DOI: 10.5061/dryad.cjsxksn8j
title: Supplementary data from: Current and past climate co-shape community-level plant species richness in the Western Siberian Arctic

description: 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.
description: 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: 
 <strong>YANAO_PGF_final_PP_test_paleo.</strong> The script
 contains predictive power and autocorrelation test; 
 <strong>GLM_GBM_random_forest_GAM_fitting_paleoclimate.
 </strong>The script includes fitting of four models (GLM, GAM, GBM,
 Random forest) used for the analysis; 
 <strong>cross-validation</strong>. Cross-validation of the
 model; 
 <strong>spatial_projections_paleo</strong>. Spatial
 projections of the models. The data includes six csv files:
 
 <strong>YANAO_PGF_full_with_paleoclimate</strong> (contains
 all the sampled predictors we used for testing); 
 <strong>YANAO_final_predictors_paleo</strong> (contains only
 predictors selected for model’s fitting) <strong>Cryo_db_main
 </strong>(sampled raw paleoclimatic data from CHELSA-TraCE21k dataset) <strong>paleoprecip_data_new </strong>(paleoprecipitation data prepared for tests) <strong>paleotemperature_data_new</strong> (paleotemperature data prepared for tests) <strong>distance_to_land_ice_data_new</strong> (distance to land ice data prepared for tests) A detailed description of the methodology could be found in the paper.
description: The scripts were written and used in <strong>R programming
 environment</strong>, version 4.1.2. <strong>Microsoft
 Excel </strong>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. <em>Ecology and Evolution</em>, 14,
 e11140. https://doi.org/10.1002/ece3.11140
description: # 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 rate to 0.1 and the error distribution to ‘poisson’. For GLM and
 GAM we assumed a Poisson error distribution and used the ‘log’ link
 function. For GLMs, we defined linear and quadratic terms for each
 predictor. For GAMs, we used smooth terms with four degrees of freedom.
 For GLM and GAM, we step-wise optimized the Akaike information criterion
 by removing uninformative predictor terms from the model equation. 3.
 **cross-validation.R** Five-fold cross-validation of the model; 4.
 **spatial_projections_paleo.R** The scripts perform spatial projections of
 the models on a prepared raster stack of the selected predictors, as well
 as calculate the model means, the natural reserve shape file overlap, and
 display the model uncertainty map. The following R packages used in the
 scripts: gam (Hastie & Tibshirani, 1990), gbm (Friedman, 2001),
 randomForest (Breiman, 2001), raster (Hijmans et al., 2015), rgdal (Bivand
 et al., 2021), RColorBrewer (Neuwirth & Neuwirth, 2014), ecospat
 (Broennimann et al., 2014).\ \ The data includes six .csv files: 1.
 **YANAO_PGF_full_with_paleoclimate.csv** – is the main dataset, containing
 the field data (columns A-K) as well as the all the sampled predictor data
 we used for testing (columns L onwards). \ \ Sampled environmental
 variables, including abbreviations, are described in details Appendix
 Table S1. \ \ A. plots – plot names\ B. Vascular – number of vascular
 plant species\ C. Moss – number of moss species\ D. Liverwort – number of
 liverwort species\ E. Lichen – number of lichen species\ F. Other – number
 of algae or fungi species (not included in the total species count)\ G.
 Total  –  the full species richness of lichens, bryophytes and vascular
 plants\ H. Total_without_liverworts - the full species richness for each
 plot without liverworts. Main dependent variable of the models.\ I.
 Location – the site name.\ J. Latitude – decimal latitude\ K. Longitude –
 decimal latitude.\ \ The following columns contain sampled predictor
 data:\ \ L. BIO10_01_1 - Mean annual air temperature. Source: CHELSA
 Bioclim (Karger et al., 2016)\ M. BIO10_03 – Isothermality. Source: CHELSA
 Bioclim (Karger et al., 2016).\ N. BIO10_04_1 - Temperature seasonality.
 Source: CHELSA Bioclim (Karger et al., 2016)  \ O. bio10_09_1 - Mean daily
 mean air temperatures of the driest quarter. Source: CHELSA Bioclim
 (Karger et al., 2016)\ P. bio10_14_1 Precipitation amount of the driest
 month. Source: CHELSA Bioclim (Karger et al., 2016)\ Q. bio10_19_1 - Mean
 monthly precipitation amount of the coldest quarter. Source: CHELSA
 Bioclim (Karger et al., 2016).\ R. gdd_5_1979_2013_1 - Growing degree days
 heat sum above 5°C. Source: CHELSA new (Brun et al., 2022)\ S.
