Landscape and Pleistocene refuges played a major role in shaping the population structure of endemic Atlantic Forest spiny rats Trinomys dimidiatus and Trinomys iheringi (Echymidae)

doi:10.21203/rs.3.rs-4837229/v1

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Landscape and Pleistocene refuges played a major role in shaping the population structure of endemic Atlantic Forest spiny rats Trinomys dimidiatus and Trinomys iheringi (Echymidae)

https://doi.org/10.21203/rs.3.rs-4837229/v1

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The Atlantic Forest has experienced a significant loss of its original vegetation, with more than 80% of the biome now comprising small and isolated fragments. To understand species distribution, it is crucial to comprehend the impact of fragmentation on species persistence within this biome. Previous phylogeographic inferences have indicated that Trinomys iheringi and T. dimidiatus are structured into two populations. In this study, we employed a more comprehensive database to re-examine the phylogeography and conduct a detailed analysis of the demographic history and population structure of two Trinomys species. The results indicate that T. dimidiatus is structured into at least three populations: Serra dos Órgãos, South Center of Serra do Mar, and North of Serra do Mar. In contrast, T. iheringi is highly structured into two populations: São Paulo and Ilha Grande. The demographic analysis indicated T. dimidiatus from Serra do Mar exhibited an expansionary trend during periods of reduced sea level with an optimal area reduction during LGM. In the past, T. iheringi presented the expansion of suitable areas onto the emerging continental shelf during the Last Glacial Maximum (LGM) but it did not impact the population's structure. The results suggest that retreat forest events during the Pleistocene and landscape factors were instrumental in shaping the population structure of the Trinomys species. Additionally, T. iheringi represents an entirely isolated island population exhibiting no gene flow, and analysis suggests the presence of a new microendemic species.

mt-Cytb

forest refuge

genetic diversity

rodents

species distribution models

Tropical forests, such as the Atlantic Forest (AF), are of great conservation importance, harboring nearly half of the world's biodiversity (Olson and Dinerstein 2002). Over the past five decades, deforestation in tropical forests has increased significantly, with the deforested area now exceeding one-third of the original vegetation that covered the planet (Hansen et al. 2013). Anthropogenic impacts have increased in recent decades, and such activities threaten about a quarter of all mammal species due to habitat fragmentation, excessive hunting, and pollution (IUCN 2024; Haddad et al. 2015; Tilman et al. 1994). The Atlantic Forest has a high degree of endemism, with a high proportion of endemic vertebrate species, including approximately 30% of endemic mammals (Costa et al. 2000; Dalapicolla et al. 2021). However, the AF is highly threatened (Brown and Brown 1992; Ribeiro et al. 2010), with only about 11–16% of the original biome left in forest remnants (Ribeiro et al. 2009). The high fragmentation makes populations highly vulnerable (Lugo 1995), being the main threat to species of terrestrial mammals (Joppa et al. 2016).

High endemism several restricts several rare species to small and isolated habitat patches, increasing extinction risks due to demographic, environmental, and/or genetic stochasticity (Haddad et al. 2015). Reduced gene flow and increased genetic drift are the possible genetic consequences of habitat fragmentation (Keyghobadi 2007), increasing the risk of extinction due to inbreeding and reduced genetic diversity (Gaines et al. 1997; Ralls et al. 2018). Despite these challenges, few studies on Atlantic Forest mammal population structure exist, making them crucial for understanding fragmentation effects and elucidating ecological and behavioral patterns (DeWoody 2010). Small mammals, such as spiny rats (Trinomys spp.), play an important role in food chains with a diet largely based on seeds, fruits, fungi, some leaves, and insects (Cáceres and Monteiro-Filho 2001; Pinotti et al. 2011; Vieira et al. 2006) and act as dispersers for several plant species (Cáceres and Monteiro-Filho 2007; Vieira et al. 2006). Therefore, understanding the population structure of wild rodents is critical information for biodiversity conservation (Gaines et al. 1997).

The great tropical forest diversity, including mammals, has long been attributed to the theory of forest refugia. (Haffer 1969; Vanzolini and Williams 1981) This theory suggests that Pleistocene climatic fluctuations fragmented forests into isolated patches of favorable habitat. Paleoclimatic studies revived this theory, linking high AF diversity and endemism to historically stable areas. These include three main areas: Bahia Refugium (north of the São Francisco River), Pernambuco Refugium (between Rio São Francisco and Rio Doce), and São Paulo Refugium, extending along the Serra do Mar and inland (Carnaval and Moritz 2008; Carnaval et al. 2009).

