Sampling effect in predicting the evolutionary response of populations to climate change

Area of Science:

• Genomics and Bioinformatics
• Conservation Biology
• Climate Change Ecology

Background:

• Genomic data and machine learning identify adaptive genetic variation and assess species vulnerability to climate change.
• Gene-environment associations project future genetic composition changes (genetic offsets) indicating population maladaptation.
• Genetic offsets can inform conservation priorities, but their sensitivity to sampling intensity is unclear.

Purpose of the Study:

• To evaluate the sensitivity of genetic offset estimations to varying population and individual sampling intensities.
• To determine optimal sampling strategies for robust genetic offset calculations in conservation.

Main Methods:

• Utilized five genomic datasets with diverse single nucleotide polymorphism (SNP) counts, sampled populations, and individuals.
• Assessed genetic offset estimation sensitivity across different sampling intensities.
• Analyzed the impact of population structure and future climate scenarios on estimation accuracy.

Main Results:

• Genetic offsets are highly sensitive to the number of populations sampled, particularly with fewer than 10 populations or high genetic structure.
• The number of individuals sampled per population has minimal impact; robust results are achieved with five or more individuals.
• Uncertainty from future climate scenarios slightly increases estimation uncertainty in genetic offsets.

Conclusions:

• Sampling efforts for genetic offset estimation should prioritize increasing the number of sampled populations over individuals per population.
• Evaluating multiple future climate scenarios is recommended to ensure robust genetic offset estimations.
• Findings provide guidance for optimizing genomic data collection for effective conservation and management strategies.