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INTRODUCTION

The white-eared opossum, Didelphis albiventris Lund, 1840, is one of the largest species of South American opossums and inhabits southern Bolivia, Paraguay, Uruguay, northeastern, central and southern Brazil, and Argentina southward to Río Negro and Neuquén provinces (Cerqueira & Tribe 2008; Carrera & Udrizar Sauthier 2014; Astúa 2015; Bianchini 2018). Due to this large distribution, the species is adapted to a wide range of environments, from gallery forests and wet areas such as the Paranaense Forest to the dry Monte Desert (Astúa 2015). Also, the species is highly adapted to urban environments, including large cities, as well as other human-modified environments such as croplands (Smith 2007; Costa et al. 2015).

Regarding its diet, D. albiventris is a generalist species, feeding on arthropods, vertebrates, fruits, and other plant items (Vieira & Astúa 2003). It plays a role in seed dispersal in several environments, and consequently, it may be an important part in the recovery of degraded areas through the dispersal of seeds of pioneer plants (e.g., Cantor et al. 2010; Rodrigues Da Silva et al. 2014). The white-eared opossum is also a generalist regarding the use of the vertical strata, being found both on the canopy and on the ground (Vieira & Camargo 2012), and it occupies a large home range of 2.33±2.32 ha on average (Sanches et al. 2012).

Despite being such a common species, little is known about its genetic variability and the phylogeographic structure of its populations. For Brazil, there are some analyses using cytochrome oxidase I (COI), including samples from Rio Grande do Sul, Minas Gerais, and the northeast of the country (Paraíba, Sergipe, Pernambuco, Ceará, Piauí, and Maranhäo), which found two separated groups, one including samples from Rio Grande do Sul, and the other for the remaining localities (Sousa et al. 2012; Nascimento et al. 2019). Both groups, however, showed little vari- ation, and only the one from the northeast revealed some phylogeographic structure (Nascimento et al. 2019).

In another study, Rocha et al. (2015) analyzed, using cytochrome b, samples from Brazil and Bolivia, although their sampling was much smaller than the ones from Sousa et al. (2012) and Nascimento et al. (2019). For Brazil, they included samples from Minas Gerais, Mato Grosso, Tocantins, Pará, and Paraná, and for Bolivia, samples from Chuquisaca and Tarija. Again, two groups were found, one including specimens from Paraná and Bolivia, and other comprising samples from central Brazil. Although the analyses showed some genetic structuring, which separated a group from the Cerrado from another group encompassing samples from the ecotone Amazonia-Cerrado, there were some haplotypes from one re gion that were more closely related to those of the other region (Rocha et al. 2015).

To date, there are no published phylogeographic analyses of D. albiventris based on populations from Argentina. So the aim of this contribution is to analyze the genetic structure of the species in this country, using two mitochondrial markers (cytochrome b and D-loop) and a wide geographic sample.

MATERIALS AND METHODS

Samples and molecular methods

We analyzed sequences of 32 specimens of D. albiventris from most of its distribution range in Argentina (Misiones, Buenos Aires, Córdoba, La Rioja, Santa Fe, Entre Ríos, Salta, Chaco, and Tucumán; Table 1; Fig. 1). Tissues were collected from road-killed animals or obtained from specimens housed at scientific collections (Table 1).

Total DNA was extracted using an SDS-proteinase K-NaCl protocol (modified from Miller et al. 1988). We amplified two mitochondrial markers: a fragment of cytochrome b (cyt b from here on), using primers CYTB- F1-Didelphidae, CYTB-R1-Didelphidae, PF, and RF (Giarla et al. 2010; Chemisquy & Flores 2012), and D-loop using primers L0 and E3 (Huchon et al. 1999). Polymerase chain reactions (PCR) were performed in a final volume of 15 µl. Each reaction contained between 50 and 100ng of DNA, 1.5 units of Taq polymerase, 1x PCR Buffer, 5 mM MgCl2, 0.2 µM of each primer, and 0.025 mM dNTP each. PCR amplifications were carried out as follows: a first denaturation period at 94° C for 5 min, followed by 35 cycles of denaturation at 94° C for 45 s, annealing at 50- 56° C for 1 min, and extension at 72° C for 1 min. A final extension at 72° C for 6 min terminated the reactions. A negative control with no template was included for each series of amplifications to test for contamination. PCR products were electrophoresed on a 1% TBE agarose gel stained with ethidium bromide. Sequencing was performed by MACROGEN (Korea). All sequences were deposited in GenBank (see accession numbers on Table 1).

