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.