.html?=&t=downloads . [Google Scholar]
  5. R Core Team . R: A Language and Environment for Statistical. R Foundation for Statistical Computing; Vienna: 2018. [7 July 2021]. https://www.r-project.org . [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Table S1 -. Location of samples used in the study.
1415-4757-GMB-47-1-e20230252-s1.pdf (143.9KB, pdf)
Figure S1 -. Delta K (a) and L(K) (b) values without prior information (no LOCPRIOR) obtained from the Bayesian clustering analysis performed in Structure.
1415-4757-GMB-47-1-e20230252-s2.pdf (70.2KB, pdf)
Figure S2 -. Delta K (a) and L(K) (b) values with prior information (LOCPRIOR) obtained from the Bayesian clustering analysis performed in Structure.
1415-4757-GMB-47-1-e20230252-s3.pdf (90.4KB, pdf)
Figure S3 -. Results of the Bayesian clustering analysis performed in Structure, showing the percentage of membership of each individual to each cluster.
1415-4757-GMB-47-1-e20230252-s4.pdf (166.4KB, pdf)

Articles from Genetics and Molecular Biology are provided here courtesy of Sociedade Brasileira de Genética

RESOURCES