Predicting Thermal Adaptation by Looking Into Populations’ Genomic Past

Summary

This narrative review synthesises population genomic and quantitative genetic approaches for understanding and forecasting thermal adaptation in organisms. The authors bridge multiple temporal scales—from recent selection detected via coalescent methods, through intermediate-term patterns revealed by genome-wide association scans, to deep evolutionary histories reconstructed via phylogeography—and propose that genomic prediction and machine learning frameworks offer promising tools for predicting populations' responses to future thermal variation.

Regional applicability

The paper is methodological and global in scope rather than geographically specific. Its recommendations for integrating genomic prediction with climate change research are broadly applicable to crop and livestock breeding programmes in the United Kingdom and elsewhere, though specific validation would depend on the species and environmental context of interest.

Key measures

Genomic signatures of thermal selection; coalescent-inferred selective and demographic responses; genome-wide association and selection scan results; genomic prediction model outputs; phylogeographic patterns of thermal divergence

Outcomes reported

The study reviewed population genomic tools and methods for inferring thermal adaptation from genomic data across multiple temporal scales, from recent selective responses detectable via coalescent methods to evolutionary-timescale signatures identifiable through phylogeographic approaches. It synthesised approaches for predicting future thermal adaptive responses in populations using genomic prediction and machine learning frameworks.

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