DOI: 10.5061/dryad.05qfttfdd
title: Data and code from: Adaptive potential and genomic vulnerability of keystone forest tree species to climate change: A case study in Scots pine

description: A better understanding of the possible adaptive response and genomic
 vulnerability of forest trees is needed to properly assist future forest
 management and develop adequate resilience strategies to changing
 environments. Scots pine (Pinus sylvestris L.), a keystone species with
 extensive distribution and a broad ecological niche, is expected to be
 directly impacted by climate change due to fitness loss and genetic
 maladaptation on a large spatial scale. Despite extensive studies that
 have clarified the broad-scale history and genetic structure of the
 species, understanding the genetic basis for the local adaptation and
 genomic vulnerability of Scots pine remains incomplete. Here, we used
 thousands of genotyped SNP markers in 39 natural populations (440 trees)
 along a broad latitudinal gradient of species distribution to examine
 molecular signatures of local adaptation. Specifically, this landscape
 genomics approach aimed to assess fine-scale patterns of SNPs associated
 with environmental gradients, predict vulnerability to climate change
 using genomic offset, and evaluate the adaptive response of populations to
 projected climate shifts. The variation of outlier SNPs, which exhibits
 selection signatures between genetically very similar populations in the
 analysed distribution range, was highly correlated with mean temperature,
 a key limiting factor for the growth and survival of tree species.
 Furthermore, our simulation results indicated a high genomic vulnerability
 on a large spatial scale in P. sylvestris, with the time frame required to
 close the offset gap by natural selection estimated to be in the range of
 hundreds of years. The study evaluates current genomic offset and
 indicates the optimal allelic frequency spectra to ensure resilience of
 Scots pine populations. It highlights the potential of forest assisted
 migration (FAM) as a management strategy, involving the relocation of
 genotypes to areas with matching environmental conditions. However,
 empirical validation through progeny tests of provenance regions and
 careful selection of source populations are crucial before implementation.
 By evaluating adaptive responses, the study adds to the discussion on the
 long-term sustainability of forest ecosystems in the face of ongoing
 environmental change.
description: # Data and code from: Adaptive potential and genomic vulnerability of
 keystone forest tree species to climate change: A case study in Scots pine
 [https://doi.org/10.5061/dryad.05qfttfdd](https://doi.org/10.5061/dryad.05qfttfdd) ## Dataset Description This dataset includes SNP genotypic data for 440 individuals of Scots pine (Pinus sylvestris) sampled from 39 populations across Europe. The dataset is structured in PLINK format, comprising `.map` and `.ped` files, and was generated using a custom Axiom PineGAP SNP array. ## Sampling Information We sampled 39 populations covering a broad latitudinal range (49.1 °N-69.8 °N) to capture the environmental variation within the species distribution. Populations range from northern Finland (FIN1-21) to southern Poland (POL1-15) and include three populations from the Baltic Region (LVA, LTU, EST). Needles were collected from 5 to 23 mature trees per population, with a median sample size of 10 trees per population, ensuring a minimum distance of 50 m between sampled individuals. ## SNP Genotyping and Data Processing * **DNA Extraction:** Genomic DNA was extracted from needles using the Genomic Mini AX Plant kit (A&A Biotechnology, Poland) following the manufacturer’s instructions. * **DNA Quantification:** DNA concentration was measured using a Qubit 4 fluorometer with the Broad Range (BR) Assay Kit and diluted to a working concentration of 40 ng/µl. * **Genotyping Platform:** Samples were genotyped using a custom Axiom PineGAP SNP array (Affymetrix, Thermo Fisher Scientific, Santa Clara, CA, USA), designed based on targeted sequencing of candidate genes and transcriptomes from multiple pine species. * **SNP Selection:** A total of 49,829 SNPs were initially included in the array, ensuring wide genomic coverage across different linkage groups in Scots pine. * **Genotype Calling:** Axiom Analysis Suite software (Applied Biosystems, Waltham, MA, USA) was used for genotype calling, with quality filters set to: * QC call_rate: 95% * Average cut-rate for passing samples: 95% * Cr-cutoff: 95% ## Data Filtering and Quality Control After initial genotype calling, the following filtering steps were applied using PLINK v.1.07 (Purcell et al., 2007): 1. Removal of 93 SNPs mapping to Pinus taeda mitochondrial genome (NC_039746.1) and Pinus sylvestris chloroplast genome (NC_035069.1). 2. Exclusion of SNPs with minor allele frequency (MAF) &lt; 0.01. 3. Removal of loci and individuals with more than 10% missing data. 4. The final dataset contains 10,597 high-confidence SNPs. 5. For population structure analyses (PCA, LEA), linkage disequilibrium (LD) pruning was applied to exclude SNPs with LD r² &gt; 0.7, resulting in a subset of 6,995 SNPs. ### Files and variables #### File: Dataset_Łabiszak_Wachowiak.zip * **`dataset.ped`**: Pedigree file containing SNP genotypic data for all sampled individuals. * **`dataset.map`**: Map file providing SNP marker positions. * **`Allele_shift_simulations.R`**: custom R script for calculation Allele Shift Simulations ## Code/software The script is designed to provide a lower-bound estimate of the time required for allele frequency changes under selection pressure, ignoring factors like dominance effects and effective population size, which would only extend the time required for change. he allele shift simulation script (`Allele_shift_simulations.R`) executes the following steps: 1. **Define Parameters:** The script sets a range of selection coefficients (0.1 - 0.9) and applies the basic population genetic formula for allele frequency change per generation. 2. **Simulation of Allele Frequency Change:** Using the formula Δp = (sp0q02) / (1 - s *q02 ), the script calculates allele frequency shifts over multiple generations. 3. **Conversion to Calendar Years:** The script converts generational shifts into calendar years, assuming an average generation time of 20-25 years for Scots pine. 4. **Visualization:** The results are visualized using R plotting functions, illustrating the rate of allele frequency changes across different selection pressures.