Data analyses

Sequences were edited and hand-aligned (since the alignments were trivial and showed no gaps or insertions) using BioEdit (Hall 1999). Matrices were combined using SequenceMatrix 1.8 (Vaidya et al. 2011). Some analyses were performed on each marker separately, while others were conducted only for the combined matrix. For the cyt b matrix, we included sequences from GenBank of samples from Brazil, Bolivia, and Paraguay (Table 1). We could not do the same for the D-loop since there are no sequences of that marker available for D. albiventris.

We analyzed four different matrices: 1) D-loop only (30 individuals); 2) cyt b only (27 individuals); 3) the combined matrix (25 individuals that have both markers). In this latter matrix individuals that only have one genetic marker were discarded and consequently, the dataset does not have missing data; this matrix was used in most of the analyses; 4) the full matrix (38 individuals), consisting of all individuals having both markers and those having only cyt b or only D-loop; therefore, it has a high percentage of missing data; this matrix was only used for the phylogenetic analyses.

The number of haplotypes (h), the number of polymorphic sites (S), haplotype (Hd), and nucleotide (π) diversity values were estimated using DnaSP 6.12.03 (Rozas et al. 2017). Pairwise distances among haplotypes were calculated using the Kimura 2-parameter implemented in Mega X (Kumar et al. 2018). In order to check if there is a correlation between genetic distances and geographic distances, we performed a Mantel test in R, using the packages vegan, Ape, and geosphere (Paradis et al. 2004; Oksanen et al. 2019; Hijmans 2019).

Deviation from neutrality was checked using the indexes D (Tajima 1989) and Fs (Fu 1997) calculated in Arlequin 3.5 (Excoffier & Lischer 2010). We also performed the R2 test, which has a better performance in small data sets (Ramos-Onsins & Rozas 2002) using the package pegas for R (Paradis 2010). Historical demography was analyzed by performing a mismatch distribution analysis, which tests for recent range expansion, using Arlequin 3.5 (Excoffier & Lischer 2010). We also conducted a Bayesian skyline plot (BSP) reconstruction in BEAST 1.10.4 (Suchard et al. 2018). Coalescent reconstructions used a lognormal relaxed clock with a substitution rate of 0.017 per site per million years (Rocha et al. 2015), the HKY substitution model as determined by jModeltest 2 (Darriba et al. 2012), and a TMRCA (time to the most recent common ancestor of all the taxa) of 0.76 Myr BP (taken from Rocha et al. 2015). Analyses were run for 10 million MCMC steps, discarding the first 10% as “burn-in.” Results were checked for convergence to a stationary distribution using Tracer

1.7 (Rambaut et al. 2018). Mismatch distribution and BSP were performed on the combined matrix only.

To analyze patterns of geographical distribution and haplotype relationships, we performed a Median-Joining network implemented in PopART 1.7 (Leigh & Bryant 2015) using the combined matrix. We also built a network using the cyt b matrix in order to analyze the placement of sequences from Bolivia, Brazil and Paraguay. We tried to run a network analysis using the full matrix (i.e., including sequences from GenBank), but due to the large amount of missing data many sequences could not be placed correctly and results were inconclusive (data not shown). For the biogeographic grouping of the samples we followed the biogeographic provinces delimited by Cabrera & Willink (1980).

To estimate the number of genetic clusters and their geographic distribution on the combined matrix, we used the software Geneland 3.0 (Guillot et al. 2005), testing up to k=6, running 20 million MCMC steps and 2,000 thinnings. Final results were checked by performing three independent runs with a fixed k to avoid ghost populations.

We also performed a maximum likelihood phylogenetic tree using the estimated haplotypes for each matrix. Sequences of D. marsupialis, downloaded from GenBank, were used as outgroups (accession numbers KT447521 and JQ478421). Phylogenetic analyses were performed on the cyt b matrix, the D-loop matrix and the full matrix. Analyses were run on raxmlGUI 2.0 (Edler et al. 2021) using the HKY substitution model, and 10,000 bootstrap replicates.