 Ist_januar_1 - Mean January temperature (K). Source: MODIS derived
 2000-2019 (MOD11A2 MODIS/Terr a Land Surface Temperature /Emissivity 8-
 Day L3 Global 1km SIN Grid V006 [Data set])\ T. Ist_juli_1 - Mean July
 Temperature (K). Source: MODIS derived 2000-2019 (MOD11A2 MODIS/Terr a
 Land Surface Temperature /Emissivity 8- Day L3 Global 1km SIN Grid V006
 [Data set]).\ U. modcf_intraanualsd_1. Source: CHELSA Bioclim (Karger et
 al., 2016).\ V. Altitude – altitude. ArcticDEM based (Morin et al., 2016)\
 W. Aspect - aspect calculated in QGIS. ArcticDEM based (Morin et al.,
 2016)\ X. Slope  - slope calculated in QGIS. ArcticDEM based (Morin et
 al., 2016)\ Y. log_slope – log transformed slope. ArcticDEM based (Morin
 et al., 2016)\ Z. MGTM - Mean annual ground temperatures. Source: Global
 Permafrost project (Obu, et al., 2019)\ AA. beam - Beam irradiance.
 ArcticDEM based (Morin et al., 2016)\ AB. Diff - Diffuse irradiance.
 ArcticDEM based (Morin et al., 2016)\ AC. Global - Global (total)
 irradiance. ArcticDEM based (Morin et al., 2016)\ AD. Inc - Incidence
 angle raster map. ArcticDEM based (Morin et al., 2016)\ AE. Wind – Mean
 wind speed. Source: Global Wind Atlas ([https://globa](https://globa)
 lwindatlas.info/). The data from AF-AO columns originates from CHELSA new
 dataset (Brun et al., 2022)\ \ AF. humidity_penman_max_1981-2010_V.2.1 -
 Climate moisture index max. Source: CHELSA new (Brun et al., 2022)\ AG.
 humidity_penman_mean_1981-2010_V.2.1 - Climate moisture index mean.
 Source: CHELSA new (Brun et al., 2022)\ AH.
 humidity_penman_min_1981-2010_V.2.1 - Climate moisture index range.
 Source: CHELSA new (Brun et al., 2022)\ AI. humidity_penman_range -
 Climate moisture index range. Source: CHELSA Bioclim (Karger et al.,
 2016).\ AJ. pet_penman_max_1981-2010_V.2.1 - Potential evapotranspiration
 max. Source: CHELSA new (Brun et al., 2022).\ AK.
 pet_penman_mean_1981-2010_V.2.1 - Potential evapotranspiration min.
 Source: CHELSA new (Brun et al., 2022)\ AL. pet_penman_min - Mean
 potential evapotranspiration. Source: CHELSA new (Brun et al., 2022).\ AM.
 pet_penman_range_1981-2010_V.2.1 Potential evapotranspiration range.
 Source: CHELSA new (Brun et al., 2022)\ AN. swb_1981-2010_V.2.1 - Site
 water balance. Source: CHELSA new (Brun et al., 2022).\ AO.
 tcc_max_1981-2010_V.2.1 - Cloud area fraction. Source: CHELSA new (Brun et
 al., 2022). AP. Tpi - Topographic position index. ArcticDEM based (Morin
 et al., 2016).\ AQ. Distance - distance between the plots and the closest
 infrastructure. OSM based ([https://www](https://www). openstreetm
 ap.org/)\ AR. NDVI - Sentinel 2 derived NDVI (mean June-August 2019-2020).
 The data from AS-BE columns originates from CHELSA Bioclim (Karger et al.,
 2016):\ \ AS. BIO10_02 - Mean diurnal air temperature range\ AT. BIO10_05
 - Mean daily maximum air temperature of the warmest month\ AU. BIO10_06 -
 Mean daily minimum air temperature of the coldest month\ AV. BIO10_07 -
 Annual range of air temperature\ AW. BIO10_08 - Mean daily mean air
 temperatures of the wettest quarter\ AX. BIO10_10 - Mean daily mean air
 temperatures of the warmest quarter\ AY. BIO10_11 - Mean daily mean air
 temperatures of the coldest quarter\ AZ. BIO10_12 - Annual precipitation
 amount\ BA. BIO10_13 - Precipitation amount of the wettest month\ BB.
 BIO10_15 - Precipitation seasonality\ BC. BIO10_16 - Mean monthly
 precipitation amount of the wettest quarter\ BD. BIO10_17 - Mean monthly
 precipitation amount of the driest quarter  \ BE. BIO10_18 - Mean monthly
 precipitation amount of the warmest quarter. The columns from BF onwards
 contain sampled raw paleoclimatic data from the CHELSA-TraCE21k dataset).
 Their content is described below (files 3-6).  \ \ Other CSV files are
 subsets of the main file, convenient for modeling (file 2) and testing
 (files 4-6), or preparatory steps to compile the main file (files 3).
 **2.      YANAO_final_predictors_paleo.csv** is the subset of the main
 dataset containing only the predictors selected for model fitting.
