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. 2024 Aug 8;14:18429. doi: 10.1038/s41598-024-69299-2

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© The Author(s) 2024

Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.

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Results of partial least squares regression: (A) relationship between real (features) and latent (factors) environmental covariates (biplot of the predictors’ and responses’ loadings considering the first two components); and (B) relationship between environments based on both environmental features and factors (biplot of predictors’ scores considering the first two components). FA1, FA2 and FA3 are the first, second and third factors, respectively. For the environmental features’ acronyms, see Table 1. In (A), colour scale represents the variable importance in projection (VIP), in percentage, of each environmental feature.