Matthew C. Fitzpatrick (1), Stephen R. Keller (2), Vikram Chhatre (3)
1 University of Maryland Centre for Environmental Science, Appalachian Lab, 301 Braddock Road, Frostburg, Maryland, 21532, USA.
2 Department of Plant Biology, 111 Jeffords Hall, 63 Carrigan Drive, University of Vermont, Burlington , VT 05405, USA.
3 Department of Plant Biology, 111 Jeffords Hall, 63 Carrigan Drive, University of Vermont, Burlington , VT 05405, USA.
A critical component of understanding and predicting biological responses to changes in climate is deciphering the underlying genetic basis of climate adaptation. Advances in molecular ecology and genomics are providing unparalleled, genome-‐wide insight into the molecular diversity present within organisms that can be used to identify gene-‐environment relationships and the molecular basis of climate adaptation at landscape scales. However, statistical frameworks that can translate these new genomic insights into spatially explicit predictions of adaptive variation under current and future climate have remained elusive. In this talk, we discuss how two relatively new biodiversity modeling techniques based on the concept of community-‐level compositional turnover functions – Generalized Dissimilarity Modeling and Gradient Forests – can be powerfully applied to analyzing and mapping range-‐wide climate adaptation, including the identification of predicted differences in the genetic composition of populations under current and future climate. Using balsam poplar as a case study and data on than 150,000 single nucleotide polymorphisms (SNPs), we demonstrate how these methods can (i) accommodate nonlinear responses of loci to environmental gradients, (ii) determine where along gradients changes in allele frequencies are most pronounced, and (iii) identify SNPs that are candidates for climate adaptation. More broadly, these methods represent an important advance in landscape genomics and spatial modeling of biodiversity that moves beyond species-‐level assessments of climate change vulnerability.