Collection acronyms: CRILAR-Ma-CT: tissue collection of the mammalogy division of the CRILAR, La Rioja, Argentina; MACN-CT: tissue collection of the Museo Argentino de Ciencias Naturales “Bernardino Rivadavia”, Buenos Aires, Argentina; LIEB: Laboratorio de Investigaciones en Evolución y Biodiversidad (LIEB), CIEMEP, Esquel, Argentina; MG-ZV-M: Museo Provincial de Ciencias Naturales Dr. Angel Gallardo, Rosario, Argentina; ZV-M-H: Museo Provincial de Ciencias Naturales “Florentino Ameghino”, Santa Fe, Argentina.

RESULTS

For the D-loop, we successfully amplified and sequenced 460 bp from 30 individuals of D. albiventris, obtaining 11 variable sites that defined 11 haplotypes (Hd=0.75 [sd=0.089]; π=0.0041 [sd=0.0007]).

The most common haplotype, H1, was present in 15 individuals; H5 was present in three individuals, and the remaining haplotypes were found in one or two individuals (Table 1). The overall mean distance among sequences of D-loop was 0.39%, with pairwise distances between sequences ranging from 0 to 0.66% (Table S1).

We successfully amplified and sequenced 511 bp from 27 individuals, and with the addition of six sequences from GenBank, resulted in 8 variable sites defining 7 haplotypes (Hd=0.676 [sd=0.079]; π=0.0027 [sd=0.0004]). The most common haplotype, H2, was present in 18 individuals, followed by H5 in five individuals, H4 in four individuals, H3 and H7 in two individuals, and the remaining haplotypes present in one individual each (Table 1). The overall mean distance among sequences of cyt b was 0.27%, with pairwise distance values ranging from 0 to 0.8% (Table S2). Due to technical difficulties, we were not able to amplify the same number of samples for both markers, and consequently, we have fewer samples for the cyt b and the combined matrix.

The combined matrix consisted of 25 individuals (the ones that only have one marker were discarded for most of the analyses) and 971 bp, with 14 variable sites defining 9 haplotypes (Hd=0.76 [sd=0.092]; π=0.003 [sd=0.0005]). The most common haplotype, H1, was present in 13 individuals, H5 in three, and the remaining in one or two individuals (Table 1). The overall mean distance among individuals in the combined matrix was 0.3%, with pairwise distance values between sequences ranging from 0 to 0.73% (Table S3).

Neutrality tests were consistent with a constant population model (i.e., were not statistically significant) for the combined data set: D=-0.192, Fs=-1.003, R2=0.083; the cyt b: D=-0.927, Fs=-1.537, R2=0.097; and the D-loop: D=-1.299, Fs=-1.332. The only exception was the R2 that was statistically significant (although marginally) for the D-loop (R2=0.065, p=0.034).

The Mantel test showed a significant correlation between geographic and genetic distances in the three data sets (cyt b r=0.3125, p=0.0001; D- loop r=0.3036, p=0.0011; combined matrix r=0.51, p=0.00009). All the analyses performed in Geneland using the combined matrix found strong support for the subdivision of the sample in two populations, but since we have little genetic variation, the analyses did not have enough power to discriminate the individuals correctly in two populations, and the different runs resulted in an inconsistent placement of the boundaries among populations (data not shown). The mismatch distribution analysis was also consistent with a model of demographic equilibrium, instead of an expanding model (θ0=0; θ1=4.5; SSD=0.056, p>0.5; raggedness index=0.11, p>0.5; τ =5.92; Fig. 2a). The same pattern was observed in the Bayesian skyline plot where the effective population size has apparently been kept stable during the last 600,000 years (Fig. 2b).

