Phenotypic drought stress prediction of European beech (Fagus sylvatica) by genomic prediction and remote sensing

Abstract

AbstractCurrent climate change species response models usually not include evolution. We integrated remote sensing with population genomics to improve phenotypic response prediction to drought stress in the key forest tree European beech (Fagus sylvaticaL.). We used whole-genome sequencing of pooled DNA from natural stands along an ecological gradient from humid-cold to warm-dry climate. We phenotyped stands for leaf area index (LAI) and moisture stress index (MSI) for the period 2016-2022. We predicted this data with matching meteorological data and a newly developed genomic population prediction score in a Generalised Linear Model. Model selection showed that addition of genomic prediction decisively increased the explanatory power. We then predicted the response of beech to future climate change under evolutionary adaptation scenarios. A moderate climate change scenario would allow persistence of adapted beech forests, but not worst-case scenarios. Our approach can thus guide mitigation measures, such as allowing natural selection or proactive evolutionary management.