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Baseline and Future (2050s and 2090s) Climate Suitability Scores for 137 Useful Tree Species and 273 locations from the United Republic of Tanzania

Climate suitability scores were calculated for 137 Useful Tree Species identified by filtering native tree species from the United Republic of Tanzania via the <a href="https://patspo.shinyapps.io/GlobalUsefulTrees/">GlobalUsefulNativeTrees</a> database, matching species with those described in the <a href="https://apps.worldagroforestry.org/usefultrees/">RELMA-ICRAF Useful Tree and Shrub Species for Tanzania manual</a> and checking for the availability of globally observed environmental ranges from the <a href="https://doi.org/10.5281/zenodo.13132613">TreeGOER</a> database. <ul> <li>Score = 3 means that in 'environmental space' the planting site occurs within the 25% - 75% species's range (as documented in the <a href="https://onlinelibrary.wiley.com/doi/full/10.1111/gcb.16914" target="_blank" rel="noopener">TreeGOER</a> ) for all variables</li> <li>Score = 2 corresponds to the 5% - 95% species's range for all variables</li> <li>Score = 1 corresponds to the 0% - 100% species's range for all variables</li> <li>Score = 0 means that the planting site occurs outside the 0% - 100% species's range for some of the variable</li> <li>Score = -1 means that the species is not documented by TreeGOER</li> </ul>   Locations corresponded to cities within the target countries sourced from the <a href="https://doi.org/10.5281/zenodo.10004594">CitiesGOER</a> database. This database provides bioclimatic conditions for the historical (baseline) and three future climate change scenarios. Bioclimatic variables for future climates correspond to the median values from 24 Global Climate Models (GCMs) for Shared Socio-Economic Pathway (SSP) 1-2.6 for the 2050s (2041-2060), from 21 GCMs for SSP 3-7.0 for the 2050s and from 13 GCMs for SSP 5-8.5 for the 2090s. Investigations were made for two different sets of bioclimatic variables, allowing for sensitivity analysis: <ul> <li>One set of bioclimatic variables included BIO01, BIO12, climaticMoistureIndex, monthCountByTemp10, growingDegDays5, BIO05, BIO06, BIO16, BIO17 and MCWD. These are the same bioclimatic variables available internally in the <a href="https://worldagroforestry.org/output/globalusefulnativetrees">GlobalUsefulNativeTrees</a> for climate filtering.</li> <li>One set only included BIO01 (= Mean Annual Temperature), which is the same bioclimatic variables available from the BGCI <a href="https://cat.bgci.org/">Climate Assessment Tool</a>.</li> </ul> Calculations were made with similar scripting pipelines in the R statistical environment as documented here: <a href="https://rpubs.com/Roeland-KINDT/1168650">https://rpubs.com/Roeland-KINDT/1168650</a>. These scripts use similar calculations methods as those used for the global case studies of the TreeGOER manuscript (Kindt <a href="https://onlinelibrary.wiley.com/doi/10.1111/gcb.16914">2023</a>), and used internally in the <a href="https://worldagroforestry.org/output/globalusefulnativetrees">GlobalUsefulNativeTrees</a> online database. Interested readers should especially refer to the manuscript for further details on methods used and their justification. The maps show the frequency distribution of tree species with climate scores 3, 2, 1 and 0, excluding 6 species not documented by the TreeGOER.   The Excel database allows filtering useful tree species by some of the attributes available in the <a href="https://patspo.shinyapps.io/GlobalUsefulTrees/">GlobalUsefulNativeTrees</a> database. Species can be filtered for ten categories of documented human uses (see <a href="https://kew.iro.bl.uk/concern/datasets/7243d727-e28d-419d-a8f7-9ebef5b9e03e">Diazgranados et al. 2020</a> for details): <ul> <li>AF: Animal Food.</li> <li>EU: Environmental Uses.</li> <li>FU: Fuel.</li> <li>GS: Gene Sources.</li> <li>HF: Human Food.</li> <li>IF: Invertebrate Food.</li> <li>MA: Materials.</li> <li>ME: Medicines.</li> <li>PO: Poisons.