DESCRIPTION OF DATA in “Global trends in grassland carrying capacity and relative stocking density of livestock” This file describes the provided data sets and illustrates how we use them. More details in R codes: https://github.com/jpiippon/cc_rsd_repo * The results of the article are based on R code following the tag vers_provide. Feel free to propose improvements to the code. INPUT FOLDER xxx refers to varying names of input files. MCD_mode2001_2015-xxx.tif * This data is used to define our study area. We first calculated mode value of MODIS land cover classes (MCD) between 2001 and 2015 in Google Earth Engine (GEE). Then we reclassified the data so that classes savannas, woody savannas and grasslands get value 1 and all other areas value 0. The GEE code used is provided in a separate file GEE_codes.txt. o see also merged file mcd_mode_0_or_1_res000449.tif in the Output folder and rast_processing_res000449.Rmd o “MODIS data and products acquired through the LP DAAC have no restrictions on subsequent use, sale, or redistribution.” see https://developers.google.com/earth-engine/datasets/catalog/MODIS_006_MCD12Q1?hl=en o Ref: Sulla-Menashe, D., & Friedl, M. A. (2018). User guide to collection 6 MODIS land cover (MCD12Q1 and MCD12C1) product. USGS: Reston, VA, USA, 1–18. https://doi.org/10.5067/MODIS/MCD12Q1.006 NPPyear-xxx.tif * MODIS NPP data MOD17A3HGF.006 downloaded from: https://developers.google.com/earth-engine/datasets/catalog/MODIS_006_MOD17A3HGF?hl=en#bands * The unit of NPP is originally kg * C /m2 but the MODIS webpage notifies original scaling factor of NPP to be 0.0001 and therefore values of this file should be divided by 10 000 to get the unit in kg * C /m2. o “MODIS data and products acquired through the LP DAAC have no restrictions on subsequent use, sale, or redistribution.” See: https://developers.google.com/earth-engine/datasets/catalog/MODIS_006_MOD17A3HGF?hl=en#terms-of-use o Ref: Running, S. W., & Zhao, M. (2019). MOD17A3HGF MODIS/Terra Net Primary Production Gap-Filled Yearly L4 Global 500 m SIN Grid V006. 2019, distributed by NASA EOSDIS Land Processes DAAC. 35. https://doi.org/10.5067/MODIS/MOD17A3HGF.006 Forest_yearxxx.tif * Tree canopy coverage data from: https://developers.google.com/earth-engine/datasets/catalog/NASA_MEASURES_GFCC_TC_v3?hl=en * see TreeCoverMultiplier in the main text and S2.4 in the Appendix, as well as GEE_codes.txt * Includes bottom 2.5%, median and top 97.5% TreeCoverMultiplier curves * First, we recoded the 30m*30m data based on Equation 2, then aggregated and resampled to MODIS resolution (500 m). * see merged file treecover_res000449.tif in the Output folder and rast_processing_res000449.Rmd * “Intellectual property rights to this dataset belong to University of Maryland, Department of Geographical Sciences and NASA. Usage is free if acklowedgement is made.” See: https://developers.google.com/earth-engine/datasets/catalog/NASA_MEASURES_GFCC_TC_v3?hl=en * Ref: Sexton, J. O., Song, X.-P., Feng, M., Noojipady, P., Anand, A., Huang, C., Kim, D.