Baseline and Future (Shared Socio-economic Pathways 1-2.6 and 3-7.0 for the 2050s) Climate Suitability Maps for 484 Useful Tree Species for Kenya

Climate suitability scores were calculated for 484 useful tree species included in the Useful trees and shrubs for Kenya or in the list of native useful tree species obtained from the GlobalUsefulNativeTrees database (Kindt et al. 2023a). After compiling the list of species, we checked afterwards for the availability of globally observed environmental ranges for these species from the TreeGOER database (Kindt 2023). Species with fewer than 10 observations in TreeGOER were excluded.

The climate scoring system is the same that is used in the GlobalUsefulNativeTrees database:

• Score = 3 means that in 'environmental space' the planting site occurs within the 25% - 75% species's range (as documented in the TreeGOER) for all variables
• Score = 2 corresponds to the 5% - 95% species's range for all variables
• Score = 1 corresponds to the 0% - 100% species's range for all variables
• Score = 0.5 means that the planting site occurs outside the 0% - 100% species's range for some of the variables, but for heat-related bioclimatic variables (used to produce the maps shown here: BIO01, monthCountByTemp10, growingDegDays5, BIO05 and BIO06) to be below the minimum (‘too cold but not too hot’) and for water-related bioclimatic variables (used here: BIO12, climaticMoistureIndex, BIO16, BIO17 and MCWD) to be above the maximum (‘too wet but not too dry’)
• Score = 0 means that the planting site occurs outside the 0% - 100% species's range for some of the variables

Climate scores were obtained for future climates (2050s: 2041-2060) from the median values from 24 Global Climate Models (GCMs) for Shared Socio-Economic Pathway (SSP) 1-2.6 and from 22 GCMs for SSP 3-7.0. Future and baseline bioclimatic layers were processed from raster layers obtained from WorldClim 2.1 at resolutions of 2.5 arc-minutes. Similar methods were used to obtain median values for the ClimateForecasts and CitiesGOER databases.

Calculations of climate scores were made with similar scripting pipelines in the R statistical environment as documented here: https://rpubs.com/Roeland-KINDT/1168650. These scripts use similar calculations methods as those used for the global case studies of the TreeGOER manuscript (Kindt 2023), and used internally in the GlobalUsefulNativeTrees online database. Interested readers should especially refer to the manuscript for further details on methods used and their justification.

Alternative future climate maps were obtained by calculating climate scores for each GCM and SSP separately, then counting the number of GCMs that projected that the species would be suitable under the future climatic conditions. A species was estimated to be suitable for a particular combination of GCM and SSP if its score was 1 or above. The maps distinguished areas where 33% or fewer of the GCMs predicted that the species would be suitable, and areas where 66% or more of the GCMs predicted that the species would be suitable. Those thresholds correspond to the Mastrandea et al. (2011) likelihood scale, which was adopted earlier in another climate change atlas (Kindt et al. 2023b; https://atlas.worldagroforestry.org/). A separate mapping category shows where 66% or more GCMs had a climate score of 2 or 3.

The maps include a red polygon showing the country outline of Kenya obtained from the GADM database. The first map for the baseline climate includes presence observations in the country obtained from the RAINBIO database (Dauby et al. 2016) and from the Global Biodiversity Information Facility (filtered from the occurrences that informed the TreeGOER database; GBIF.org 2021 GBIF Occurrence Download https://doi.org/10.15468/dl.77gcvq).

The MS Excel file contains columns that include information from World Flora Online, including hyperlinks to this online flora. Other columns indicate whether the species was included among assemblages of native useful tree species for the country in the GlobalUsefulNativeTrees database.

References

• Maundu P.M. & Tengnas T. 2005. Useful trees and shrubs for Kenya. World Agroforestry Centre. Accessed online X-2016 via http://www.worldagroforestry.org/usefultrees
• Kindt, R. (2023). TreeGOER: A database with globally observed environmental ranges for 48,129 tree species. Global Change Biology, 00, 1–16. https://onlinelibrary.wiley.com/doi/10.1111/gcb.16914.
• Kindt, R. (2024). TreeGOER: Tree Globally Observed Environmental Ranges (2024.07) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.13132613
• Kindt, R., Graudal, L., Lillesø, JP.B. et al. (2023a). GlobalUsefulNativeTrees, a database documenting 14,014 tree species, supports synergies between biodiversity recovery and local livelihoods in landscape restoration. Sci Rep 13, 12640. https://doi.org/10.1038/s41598-023-39552-1
• Kindt R, Abiyu A, Borchardt P, Dawson IK, Demissew S, Graudal L, Jamnadass R, Lillesø J-PB, Moestrup S, Pedercini F, Wieringa JJ, Wubalem T. 2023. The Climate change atlas for Africa of tree species prioritized for forest landscape restoration in Ethiopia: A description of methods used to develop the atlas. Working Paper No. 17. Bogor, Indonesia; and Nairobi, Kenya: Center for International Forestry Research and World Agroforestry (CIFOR-ICRAF). https://doi.org/10.17528/cifor-icraf/008977
• Kindt, R. (2023). CitiesGOER: Globally Observed Environmental Data for 52,602 Cities with a Population ≥ 5000 (2023.10) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.10004594
• Kindt, R. (2024). ClimateForecasts: Globally Observed Environmental Data for 15,504 Weather Station Locations (2024.07) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.12679832
• Fick, S. E., & Hijmans, R. J. (2017). WorldClim 2: New 1‐km spatial resolution climate surfaces for global land areas. International Journal of Climatology, 37(12), 4302–4315. https://doi.org/10.1002/joc.5086
• Title, P. O., & Bemmels, J. B. (2018). ENVIREM: An expanded set of bioclimatic and topographic variables increases flexibility and improves performance of ecological niche modeling. Ecography, 41(2), 291–307. https://doi.org/10.1111/ecog.02880
• Mastrandrea, M.D., Mach, K.J., Plattner, GK. et al. The IPCC AR5 guidance note on consistent treatment of uncertainties: a common approach across the working groups. Climatic Change 108, 675 (2011). https://doi.org/10.1007/s10584-011-0178-6
• Dauby G, Zaiss R, Blach-Overgaard A, Catarino L, Damen T, Deblauwe V, Dessein S, Dransfield J, Droissart V, Duarte MC, Engledow H, Fadeur G, Figueira R, Gereau RE, Hardy OJ, Harris DJ, de Heij J, Janssens S, Klomberg Y, Ley AC, Mackinder BA, Meerts P, van de Poel JL, Sonké B, Sosef MSM, Stévart T, Stoffelen P, Svenning J-C, Sepulchre P, van der Burgt X, Wieringa JJ, Couvreur TLP (2016) RAINBIO: a mega-database of tropical African vascular plants distributions. PhytoKeys 74: 1-18. https://doi.org/10.3897/phytokeys.74.9723

Funding

The development of this climate change atlas for Kenya was supported by the Bezos Earth Fund to the Quality Tree Seed for Africa in Kenya and Rwanda project, by the Wellcome Trust to the Visibilize4ClimateAction in East Africa Project: Visibilizing climate change impacts on nutrition and mental health among vulnerable populations in East African drylands to catalyse climate action at scale project and by the German International Climate Initiative (IKI) to the regional tree seed programme on The Right Tree for the Right Place for the Right Purpose in Africa.