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.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.

Methods
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 for tests)

A detailed description of the methodology could be found in the paper.