Data from: Eco-evolutionary genomics reveal mountain range-specific adaptation and intraspecific variation in vulnerability to climate change of alpine endemics
Data files
Mar 30, 2026 version files 69.95 MB
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geo_coor_bioclimatic.csv
4.71 KB
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gl_all_polymorphic_loci.rdata
67.29 MB
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README.md
6.48 KB
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tfreq2_1row.csv
2.66 MB
Abstract
Alpine chasmophytes exhibit intraspecific diversification due to range fragmentation during Holocene warming, complicating predictions of their climate vulnerability. A lack of understanding of eco-evolutionary mechanisms driving their response to climate change results in ineffective conservation efforts. To uncover the genomic basis of their diversification and explain spatial patterns of their vulnerability, we combine landscape genomics and species distribution modeling. Our model, the Campanula lehmanniana complex, occurs in three distinct central Asian mountain ranges, considered both a biodiversity hotspot and a vascular plant diversity darkspot. Genome-environment association confirmed the adaptive basis of intraspecific diversification, driven by numerous loci of small effect. Genomic and ecological data indicate mountain range-specific climate sensitivity driven by altitude, temperature, and precipitation. The cold-dry adapted group from Zeravshan-Hissar Mts will face niche decline but show a higher degree of preadaptation to future climate, while the temperate-humid group from Tian Shan shows an opposite response, with a higher risk of maladaptation despite predicted niche expansion. Maladapted populations at northern margins may require an influx of adaptive variation to cope with predicted changes. However, limited landscape connectivity between sky island habitats, combined with long migration distances required to minimize genotype-environment disruption, highlights the role of human-assisted migration in enabling evolutionary rescue. These results underscore the need to facilitate gene flow from pre- to maladapted populations and the importance of population-specific approaches to inform effective conservation strategies in heterogeneous mountain ecosystems. The results may be relevant to numerous Central Asian mountain species that show similar phylogeographic patterns.
Dataset DOI: 10.5061/dryad.4f4qrfjqb
Description of the data and file structure
Description: This README file describes the data accompanying the above-mentioned publication.
GENERAL INFORMATION
- Title of dataset
Supplementary Material associated with the article "Eco-evolutionary genomics unveils adaptive intraspecific diversification, differences in vulnerability to climate change, and low potential for the evolutionary rescue of alpine chasmophytes."
- Author Information
A. Corresponding Author Contact Information
Name: Ewelina Klichowska
ORCID: 0000-0001-9641-5750
Institution: Institute of Botany, Faculty of Biology, Jagiellonian University
Address: Gronostajowa 3, 30-387 Krakow, Poland
Email: ewelina.klichowska@uj.edu.pl
ResearchGate profile: https://www.researchgate.net/profile/Ewelina-Klichowska
B. Senior Author Contact Information
Name: Marcin Nobis
ORCID: 0000-0002-1594-2418
Institution: Institute of Botany, Faculty of Biology, Jagiellonian University
Address: Gronostajowa 3, 30-387 Krakow, Poland
Email: m.nobis@uj.edu.pl
ResearchGate profile: https://www.researchgate.net/profile/Marcin-Nobis
- Date of material collection: 2015-2019
- Geographic location of data collection: Zeravshan - Hissar Mts, Alai Mts, and Tian-Shan Mts; Central Asia; Tajikistan and Kirgistan
- Examined species: Campanula lehmanniana Bunge, C. lehmanniana var. capusii Franch., C. eugeniae Fed. (Campanulaceae)
- Funding:
National Science Centre, Poland (grant number 2018/29/B/NZ9/00313 to Marcin Nobis)
Data and Supplementary Materials
Files and variables
This repository contain three files: geo_coor_bioclimatic.csv, tfreq2_1row.csv, and gl_all_polymorphic_loci.rdata:
- The geo_coor_bioclimatic is a CSV file with population IDs, geographical coordinates and values of bioclimatic variables derived from WorldClim version 2.1 climate data for 1970-2000 (https://www.worldclim.org/data/bioclim.html) with spatial resolutions 30 seconds (~1 km2).
