DOI: 10.5061/dryad.wwpzgmsxc
title: Patterns of genotype-environment association in the eastern North American yellow birch (<em>Betula alleghaniensis</em> Britt.)

description: Understanding how genomic adaptation shapes species’ responses to climate
 change is essential for developing climate-resilient forests, as shifting
 conditions increasingly drive range shifts and maladaptation. This study
 investigates adaptive genomic variation in Betula alleghaniensis (yellow
 birch), a widely distributed hardwood of eastern North America.
 Genome-wide SNP variation from 27 populations was analyzed using
 3D-genotype-by-sequencing and two genotype–environment association
 methods: redundancy analysis and Gradient Forests. A total of 124
 putatively adaptive loci were identified, linked to extreme minimum
 temperature, degree-days below 0°C, winter precipitation, and snowfall.
 Functional annotation revealed roles in stress response and
 transcriptional regulation. Patterns of adaptive variation showed a
 latitudinal gradient tied to winter severity and spatially heterogeneous
 responses to snowfall. Two distinct clusters of adaptive loci were
 identified along climate gradients, suggesting winter climate plays a
 dominant role in shaping local adaptation. Future climate projections
 (SSP5-8.5, 2041–2070) predict substantial shifts in adaptive alleles in
 the Northeastern Appalachians, Maritimes, and St. Lawrence River regions.
 Nevertheless, genetic offset across the range was relatively low,
 suggesting genomic resilience potentially supported by yellow birch’s
 autohexaploid genome and extensive gene flow, including adaptive
 introgression from hybridization with other Betula species. These findings
 support integrating genomic data into forest management.
description: # Patterns of genotype-environment association in the eastern North
 American yellow birch (*Betula alleghaniensis* Britt.) Dataset DOI:
 [10.5061/dryad.wwpzgmsxc](https://doi.org/10.5061/dryad.wwpzgmsxc) ##
 Description of the data and file structure This dataset was collected to
 investigate spatial patterns of genomic variation and climate adaptation
 in the eastern North American tree *Betula alleghaniensis* (yellow birch).
 Leaf tissue or seedlings were sampled from 27 populations across the
 species’ range, and genomic DNA was extracted and genotyped using a 3D-GBS
 protocol. The resulting SNP data were used to characterize population
 structure, identify climate-associated loci through multiple
 genotype–environment association methods, model adaptive genomic variation
 under current and future climate scenarios, and evaluate evolutionary
 capacity using standing genetic variation and population adaptive index
 metrics. The associated scripts reproduce all filtering, analysis, and
 mapping steps used in the study. ### Files and variables #### File:
 Cummins_etal._PolyRAD_GEA_R_code.txt **Description:** R code implementing
 the complete analysis pipeline used in the manuscript, including: *
 polyRAD filtering of paralogous loci * hybrid detection and removal *
 weighted-mean genotype calculation * PCA of population structure * dbMEM
 geographic structure modeling * climate variable processing * four GEA
 methods (RDA-R, RDA-X, GF-R, GF-X) * adaptive index computation under
 current and future climates * genetic offset estimation * SGV and PAI
 calculation\ Paths are generic and require user modification. #####
 Variables * Not applicable — this is a code file. #### Cummins: * et al.
 (2025) - Patterns of genotype-environment association in the eastern North
 American yellow birch (Betula alleghaniensis Britt.) ####: #### File:
 Cummins_etal_gene_ontology_code.txt **Description:** Unix-based workflow
 used to perform functional annotation of putatively adaptive loci.
 Includes commands for: * AUGUSTUS gene prediction * conversion to GFF3 and
 BED formats * BEDTools intersections between SNPs and predicted genes *
 eggNOG-mapper functional annotation\ Used to assign gene functions, GO
 terms, and pathways to climate-associated SNPs. ##### Variables * Not
 applicable — this is a code file. #### Cummins: * et al. (2025) - Patterns
 of genotype-environment association in the eastern North American yellow
 birch (Betula alleghaniensis Britt.) ####: #### File:
 second_filters_m4_p80_x0_S3.singleton.unlinked_100k_0.5.imputed_k1.vcf.gz
 **Description:** Compressed VCF file containing 32,295 high-confidence
 SNPs genotyped from 221 *Betula alleghaniensis* individuals using the
 3D-GBS protocol. This file was generated by the IBIS bioinformatics
 pipeline (adapter trimming, quality filtering, alignment to *Betula
 pendula*, SNP calling with STACKS) and represents the input dataset for
 all downstream filtering, GEA analyses, adaptive index calculations, and
 SGV/PAI metrics. **Variables (VCF fields):** * `#CHROM` — Chromosome or
 scaffold name * `POS` — Genomic position (base pair) * `ID` — SNP
 identifier * `REF` — Reference allele * `ALT` — Alternate allele(s) *
 `QUAL` — Phred-scaled quality score * `FILTER` — Filter status * `INFO` —
 Site-level metadata (e.g., `NS`, `AF`, `DP`) * `FORMAT` — Per-sample
 fields (e.g., `GT`, `AD`, `DP`, `GQ`, `GL`) * Individual columns —
 Hexaploid genotype calls or allele depths per sample **Missing values:**\
 Missing genotypes indicated by `./.` or `.` according to VCF 4.2
 specification. ## Code/software The primary genotype dataset is provided
 as a compressed VCF (`.vcf.gz`) and can be viewed with any VCF-compatible
 tool, including free/open-source software such as bcftools/htslib,
 vcftools, and IGV. The analyses can also be read directly in R using
 packages such as VariantAnnotation or vcfR. All downstream analyses are
 reproduced in the accompanying scripts: 1.
 **`Cummins_etal._PolyRAD_GEA_R_code.txt`** (R workflow)
 Cummins_etal._PolyRAD_GEA_R_code \ This script was executed in **R
 v4.4.1** and reproduces paralog filtering, hybrid removal, population
 structure PCA, climate processing, four genotype–environment association
 (GEA) methods, adaptive index mapping under current and future climates,
 genetic offset calculation, and SGV/PAI metrics.\ Key R packages used (all
 open source from CRAN/R-Forge/Bioconductor) include:\ `polyRAD`,
 `VariantAnnotation`, `adegenet`, `vegan`, `adespatial`, `igraph`, `dplyr`,
 `ggplot2`, `ggforce`, `terra`, `sf`, `raster`, `ggcorrplot`, `robust`,
 `qvalue`, `gradientForest`, `rjags`, `coda`, `VennDiagram`,
 `rnaturalearth`, `rnaturalearthdata`, `tibble`, `tidyr`.\ Users should
 adjust the generic file paths at the top of each section to match their
 local folder structure. 2. **`Cummins_etal_gene_ontology_code.txt`**
 (command-line ontology workflow) Cummins_etal_gene_ontology_code \ This
 file contains Unix command-line scripts used for gene prediction and
 functional annotation of climate-associated loci. Required free/open tools
 and versions: * **AUGUSTUS v3.5.0** (gene prediction) * **BEDTools
 v2.30.0** (SNP–gene intersection and nearest-gene assignment) *
 **eggNOG-mapper v2** (orthology-based functional/GO annotation)\
 Supporting standard Unix utilities used include `gffread`, `awk`, `sort`,
 and `bedtools closest/intersect`. Together, these scripts fully document
 the relationship between the raw SNP dataset and all derived results
 reported in Cummins et al. (2025). ## Access information Other publicly
 accessible locations of the data: * N/A Data was derived from the
 following sources: * N/A