This file was generated on 2026-02-10 by MAUREEN NABATANZI A GENERAL INFORMATION 1. Title of the dataset: Ecological niche and presence maps for Wesselsbron, Sindbis and Middelburg viruses and their vectors in Africa 2. Brief description of the research project and its aims: This dataset comprises ecological niche models of Wesselsbron, Sindbis, and Middelburg viruses and five suspected mosquito vectors (Aedes circumluteolus, Aedes mcintoshi, Culex univittatus, Culex pipiens, and Mansonia africana) in Africa. The models predict the potential distribution and presence of species under current (year 2015) and future (years 2021 - 2040) environmental and climatic conditions. For the future ecology, 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 used in ecological niche modeling comprise coordinates of locations of samples from which the mosquito and virus species were previously identified. We applied the Maxent algorithm to predict habitat suitability based on species presence and ecological conditions. The output models presented show average predicted habitat suitability probabilities ranging from 0 (least suitable) to 1 (most suitable). The suitability models were converted into species presence models by assigning presence to cells with suitability above four thresholds: 1) Equal training sensitivity and specificity; 2) Maximum training sensitivity plus specificity; 3) Balanced training omission; and 4) Ten-percentile training presence. This created a stack of four presence/absence models per species and scenario, showing the number of threshold criteria (0 - 4) under which a given cell is predicted as suitable. For viruses, we combined these stacks to produce hotspot maps highlighting areas with the highest certainty of species presence (value 12) and least certainty (value 0). The dataset used to generate these models is published, https://doi.org/10.60507/FK2/LA6LJW. 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; mrbnabatanzi@gmail.com 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; mrbnabatanzi@gmail.com 4. Date of data collection: 2024 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: Germany B DATA & FILE OVERVIEW 1. File List: a) Habitat suitability models for the respective species currently (2015) and under future (2021 – 2040, SSP2-4.5 and SSP5-8.5) ecology. File names include a description of the species, average model for the two GCMs per corresponding SSP and the time period e.g., AedesMcintoshi_245mean_21_40.asc corresponds to the model for Aedes mcintoshi for SSP2-4.5 (average of 2 GCMs HadGEM - GC31 – LL and IPSL CM6ALR) for 2021 - 2040 while AedesMcintoshi_avg.asc corresponds to the suitability model for Aedes mcintoshi for 2015: AedesMcintoshi_245mean_21_40.asc = Suitability model for Aedes mcintoshi under SSP2-4.5 for 2021 - 2040 AedesMcintoshi_585mean_21_40.asc = Suitability model for Aedes mcintoshi under SSP5-8.5 for 2021 - 2040 AedesMcintoshi_avg.asc = Suitability model for Aedes mcintoshi for 2015 Aedes_circumluteolus_245mean_21_40.asc = Suitability model for Aedes circumluteolus under SSP2-4.5 for 2021 - 2040 Aedes_circumluteolus_585mean_21_40.asc = Suitability model for Aedes circumluteolus under SSP5-8.5 for 2021 - 2040 Aedes_circumluteolus_avg.asc = Suitability model for Aedes circumluteolus for 2015 Culex_pipiens_245mean.asc = Suitability model for Culex pipiens under SSP2-4.5 for 2021 - 2040 Culex_pipiens_585mean.asc = Suitability model for Culex pipiens under SSP5-8.5 for 2021 - 2040 Culex_pipiens_avg.asc = Suitability model for Culex pipiens for 2015 Culex_univittatus_245mean.asc = Suitability model for Culex univittatus under SSP2-4.5 for 2021 - 2040 Culex_univittatus_585mean.asc = Suitability model for Culex univittatus under SSP5-8.5 for 2021 - 2040 Culex_univittatus_avg.asc = Suitability model for Culex univittatus for 2015 Mansonia_africana_245mean_21_40.asc = Suitability model for Mansonia africana under SSP2-4.5 for 2021 - 2040 Mansonia_africana_585mean_21_40.asc = Suitability model for Mansonia africana under SSP5-8.5 for 2021 - 2040 