The haplotype network of the combined matrix showed that haplotypes were mostly separated by one or two mutational steps. Notwithstanding, two haplogroups, separated by two mutational steps, can be identified. The largest group (Fig. 3) included haplotypes from the Pampas, Chaco, Monte, and Espinal phytogeographic provinces. H1 (which is present in Pampas, Espinal, and Chaco), occupying a central position in the network, would be the most ancestral one, while derived haplotypes would have a more recent origin. The second group has a more heterogeneous composition, given that haplotypes are related through a median vector (a possible unsampled sequence or extinct ancestral sequence; Fig. 3). This second group enclosed haplotypes from more humid environments like the Yungas and the Parana forest. The Chaco biogeographic province presented haplotypes distributed in the two haplogroups (H3 and H5 from haplogroup 2 and H1 from haplogroup 1; see Table 1) and thus, admixture of the two haplogroups could be occurring in this ecoregion (Fig. 3).

The haplotype network of the cyt b matrix was star-shaped (Fig. 4). The central haplotype (H7) corresponds to sequences from Chuquisaca (Bolivia). The most common haplotype (H2; see Table 1) is present in several biogeographic provinces: Pampas, Espinal, Monte and Chaco. Sequences from Presidente Hayes (Paraguay) and Tarija (Bolivia) shared the haplotype (H4) with sequence DA14 from Chaco, and contrary to what we expected, the sequence from Foz do Iguaçu (Brazil) did not share the haplotype with sequences from Puerto Iguazú (Argentina) (H6 and H3, respectively; Table 1).

The haplotype phylogenetic trees were quite uninformative, and the three trees have different structure (Figs. S1-S3). Regarding the haplogroups of the network analyses, we only recovered haplogroup two (formed by haplotypes H2, H3, H5, and H9) in the analysis of the D-loop matrix, where H11 of the D-loop matrix have the same individuals as H9 of the combined matrix (Fig. S2; Table 1), while none of the other analyses recover any of the haplogroups from the network. In the analysis of the full matrix (Fig. S1), the haplotype that includes specimens from Paraguay and Tarija (Bolivia) (H10*; Table 1) was grouped with haplotype H3 (which includes a sample from Chaco Province, Argentina) while the haplotypes that included sequences from Chuquisaca (Bolivia) and Foz do Iguaçu (Brazil) (H14* and H16*, respectively) could not be correctly assigned to any group and were placed in a politomy (Fig. S1). The same pattern was observed in the phylogenetic analysis of the cyt b matrix (Fig. S3). Branch lengths were short in all the analyses, confirming the lack of information of these markers found in other analyses, as well as their lack of phylogenetic signal (Figs. S1-S3).

DISCUSSION

The main finding of this contribution is the little genetic variation present in the Argentinean populations of D. albiventris. Although we sampled a relatively small number of individuals, we covered a large portion of the species distribution in the country (Fig. 1), including samples from different environments, and consequently, we expected to find more variation.

The D-loop is known to be a variable marker in placental mammals, while in marsupials Nilsson (2009) found that it is more conserved. Despite those findings, the D-loop was used in phylogeographic analyses of Australian marsupials (e.g., Neaves et al. 2016; Umbrello et al. 2020) showing high levels of variation. In American marsupials, the D-loop has been mostly disregarded in phylogeographic analyses, and the few published articles, both in the genus Marmosa, reported low levels of genetic variation (Steiner & Catzefliz 2003; Rocha et al. 2012). On the contrary, cyt b has been widely used in phylogeographic analyses of American marsupials (e.g., Carvalho et al. 2011; Gutiérrez et al. 2014; Rocha et al. 2015; Sartorato Zanchetta et al. 2019). In addition, Steiner & Catzefliz (2003) found in their analyses in Marmosa murina that cyt b was more variable than D-loop.

Our findings in D. albiventris contrast with what was previously reported for South American marsupials since the cyt b data showed extremely low variation (only eight variable sites), and contrary to what Steiner & Catzefliz (2003) reported, the D-loop was more variable than the cyt b. Voss & Jansa (2019) analyzed cyt b sequences of five specimens of Chironectes minimus from widely separated samples across the distribution range of the species and found low levels of genetic variation (uncorrected pairwise distances from 0.1 to 0.6 % in the studied samples from South America). The overall mean genetic distance among our samples (Kimura 2-parameter distance of 0.3 % for the combined data set) was on the lower bound of the values reported by Voss & Jansa (2019), and individuals from widely separated localities and completely different environments (such as Buenos Aires and La Rioja) shared the same haplotype (Table 1).

