Supplementary data from: Current and past climate co-shape community-level plant species richness in the Western Siberian Arctic

The Arctic ecosystems and their species are exposed to amplified climate warming and, in some regions, to rapidly developing economic activities. We used macroecological modeling to estimate the community-level species richness across the Western Siberian tundra, with climate variables and anthropog...

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Bibliographic Details
Main Authors: Zemlianskii, Vitalii, Brun, Philipp, Zimmermann, Niklaus, Ermokhina, Ksenia, Khitun, Olga, Koroleva, Natalia, Schaepman-Strub, Gabriela
Format: Other/Unknown Material
Language:unknown
Published: Zenodo 2024
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Online Access:https://doi.org/10.5061/dryad.cjsxksn8j
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Summary:The Arctic ecosystems and their species are exposed to amplified climate warming and, in some regions, to rapidly developing economic activities. We used macroecological modeling to estimate the community-level species richness across the Western Siberian tundra, with climate variables and anthropogenic influence identified as main explanatory factors. Our results reveal complex spatial patterns of community-level species richness in the Western Siberian Arctic. We show that climatic factors such as temperature (including paleotemperature) and precipitation are the main drivers of plant species richness in this area, and the role of relief is clearly secondary. Here we present a supplementing dataset to the analysis of our paper "Current and past climate co-shape community-level plant species richness in the Western Siberian Arctic" ( https://doi.org/10.1002/ece3.11140 ). Our research is based on the Western Siberian part of the Russian Arctic Vegetation Archive (AVA-RUS, http://avarus.space ), with 1483 Braun-Blanquet plots observed from 2005-2018. The dataset contains geolocated species richness data along with sampled raster data on environmental and anthropogenic predictors used for modeling. The scripts are used for paleoclimatic data sampling; testing univariate predictive performance and limited collinearity for all predictors; fitting four different modes: random forest, gradient boosting machine, generalized linear model, and generalized additive model; their validation and projection. Detailed information regarding the data structure and the applied methods could be found in the paper. The scripts were written and used in R programming environment , version 4.1.2. Microsoft Excel can be used to view csv files. The scripts and data are free for non-commercial use. We kindly request to cite the original publication while referencing them. Zemlianskii, V., Brun, P., Zimmermann, N. E., Ermokhina, K., Khitun, O., Koroleva, N., & Schaepman-Strub, G. (2024). Current and past climate co-shape ...