-H., Collins, K. M., Channan, S., DiMiceli, C., & Townshend, J. R. (2013). Global, 30-m resolution continuous fields of tree cover: Landsat-based rescaling of MODIS vegetation continuous fields with lidar-based estimates of error. International Journal of Digital Earth, 6(5), 427–448. https://doi.org/10.1080/17538947.2013.786146 TerraClimate_xxx.nc * Temperature Abatzoglou et al. (2018) downloaded from terraclim: https://www.nature.com/articles/sdata2017191 (We did not use GEE for temperature) * We disaggregated and resampled the original temperature data (see files TerraClimate_tmax_year.nc / TerraClimate_tmin_year.nc) into 500m (000449 degree) resolution and derived average annual temperature based on monthly values o see file temperature_res000449.tif in the Output folder and rast_processing_res000449.Rmd o Creative Commons Attribution Non-Commercial Share-Alike o Ref: Abatzoglou, J. T., Dobrowski, S. Z., Parks, S. A., & Hegewisch, K. C. (2018). TerraClimate, a high-resolution global dataset of monthly climate and climatic water balance from 1958–2015. Scientific Data, 5(1), 170191. https://doi.org/10.1038/sdata.2017.191 dtm_slope_merit.dem_m_250m_s0..0cm_2018_v1.0.tif * Data Amatulli et al. (2020) from: https://www.nature.com/articles/s41597-020-0479-6 * Expressed originally in degrees*100 but we converted them to slope% and reclassified as explained in the main text (Figure 1) and in the Appendix (see S2.7) o see file terrain_slope_res000449.tif in the output folder and rast_processing_res000449.Rmd o Creative Commons Attribution 4.0 International (CC-BY-4.0) o Ref: Amatulli, G., McInerney, D., Sethi, T., Strobl, P., & Domisch, S. (2020). Geomorpho90m, empirical evaluation and accuracy assessment of global high-resolution geomorphometric layers. Scientific Data, 7(1), 162. https://doi.org/10.1038/s41597-020-0479-6 5_Ct_2010_Da.tif 5_Sh_2010_Da.tif 5_Bf_2010_Da.tif 5_Ho_2010_Da.tif 5_Gt_2010_Da.tif 8_Areakm.tif * Gridded livestock of the world data. See Appendix S2.8 and https://www.fao.org/livestock-systems/global-distributions/en/ o see also file data_for_simulations.csv in the Output folder o Creative Commons Attribution 4.0 International (CC-BY-4.0) o Ref: Gilbert, M., Nicolas, G., Cinardi, G., Van Boeckel, T. P., Vanwambeke, S. O., Wint, G. R. W., & Robinson, T. P. (2018). Global distribution data for cattle, buffaloes, horses, sheep, goats, pigs, chickens and ducks in 2010. Scientific Data, 5, 180227. https://doi.org/10.1038/sdata.2018.227 ne_10m_admin_0_countries --> .cpg, .dbf, .prj, .shp, .shx * For country polygons we used 10m data from Natural Earth. See https://www.naturalearthdata.com/ reg_mollw.gpkg * regional macroregion polygons adapted from Kummu et al. (2010). * Ref: Kummu, M., Ward, P. J., Moel, H. de, & Varis, O. (2010). Is physical water scarcity a new phenomenon? Global assessment of water shortage over the last two millennia. Environmental Research Letters, 5(3), 034006. https://doi.org/10.1088/1748-9326/5/3/034006 OUTPUT FOLDER mcd_mode_0_or_1_res000449.tif * This data is used to define our study area. We first calculated mode value of MODIS land cover classes (MCD) between 2001 and 2015 in Google Earth Engine (GEE). Then we reclassified the data so that classes savannas, woody savannas and grasslands get value 1 and all other areas value 0. The GEE code used is provided in a separate file GEE_codes.txt. o 1 layer: “mcd” o Resolution of this dataset is “500 m” as documented in MODIS webpage (000449 degrees) o “MODIS data and products acquired through the LP DAAC have no restrictions on subsequent use, sale, or redistribution.” o Ref: Sulla-Menashe, D., & Friedl, M. A. (2018). User guide to collection 6 MODIS land cover (MCD12Q1 and MCD12C1) product. USGS: Reston, VA, USA, 1–18. https://doi.org/10.5067/MODIS/MCD12Q1.006 