This file was generated on 2025-04-09 by MAUREEN NABATANZI A GENERAL INFORMATION 1. Title of the dataset: Data for Modeling the potential distribution of Wesselsbron, Sindbis, and Middelburg viruses and their vectors in Africa under future climatic and land-use changes 2. Brief description of the research project and its aims: This dataset comprises ecological variables and species presence points used to develop current and future species distribution models for Wesselsbron, Sindbis, and Middelburg viruses and five mosquito vectors (Aedes circumluteolus, Aedes mcintoshi, Culex univittatus, Culex pipiens and Mansonia africana). Ecological data include 19 Bioclimatic variables, Normalized Difference Vegetation Index, Built-Up areas, Settlement model grid, Human population, Forested areas, Livestock density, and Croplands. Two time periods were selected, current (reference year 2015) and future (2021 – 2040); current bioclimatic comprised data collected over 1970 – 2000. For the future, we utilized the Intergovernmental Panel on Climate Change’s Shared Socioeconomic Pathways (SSPs), which are projections of future greenhouse gas emissions and climate. We chose SSP2-4.5 for moderate and SSP5-8.5 for severe conditions. Based on the Coupled Model Intercomparison Project Phase 6, we selected two Global Climate Models (GCM), IPSL - CM6A - LR and HadGEM - GC31 – LL. Therefore, per GCM, we extracted ecological data for the two SSPs for the period 2021 – 2040. Presence points comprise coordinates of locations of samples from which the species were previously identified. We applied the Maxent algorithm to predict habitat suitability based on species presence and ecological conditions. 3. Author Information A. Investigator Contact Information Name: Maureen Nabatanzi Institution: Center for Development Research (ZEF), University of Bonn Address: Genscherallee 3, 53113, Bonn Email: maureen.nabatanzi@uni-bonn.de; mnabatanzi@musph.ac.ug B. Project Supervisor (Principal Investigator) Contact Information Name: Lisa Biber-Freudenberger Institution: Center for Development Research (ZEF), University of Bonn Address: Genscherallee 3, 53113, Bonn Email: lfreuden@uni-bonn.de C. In case of questions related to this dataset, please contact: Name: Maureen Nabatanzi Institution: Center for Development Research (ZEF), University of Bonn Address: Genscherallee 3, 53113, Bonn Email: maureen.nabatanzi@uni-bonn.de; mnabatanzi@musph.ac.ug 4. Date of data collection: 1954-01-01 to 2024-09-19 5. Information about funding sources that supported the collection of the data: This study was funded by the German Research Foundation (DFG) under the grant titled, “The Effects of Biodiversity loss and Land-Use Change on the Occurrence of Novel Infectious Diseases”, project number, 458328858. 6. Language of the dataset: English 7. Geographic location of data collection: Africa B DATA & FILE OVERVIEW 1. File List: a) Species presence points are coordinates of species sampling: AeCirc_17May24.csv = Aedes circumluteolus AeMc_17May24.csv = Aedes mcintoshi culexU_2Aug24.csv = Culex univittatus culexP_19Sept24.csv = Culex pipiens manAfr_6Aug24.csv = Mansonia africana Midv_5Aug24.csv = Middelburg virus Sinv_31July24.csv = Sindbis virus Wslv_5June24.csv = Wesselsbron virus b) 19 Bioclimatic variables 1 - 19 for Current time period (1970 - 2000): Bio1.asc = Annual Mean Temperature Bio2.asc = Mean Diurnal Range (Mean of monthly (max temp - min temp)) Bio3.asc = Isothermality (Bio2/Bio7) (×100) Bio4.asc = Temperature Seasonality (standard deviation ×100) Bio5.asc = Max Temperature of Warmest Month Bio6.asc = Min Temperature of Coldest Month Bio7.asc = Temperature Annual Range (Bio5-Bio6) Bio8.asc = Mean Temperature of Wettest Quarter Bio9.asc = Mean Temperature of Driest Quarter