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. 2023 May 2;13:7156. doi: 10.1038/s41598-023-33993-4

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© The Author(s) 2023

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, 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 changes were made. 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/4.0/.

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(a) Principal component analysis (PCA) biplot of forest soil samples according to their physicochemical properties. The influence of each variable on sample distribution is represented by the arrows radiating from the center of the PCA plot. (b) The sample clusters corresponding to different ecoregions are highlighted within ellipses of the same color. (c) Pearson correlation plot between measured soil physicochemical properties. Positive and negative correlations are displayed in blue and red shades, respectively, while the size and intensity of matrix circles is proportional to correlation coefficient between variables.