mcd_fraction_of_grassland_in_cell_5arcmin.tif * mcd_mode_0_or_1_res000449.tif aggregated and resampled to 5arcmin. This expresses a fraction of grassland that exist in each 5arcmin cell o “MODIS data and products acquired through the LP DAAC have no restrictions on subsequent use, sale, or redistribution.” o Ref: Sulla-Menashe, D., & Friedl, M. A. (2018). User guide to collection 6 MODIS land cover (MCD12Q1 and MCD12C1) product. USGS: Reston, VA, USA, 1–18. https://doi.org/10.5067/MODIS/MCD12Q1.006 treecover_res000449.tif * Tree canopy coverage data from: https://developers.google.com/earth-engine/datasets/catalog/NASA_MEASURES_GFCC_TC_v3?hl=en * see TreeCoverMultiplier in the main text and S2.4 in the Appendix, as well as GEE_codes.txt * Includes bottom 2.5%, median and top 97.5% TreeCoverMultiplier curves * First, we recoded the 30m*30m data based on Equation 2, then aggregated and resampled to MODIS resolution (500 m). * Resolution of this dataset is “500 m” as documented in MODIS webpage (000449 degrees) o 45 layers:"tc2001bot", "tc2001med", "tc2001up", "tc2002bot", "tc2002med", "tc2002up", "tc2003bot", "tc2003med", "tc2003up", "tc2004bot", "tc2004med", "tc2004up", "tc2005bot", "tc2005med", "tc2005up", "tc2006bot", "tc2006med", "tc2006up", "tc2007bot", "tc2007med", "tc2007up", "tc2008bot", "tc2008med", "tc2008up", "tc2009bot", "tc2009med", "tc2009up", "tc2010bot", "tc2010med", "tc2010up", "tc2011bot", "tc2011med", "tc2011up", "tc2012bot", "tc2012med", "tc2012up", "tc2013bot", "tc2013med", "tc2013up", "tc2014bot", "tc2014med", "tc2014up", "tc2015bot", "tc2015med", "tc2015up" * “Intellectual property rights to this dataset belong to University of Maryland, Department of Geographical Sciences and NASA. Usage is free if acklowedgement is made.” * Ref: Sexton, J. O., Song, X.-P., Feng, M., Noojipady, P., Anand, A., Huang, C., Kim, D.-H., Collins, K. M., Channan, S., DiMiceli, C., & Townshend, J. R. (2013). Global, 30-m resolution continuous fields of tree cover: Landsat-based rescaling of MODIS vegetation continuous fields with lidar-based estimates of error. International Journal of Digital Earth, 6(5), 427–448. https://doi.org/10.1080/17538947.2013.786146 temperature_res000449.tif * Temperature Abatzoglou et al. (2018) downloaded from terraclim: https://www.nature.com/articles/sdata2017191 (We did not use GEE for temperature) * We disaggregated and resampled the original temperature data (see files TerraClimate_tmax_year.nc / TerraClimate_tmin_year.nc) into 500m (000449 degree) resolution * Average annual temperature, see rast_processing_res000449.Rmd o 15 layers: "avgtemp_2001", "avgtemp_2002", "avgtemp_2003", "avgtemp_2004", "avgtemp_2005", "avgtemp_2006", "avgtemp_2007", "avgtemp_2008", "avgtemp_2009", "avgtemp_2010", "avgtemp_2011", "avgtemp_2012", "avgtemp_2013", "avgtemp_2014", "avgtemp_2015" o Creative Commons Attribution Non-Commercial Share-Alike o Ref: Abatzoglou, J. T., Dobrowski, S. Z., Parks, S. A., & Hegewisch, K. C. (2018). TerraClimate, a high-resolution global dataset of monthly climate and climatic water balance from 1958–2015. Scientific Data, 5(1), 170191. https://doi.org/10.1038/sdata.2017.191 terrain_slope_res000449.tif * Data Amatulli et al. (2020) from: https://www.nature.com/articles/s41597-020-0479-6 * Expressed originally in degrees*100 but we converted them to slope% and reclassified as explained in the main text (Figure 1) and in the Appendix (see