Leite et al. (2016) tested diversification hypotheses for small mammals in the AF and found that suitable habitats were more fragmented during the Last interglacial (LIG) and present than during the Last Glacial Maximum (LGM). In addition, they found that during the LGM, suitable conditions extended to the exposed continental shelf, allowing demographic expansion. Species distribution modeling is crucial for understanding geographic distribution and predicting responses to environmental change, aiding the identification of priority areas for conservation, and the inform management strategies (Elith et al. 2006).

In this work, we revisited phylogenetic and demographic analyses to understand the events contributing to genetic diversity in the endemic species Trinomys dimidiatus (Günther 1876) and Trinomys iheringi (Thomas 1911). The distribution of T. dimidiatus, with a fixed type locality in the Parque Nacional da Tijuca, Rio de Janeiro, includes the entire state of Rio de Janeiro to the northern coast of the state of São Paulo (Attias et al. 2009; Tavares et al. 2015).The distribution of T. iheringi is restricted to the coastal region, extending along the plateau and coast of São Paulo state, with a disjunct distribution in Ilha Grande, on the south coast of Rio de Janeiro (Attias et al. 2009; Tavares et al. 2015).

Previous molecular studies recover these two species as sisters, in addition to indicating that both taxa are monotypic. (Lara et al. 1996, 2006; Galewski et al. 2005; Tavares et al. 2015, 2016) However, a more recent paper with a larger sample size identified two operational taxonomic units (OTUs) within each species. (Leitão Nacif et al. 2022) Nevertheless, it does not explore neither potential historical processes contributing to the diversity nor demographic history of the two species. So, the influence of Pleistocene Forest retreats on the emergence of T. dimidiatus and T. iheringi lineages remains untested.

Here, we examined the population structure and phylogeography of T. dimidiatus and T. iheringi using phylogeographic, population, and demographic analyses of mitochondrial gene cytochrome b (mt-Cytb); and simulated potential distributions for both species in three scenarios: Present, LIG, and LGM to identify temporally stable (refugial) and unstable (recently colonized) regions for species. We also tested whether the population structure found for species correlated with Pleistocene events and whether there was a signature of population expansion during the LGM, as seen for other mammals (Abreu et al. 2022; Leite et al. 2016).

Sampling, DNA amplification, and sequencing

We used a dataset covering the entire range occupied by T. iheringi and T. dimitiatus in the Atlantic Forest (Fig. 1). The dataset included 202 specimens of T. dimitiatus and 75 specimens of T. iheringi (Table S1). Genomic DNA extraction from tissue samples was performed using a phenol-chloroform protocol (Sambrook et al. 1989). Polymerase chain reactions (PCR) were performed as described by. Leitão Nacif et al. (2022). PCR products were purified using PureLink^tm - Quick Gel Extraction and PCR Purification Combo Kit (Invitrogen). Sequencing was performed on an ABI PRISM 3730xl sequencer using four primers - two external, L14724 (Irwin et al. 1991) and CitbRev (Casado et al. 2010), and two internal, CBin1 and CBin2 (Cassens et al. 2000) - and dye terminator chemistry (BigDye Terminator 3.1; Life Technologies). The sequences generated in this study have been deposited in GenBank under accession numbers PQ072905 to PQ073076.

