Assessing uncertainty in genomic offset forecasts from landscape genomic models (and implications for restoration and assisted migration)

• Published in: Frontiers in ecology and evolution Vol. 11
• Main Authors: Lachmuth, Susanne, Capblancq, Thibaut, Keller, Stephen R., Fitzpatrick, Matthew C.
• Format: Journal Article
• Language: English
• Published: Frontiers Media S.A 19.06.2023
• Subjects: climate change; conservation genomics; donor and recipient importance; forest management; gradient forest; predictive ecological genomics
• ISSN: 2296-701X; 2296-701X
• DOI: 10.3389/fevo.2023.1155783

Introduction Ecological genomic models are increasingly used to guide climate-conscious restoration and conservation practices in the light of accelerating environmental change. Genomic offsets that quantify the disruption of existing genotype–environment associations under environmental change are a promising model-based tool to inform such measures. With recent advances, potential applications of genomic offset predictions include but are not restricted to: (1) assessing in situ climate risks, (2) mapping future habitat suitability while accounting for local adaptations, or (3) selecting donor populations and recipient areas that maximize genomic diversity and minimize maladaptation to future environments in assisted migration planning. As for any model-based approach, it is crucial to understand how arbitrary decisions made during the modeling process affect predictions and induce uncertainty. Methods Here, we present a sensitivity analysis of how various modeling components influence forecasts of genomic offset-based metrics, using red spruce ( Picea rubens ), a cool-temperate tree species endemic to eastern North America, as a case study. We assess the effects of genomic marker set, climatic predictor set, climate change scenario, and “not-to-exceed” offset threshold and evaluate how uncertainty in predictions varies across space. Results Climate change scenario induced by far the largest uncertainty to our forecasts; however, the choice of predictor set was also important in regions of the Southern and Central Appalachians that are of high relevance for conservation and restoration efforts. While much effort is often expended in identifying candidate loci, we found that genomic marker set was of minor importance. The choice of a maximum offset threshold to limit transfers between potential donor and recipient locations in assisted migration programs has mostly affected the magnitude rather than geographic variation in our predictions. Discussion Overall, our model forecasts suggest high climate change risks across the entire distributional range of red spruce and strongly underscore the potential for assisted migration to help ameliorate these risks. In that regard, populations in the Southern and Central Appalachians as well as along the US and Canadian east coast seem the best candidates for both in situ conservation and relocation.