S2.7) * Resolution of this dataset is “500 m” as documented in MODIS webpage (000449 degrees) * See rast_processing_res000449.Rmd o 1 layer: “slope” o Creative Commons Attribution 4.0 International (CC-BY-4.0) o Ref: Amatulli, G., McInerney, D., Sethi, T., Strobl, P., & Domisch, S. (2020). Geomorpho90m, empirical evaluation and accuracy assessment of global high-resolution geomorphometric layers. Scientific Data, 7(1), 162. https://doi.org/10.1038/s41597-020-0479-6 data_for_simulations.csv * This includes all the 0.004491576 resolution datasets we described above in 5 arc-minute resolution. We aggregated the abovementioned layers and resampled to 5 arc-minutes using gridded livestock of the world (GLW) datasets as template. That said, this csv file contains also gridded livestock estimates for different animal species as well as pixel areas in km-2. * See rast_processing_res000449.Rmd * Licences and references as presented above o 84 columns: "x”, “y”, npp2001", "npp2002","npp2003","npp2004","npp2005","npp2006","npp2007","npp2008", "npp2009","npp2010","npp2011","npp2012","npp2013","npp2014","npp2015","avgtemp_2001",”avgtemp_2002", "avgtemp_2003","avgtemp_2004","avgtemp_2005","avgtemp_2006","avgtemp_2007","avgtemp_2008", "avgtemp_2009","avgtemp_2010","avgtemp_2011","avgtemp_2012","avgtemp_2013","avgtemp_2014", "avgtemp_2015","tc2001bot", "tc2001med", "tc2001up","tc2002bot","tc2002med", "tc2002up" , "tc2003bot","tc2003med","tc2003up","tc2004bot","tc2004med","tc2004up","tc2005bot","tc2005med", "tc2005up","tc2006bot","tc2006med","tc2006up","tc2007bot","tc2007med","tc2007up","tc2008bot", "tc2008med","tc2008up", tc2009bot" , "tc2009med","tc2009up","tc2010bot","tc2010med","tc2010up", "tc2011bot","tc2011med","tc2011up","tc2012bot","tc2012med","tc2012up", “tc2013bot","tc2013med", "tc2013up","tc2014bot","tc2014med","tc2014up", "tc2015bot","tc2015med","tc2015up","slope","cattle","sheep" "buffalo","horse" ,"goat","pix_area_km2" simulation_results_n1000.csv * simulated data (n = 1000) with a resolution of 5 arc-minutes. This data, based on MODIS NPP, is used to produce maps presented in the main text. See Appendix for the uncertainty assessment and simulation.Rmd. * layers: "x", "y", "ab_med_2001", "ab_cv_2001", "cc_med_2001", "cc_cv_2001", "rsd_med_2001", "rsd_cv_2001", "au_pkm2_med_2001", "ab_med_2002", "ab_cv_2002", "cc_med_2002", "cc_cv_2002", "rsd_med_2002", "rsd_cv_2002", "au_pkm2_med_2002", "ab_med_2003", "ab_cv_2003", "cc_med_2003", "cc_cv_2003", "rsd_med_2003", "rsd_cv_2003", "au_pkm2_med_2003", "ab_med_2004", "ab_cv_2004", "cc_med_2004", "cc_cv_2004", "rsd_med_2004", "rsd_cv_2004", "au_pkm2_med_2004", "ab_med_2005", "ab_cv_2005", "cc_med_2005", "cc_cv_2005", "rsd_med_2005", "rsd_cv_2005", "au_pkm2_med_2005", "ab_med_2006", "ab_cv_2006", "cc_med_2006", "cc_cv_2006", "rsd_med_2006", "rsd_cv_2006", "au_pkm2_med_2006", "ab_med_2007", "ab_cv_2007", "cc_med_2007", "cc_cv_2007", "rsd_med_2007", "rsd_cv_2007", "au_pkm2_med_2007", "ab_med_2008", "ab_cv_2008", "cc_med_2008", "cc_cv_2008", "rsd_med_2008", "rsd_cv_2008", "au_pkm2_med_2008", "ab_med_2009", "ab_cv_2009", "cc_med_2009", "cc_cv_2009", "rsd_med_2009", "rsd_cv_2009", "au_pkm2_med_2009", "ab_med_2010", "ab_cv_2010", "cc_med_2010", "cc_cv_2010", "rsd_med_2010", "rsd_cv_2010", "au_pkm2_med_2010", "ab_med_2011", "ab_cv_2011", "cc_med_2011", "cc_cv_2011", "rsd_med_2011", "rsd_cv_2011", "au_pkm2_med_2011", "ab_med_2012", "ab_cv_2012", "cc_med_2012", "cc_cv_2012", "rsd_med_2012", "rsd_cv_2012", "au_pkm2_med_2012", "ab_med_2013", "ab_cv_2013", "cc_med_2013", "cc_cv_2013", "rsd_med_2013", "rsd_cv_2013", "au_pkm2_med_2013", "ab_med_2014", "ab_cv_2014", "cc_med_2014", "cc_cv_2014", "rsd_med_2014", "rsd_cv_2014", "au_pkm2_med_2014", "ab_med_2015", "ab_cv_2015", "cc_med_2015", "cc_cv_2015", "rsd_med_2015", "rsd_cv_2015", "au_pkm2_med_2015" * as