2.7 Quantifying the relative impact of IBE on genomic differentiation
We used GDM to compare the importance of IBE to isolation by landscape barriers (IBB) or Pleistocene refugia (IBP) on patterns of genomic turnover, as detailed in Appendix 1. GDM is a matrix regression technique that evaluates the relationship between site-site dissimilarities in environmental or landscape ‘predictor’ variables and a biotic ‘response’ variables (e.g. pairwise genetic distances). A major advantage of GDM over other modelling methodologies is that it can fit non-linear relationships between environmental variables and the biological response variable through the use of I -spline basis functions (Ferrier et al., 2007). This approach can also incorporate a range of environmental data layers, resistance surfaces, and straight-line geographic distance as different predictors.
Pairwise dissimilarity in genomic composition between sites was modeled using two measures: 1) pairwise FST values and 2) a pairwise Bray-Curtis dissimilarity index based on the presence or absence of a SNP at each locus. IBE was represented by the set of 12 uncorrelated environmental variables described previously. In addition to these environmental variables, a set of predictor variables were generated to model the effect of landscape barriers (elevation and rivers) and hypothesized Pleistocene refugia under the Last Glacial Maximum (LGM) approximately 21,000 years ago. Pairwise resistance distances for IBB were generated by creating raster layers of resistance surfaces based on landscape features, elevation and rivers, using the raster calculator available in QGIS v.2.18. We then calculated pairwise resistance distances from these raster layers with CIRCUITSCAPE 4.0 (McRae et al., 2013). Two IBB matrices were generated, IBB1 and IBB2. For IBB1, resistance values increased with increasing elevation and rivers were treated as impenetrable. For IBB2, resistance increased with increasing elevation and also with Strahler order which reflects size and strength of perennial river systems. For IBP, we first projected habitat suitability for P. auritus under climate conditions during the LGM using two global climate models (MIROC and CCSM). We then created resistance surfaces where resistance was considered to be inversely proportional to habitat suitability, and finally, calculated pairwise resistance distances from this raster layer with CIRCUITSCAPE. Further details on how these predictor variables were generated can be found in Appendix 1. We ran four models for each genomic dataset with different configurations of these predictor variables: 1) IBE, IBB1, IBP-MIROC, 2) IBE, IBB1, IBP-CCSM, 3) IBE, IBB2, IBP-MIROC, 4) IBE, IBB2, IBP-CCSM.