Bio10.asc = Mean Temperature of Warmest Quarter Bio11.asc = Mean Temperature of Coldest Quarter Bio12.asc = Annual Precipitation Bio13.asc = Precipitation of Wettest Month Bio14.asc = Precipitation of Driest Month Bio15.asc = Precipitation Seasonality (Coefficient of Variation) Bio16.asc = Precipitation of Wettest Quarter Bio17.asc = Precipitation of Driest Quarter Bio18.asc = Precipitation of Warmest Quarter Bio19.asc = Precipitation of Coldest Quarter c) Other ecological variables for current time period (reference year, 2015): NDVI.asc = Normalized Difference Vegetation Index (NVDI) for 2015 Hpop.asc = Human population distribution by number of people per grid cell for 2015 Built.asc = Built-Up areas for 2015. Proportion of the building footprint area within the total size of the grid cellfor 2014 Smod.asc = Settlement model grid for 2015. Grid cells assigned with 2-digit codes in the following classes: 30 = "Urban Centre"; 23 = "Dense Urban Cluster"; 22 = "Semi-dense Urban Cluster"; 21 = "Suburban or peri-urban grid cells"; 13 = "Rural cluster"; 12 = "Low Density Rural grid cells"; 11 = "Very low density rural grid cells"; 10 = "Water grid cells" Forest = Forested areas for 2015 Livestock.asc = Livestock density for 2015. The following species: cattle, sheep, goats, horses, pigs and chicken; absolute number of animals per pixel. Cropland = Croplands for 2015 d) 19 Bioclimatic variables 1 - 19 for for future climate projections: 19 Bioclimatic variables for Global Climate Models (GCM) IPSL CM6ALR and HadGEM3-GC3I-LL under Shared Social-economic Pathways (SSP) SSP2-4.5 and SSP5-8.5 scenarios. Same 19 Bio climatic variables; file names include a description of the corresponding SSP and GCM e.g. Bio1_SSP245_hadgem_2040 corresponds to Bio1 for SSP2-4.5 under GCM HadGEM - GC31 – LL; Bio1_SSP245_ipsl_2040 corresponds to Bio1 for SSP2-4.5 under GCM IPSL CM6ALR; Bio1_585_hadgem_2040 corresponds to Bio1 for SSP5-8.5 under GCM HadGEM - GC31 – LL and Bio1_585_ipsl_2040 corresponds to Bio1 for SSP5-8.5 under GCM IPSL CM6ALR. e) Other ecological variables for future time period: Cropland_ssp245_hadgem_2040.asc = Croplands for SSP2-4.5 under HadGEM - GC31 – LL Cropland_ssp245_ipsl_2040.asc = Croplands for SSP2-4.5 under IPSL CM6ALR Cropland_ssp585_hadgem_2040.asc = Croplands for SSP5-8.5 under HadGEM - GC31 – LL Cropland_ssp585_ipsl_2040.asc = Croplands for SSP5-8.5 under IPSL CM6ALR Forest_ssp245_hadgem_2040.asc = Forested areas for SSP2-4.5 under HadGEM - GC31 – LL Forest_ssp245_ipsl_2040.asc = Forested areas for SSP2-4.5 under IPSL CM6ALR Forest_ssp585_hadgem_2040.asc = Forested areas for SSP5-8.5 under HadGEM - GC31 – LL Forest_ssp585_ipsl_2040.asc = Forested areas for SSP5-8.5 under IPSL CM6ALR Urban_ssp245_hadgem_2040 = urban areas for SSP2-4.5 under HadGEM - GC31 – LL Urban_ssp245_ipsl_2040 = urban areas for SSP2-4.5 under IPSL CM6ALR Urban_ssp585_hadgem_2040.asc = urban areas for SSP5-8.5 under HadGEM - GC31 – LL Urban_ssp585_ipsl_2040.asc = urban areas for SSP5-8.5 under IPSL CM6ALR Hpop_SSP2_2040.asc = Population distribution by number of people per grid cell under the SSP 2 projection Hpop_SSP5_2040.asc = Population distribution by number of people per grid cell under the SSP 5 projection Livestock_2020.asc = Livestock density for 2020. Animals or birds/pixel f) List references for the data sources 2. Are there multiple versions of the dataset? No 3. Relationship between files: 4. Additional related data collected that was not included in the current data package: C SHARING/ACCESS INFORMATION 1. Was data derived from another source?: yes a) Chen, M., Vernon, C. R., Graham, N. T., Hejazi, M., Huang, M., Cheng, Y., & Calvin, K. (2020). Global land use for 2015–2100 at 0.05° resolution under diverse socioeconomic and climate