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 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.sp..., The dataset consists of R scripts we used for the analysis as well as the training data in .csv format. The R script is separated into five .R files: YANAO_PGF_final_PP_test_paleo. The script contains predictive power and autocorrelation test; GLM_GBM_random_forest_GAM_fitting_paleoclimate. The script includes fitting of four models (GLM, GAM, GBM, Random forest) used for the analysis; cross-validation. Cross-validation of the model; spatial_projections_paleo. Spatial projections of the models. The data includes six csv files: YANAO_PGF_full_with_paleoclimate (contains all the sampled predictors we used for testing); YANAO_final_predictors_paleo (contains only predictors selected for model’s fitting) Cryo_db_main (sampled raw paleoclimatic data from CHELSA-TraCE21k dataset) paleoprecip_data_new (paleoprecipitation data prepared for tests) paleotemperature_data_new (paleotemperature data prepared for tests) distance_to_land_ice_data_new (distance to land ice data prepared fo..., 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 community-level plant species richness in the Western Siberian Arctic. Ecology and Evolution, 14, e11140. https://doi.org/10.1002/ece3.11140, # Supplementary data from: Current and past climate co-shape community-level plant species richness in the Western Siberian Arctic The dataset consists of R scripts we used for the analysis as well as the training data in .csv format. The R script is separated into four .R files: 1. **YANAO_PGF_final_PP_test_paleo.R** The script performing a test of univariate predictive performance and limited collinearity for the predictors. 2. **GLM_GBM_random_forest_GAM_fitting_paleoclimate.R**\ The script includes fitting of four models (GLM, GAM, GBM, Random\ forest) used for the analysis. We modeled species richness as a function of non-anthropogenic predictors using four different model algorithms: random forest, gradient boosting machine, generalized linear model, and generalized additive model. For RF, we fitted 500 regression trees, considering three predictors for each tree. For GBMs, we set the number of trees to 80, the minimum number of data points per leaf to 10, the learning ra...北极生态系统及其物种正面临加剧的气候变暖威胁，部分区域同时伴随快速发展的经济活动。我们采用宏观生态建模（macroecological modeling）方法，估算了西西伯利亚苔原的群落水平物种丰富度，其中气候变量与人为影响被确定为核心解释因子。本研究结果揭示了西西伯利亚北极地区群落水平物种丰富度的复杂空间分布格局。我们发现，温度（包括古温度）与降水等气候因子是该区域植物物种丰富度的主要驱动因素，地形起伏的作用则明显居于次要地位。 本研究配套提供本论文"Current and past climate co-shape community-level plant species richness in the Western Siberian Arctic"（DOI: 10.1002/ece3.11140）的补充分析数据集。本研究基于俄罗斯北极植被档案（Russian Arctic Vegetation Archive, AVA-RUS, http://avarus.sp...）的西西伯利亚分支构建，数据集包含分析所用的R脚本与逗号分隔值（Comma-Separated Values, CSV）格式的训练数据。 本次分析所用的R脚本共分为5个.R文件： 1. YANAO_PGF_final_PP_test_paleo.R：该脚本用于执行预测能力与自相关检验； 2. GLM_GBM_random_forest_GAM_fitting_paleoclimate.R：该脚本涵盖本研究所用的四类模型（广义线性模型（Generalized Linear Model, GLM）、广义可加模型（Generalized Additive Model, GAM）、梯度提升机（Gradient Boosting Machine, GBM）、随机森林（Random Forest））的拟合流程； 3. cross-validation.R：用于执行模型的交叉验证； 4. spatial_projections_paleo.R：用于执行模型的空间投影运算。 本数据集包含6个CSV格式文件： 1. YANAO_PGF_full_with_paleoclimate：包含测试所用的全部采样预测变量； 2. YANAO_final_predictors_paleo：仅包含经筛选用于模型拟合的预测变量； 3. Cryo_db_main：源自CHELSA-TraCE21k数据集的采样原始古气候数据； 4. paleoprecip_data_new：为测试制备的古降水数据； 5. paleotemperature_data_new：为测试制备的古温度数据； 6. distance_to_land_ice_data_new：为测试制备的距陆冰距离数据。 本套脚本均在R 4.1.2版本编程环境中编写与运行。可使用Microsoft Excel查看CSV格式文件。 本脚本与数据可免费用于非商业用途。引用本数据集时请务必标注原研究论文。 参考文献：Zemlianskii, V., Brun, P., Zimmermann, N. E., Ermokhina, K., Khitun, O., Koroleva, N., & Schaepman-Strub, G. (2024). Current and past climate co-shape community-level plant species richness in the Western Siberian Arctic. Ecology and Evolution, 14, e11140. https://doi.org/10.1002/ece3.11140 # 补充数据来自：Current and past climate co-shape community-level plant species richness in the Western Siberian Arctic 本数据集包含分析所用的R脚本与CSV格式的训练数据。 本次分析所用的R脚本共分为4个.R文件： 1. **YANAO_PGF_final_PP_test_paleo.R**：该脚本用于执行预测变量的单变量预测性能检验与共线性限制检验； 2. **GLM_GBM_random_forest_GAM_fitting_paleoclimate.R**：该脚本涵盖本研究所用的四类模型（广义线性模型（Generalized Linear Model, GLM）、广义可加模型（Generalized Additive Model, GAM）、梯度提升机（Gradient Boosting Machine, GBM）、随机森林（Random Forest））的拟合流程。我们以四种不同的模型算法，将物种丰富度建模为非人为影响预测变量的函数：随机森林、梯度提升机、广义线性模型与广义可加模型。其中随机森林拟合了500棵回归树，每棵树选取3个预测变量；对于梯度提升机，我们设置树的数量为80，单叶最小数据点数为10，学习率（learning rate）。