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 GlobalUsefulNativeTrees database, matching species with those described in the RELMA-ICRAF Useful Tree and Shrub Species for Tanzania manual and checking for the availability of globally observed environmental ranges from the TreeGOER 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 means that the planting site occurs outside the 0% - 100% species's range for some of the variable Score = -1 means that the species is not documented by TreeGOER Locations corresponded to cities within the target countries sourced from the CitiesGOER 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: 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 GlobalUsefulNativeTrees for climate filtering. One set only included BIO01 (= Mean Annual Temperature), which is the same bioclimatic variables available from the BGCI Climate Assessment Tool. Calculations 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. 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 GlobalUsefulNativeTrees database. Species can be filtered for ten categories of documented human uses (see Diazgranados et al. 2020 for details): AF: Animal Food. EU: Environmental Uses. FU: Fuel. GS: Gene Sources. HF: Human Food. IF: Invertebrate Food. MA: Materials. ME: Medicines. PO: Poisons. SU: Social Uses Species can also be filtered for the Climatic Moisture Index (CMI). See this Zenodo archive (https://zenodo.org/records/8252756) 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). CMI.A (CMI ≥ 0.5 ; P >= 2 * PET; ‘extremely humid’ lands) CMI.B (0 ≤ CMI < 0.5 ; PET <= P < 2 * PET ; ‘very humid’ lands) CMI.C (−0.35 ≤ CMI < 0 ; 0.65 <= P/PET < 1 ; ‘humid’ lands) CMI.D ( −0.5 ≤ CMI < −0.35 ; 0.50 <= P/PET < 0.65 ; dry sub-humid drylands) CMI.E (−0.8 ≤ CMI < −0.5 ; 0.20 <= P/PET < 0.50 ; semi-arid drylands) CMI.F (−0.95 ≤ CMI < −0.8 ; 0.05 <= P/PET < 0.20 ; arid drylands) CMI.G (CMI < −0.95 ; P/PET < 0.05 ; hyper-arid drylands) References 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. (2023). 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. (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 Opendatasoft (2023) Geonames - All Cities with a population > 1000. https://public.opendatasoft.com/explore/dataset/geonames-all-cities-with-a-population-1000/information/?disjunctive.cou_name_en&sort=name (accessed 22-JULY-2023) Meteostat (2024) Weather stations: Lite dump with active weather stations. https://github.com/meteostat/weather-stations (accessed 17-FEB-2024) 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. https://knb.ecoinformatics.org/view/doi:10.5063/F1CV4G34 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.本数据集针对137种有用树种计算了气候适宜性评分：首先通过全球有用乡土树种数据库（GlobalUsefulNativeTrees）从坦桑尼亚联合共和国的乡土树种中筛选目标物种，匹配《RELMA-ICRAF坦桑尼亚有用乔灌木树种手册》中记载的物种，并核查TreeGOER数据库中记录的全球观测环境范围数据。 气候适宜性评分规则如下： - 评分3：所有生物气候变量对应的种植位点均处于该物种25%~75%的分布区间内（依据TreeGOER数据库记录） - 评分2：所有生物气候变量对应的种植位点均处于该物种5%~95%的分布区间内 - 评分1：所有生物气候变量对应的种植位点均处于该物种0%~100%的分布区间内 - 评分0：存在至少一项生物气候变量，其种植位点超出该物种0%~100%的分布区间 - 评分-1：该物种未被TreeGOER数据库收录 研究位点取自CitiesGOER数据库中的目标国家城市，该数据库提供了历史（基准期）及三种未来气候变化情景下的生物气候条件。