Genetic diversity analyses, phylogeographic and molecular dating

DnaSP 6.12 (Rozas et al. 2017) was used to estimate the number of polymorphic sites (S), number of haplotypes (k), haplotype diversity (HD), and nucleotide diversity (π) (TableS2). For sites sampled with N = 1, the evaluation was limited to the estimation of general diversity rates for each species. Echimyidae sequences, including 45 sequences of other species in the genus Trinomys retrieved from GenBank, were used as an external group (Table S3), following previous publications. (Olivares et al. 2017) Estimates of divergence times were based on the haplotypes obtained for the two focal species. Molecular dating analyses and Bayesian inference were performed using BEAST 2 (Bouckaert et al. 2014), applying a relaxed molecular clock (Drummond et al. 2006) and uncorrelated lognormal and tree sampling using the birth and death process. (Drummond 2005) Estimation was performed under the GTR + I + G nucleotide substitution model selected in JModeltest 2.1.10. The initial tree was randomly generated, and the chain length was set to 10⁸ generations, with sampling every 10³ generations. TRACER 1.7 software (Rambaut et al. 2018) was used to check whether the effective sample sizes (ESS) values were greater than 200. The consensus topology was generated in TreeAnnotator v.1.7.5 with a burn-in of 10% and visualized in FigTree v.1.4.0. (Drummond et al. 2012). Following previous work (Tavares et al. 2015, 2016; Leitão Nacif et al. 2022), four calibration points were used. The first calibration point corresponded to the root of Octodontidae + Ctenomyidae, with an offset of 25 Myr, mean (M). The second calibration point corresponded to the fossil Paradelphomys (Patterson and Pascual 1968), with a minimum age of 20 Myr. The third calibration point corresponded to the fossil Pampamys, a sister taxon of Thrichomys, from the Cerro Azul Formation (Olivares et al. 2012). The fourth calibration is based on the dating of Theridomysops parvulus, a late Miocene Euryzygomatomyini genus from Catamarca (Vucetich 1995; Olivares et al. 2012). A minimum age of 6 Myr was used for the third and fourth calibrations. We used a lognormal mean of 0.01 and a lognormal standard deviation of 0.6 for all calibration points, following best practices for molecular clock estimation (Parham et al. 2012). The relationships between haplotypes and their geographic distribution were investigated using a median-joining (MJ) network (Bandelt et al. 1999) constructed with the PopART 1.7 program (Leigh and Bryant 2015). Interspecific genetic distances were calculated in Mega 11 to compare the percentage of the divergence between the most divergent strains of T. dimidiatus (Serra dos Órgãos + South Center of Serra do Mar vs North of Serra do Mar) and T. iheringi (Ilha Grande vs São Paulo) with recognized species and/or subspecies of the genus. For this purpose, the Tamura-Nei 93 model at uniform rates was used to best describe mitochondrial evolution in mammals and is the closest among the models estimated in the present study (Tamura and Nei 1993).

Population Structure and Historical Analyses

Pairwise fixation indices Φ_ST (Excoffier et al. 1992) were estimated using Arlequin 3.5 (Excoffier and Lischer 2010), and their significance was tested using 10⁵ permutations. Sites with only one sample were excluded from population analyses. Population differentiation was tested using analysis of molecular variance (AMOVA), performed in Arlequin 3.5. We used all samples to perform AMOVA, testing groups as obtained in the median-joining network. To assign individuals to different groups, a Bayesian Analysis of Population Structure (BAPS) was also performed in the BAPS 6 (Corander et al. 2008), using Maximum Likelihood and MCMC under the non-reversible Metropolis-Hasting method, considering linked loci, assigning maximum values of different partitions, and varying the values of k from 1 to 3. Five simulations were performed for each value of k, over 10³ generations. The T. dimidiatus population structure hypotheses tested included the following scenarios tested in AMOVA (Table S5): i) under Panmixia; ii) with two populations (Serra dos Órgãos + South Center of Serra do Mar vs North of Serra do Mar) and with three populations (iii.a) Serra dos Órgãos vs South Center of Serra do Mar vs North of Serra do Mar or (iii.b) Serra dos Órgãos + Petrópolis vs. South Center of Serra do Mar vs North of Serra do Mar. T. dimidiatus population structuring was evident even without the highly differentiated samples from North of Serra do Mar.

For T. iheringi, we tested three population structure hypotheses (K = 1 to 3) using AMOVA (Table S7). These hypotheses included i) panmixia, ii) two populations (Ilha Grande vs. São Paulo), and iii) three populations (West of Ilha Grande-WIG vs. East of Ilha Grande-EIG vs. São Paulo). The third hypothesis (K = 3) was carried out to verify whether the process of separation of the island and the geography of this place influenced the population structure of the species. To test the latter hypothesis, we used two different datasets, one including SP samples and the other excluding the São Paulo (SP) population, to investigate Ilha Grande (IG) substructure. The western part of the region separated about 10,000 years ago and the eastern part about 5,100 years ago (Gama et al. 2009). The disjunct separation and the difference in elevation between the two sides of Ilha Grande (elevations greater than 500 m are concentrated in the central-eastern region; Gama et al. 2009). To test for the presence of a correlation between geographic distance (kilometers) and genetic distance (using the FST/(1-FST)), a Mantel test (Mantel 1967) was performed using Arlequin 3.5 (Excoffier and Lischer 2010) and significance was tested using 10⁵ permutations. Geographic distances were obtained using R software (version 4.0.4) via the geosphere package (Hijmans et al. 2022).

