Current climate change species response models usually do not include evolution. We integrated remote sensing with population genomics to improve phenotypic response prediction to drought stress in the key forest tree species European beech (Fagus sylvatica L.). 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 the 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.

The sites selected for this study were in many regards typical for German beech forests. They comprised a large ecological amplitude with regard to long-term conditions. Within the range from humid-cold to warm-dry climatic conditions, important demographic and genetic processes in beech forests are taking place. Therefore, the aim of selection and stratification was to cover the climatic spectrum of forested areas in central and southern Germany to a large extent. For the characterisation of the climatic niche, we used the climatic marginality towards the rear edge, i.e. the dry and warm border of species distributions.

Construction of population pools, sequencing, population structure and genetic diversity

From each tree, 1-2 buds or 3-4 leaf discs of 0.5 mm diameter (approx. 50 mg of fresh plant material) were dried in Silicagel prior to homogenization. DNA was extracted using an in-house protocol. We constructed DNA pools per population using the same DNA quantity per individual beech. DNA concentration was measured using a Quantus fluorometer (Promega). Library preparation and 150bp paired-end sequencing with 450bp insert was conducted at Novogene.

Reads were trimmed using Trimmomatic v.0.39 and quality controlled with FastQC v.0.11.9. We used BWA mem v.0.7.17 to map the reads onto the newest version of the beech reference genome and Samtools v.1.10 to convert, sort and pile up the bam files. Duplicates were marked and removed with Picard v.2.20.8 (https://github.com/broadinstitute/picard). PoPoolation2 v.2.201 pipeline was used to remove indels, and calculated allele frequencies for every position and pairwise FSTs in non-overlapping 1kb windows. Genetic variation (ϴ) and nucleotide diversity (π) were estimated in the same windows with PoPoolation1 v.1.2.2. We considered only sites within a coverage range of 15-50X.