未来气候的生物气候变量采用24个全球气候模型（Global Climate Model, GCM）在2050年代（2041-2060年）共享社会经济路径（Shared Socio-Economic Pathway, SSP）1-2.6情景下的中位数，21个GCM在2050年代SSP3-7.0情景下的中位数，以及13个GCM在2090年代SSP5-8.5情景下的中位数。 本研究设置了两组生物气候变量以开展敏感性分析： 1. 第一组变量包含BIO01、BIO12、气候湿度指数（Climatic Moisture Index, CMI）、monthCountByTemp10、growingDegDays5、BIO05、BIO06、BIO16、BIO17及MCWD，与GlobalUsefulNativeTrees数据库中用于气候筛选的内置生物气候变量一致。 2. 第二组变量仅包含BIO01（即年平均温度），与BGCI气候评估工具（BGCI Climate Assessment Tool）提供的生物气候变量一致。 本研究的计算流程采用R统计环境（R statistical environment）下的脚本管道完成，具体实现可参考：https://rpubs.com/Roeland-KINDT/1168650。这些脚本采用了与TreeGOER论文（Kindt, 2023）中全球案例研究及GlobalUsefulNativeTrees在线数据库内部计算方法一致的算法。感兴趣的读者可参考该论文以获取方法细节及合理性依据。 本数据集附带的地图展示了气候评分分别为3、2、1、0的树种的频率分布，排除了6种未被TreeGOER数据库收录的物种。 本Excel数据库支持通过GlobalUsefulNativeTrees数据库中的部分属性对有用树种进行筛选： 1. 可基于10类已记载的人类用途进行筛选（详细分类见Diazgranados等, 2020）： - AF：动物食物（Animal Food） - EU：环境用途（Environmental Uses） - FU：燃料（Fuel） - GS：基因资源（Gene Sources） - HF：人类食物（Human Food） - IF：无脊椎动物食物（Invertebrate Food） - MA：材料（Materials） - ME：医药用途（Medicines） - PO：毒物（Poisons） - SU：社会用途（Social Uses） 2. 也可基于气候湿度指数（CMI）进行筛选。可参考该Zenodo存档（https://zenodo.org/records/8252756）查看坦桑尼亚联合共和国境内CMI分区的分布情况：编码2代表物种分布区间上限覆盖该分区，编码9代表该分区处于物种分布区间中部，编码3代表物种分布区间下限覆盖该分区。 CMI分区定义如下： - CMI.A（CMI≥0.5；P≥2×PET；极端湿润区） - CMI.B（0≤CMI<0.5；PET≤P<2×PET；极湿润区） - CMI.C（−0.35≤CMI<0；0.65≤P/PET<1；湿润区） - CMI.D（−0.5≤CMI<−0.35；0.50≤P/PET<0.65；干燥半湿润旱地） - CMI.E（−0.8≤CMI<−0.5；0.20≤P/PET<0.50；半干旱旱地） - CMI.F（−0.95≤CMI<−0.8；0.05≤P/PET<0.20；干旱旱地） - CMI.G（CMI<−0.95；P/PET<0.05；超干旱旱地） 参考文献 1. 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. 2. Kindt, R. (2024). TreeGOER: Tree Globally Observed Environmental Ranges (2024.07) [数据集]. Zenodo. https://doi.org/10.5281/zenodo.13132613 3. 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. https://doi.org/10.1038/s41598-023-39552-1 4. Kindt, R. (2023). CitiesGOER: Globally Observed Environmental Data for 52,602 Cities with a Population ≥ 5000 (2023.10) [数据集]. Zenodo. https://doi.org/10.5281/zenodo.10004594 5. Kindt, R. (2024). ClimateForecasts: Globally Observed Environmental Data for 15,504 Weather Station Locations (2024.07) [数据集]. Zenodo. https://doi.org/10.5281/zenodo.12679832 6. 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 7. 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 8. Opendatasoft (2023) Geonames - All Cities with a population > 1000. https://public.opendatasoft.com/explore/dataset/geonames-all-cities-with-a-population-1000/information/?disjunctive.cou_name_en&sort=name (访问时间：2023年7月22日) 9. Meteostat (2024) Weather stations: Lite dump with active weather stations. https://github.com/meteostat/weather-stations (访问时间：2024年2月17日) 10. Diazgranados, M. et al. (2020). World checklist of useful plant species. 英国皇家植物园邱园（Royal Botanic Gardens, Kew）. https://knb.ecoinformatics.org/view/doi:10.5063/F1CV4G34 资助说明 本存档中的数据集与地图由美国国际开发署（USAID, U.S. Agency for International Development）向世界农林中心-国际林业研究中心（CIFOR-ICRAF）资助完成，具体项目为“生计与环境效益农田土地恢复”（On-farm Land Restoration for Livelihoods and Environmental Benefits）项目。