Two neutrality tests, namely Fu Fs (Fu 1997) and Tajima's D, were performed in Arlequin 3.5 (Excoffier and Lischer 2010). Historical demographic trend of each population (except for the groups represented by N < 20, which precluded reliable estimates) was examined by Bayesian skyline plots (BSP) (Drummond 2005) using Beast 2.0 (Drummond et al. 2012; Bouckaert et al. 2014), running on the CIPRES platform (Miller et al. 2010). Coalescent reconstructions used a strict molecular clock with a mutation rate of 0.098 substitutions/site/million years, estimated for the third codon position of mammalian mt-Cytb. (Nabholz et al. 2008) The model GTR + I + G was selected for this analysis using the program JModeltest 2.1.10 (Darriba et al. 2012). The BSP was performed on each population using 3x10⁷ Markov Chain Monte Carlo (MCMC) steps, with a burn-in of 10%, to obtain ESS > 200 for all parameters.

Species distribution models (SDM)

The maximum entropy niche modeling approach was performed using 29 georeferenced data of T. dimidiatus and 24 occurrence data of T. iheringi in Maxent version 3.4.4. (Phillips et al. 2006) The background extension was defined for each species distribution model (SDM) using the Atlantic Forest shapefile (http://terrabrasilis.dpi.inpe.br/), and the base area was defined by the restricted range of the species in the AF in southeastern Brazil. All SDM analyses were performed on the R platform (R Core Team 2022) using the "raster", "dismo", and "rJava" packages. The following parameters were used for all models: format cloglog (habitat suitability measured on a scale from 0 to 1), 80% of datasets used for training and 20% for testing, maximum 500 iterations, 5 replicates, 10,000 background points. (Phillips et al. 2006) We used 19 bioclimatic variables available in WorldClim 2.1 (Hijmans et al. 2005; Fick and Hijmans 2017) with 2.5′ resolution (~ 5 km²) for current projections (1970s to 2000s), LIG (~ 120–140 kya), and LGM (~ 22 kya). LGM distribution projections were made using the average of projections from three global climate models (GCMs): CCSM4, MIROC-ESM, and MPI-ESM-P. The models were considered using a 10% threshold. The proportional contributions (%) of each environmental variable for each model were analyzed using jackknife tests. (Phillips et al. 2006) To estimate the predictive accuracy of potential habitat models, we used the area under the curve (AUC) metric, based on the area under the receiver operating characteristic (ROC) curve (Swets 1988; Elith et al. 2006). For comparative purposes, the images resulting from each model (with continuous values from 0 to 1) were reclassified into three environmental suitability classes: poor (0 - MTP value), moderate suitability (MTP value − 0.5), and high suitability (0.5–1). A final map of occurrences and distribution model was plotted in ArcGIS software.

Diversity analyses, phylogeographic and phylogenetic inferences

Trinomys. dimidiatus data set included 202 sequences with 100 polymorphic sites defining 43 haplotypes. Haplotype diversity (Hd) was 0.831 (sd = 0.019), and nucleotide diversity (π) ranged from 0.0008 to 0.0271 (Table S2). In 75 analyzed sequences of T. iheringi, 17 haplotypes with 42 polymorphic sites were recovered (Table S2). Nucleotide diversity ranged from 0.00162 to 0.01142, and haplotypic diversity was 0.813 (sd = 0.031), lower values than those found in T. dimidiatus. Bayesian inference (BI) recovered T. dimidiatus and T. iheringi as sister species (posterior probability = 0.92), and the diversification between the two species was dated to 3.84 Ma (2.35–5.45 Ma; Fig. 2). The T. dimidiatus and T. iheringi have, respectively, four and two lineages, according to the results obtained by BI and median-joining network (MJ) (Fig. 2, 3).

The four lineages of T. dimidiatus are: (1) North of Serra do Mar (NSM);(2) Serra dos Órgãos (SO) with two haplotypes from Petrópolis, one from Cachoeira de Macacu and one shared by SO and Serra da Bocaina (SB); (3 and 4) South Center of Serra do Mar (SCSM), the SCSM2 with only two samples from Picinguaba (H38) and Tarituba (H35), and SCSM1 with haplotypes from South Center of Serra do Mar, including Picinguaba and Tarituba (Fig. 2, 3A). The lineage SCSM2 differs from lineage SCSM1 by at least 13 mutational steps. The BI and MJ of T. iheringi (Fig. 2, 3B) revealed two deeply divergent lineages, lineage IG with haplotypes from Ilha Grande (H44-49, H51-53) and lineage SP formed by samples from São Paulo (H50, H55-60). The genetic distance results for the most divergent lineages are 4.80% between lineages (SO + SCSM vs NSM) of T. dimidiatus, and 3.94% for two lineages (SP vs IG) of T. iheringi (Table 1).