</li> <li>SU: Social Uses</li> </ul> Species can also be filtered for the Climatic Moisture Index (CMI). See this Zenodo archive (<a href="https://zenodo.org/records/8252756">https://zenodo.org/records/8252756</a>) to see the distribution of CMI zones across the United Republic of Tanzania. Codings refer to the species reaching the upper part of the range in the zone (code: 2), the zone being included in teh middle part of the ranage (code: 9) or the species reaching the lower part of the range in this zone (code:3). <ul> <li>CMI.A (CMI ≥ 0.5 ; P >= 2 * PET; ‘extremely humid’ lands)</li> <li>CMI.B (0 ≤ CMI < 0.5 ; PET <= P < 2 * PET ; ‘very humid’ lands)</li> <li>CMI.C (−0.35 ≤ CMI < 0 ; 0.65 <= P/PET < 1 ; ‘humid’ lands)</li> <li>CMI.D ( −0.5 ≤ CMI < −0.35 ; 0.50 <= P/PET < 0.65 ; dry sub-humid drylands)</li> <li>CMI.E (−0.8 ≤ CMI < −0.5 ; 0.20 <= P/PET < 0.50 ; semi-arid drylands)</li> <li>CMI.F (−0.95 ≤ CMI < −0.8 ; 0.05 <= P/PET < 0.20 ; arid drylands)</li> <li>CMI.G (CMI < −0.95 ; P/PET < 0.05 ; hyper-arid drylands)</li> </ul>   References <ul> <li>Kindt, R. (2023). TreeGOER: A database with globally observed environmental ranges for 48,129 tree species. Global Change Biology, 00, 1–16. <a href="https://onlinelibrary.wiley.com/doi/10.1111/gcb.16914">https://onlinelibrary.wiley.com/doi/10.1111/gcb.16914</a>.</li> <li>Kindt, R. (2024). TreeGOER: Tree Globally Observed Environmental Ranges (2024.07) [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.13132613" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.13132613</a></li> <li>Kindt, R., Graudal, L., Lillesø, JP.B. et al. (2023). GlobalUsefulNativeTrees, a database documenting 14,014 tree species, supports synergies between biodiversity recovery and local livelihoods in landscape restoration. Sci Rep 13, 12640. <a href="https://doi.org/10.1038/s41598-023-39552-1">https://doi.org/10.1038/s41598-023-39552-1</a></li> <li>Kindt, R. (2023). CitiesGOER: Globally Observed Environmental Data for 52,602 Cities with a Population ≥ 5000 (2023.10) [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.10004594" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.10004594</a></li> <li>Kindt, R. (2024). ClimateForecasts: Globally Observed Environmental Data for 15,504 Weather Station Locations (2024.07) [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.12679832" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.12679832</a></li> <li>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. <a href="https://doi.org/10.1002/joc.5086">https://doi.org/10.1002/joc.5086</a></li> <li>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. <a href="https://doi.org/10.1111/ecog.02880">https://doi.org/10.1111/ecog.02880</a></li> <li>Opendatasoft (2023) Geonames - All Cities with a population > 1000. <a href="https://public.opendatasoft.com/explore/dataset/geonames-all-cities-with-a-population-1000/information/?disjunctive.cou_name_en&sort=name">https://public.opendatasoft.com/explore/dataset/geonames-all-cities-with-a-population-1000/information/?disjunctive.cou_name_en&sort=name</a> (accessed 22-JULY-2023)</li> <li>Meteostat (2024) Weather stations: Lite dump with active weather stations. <a href="https://github.com/meteostat/weather-stations">https://github.com/meteostat/weather-stations</a> (accessed 17-FEB-2024)</li> <li>Diazgranados, M., Allkin, B., Black, N., Cámara-Leret, R., Canteiro, C., Carretero, J., Eastwood, R., Hargreaves, S., Hudson, A., Milliken, W. and Nesbitt, M., 2020. World checklist of useful plant species. Royal Botanic Gardens, Kew. <a href="https://knb.ecoinformatics.org/view/doi:10.5063/F1CV4G34">https://knb.ecoinformatics.org/view/doi:10.5063/F1CV4G34</a></li> </ul>   Funding The data sets and maps available in this archive were created through funding by the U. S. Agency for International Development (USAID) to CIFOR-ICRAF, here specifically in the context of the On-farm Land Restoration for Livelihoods and Environmental Benefits project.

ShareScore

28/100

Overall dataset sharing score

Score breakdown

These five areas show where the dataset supports — or may limit — practical reuse.

Stewardship: 4
Harmonization: 4
Access: 16
Reuse readiness: 0
Engagement: 4

Baseline and Future (2050s and 2090s) Climate Suitability Scores for 137 Useful Tree Species and 273 locations from the United Republic of Tanzania

Score breakdown

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