we have GLW data only in 2010, au_per_km2 and rsd_ variables are valid only for 2010. We have these redundant variables here as the future versions of the GLW will have data for multiple years and one can easily simulate AGB and CC based on our simulation.Rmd script. However, only variables “rsd_med_2010" "rsd_cv_2010" "au_pkm2_med_2010 used in the analysis * aboveground biomass abbreviated here as ab lm_rast_5arcmin.tif * Linear trend for carrying capacity (CC) calculated using linear regression o 2 layers: "estimate" "p.value" o “MODIS data and products acquired through the LP DAAC have no restrictions on subsequent use, sale, or redistribution.” SUPPLEMENTARY FOLDER Sheet1_total_cc_in_AUs_2001_2015_with_Kendall_rank_correlation_countries.csv Sheet2_country_specific_data_based_on_median_cc_of_2008_2012.csv Sheet3_varying_grasslandarea_in_km2_2001_2015_countries.csv * See Figure 3b, Figure 5 and Figure S6. sensitivity_results_2010_n1000.csv * This file includes sensitivity analysis related to different variables. See Figure S8 in the Appendix and simulation.Rmd caraib_gswp3_nobc_hist_varsoc_co2_npp_global_monthly_1971_2010.nc4 caraib_princeton_nobc_hist_varsoc_co2_npp_global_monthly_1971_2012.nc4 caraib_watch-wfdei_nobc_hist_varsoc_co2_npp_global_monthly_1971_2010.nc4 dlem_gswp3_nobc_hist_varsoc_co2_npp_global_monthly_1971_2010.nc4 dlem_princeton_nobc_hist_varsoc_co2_npp_global_monthly_1971_2012.nc4 dlem_watch-wfdei_nobc_hist_varsoc_co2_npp_global_monthly_1971_2010.nc4 lpjml_gswp3_nobc_hist_varsoc_co2_npp_global_monthly_1971_2010.nc4 lpjml_princeton_nobc_hist_varsoc_co2_npp_global_monthly_1971_2012.nc4 lpjml_watch-wfdei_nobc_hist_varsoc_co2_npp_global_monthly_1971_2010.nc4 orchidee_gswp3_nobc_hist_varsoc_co2_npp_global_monthly_1971_2010.nc4 orchidee_princeton_nobc_hist_varsoc_co2_npp_global_monthly_1971_2012.nc4 orchidee_watch-wfdei_nobc_hist_varsoc_co2_npp_global_monthly_1971_2010.nc4 * Modelled NPP/ISIMIP2a based NPP combined in gather_isimip_data.Rmd * CC BY 4.0 * Ref: Reyer, C., Asrar, G., Betts, R., Chang, J., Chen, M., Ciais, P., Dury, M., François, L., Henrot, A.-J., Hickler, T., Ito, A., Jacquemin, I., Nishina, K., Mishurov, M., Morfopoulos, C., Munhoven, G., Ostberg, S., Pan, S., Rafique, R., … Büchner, M. (2019). ISIMIP2a Simulation Data from Biomes Sector (V. 1.1). https://doi.org/10.5880/PIK.2019.005 isimip_agb_1000sim.csv * This file includes simulated aboveground biomass based on modelled NPP data. See Appendix S2.10 for more details. * First we combined ISIMIP2a data in gather_isimip_data.Rmd and then simulated in simulate_isimip_agb.Rmd * We converted this to raster in figures_supplementary.Rmd * CC BY 4.0 * Ref: Reyer, C., Asrar, G., Betts, R., Chang, J., Chen, M., Ciais, P., Dury, M., François, L., Henrot, A.-J., Hickler, T., Ito, A., Jacquemin, I., Nishina, K., Mishurov, M., Morfopoulos, C., Munhoven, G., Ostberg, S., Pan, S., Rafique, R., … Büchner, M. (2019). ISIMIP2a Simulation Data from Biomes Sector (V. 1.1). https://doi.org/10.5880/PIK.2019.005