DOI: 10.5061/dryad.cvdncjtfn
title: Data from: Local adaptation has a role in reducing vulnerability to climate change in a widespread Amazonian forest lizard

description: 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.
description: # Local adaptation has a role in reducing vulnerability to climate change
 in a widespread Amazonian forest lizard Dataset DOI:
 [10.5061/dryad.cvdncjtfn](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](mailto: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.