Population structure and landscape genetics in the endangered subterranean rodent Ctenomys porteousi

Abstract

In order to devise adequate conservation and management strategies for endangered species, it is important to incorporate a reliable understanding of its spatial population structure, detecting the existence of demographic partitions throughout its geographical range and characterizing the distribution of its genetic diversity. Moreover, in species that occupy fragmented habitats it is essential to know how landscape characteristics may affect the genetic connectivity among populations. In this study we use eight microsatellite markers to analyze population structure and gene flow patterns in the complete geographic range of the endangered rodent Ctenomys porteousi. Also, we use landscape genetics approaches to evaluate the effects of landscape configuration on the genetic connectivity among populations. In spite of geographical proximity of the sampling sites (8-27 km between the nearest sites) and the absence of marked barriers to individual movement, strong population structure and low values of gene flow were observed. Genetic differentiation among sampling sites was consistent with a simple model of isolation by distance, where peripheral areas showed higher population differentiation than those sites located in the central area of the species' distribution. Landscape genetics analysis suggested that habitat fragmentation at regional level has affected the distribution of genetic variation among populations. The distance of sampling sites to areas of the landscape having higher habitat connectivity was the environmental factor most strongly related to population genetic structure. In general, our results indicate strong genetic structure in C. porteousi, even at a small spatial scale, and suggest that habitat fragmentation could increase the population differentiation.

Population Genetic Structure of the Baird'S Pocket Gopher,Geomys breviceps, in Eastern Texas

“…In general, population genetic studies focusing on a solitary species with low vagility, such as the Baird's pocket gopher, are rare (e.g., Hambuch and Lacey 2000, Connior and Risch 2010, Lopes and De Freitas 2012, Mapelli et al 2012). This study found that both mitochondrial and microsatellite data were informative in understanding population processes across a series of localities of G. breviceps in the Brazos Valley of eastern Texas.…”

Functional connectivity and home range inferred at a microgeographic landscape genetics scale in a desert‐dwelling rodent

“…Five loci (Ds1, Ds3, Ds19, Ds46, and DS98) deviated significantly from HWE after Bonferroni correction, while there was no evidence of LD (Bonferroni corrected p < 0.05); these same loci showed evidence of null alleles but not stuttering errors were detected. Because D. merriami's social structure encompasses groups of individuals with different degrees of relatedness (see relatedness results below), some evidence of deviation from HWE or null alleles is expected (i.e., not resulting from a systematic nonamplification of alleles; Bergl & Vigilant, 2007; Mapelli, Mora, Mirol, & Kittlein, 2012). Hence, all eight loci were included in the following analyses.…”

Landscape Genetics of Mammals in American Ecosystems

“…Studies involving rodents in America have been conducted mainly in areas with anthropogenic impact, including urban areas (Gardner-Santana et al 2009;Chambers and Garant 2010;Mapelli et al 2012;Munshi-South 2012;Marrotte et al 2014;Anderson et al 2015;Chiappero et al 2016;Combs et al 2018). On the other hand, we found only two works that were conducted with rodents in desert environments, specifically kangaroo rats in the Chihuahuan desert.…”

“…An aspect worth mentioning is the development of additional methods to assess connectivity in terms of the relationship between effective and genetic distances, like the generalized linear models, which prevailed in studies with rodents (Kittlein and Gaggiotti 2008;Mapelli et al 2012;Marrotte et al 2014;Cosentino et al 2015). We should also highlight the use of species distribution models for the construction of connectivity hypotheses in landscape genetics, as these combine presence data (localities where the species has been recorded) and climate data associated with that locality, also considering vegetation cover, topography, and other environmental variables (Rolland et al 2015).…”

“…With our revision we evidence that one of the most innovative aspects in landscape genetics studies is the use of remote sensing information to achieve a more realistic interpretation of the landscape studied. For instance, Mapelli et al (2012) used Landsat ETM+ sensor images to identify habitat patches in their study area and extract reflectance values of each, which were used to generate predictive variables. Thereby, the distance between good-quality patches was determined to be the most significant factor associated with the genetic structure patterns in Ctenomys porteousi.…”