Table 1

Pairwise genetic distance between *Trinomy*s species /subspecies. SO = Serra dos Órgãos; CSSM = South Center of Serra do Mar; NSM = North of Serra do Mar; IG = Ilha Grande; SP = São Paulo
	1	2	3	4	5	6	7	8	9	10	11	12	13	14	15
1. T. dimidiatus (SCSM + SO)
2. T. dimidiatus (NSM)	0.048
3. T. iheringi (IG)	0.153	0.169
4. T. iheringi (SP)	0.146	0.160	0.039
5. T. albispinus albispinus	0.201	0.194	0.213	0.217
6. T. albispinus minor	0.208	0.198	0.216	0.220	0.042
7. T. setosus setosus	0.204	0.208	0.186	0.191	0.193	0.188
8. T. gratiosus bonafidei	0.171	0.168	0.191	0.191	0.185	0.180	0.185
9. T. paratus	0.196	0.193	0.209	0.213	0.190	0.199	0.161	0.174
10. T. setosus elegans	0.203	0.210	0.192	0.195	0.196	0.191	0.043	0.175	0.158
11. T. eliasi 1	0.193	0.189	0.204	0.212	0.187	0.203	0.154	0.164	0.055	0.149
12. T. eliasi 2	0.199	0.195	0.211	0.212	0.196	0.208	0.167	0.173	0.054	0.163	0.047
13. T. gratiosus gratiosus	0.163	0.164	0.185	0.184	0.184	0.181	0.182	0.024	0.169	0.175	0.164	0.172
14. T. mirapitanga	0.154	0.151	0.147	0.144	0.200	0.187	0.187	0.164	0.199	0.183	0.188	0.199	0.160
15. T. moojeni	0.177	0.177	0.183	0.179	0.177	0.190	0.185	0.107	0.190	0.174	0.178	0.187	0.109	0.167
16. T. yonenagae	0.190	0.181	0.219	0.215	0.169	0.169	0.138	0.164	0.148	0.136	0.140	0.149	0.159	0.169	0.160

Population Structure and Historical Analyses of Trinomys dimidiatus

The SCSM2 lineage was removed from population analyses due to a low sample size (N = 2). Φ_ST values were significant for almost all comparisons between samples of T. dimidiatus, even between localities only a few kilometers. The highest significant Φ_ST value was 0.9534 between Teresópolis and Nova Friburgo, and the lowest (0.1887) between Rio Claro and Petrópolis (Table S4, Fig. 1). The Mantel test did not support isolation by distance (p = 0.29), suggesting that the genetic distance found is correlated with landscape isolation factors.

The AMOVA results showed that the scenario that maximized the variance between groups (ΦCT = 0.77; p-value = 0.01) was the two-population scenario (SCSM + SO vs. NSM; Table S5). The best AMOVA scenario identified was that of two populations (SO vs. SCSM), with stronger support for a k = 2 scenario when samples from SO were separated from samples from SCSM (Φ_CT = 0.61; p-value = 0.02). Bayesian Analysis of Population Structure (BAPS) analysis revealed three clusters: (1) SO, (2) SCSM, and (3) NSM (Fig. 4A). Our analyses (BAPS and AMOVA) suggest the existence of three populations of T. dimidiatus (SO/SCSM/NSM). The populations NSM and SCSM + SO diverged at 1.43 Ma (0.85–2.18 Ma). The SO and SCSM populations diverged at 0.48 Ma (0.28–0.73 Ma) (Fig. 2). Tajima's D test suggested that SCSM and NSM populations were not stable over time (D < 0; p < 0.05), and the SO population was not different from the null hypothesis of neutrality (D > 0; p < 0.05). The Fu Fs test was not significant for any of the three populations (p > 0.02; Table S8).

The NSM population was excluded from the Bayesian Skyline Plot (BSP) analysis due to the limited number of samples, which prevented the generation of reliable estimates. BSP revealed contrasting demographic histories for the two T. dimidiatus populations (SCSM and SO). The SO population demonstrated a consistent population size over time, whereas the SCSM population exhibited a recent expansion approximately 20,000 years ago (Fig. 5A and 5B).

Population Structure and Historical Analyses of Trinomys iheringi

The Φ_ST values obtained for T. iheringi (Table S6) were significant in all different localities of two lineages (IG vs SP) and some comparisons within IG. Regarding the four localities of IG, Abraão and Parnaioca differed from the other localities, and Dois Rios differed from Aventureiro, but small sample sizes may explain these results. Φ_ST values were high between the two lineages (0.90 < Φ_ST < 0.98), and comparatively low between localities within Ilha Grande (0.24 < Φ_ST < 0.49), indicating limited restriction of gene flow on the island. The Mantel test was not significant and did not support the hypothesis of isolation by distance (p > 0.05). AMOVA results showed that the two population scenarios (IG/SP; Table S7) had higher support (ΦCT = 0.91; p = 0.008). The analysis carried out in BAPS for T. iheringi revealed two distinct populations: one formed by the IG lineage, and another by SP lineage (Fig. 4B), that diverged at 0.93 Ma (0.45–1.6 Ma; Fig. 2).