Below are the names of the variables included in the table (used in the analysis) along with the variable type and description.
IDs - Categorical - Population ID from which samples were collected.
lon - Numeric - Longitude where samples were collected in decimal degrees.
lat - Numeric - Latitude where samples were collected in decimal degrees.
group - Categorical - Identified groups adapted to the local climatic conditions of mountain ranges.
bio_1 - Numeric - Annual Mean Temperature.
bio_2 - Numeric - Mean Diurnal Range (Mean of monthly (max temp - min temp)).
bio_3 - Numeric - Isothermality (BIO2/BIO7) (×100).
bio_4 - Numeric - Temperature Seasonality (standard deviation ×100).
bio_5 - Numeric - Max Temperature of Warmest Month.
bio_6 - Numeric - Min Temperature of Coldest Month.
bio_7 - Numeric - Temperature Annual Range (BIO5-BIO6).
bio_8 - Numeric - Mean Temperature of Wettest Quarter.
bio_9 - Numeric - Mean Temperature of Driest Quarter.
bio_10 - Numeric - Mean Temperature of Warmest Quarter.
bio_11 - Numeric - Mean Temperature of Coldest Quarter.
bio_12 - Numeric - Annual Precipitation.
bio_13 - Numeric - Precipitation of Wettest Month.
bio_14 - Numeric - Precipitation of Driest Month.
bio_15 - Numeric - Precipitation Seasonality (Coefficient of Variation).
bio_16 - Numeric - Precipitation of Wettest Quarter.
bio_17 - Numeric - Precipitation of Driest Quarter.
bio_18 - Numeric - Precipitation of Warmest Quarter.
bio_19 - Numeric - Precipitation of Coldest Quarter.
- The tfreq2_1row is a CSV file with allele frequencies per population computed by using function 'makefreq' from 'adegenet' package in R.
The file contains allele frequencies for a randomly selected allele among two alleles at a given locus.
Population IDs according to 'geo_coor_bioclimatic.csv'.
- The gl_all_polymorphic_loci is an .RData file, which is a genlight object containing SNP data used in studies.
These files store genetic information on the single nucleotide polymorphism markers (SNPs) in Puccinellia pamirica. The datasets were generated by Genome-Wide Restriction Fragment Analysis via the DArTseq platform (Diversity Arrays Technology Pty Ltd, Canberra, Australia), which combines complexity reduction methods, fragment size selection, and high-throughput sequencing, optimised for a target organism.
The file contains data of 200,555 polymorphic loci for 247 individuals assigned to 31 populations (IDs according to 'geo_coor_bioclimatic.csv').
Code/software
Software necessary to run the file
To import, process and analyse these data files as objects of a class genlight the gl_all_polymorphic_loci.rdata we used R (version 4.2.2, 2022-10-31; https://www.R-project.org/) and RStudio (version 2022.12.0+353 "Elsbeth Geranium" Release (7d165dcf, 2022-12-03) for Windows; http://www.rstudio.com/) on Windows 10. We used the dartR R-package (version 2.9.7) with necessary dependencies in the R environment. You may also handle objects of a class genlight using the adegenet and ade4 R-packages. To learn more about the installation procedure and how to use the R-packages, visit: https://cran.r-project.org/web/packages/available_packages_by_name.html
Access information
Link to other publication connected with the data:
Nobis, M., Klichowska, E., Vintsek, L., Wróbel, A., Nobis, A., Zalewska-Gałosz, J., & Nowak, A. (2023). Evolutionary response of cold- adapted chasmophytic plants to Quaternary climatic oscillations in the Mountains of Central Asia (a world hotspot of biodiversity). Diversity and Distributions, 29, 1458–1477. https://doi.org/10.1111/ddi.13773
With associated data available via Figshare repository https://doi.org/10.6084/m9.figshare.21710519
We would appreciate it if you could also cite the article associated with this dataset when appropriate.
