Data from: Climatic suitability, isolation by distance and river resistance explain genetic variation in a Brazilian whiptail lizard

Oliveira, Eliana F.1; Martinez, Pablo A.2; São-Pedro, Vinicius A.; Gehara, Marcelo3; Burbrink, Frank T.; Mesquita, Daniel O.; Garda, Adrian A.; Colli, Guarino R.4; Costa, Gabriel C.

Published Oct 06, 2017 on Dryad. https://doi.org/10.5061/dryad.8v5p3

Data files

Oct 06, 2017 version files 241.62 KB

Abstract

Spatial patterns of genetic variation can help understand how environmental factors either permit or restrict gene flow and create opportunities for regional adaptations. Organisms from harsh environments such as the Brazilian semiarid Caatinga biome may reveal how severe climate conditions may affect patterns of genetic variation. Herein we combine information from mitochondrial DNA with physical and environmental features to study the association between different aspects of the Caatinga landscape and spatial genetic variation in the whiptail lizard Ameivula ocellifera. We investigated which of the climatic, environmental, geographical and/or historical components best predict: (1) the spatial distribution of genetic diversity, and (2) the genetic differentiation among populations. We found that genetic variation in A. ocellifera has been influenced mainly by temperature variability, which modulates connectivity among populations. Past climate conditions were important for shaping current genetic diversity, suggesting a time lag in genetic responses. Population structure in A. ocellifera was best explained by both isolation by distance and isolation by resistance (main rivers). Our findings indicate that both physical and climatic features are important for explaining the observed patterns of genetic variation across the xeric Caatinga biome.

Table S1

Table S1. Samples of Ameivula ocellifera used in the present study (336 samples). A map code number (see Figure 1) is presented for each sample, along with locality, state, institution of origin, voucher number, laboratorial code number, geographic coordinates (longitude and latitude in decimal degrees), and GenBank accession numbers.

Table S2

Table S2. Pairwise FST distances matrix (lower diagonal) and significant FST p-values (upper diagonal) for the 46 sampled localities of Ameivula ocellifera. Negative values in FST matrix were replaced with zeros to perform the analyses of genetic differentiation (more details in the “Genetic diversity and differentiation” section).

Table S4

Table S4. Climate and environmental values for each locality used to test genetic diversity in Ameivula ocellifera through linear regression or simultaneous autoregression analyses. Nucleotide diversity (π); climatic suitability from current and Last Glacial Maximum (LGM, 21 kyr) periods; isothermality (BIO3), temperature seasonality (BIO4), minimum temperature of coldest month (BIO6), annual mean temperature (BIO1), annual precipitation (BIO12), net primary productivity (NPP), actual evapotranspiration (AET), topographic complexity (TC), and distance from center of diffusion (DCD). Letters after BIO3, BIO4 and BIO6 variables represent current (C) and LGM (L) climate conditions.

Table S6

Table S6. Pairwise connectivity distance matrix for the 46 sampled localities of Ameivula ocellifera based on its current climatic suitability.

Table S7

Table S7. Pairwise connectivity distance matrix for the 46 sampled localities of Ameivula ocellifera based on its LGM climatic suitability.

Table S8

Table S8. Pairwise connectivity distance matrix for the 46 sampled localities of Ameivula ocellifera based on Caatinga main rivers.

Table S9

Table S9. Pairwise resistance distance matrix for the 46 sampled localities of Ameivula ocellifera based on Caatinga slope gradient.

Table S10

Table S10. Pairwise resistance distance matrix for the 46 sampled localities of Ameivula ocellifera based on Caatinga roughness gradient.

Script_GDM

R-script used to evaluate the contribution of environment and space in explaining genetic differentiation in Ameivula ocellifera using Generalized Dissimilarity Modelling (GDM).