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 s..., Samples from natural populations were collected for Douglas-fir (Pseudotsuga menziesii var glauca and P. menziesii var menziesii) and jack pine (Pinus banksiana). Exome capture probes were used and pooled sequencing of equimolar quantities of individual DNA were carried out at Centre d/expertise et de services Génome Québec., , # How useful is genomic data for predicting maladaptation to future climate?

[https://doi.org/10.5061/dryad.sxksn039h](https://doi.org/10.5061/dryad.sxksn039h)

The data in this archive is the genetic, environmental, and phenotypic data as well as model outcomes from the evaluation of genomic offset models from Lind et al. (2024; citation at end of README).

Raw sequence data has been deposited on NCBI's Sequence Read Archive under bioprojects PRJNA1079709 and PRJNA744263. Analysis code is available on Zenodo (which mirrors the GitHub repositories):

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Lind BM. 2024. GitHub.com/brandonlind/offset_validation: Publication release (Version 1.1.0). Zendodo (2023): DOI: https://doi.org/10.5281/zenodo.10708661

Lind BM. 2023. GitHub.com/brandonlind/douglas_fir_natural_populations: Offset Revision 1 (v1.0.0). Zenodo. https://doi.org/10.5281/zenodo.8018894

Lind BM. 2023. GitHub.com/brandonlind/jack_pine_natural_populations: Offset Revision 1 (v1.0.0). Zenodo. https://doi.org/10.5281/zenod...