scenarios. Scientific Data, 7(1), 320. doi:10.1038/s41597-020-00669-x b) Coupled Model Intercomparison Project Phase 6 (CMIP6), W. (2024). Future climate, 30 seconds spatial resolution. Retrieved from: https://www.worldclim.org/data/cmip6/cmip6_clim30s.html c) Didan, K. (2021). MODIS/Terra Vegetation Indices Monthly L3 Global 1km SIN Grid V061. d) Fick, S. E., & Hijmans, R. J. (2017). WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas. 37(12), 4302-4315. doi:https://doi.org/10.1002/joc.5086 e) Friedl, M., Sulla-Menashe, D. (2022). MODIS/Terra+Aqua Land Cover Type Yearly L3 Global 500m SIN Grid V061. Retrieved from: https://doi.org/10.5067/MODIS/MCD12Q1.061 f) Joint Research Centre - JRC - European Commission, & Center for International Earth Science Information Network - CIESIN - Columbia University. (2021). Global Human Settlement Layer: Population and Built-Up Estimates, and Degree of Urbanization Settlement Model Grid. Retrieved from: https://doi.org/10.7927/h4154f0w g) NSAL, F. a. A. O.-. (2022). GLW 4: Gridded Livestock Density (Global - 2015 - 10 km) [mapDigital]. Retrieved from: https://data.apps.fao.org/catalog/iso/15f8c56c-5499-45d5-bd89-59ef6c026704 h) NSAL, F. a. A. O.-. (2024). GLW 4: Gridded Livestock Density (Global - 2020 - 10 km) [mapDigital]. Retrieved from: https://data.apps.fao.org/catalog/iso/9d1e149b-d63f-4213-978b-317a8eb42d02 i) Potapov, P., Turubanova, S., Hansen, M. C., Tyukavina, A., Zalles, V., Khan, A., . . . Cortez, J. (2022). Global maps of cropland extent and change show accelerated cropland expansion in the twenty-first century. Nature Food, 3(1), 19-28. doi:10.1038/s43016-021-00429-z j) Wang, X., Meng, X., & Long, Y. (2022). Projecting 1 km-grid population distributions from 2020 to 2100 globally under shared socioeconomic pathways. In: figshare. k) National Center for Biotechnology Information, NCBI Virus [Internet]. National Library of Medicine. 2024. Available from: https://www.ncbi.nlm.nih.gov/labs/virus/vssi/#/virus?SeqType_s=Nucleotide. 2. Licenses/restrictions placed on the data: 3. Links to publications that cite or use the data: 4. Links to other publicly accessible locations of the data: 5. Links/relationships to ancillary datasets: D METHODOLOGICAL INFORMATION 1. Description of methods used for collection/generation of data: Species presence points: During February – March 2024, we searched the National Center for Biotechnology Information (NCBI) Virus database, Global Biodiversity Information Facility (GBIF) database and literature to extract coordinates of sampling sites from which the species were identified. Additional presence points were obtained from mosquito sampling (2018 - 2019) in Uganda. Ecological data: Bioclimatic and land-use data were extracted as raster file from various databases listed in the sources. 2. Methods for processing the data: Species presence points were collated into single csv files per species and the duplicates were removed. All raster files were processed to cover a uniform spatial extent (Africa), with the same projection (EPSG: 4326-WGS 84), resolution (0.00833 × 0.00833 grid cell size where 1-degree latitude ~111.32 km2 per grid), alignment, and ASCII grid format. 3. Instrument- and/or software-specific information needed to interpret the data: We used R version 4.4.1 and the packages “sf” and “terra” to process the raster data. 4. People involved in sample collection, processing, analysis and/or submission: Selina Graff contributed additional mosquito presence points from sampling in Uganda. Maureen Nabatanzi was involved in data extraction, analysis and submission. 5. Describe any quality-assurance procedures performed on the data: 6. Standards and calibration information: 7. Environmental/experimental conditions: E DATA-SPECIFIC INFORMATION FOR: