How useful is genomic data for predicting maladaptation to future climate?

Methods using genomic information to forecast potential population
maladaptation to climate change or new environments are becoming
increasingly common, yet the lack of model validation poses serious
hurdles toward their incorporation into management and policy. Here, we
compare the validation of maladaptation estimates derived from two methods
– Gradient Forests (GFoffset) and the Risk Of Non-Adaptedness (RONA) –
using exome capture pool-seq data from 35 to 39 populations across three
conifer taxa: two Douglas-fir varieties and jack pine. We evaluate
sensitivity of these algorithms to the source of input loci (markers
selected from genotype-environment associations [GEA] or those selected at
random). We validate these methods against two-year and 52-year growth and
mortality measured in independent transplant experiments. Overall, we find
that both methods often better predict transplant performance than
climatic or geographic distances. We also find that GFoffset and RONA
models are surprisingly not improved using GEA candidates. Even with
promising validation results, variation in model projections to future
climates makes it difficult to identify the most maladapted populations
using either method. Our work advances understanding of the sensitivity
and applicability of these approaches, and we discuss recommendations for
their future use.