Data from: Local adaptation has a role in reducing vulnerability to climate change in a widespread Amazonian forest lizard
Data files
May 21, 2025 version files 2.08 GB
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bothGonatodes_branch5_stats.txt
39.93 KB
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bothGonatodes_branch5.geno
75.32 MB
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bothGonatodes_branch5.loci
835.32 MB
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bothGonatodes_branch5.usnps
6.10 MB
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bothGonatodes_branch5.vcf
1.16 GB
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README.md
2.60 KB
Abstract
The extant genetic variation within and among taxa reflects a long history of diversification and adaptive mechanisms in response to climate change and landscape alterations. However, the velocity of current anthropogenic changes poses an imminent threat to global biodiversity. Understanding how species and populations might respond to global climate change provides valuable information for conservation in the face of these impacts. Here, we use genomic data to look for candidate loci under climate selection and test for genetic vulnerability to climate change in a widespread Amazonian ombrophilous lizard. We found nine populations across Amazonia with a considerable amount of admixture among them. Distinct approaches of genome-environment association analyses recovered 56 candidate SNPs under climatic selection, recovering an east-to-west gradient in the adaptive landscape and showing a signal of local climate adaptation across the species range. According to our results, signals of local adaptation indicate that the species may not respond equally throughout its range, with some populations facing higher extinction risks. Genomic offset analysis predicts the south and central portions of Amazonia with higher vulnerability to future climate change. Our findings highlight the importance of considering spatially explicit contexts with large sampling coverage in evaluating how local adaptation and climatic vulnerability will affect Amazonian Forest ectothermic fauna.
Dataset DOI: 10.5061/dryad.cvdncjtfn
Description of the data and file structure
README – Data associated with the manuscript: "Local adaptation has a role in reducing vulnerability to climate change in a widespread Amazonian forest lizard"
Authors: André Yves, Josué A.R. Azevedo, Renata M. Pirani, Fernanda P. Werneck.
This dataset includes the output files generated after de-multiplexing and quality filtering of the genomic raw data (available at Dryad – DOI: 10.5061/dryad.ck223mq). These steps were performed using iPyRAD, and the resulting files were used in all subsequent downstream analyses (e.g., sNMF, GEA, genomic offset).
We used the SNPs file generated by iPyRAD and processed it in VCFtools v.0.1.13 to filter out singletons, retain only SNPs present in at least 95% of individuals, apply a minor allele frequency threshold (MAF = 0.05), and select one SNP per locus to minimize the effects of linkage disequilibrium. These filtering steps were implemented to reduce biases from sequencing errors and ensure the reliability of the data used in our population genomic and environmental association analyses.
For questions about the dataset or collaboration opportunities:
André Yves
andreyves7@gmail.com
National Institute of Amazonian Research
Files and variables
File: bothGonatodes_branch5_stats.txt
Description: Summary statistics of recovered loci de-multiplexed and filtered.
File: bothGonatodes_branch5.usnps
Description: Unlinked SNPs file, which contains one SNP sampled from each locus.
File: bothGonatodes_branch5.vcf
Description: This VCF format file includes full genotype information for all bases in all loci, including information about genotype quality. VCF is a standard format for storing and manipulating sequence data
File: bothGonatodes_branch5.geno
Description: SNP-based format file, in which each line corresponds to one SNP with one column per sample. The value in the sample column indicates the number of copies of the reference allele each individual has. 9 indicates missing data.
File: bothGonatodes_branch5.loci
Description: Each individual locus with variable sites indicated.
Code/software
To process the data, we used VCFtools v.0.1.13 for SNP filtering as described in our paper's methods section. All downstream analyses (e.g., sNMF, GEA, genomic offset) were conducted in R.