The genetic variation found in this contribution also contrasts with the findings of Rocha et al. (2015) for specimens of Didelphis albiventris from the Amazonia and Cerrado of Brazil, which showed higher levels of variation in the cyt b sequences (between 1 and 6 % of genetic variation), a between clades divergence of 6%, a higher haplotype diversity (0.87 versus 0.53 reported here), and more polymorphic sites (18 versus 5 reported here). It is important to mention that Rocha et al. (2015) only analyzed 15 specimens, so differences could not be explained by sample size. On the contrary, the findings of Rocha et al. (2015) for the closely related species, Didelphis marsupialis, are similar in levels of haplotype diversity and polymorphic sites to the ones reported here (5 haplotypes with 6 polymorphic sites for D. marsupialis, while we found 8 polymorphic sites defining 7 haplotypes).

The reason for these low levels of genetic differentiation among populations found in our analyses, and the lack of phylogeographic structure could be caused by high levels of gene flow, as discussed by Nascimento et al. (2019). D. albiventris has a broad home range (mean 2.33±2.32 ha but with measurements of up to 7 ha) with an overlap between individuals of up to 34%, without differences between sexes (Sanches et al. 2012), so high levels of gene flow are expected, at least among close populations (Centeno-Cuadros et al. 2011; Richardson et al. 2021). Moreover, the species is ecologically flexible, being a generalist feeder and adapted to different environments, including large cities (Astúa 2015; Costa et al. 2015); so it is difficult to consider ecological factors as barriers to gene flow (e.g., Tammeleht et al. 2010; Latch et al. 2014; Levy et al. 2019). In the sister species, Didelphis virginiana, Beatty et al. (2012), using microsatellite loci, found no genetic structure in an agriculturally fragmented system of Indiana (USA), which was attributed to the ecological characteristics of the species, and common to D. albiventris.

The analysis of the haplotypes show that distant localities shared the same haplotype, for example haplotype H1 of the combined matrix is present in localities 1000 km apart, and haplotype H2 of the cyt b matrix is present in contrasting environments such as Pampa and Monte (Table 1). Similar results were described for Brazilian populations of D. albiventris, where Sousa et al. (2012) found haplotypes shared between distant localities in their larger haplogroup from Minas Gerais, which were interpreted as lack of impediments to gene flow. On a taxonomic distant species (the rodent Lagostomus maximus), but with a shared geographic distribution with D. albiventris, Gariboldi et al. (2019) found a similar pattern of low genetic diversity and shared haplotypes among distant populations, coupled with high levels of gene flow. However, due to the life history of the species and their home range, the authors discarded ongoing gene flow among distant populations and propose a scenario of recent range expansion which homogenized populations (Gariboldi et al. 2019).

At a macrogeographic scale, we found a subtle correlation between geographic and genetic distance. To achieve this pattern, sufficient time must have passed for a balance between drift and gene flow, which is in line with a stable population history and contradicting empirical observations. Similar results were described for the rodent Oligoryzomys flavescens, where the observed isolation-by-distance pattern suggested that the population reached stability after the range expansion, resulting in low levels of geographic differentiation (although not as low as the levels found by us; Rivera et al. 2018).

Another possible explanation for these low levels of mitochondrial gene divergence could be found in the phylogeographic history of the Argentinean populations of D. albiventris. A demographic expansion is usually associated with low genetic variability (see, for example, Poljak et al. 2018; Gariboldi et al. 2019). The analyses performed here to test for population expansion (i.e., mismatch analysis and BSP) suggest that the species in Argentina is in a demographic equilibrium. However, both analyses are affected by low levels of sequence polymorphism and small sample sizes (Grant 2015), and consequently, the most likely explanation is that the result we are obtaining is that we do not have enough statistical power to discriminate among the possible evolutionary scenarios. Something similar happens with some of the neutrality tests used (Tajima’s D, Fu’s Fs), that lack of statistical power when analyzing small samples (Ramos-Onsins & Rozas 2002). So again, results should be carefully analyzed when discarding an expansion scenario due to the non-significant values, especially since they showed negative signs in all the analyses (and even relatively large negative values in the D-loop). The R2 is more robust when dealing with small samples (Ramos-Onsins & Rozas 2002), and for the D-loop showed a statistically significant result indicating population growth. Perhaps, the species is going through an expansion period, but we do not have enough genetic signal in both mitochondrial genes of D. albiventris to detect the putative expansion.