Tajima's D and Fu's FS neutrality test values for the IG and SP populations do not deviate significantly from the null hypothesis (p > 0.05; Table S8), although the BSP pointed to a sharp increase, about 5,000 years ago to the IG population (Fig. 5C). This growth would have occurred shortly after the total separation between the island and the mainland.

Predicted Potential Distribution

Analyzing the current (1970 to 2000 years) and past (Last Glacial Maximum and Last Inter Glacial) distribution models, T. dimidiatus hotspots are concentrated in the southeastern region, especially Rio de Janeiro and São Paulo (Fig. 6A). Two main areas of high environmental suitability for the occurrence of T. dimidiatus were identified: i) Serra do Mar, and ii) Serra dos Órgãos. The current suitable area for T. dimidiatus occurrence is 27,895.00 km². In past models, its range increased by 27% in LGM, and 628% in LIG (Fig. 6A). Key bioclimatic variables were isothermality (BIO 3), seasonality of temperature (BIO 4) and seasonality of precipitation (BIO 15; Table S7).

The current potential distribution of T. iheringi is 82,714.00 km² mainly the coastal regions of Rio de Janeiro e São Paulo (Fig. 6B). In the LGM model, there was an increase in potential area of 93%. During LGM, its area increased by 93% but decreased by 39% during LIG, indicating post-LGM expansion (Fig. 6B). The bioclimatic variables that most influenced the distribution of T. iheringi were mean diurnal interval (BIO 2), isothermality (BIO 3), and warmest quarter precipitation (BIO 18; Table S6).

Trinomys dimidiatus

The origin of T. dimidiatus approximately 3.84 million years ago in the Miocene is consistent with previous studies suggesting a Middle Miocene origin for this species (Álvarez et al. 2017; Fabre et al. 2016; Lara and Patton 2000; Leitão Nacif et al. 2022; Olivares et al. 2020; Tavares et al. 2015). Here, compelling evidence of significant genetic differences was found among all the localities analyzed, including two localities within Serra dos Órgãos National Park, which showed low but still significant differentiation. The genetic divergence between the two sampling sites may be related to the choice of environments close to water sources (Tavares et al. 2015) influencing the structuring of populations between these two localities. Given the absence of a continuum of water sources between these localities, species distribution can be limited, reinforcing the idea of population-specific habitat preference. Additionally, different altitudes these two localities (Teresópolis ranging from 1000 to 1200 meters, and Guapimirim from 522 to 748 meters) could create microclimate variations, potentially reducing dispersal between the localities. Trinomys dimidiatus exhibits high haplotypic diversity, with only Teresópolis/Guapimirim/Serra da Bocaina, and Rio Claro/Paraty sharing haplotypes. Our analyses suggest the existence of three subpopulations: SO (Guapimirim + Teresópolis + Cachoeira de Macacú), SCSM (Petrópolis + Tijuca + Rio Claro + Serra da Bocaina + Paraty + Trindade + Tarituba), and NSM (Nova Friburgo + Santa Maria Madalena). Rio Claro (SCSM population) includes one sample sharing the most frequent haplotype of the SO population (Fig. 3). One hypothesis to explain this result is an ancestral polymorphism, suggesting that one Rio Claro specimen retains haplotypes from the ancestral population of SO and SCSM.

