Predicting Thermal Adaptation by Looking Into Populations' Genomic Past

Area of Science:

• Evolutionary Biology
• Genomics
• Climate Change Science

Background:

• Thermal adaptation is a key factor in species' responses to climate change.
• Genomic data offers insights into adaptation beyond simple range shifts.
• Distinguishing adaptive signals from other evolutionary processes is challenging.

Purpose of the Study:

• To review population genomic tools for studying thermal adaptation.
• To highlight advancements like genomic prediction and machine learning for forecasting.
• To bridge historical and predictive approaches for understanding adaptation.

Main Methods:

• Coalescent methods for inferring recent selection and demographic history.
• Quantitative genetic theory (breeder's equation) for short-term predictions.
• Genome-wide association studies (GWAS) and selection scans for detecting divergent selection.
• Genomic prediction models for multi-generational forecasting.
• Phylogeographic approaches for evolutionary time scales.
• Machine learning for long-term adaptive response predictions.

Main Results:

• Recent and past thermal adaptation signatures can be inferred using various genomic and modeling approaches.
• Genomic prediction and machine learning offer powerful tools for forecasting adaptive potential.
• Integrating different timescales and methods is crucial for robust predictions.

Conclusions:

• Forecasting future thermal adaptive responses requires integrating historical and predictive research.
• Modern cohesive approaches like genomic prediction and machine learning are essential.
• Bridging multiple spatial and temporal scales is key to understanding and predicting adaptation.