In fact, a scenario of population growth would be consistent with recent reports that suggest that D. albiventris is expanding its geographic range to the south of Argentina and is currently found in the provinces of Neuquén and Río Negro, with its southern limit approximately in the 40° 40’ S (Carrera & Udrizar Sauthier 2014; Bianchini 2018).

Also, the species is highly adapted to urban and rural environments (Smith 2007; Costa et al. 2015), so it is possible to think that it went through a demographic expansion, both caused by climate change and also following the enlargement of the agricultural frontier, especially in the Pampas region. It is noteworthy that the sister species, D. virginiana, is expanding northward in North America, and although the low temperatures were once a limitation, the global climate warming plus the increase of agricultural settlements in cold areas helped the species to overcome that limitation and allowed the northern expansion of D. virginiana (Walsh & Tucker 2018). Consequently, it is expected a similar behavior in D. albiventris, since both species share most ecological characteristics.

In Brazil, Sousa et al. (2012), Rocha et al. (2015), and Nascimento et al. (2019) found a similar pattern regarding neutrality indexes to the one recovered for Argentina (but with higher levels of genetic variation). Neutrality tests (D, R2, and Fu & Li’s F) were not significant in all their analyses (except for one of the demes in Nascimento et al.’s analyses), and the authors interpreted that result as a constant population indicator. However, Rocha et al. (2015) only analyzed 15 samples, so the failure in finding any signal (both in the analyses above mentioned and in the BSP) could be an artifact caused by the small sample size, similar to what happens in this contribution. Sousa et al. (2012) and Nascimento et al. (2019) analyzed a larger number of individuals (93 and 142, respectively), but since there is little genetic variation, D, and Fu & Li’s F might have lacked statistical power to find a significant result, and the authors did not perform the Ramos-Onsins and Rozas’ R2 test. Also, it is possible that a BSP or a mismatch analysis could have shown evidence of demographic change, but those analyses were not performed by the authors. Contrary to what we detected in Argentina, Nascimento et al. (2019) found evidence of population structuring, but this structure was not correlated with the biomes analyzed. In fact, they found haplotypes shared between the Uruguayan district (part of the Pampas biogeographic province) and the Paranaense Forest while in our analyses, both provinces (Pampas and Paranaense) were in separate groups in the haplotype network. This would suggest that there could be geographic structuring in a regional scale when considering different biogeographic regions of South America.

Finally, it is clear that the data gathered from mitochondrial markers is not sufficient enough to get a better understanding of the phylogeographic history of D. albiventris. A comprehensive phylogeographic approach would be necessary, including collecting sites from throughout its distribution range, including not only other countries apart from Argentina, but also samples from the southern limit of its distribution, currently missing in this contribution. Using a global point of view, it could be evaluated if the mitochondrial markers are really adequate to know the evolutionary history of this species. In addition, new analyses of highly variable markers, such as microsatellites or SNPs, would be recommended to improve our knowledge of the genetic structure of the white-eared opossum in Argentina in particular, and South America in general.

Supplementary materials

Acknowledgments

This article is part of a special section in honor of Elio Massoia, who made important contributions to the knowledge of Argentinean marsupials (among other mammals). We thank Javier Pereira, Andrés Pautasso, Eugenia Montani, David Flores, Gabriel Martin and Sergio Lucero for the donation of tissues of D. albiventris. Patricia Mirol, Jimena Gómez and Thamara Fariñas provided technical support in the lab. This is a contribution to PICT 2016-3151.

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Información adicional

Editor asociado: U. F. J. Pardiñas & C. A. Galliari

Cite as: Chemisquy, M. A., F. M. Morinigo, A. Fameli & R. E. González-Ittig. 2021. Genetic diversity of the white-eared opossum Didelphis albiventris (Didelphimorphia: Didelphidae) in Argentina. Mastozoología Neotropical, 28(1):e0534. https://doi.org/10.31687/saremMN.21.28.1.0.20

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