Corrigendum

Abstract

Methods in Ecology and EvolutionVolume 13, Issue 2 p. 528-530 CORRIGENDUMFree Access Corrigendum This article corrects the following: Redundancy analysis: A Swiss Army Knife for landscape genomics Thibaut Capblancq, Brenna R. Forester, Volume 12Issue 12Methods in Ecology and Evolution pages: 2298-2309 First Published online: October 18, 2021 First published: 24 December 2021 https://doi.org/10.1111/2041-210X.13782AboutSectionsPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat In the article by Capblancq and Forester (2021) ‘Redundancy analysis: A Swiss Army Knife for landscape genomics’, the function used to re-encode the genotypes from the lodgepole pine genomic datasets (C. Mahony et al., 2019; C. R. Mahony et al., 2020) contained an error. The authors have corrected the function and the core results and interpretations of the lodgepole pine data remain unchanged. There were some small changes to the results, including in the number of retained SNPs after filtering and the number of identified outlier loci, resulting in small changes in the following tables and figures: Table 1, Table 2, Figure 1 and Supporting information 3 and 4. The corrected tables and figures are given below and the R tutorial that accompanies the paper has been updated with the correct re-encoding function and with the new version of the results, tables and figures. The updated version of the R tutorial can be found at this link: https://github.com/Capblancq/RDA-landscape-genomics or from Zenodo (Capblancq & Forester, 2021). The journal editors and authors are satisfied that this error does not affect the results or conclusions of the article. The authors apologize for any confusion or inconvenience caused by this error. TABLE 1. Climatic variables identified as significantly associated with genetic variation using forward variable selection with RDA (redundancy analysis) Variables Cum F-value p-value MAR—Mean Annual solar Radiation 0.0222 0.022 7.35 0.002** EMT—Extreme Minimum Temp. 0.0223 0.045 7.53 0.002** MWMT—Mean Warmest Month Temp. 0.0099 0.054 3.92 0.002** CMD—Climatic Moisture Deficit 0.0087 0.063 3.56 0.002** Tavg wt—Winter Mean Temp. 0.0041 0.067 2.21 0.002** DD_18—Degree Days below 18°C 0.0030 0.070 1.88 0.002** MAP—Mean Annual Prec. 0.0017 0.072 1.52 0.002** Eref—Potential Evaporation (Hargreave) 0.0016 0.074 1.47 0.002** PAS—Prec. As Snow 0.0018 0.075 1.53 0.002** TABLE 2. The influence of climate, geography and neutral genetic structure on genetic variation decomposed with pRDA (partial redundancy analysis). Inertia is analogous to variance. The proportion of explainable variance represents the total constrained variation explained by the full model Partial RDA models Inertia R 2 p(>F) Proportion of explainablevariance Proportion of total variance Full model: F ~ clim. + geog. + struct. 84.6 0.146 0.001*** 1 0.17 Pure climate: F ~ clim. | (geog. + struct.) 22.5 0.039 0.001*** 0.27 0.05 Pure structure: F ~ struct. | (clim. + geog.) 17.5 0.030 0.001*** 0.21 0.04 Pure geography: F ~ geog. | (clim. + struct.) 4.2 0.007 0.003*** 0.05 0.01 Confounded climate/structure/geography 40.2 0.48 0.08 Total unexpected 408.8 0.83 Total inertia 493.4 1.00 FIGURE 1Open in figure viewerPowerPoint Results of the genotype–environment association using partial RDA (redundancy analysis). (a) The projection of loci and environmental variables along the first two RDA axes. The locus scores are rescaled to an unshown axis for better visibility. (b) A Manhattan plot with the distribution of −log10(p-values). Loci identified as outliers (p-value < 3.4 × 10−7 and the lowest p-value per contig) are coloured for both panels Supporting information 3: Venn diagram showing the degree of overlap between the genetic outliers identified with the unconstrained RDA-based genome scan and the outliers identified by Mahony and colleagues (2020) using Bayenv2 and a genotype–phenotype association (GPA) method Supporting information 4: Venn diagram showing the degree of overlap between the genetic outliers identified with the constrained or unconstrained RDA-based genome scans REFERENCES Capblancq, T., & Forester, B. R. (2021). Capblancq/RDA-landscape-genomics: v2.0.0 (v2.0.0). Zenodo, https://doi.org/10.5281/zenodo.5747827Google Scholar Mahony, C., MacLachlan, I., Lind, B., Yoder, J., Wang, T., & Aitken, S. (2019). Data from: Evaluating genomic data for management of local adaptation in a changing climate: A lodgepole pine case study. Dryad Digital Repository, https://doi.org/10.5061/dryad.56j8vq8Google Scholar Mahony, C. R., MacLachlan, I. R., Lind, B. M., Yoder, J. B., Wang, T., & Aitken, S. N. (2020). Evaluating genomic data for management of local adaptation in a changing climate: A lodgepole pine case study. Evolutionary Applications, 13(1), 116– 131. https://doi.org/10.1111/eva.12871Wiley Online LibraryPubMedWeb of Science®Google Scholar Volume13, Issue2February 2022Pages 528-530 FiguresReferencesRelatedInformation