The divergence observed between haplotypes H35 and H39 (SCSM2 population) can be attributed to the Montane Isolate Hypothesis (MIH), already postulated by other taxa from Serra do Mar region (Bonvicino et al. 1997). In the genus Trinomys, interspecific variation is primarily structured by habitat, with two main clades of species occupying perennial wet forests and semi-deciduous forests, respectively. T. dimidiatus is endemic to perennial wet forests of the Serra do Mar and Serra dos Órgãos, as it sister species, T. iheringi found only in the Serra do Mar, and the sister taxon T. gratiosus from Serra da Mantiqueira (Lara et al. 2006). Phylogenetic patterns are consistent with different mountain ranges, and population structure of T. dimidiatus may be associated with altitudinal differences within the same range. The Picinguaba locality exhibits altitude-related phytogeographic variation, potentially indicating microhabitat preferences. During Pleistocene climatic fluctuations, these altitudinal differences could have isolated lineages, establishing different optimal habitat patches. Consequently, it is conceivable that a population may have experienced retraction during the LGM, and then surviving individuals may have migrated to present SCSM population area. The SDM supports this hypothesis, showing reduced suitable habitats for T. dimidiatus during LGM compared to LIG. Furthermore, two sequences from Petrópolis were attributed to the SO population, while the remaining sequences were attributed to the SCSM population, probably due to the proximity of Petrópolis and Serra dos Orgãos National Park. Petrópolis, due to habitat fragmentation processes that reduced gene flow, become highly isolated today. Lara and Patton (2000) had already hypothesized this differentiation between populations of T. dimidiatus in the remnants of the AF. One study on the diversification of Trinomys species, using cytochrome b sequences (393bp), detected low genetic divergence in T. dimidiatus when analyzing 14 specimens from five localities. The lower levels of divergence found, even between distant locations such as Guapimirim and Angra dos Reis (180 km away), differ from the pattern found in the present study, where individuals from geographically closer locations (e.g. Guapimirim and Nova Friburgo, Petrópolis and Teresópolis) showed higher levels of divergence. This discrepancy may be a consequence of shorter sequence length of the sequences analyzed by Lara and Patton (2000), influencing genetic variability by uniting two different haplotypes into a single haplotype. In addition, reliable estimates from Φ_ST depend on minimum sample sizes from each site, and such sampling was not available in their study.

In mammalian populations, less dense populations tend to have lower dispersal rates, which increases the permanence in the habitat (Matthysen 2005). This behavior led the populations to become genetically differentiated over time, due to reduced gene flow between populations. Several hypotheses may explain this maintenance of genetic divergence over time. First, the rugged environment of the localities may be a relevant factor explaining the limited dispersal of the species between localities. Field data suggest that T. dimidiatus relative abundance is lower when compared to other small mammals (Macedo et al. 2007; Delciellos et al. 2023). Another hypothesis raised to explain the structure is the possible occurrence of local adaptation. Although it has not yet been verified at the intraspecific level, Trinomys species present a diversification related to the habitat, where different clades within the phylogeny of the genus are associated with different habitats (Lara et al. 2006). So, for T. dimitiatus the genetic structure observed here may be related to an incipient adaptation to different environments due primarily to differences of topography in the area. Also, T. dimitiatus avoid forest edges (Vieira et al. 2009), suggesting that the species is unlikely to disperse from sites with unfavorable matrices.

Demographic growth in SCSM population (22,000 years ago) may be associated with favorable areas on the continental shelf that are now submerged (Leite et al. 2016). Despite area reduction of high habitat suitability observed from LIG to LGM, these areas allowed migration and population expansion, acting as forest refugia for the species. The SDM for T. dimidiatus (Fig. 6A) shows a large area of high habitat suitability overlapping with the Serra do Mar refuge (Carnaval et al. 2009) and small areas between Bahia and Serra do Mar refuges, to Serra dos Órgãos, and north of Serra do Mar (Fig. 6A). During LIG, the area of high susceptibility for T. dimidiatus was located around the present-day region of Serra dos Órgãos (SO), and in the region of Serra da Mantiqueira and the Bahia Refuge, which explains the lack of expansion in the last 120,000 years, as its area has decreased considerably after the LGM. In LGM, only a small area north of Serra do Mar (NSM) remained susceptible for the species, isolating it from other populations. Such processes of retreat and expansion of forest refugia allowed the structuring of the three populations of T. dimidiatus, since the susceptible areas in the three regions (NSM, SO and SCSM) were isolated during the Pleistocene. The NSM population showed high genetic divergence, with values comparable to those of subspecies, as already mentioned (Leitão Nacif et al. 2022). The small favorable habitat in the LGM may have led to greater genetic differentiation, potentially given rise to a new subspecies.

Trinomys iheringi

The estimated origin of T. iheringi is in the Miocene (3.83 Mya), in agreement with previous studies (Lara and Patton 2000; Tavares et al. 2015; Fabre et al. 2016; Álvarez et al. 2017; Olivares et al. 2020; Leitão Nacif et al. 2022). Its genetic variability is lower than that found for its sister species, T. dimidiatus, likely due to lower number of sampled sites and that most of the localities being insular. The separation of Ilha Grande does not influence T. iheringi population structure. Although the western part of the Island may have remained connected to the mainland, gene flow within the island was greater than between the island and the mainland.

Isolation between Ilha Grande and São Paulo populations occurred before their separation, probably in events of forest contraction and expansion in the Pleistocene, therefore, the separation of Ilha Grande from the mainland did not affect the population structure on Ilha Grande.