 **3.      Cryo_db_main.csv** raw paleoclimatic data from the\
 CHELSA-TraCE21k dataset) later integrated into the main dataset
 **YANAO_PGF_full_with_paleoclimate.csv **. The data was sampled using a
 publicly available R script
 ([https://github.com/jakobjassmann/cryo_db_v2](https://github.com/jakobjassmann/cryo_db_v2)). \ "LonDD" and "LatDD" columns contain latitude and longitude coordinates of geobotanical plots from the main dataset. The "year_kBP" column indicates time (unit is one hundred years), and the next five columns are five sampled variables (mean annual temperature, annual precipitation sum, paleo-elevation, distance to land ice, and maximum (latest) year in the time-series when the location was covered by land ice). The "#NAME?" values in "CHELSA_TraCE21k_max_year_kBP_glaciated" indicate the absence of glaciation within the corresponding time period. The following three files consist of selected data from the "Cryo_db_main" dataset in a form convenient for testing predictive power.\ \ They include: 4. paleoprecip_data_new.csv (paleoprecipitation data); 5. paleotemperature_data_new.csv (paleotemperature data); 6. distance_to_land_ice_data_new.csv (distance to land ice data). Numbers from 0 to 22 in columns F to HR represent age (how many hundreds of years ago) and contain sampled data for the variables. The first five columns are identical to the main csv file (**YANAO_PGF_full_with_paleoclimate.csv**). The scripts were written and used in R programming environment, version 4.1.2 \ **References:**\ \ Bivand, R., Keitt, T., & Rowlingson, B. (2021). *Rgdal: Bindings for the ‘Geospatial’ data abstraction library*. [https://cran.r-project.org/src/contrib/Archive/rgdal/](https://cran.r-project.org/src/contrib/Archive/rgdal/) Breiman, L. (2001). Random forests. *Machine Learning*, 45, 5–32. Broennimann, O., Di Cola, V., Petitpierre, B., Breiner, F., Scherrer, D., Manuela, D., Randin, C., Engler, R., Hordijk, W., Mod, H., & Pottier, J. (2014). Package ‘ecospat’. [https://cran.r-project.org/web/packages/ecospat/index.html](https://cran.r-project.org/web/packages/ecospat/index.html) Brun, P., Zimmermann, N. E., Hari, C., Pellissier, L., & Karger, D. N. (2022). Global climate-related predictors at kilometre resolution for the past and future. *Earth System Science Data Discussions*, 14, 5573–5603. [https://doi.org/10.5194/essd-2022-212](https://doi.org/10.5194/essd-2022-212) Friedman, J. H. (2001). Greedy function approximation: A gradient boosting machine. *The Annals of Statistics*, 29, 1189–1232. [https://doi.org/10.1214/aos/1013203451](https://doi.org/10.1214/aos/1013203451) Hastie, T. J., & Tibshirani, R. J. (1990). Generalized additive models. Chapman & Hall/CRC. Hijmans, R. J., Van Etten, J., Cheng, J., Mattiuzzi, M., Sumner, M., Greenberg, J. A., Lamigueiro, O. P., Bevan, A., Racine, E. B., Shortridge, A., & Hijmans, M. R. J. (2015). Package ‘raster’. R package, 734, 473. [ftp://h64-50-233-100.mdsnwi.tisp.static.tds.net/pub/cran/web/packages/raster/raster.pdf](ftp://h64-50-233-100.mdsnwi.tisp.static.tds.net/pub/cran/web/packages/raster/raster.pdf) Karger, D. N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R. W., Zimmermann, N. E., Linder, H. P., & Kessler, M. (2016). CHELSA climatologies at high resolution for the earth's land surface areas (Version 1.0). World Data Center for Climate (WDCC) at DKRZ. [https://doi.org/10.1594/WDCC/CHELSA_v1](https://doi.org/10.1594/WDCC/CHELSA_v1) Karger, D. N., Nobis, M. P., Normand, S., Graham, C. H., & Zimmermann, N. E. (2022). CHELSA-TraCE21k – high-resolution (1 km) downscaled transient temperature and precipitation data since the Last Glacial Maximum. *Climate of the Past*, 19(2), 439–456. [https://doi.org/10.5194/cp-19-439-2023](https://doi.org/10.5194/cp-19-439-2023) Morin, P., Porter, C., Cloutier, M., Howat, I., Noh, M. J., Willis, M., Bates, B., Willamson, C., & Peterman, K. (2016). ArcticDEM: A publically available, high resolution elevation model of the Arctic. *Egu general assembly conference abstracts*. Neuwirth, E., & Neuwirth, M. E. (2014). Package ‘RColorBrewer’.*ColorBrewer palettes*, *991*. ORNL DAAC. (2018). *MODIS and VIIRS land products global subsetting and visualization tool*. ORNL DAAC, Oak Ridge, TN. [https://modis.gsfc.nasa.gov/](https://modis.gsfc.nasa.gov/)