The SDM for T. iheringi (Fig. 6B) shows high habitat suitability overlapping with the Serra do Mar refuge (Carnaval et al. 2009) and a small area near Serra dos Órgãos and Serra do Espinhaço. Contrary to the classical forest refugia hypothesis, our results show that the high habitat suitability in Serra do Mar expanded during colder, drier conditions in the LGM. Due to the small sample size from the Serra do Mar, BSP analysis for São Paulo population in the LGM was not possible. Similar findings were observed for the rodent Euryoryzomys russatus, which also showed population expansion in LGM (Abreu et al. 2022). These results support the hypothesis that T. iheringi lived along the Serra do Mar from São Paulo to the coastal region of Rio de Janeiro. Post-LGM forest dynamics isolated the IG population and decreased T. iheringi distribution, likely due to unfavorable environmental conditions or ecological factors like competition. The BSP analysis indicates that T. iheringi on Ilha Grande expanded about 5,000 years ago after the separation of the island from the continent. This expansion may be due to reduced competition with T. dimidiatus, its sister species, which occurs only on mainland.

The high genetic divergence between IG and SP populations (Table 1), in addition with the complete geographic isolation suggests the insular population could be a new species. Our findings support previous hypothesis (Leitão Nacif et al. 2022) suggesting that the IG population is a distinct lineage and possible a new species.

Relevance for conservation

Comparative population genetics studies are crucial for predicting habitat fragmentation effects and reveal general patterns that can help design conservation policies. Rodent diversification and distribution in the Atlantic Forest depend on ecological factors like life history, dispersal capacity and niche specificity. Several studies on many taxa show that genetic divergence and population structure can occur from generalist to specialist species. For example, Cruz-Salazar and Ruiz-Montoya (2020) found genetic structure in the common opossum Didelphis marsupialis, despite its high capacity to disperse and cross highly anthropized matrices (Prevedello and Vieira 2010), with significant levels of population structure over relatively short distances. Therefore, the isolation of populations of species with lower dispersal capacity, such as spiny rats or the sigmodontine rodent Euryoryzomys russatus, (Abreu et al. 2022) may cause a drastic reduction in genetic variability, with negative consequences for the species. Trinomys species are strongly affected by the effects of habitat fragmentation and their low dispersal capacity results in a high phylogeographic structure. Understanding species` responses to fragmentation is essential for conserving genetic variability. The results presented are relevant to the conservation status of T. iheringi from Ilha Grande, since the possible new specie may be microendemic, and its threat status needs to be carefully reassessed These patterns can guide efforts to maintain genetic variability in populations and species persistence.

Funding

Field and laboratory work were supported by grants from Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) and Fundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro (FAPERJ) to Dr Cibele R. Bonvicino (CNPq 311712/2021-5, FAPERJ E26/201.038/2022 and E-26/210.925/2019). The permit for the collection of specimens was issued by the Chico Mendes Institute for Biodiversity Conservation (Instituto Chico Mendes de Conservação da Biodiversidade—ICMBio). This study is part of the requirements for Marcelo de Assis Passos Oliveira’s doctoral degree in Genetics at the Universidade Federal do Rio de Janeiro. Marcelo de Assis Passos Oliveira was supported by a scholarship from Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) – 88887.479564/2020-00.

Competing Interests

The authors have no relevant financial or non-financial interests to disclose.

Author Contributions

Marcelo de Assis Passos Oliveira, Anderson Vilasboa and Cibele Rodrigues Bonvicino contributed to the study conception and design. Material preparation and data collection were performed by Marcelo de Assis Passos Oliveira. Analysis was performed by Marcelo de Assis Passos Oliveira and Marcione de Oliveira. The first draft of the manuscript was written by Marcelo de Assis Passos Oliveira and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Data Availability

The sequences generated during the current study are available in the GenBank Nucleotide Database at https://www.ncbi.nlm.nih.gov, under accession numbers PQ072905 to PQ073076.

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Landscape and Pleistocene refuges played a major role in shaping the population structure of endemic Atlantic Forest spiny rats Trinomys dimidiatus and Trinomys iheringi (Echymidae)

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Abstract

Figures

INTRODUCTION

MATERIALS AND METHODS

Sampling, DNA amplification, and sequencing

Genetic diversity analyses, phylogeographic and molecular dating

Population Structure and Historical Analyses

Species distribution models (SDM)

RESULTS

Diversity analyses, phylogeographic and phylogenetic inferences

Predicted Potential Distribution

DISCUSSION

Trinomys dimidiatus

Trinomys iheringi

Relevance for conservation

Declarations

References

Additional Declarations

Supplementary Files

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