Mansonia_africana_avg.asc = Suitability model for Mansonia africana for 2015 Middelburg_virus_245mean_21_40.asc = Suitability model for Middelburg virus under SSP2-4.5 for 2021 - 2040 Middelburg_virus_585mean_21_40.asc = Suitability model for Middelburg virus under SSP5-8.5 for 2021 - 2040 Middelburg_virus_avg.asc = Suitability model for Middelburg virus for 2015 Sindbis_virus_245mean.asc = Suitability model for Sindbis virus under SSP2-4.5 for 2021 - 2040 Sindbis_virus_585mean.asc = Suitability model for Sindbis virus under SSP5-8.5 for 2021 - 2040 Sindbis_virus_avg.asc = Suitability model for Sindbis virus for 2015 Wesselsbron_virus_585mean_21_40.asc = Suitability model for Wesselsbron virus under SSP5-8.5 for 2021 - 2040 Wesselsbron_virus_245mean_21_40.asc = Suitability model for Wesselsbron virus under SSP5-8.5 for 2021 - 2040 Wesselsbron_virus_avg.asc = Suitability model for Wesselsbron virus for 2015 b) Presence models are a threshold-based certainty index (from least certainty (0) to highest certainty (4)) representing the number of presence–absence models in which suitability exceeded predefined thresholds. File names include a description of the species and corresponding SSP and the time period currently (2015) and under future (2021 – 2040, SSP2-4.5 and SSP5-8.5) ecology: Aedes_circumluteolus_binarysum_245.tif = Presence model for Aedes circumluteolus under SSP2-4.5 for 2021 - 2040 Aedes_circumluteolus_binarysum_585.tif = Presence model for Aedes circumluteolus under SSP5-8.5 for 2021 - 2040 Aedes_circumluteolus_binarysum_curr.tif = Presence model for Aedes circumluteolus for 2015 Aedes_mcintoshi_binarysum_245.tif = Presence model for Aedes mcintoshi under SSP2-4.5 for 2021 - 2040 Aedes_mcintoshi_binarysum_585.tif = Presence model for Aedes mcintoshi under SSP5-8.5 for 2021 - 2040 Aedes_mcintoshi_binarysum_curr.tif = Presence model for Aedes mcintoshi for 2015 Culex_pipiens_binarysum_245.tif = Presence model for Culex pipiens under SSP2-4.5 for 2021 - 2040 Culex_pipiens_binarysum_585.tif = Presence model for Culex pipiens under SSP5-8.5 for 2021 - 2040 Culex_pipiens_binarysum_curr.tif = Presence model for Culex pipiens for 2015 Culex_univittatus_binarysum_245.tif = Presence model for Culex univittatus under SSP2-4.5 for 2021 - 2040 Culex_univittatus_binarysum_585.tif = Presence model for Culex univittatus under SSP5-8.5 for 2021 - 2040 Culex_univittatus_binarysum_curr.tif = Presence model for Culex univittatus for 2015 Mansonia_africana_binarysum_245.tif = Presence model for Mansonia africana under SSP2-4.5 for 2021 - 2040 Mansonia_africana_binarysum_585.tif = Presence model for Mansonia africana under SSP5-8.5 for 2021 - 2040 Mansonia_africana_binarysum_curr.tif = Presence model for Mansonia africana for 2015 Middelburg_virus_binarysum_245.tif = Presence model for Middelburg virus under SSP2-4.5 for 2021 - 2040 Middelburg_virus_binarysum_585.tif = Presence model for Middelburg virus under SSP5-8.5 for 2021 - 2040 Middelburg_virus_binarysum_curr.tif = Presence model for Middelburg virus for 2015 Sindbis_virus_binarysum_245.tif = Presence model for Sindbis virus under SSP2-4.5 for 2021 - 2040 Sindbis_virus_binarysum_585.tif = Presence model for Sindbis virus under SSP5-8.5 for 2021 - 2040 Sindbis_virus_binarysum_curr.tif = Presence model for Sindbis virus for 2015 Wesselsbron_virus_binarysum_245.tif = Presence model for Wesselsbron virus under SSP2-4.5 for 2021 - 2040 Wesselsbron_virus_binarysum_585.tif = Presence model for Wesselsbron virus under SSP5-8.5 for 2021 - 2040 Wesselsbron_virus_binarysum_curr.tif = Presence model for Wesselsbron virus c) For viruses, we combined the presence model stacks to produce hotspot models highlighting areas with the highest certainty of species presence (value 12) and least certainty (value 0): Combined_virusbinarysum_245.tif = Hotspot model for Wesselsbron, Sindbis and Middelburg viruses under SSP2-4.5 for 2021 - 2040 Combined_virusbinarysum_585.tif = Hotspot model for Wesselsbron, Sindbis and Middelburg viruses under SSP5-8.5 for 2021 - 204 Combined_virusbinarysum_curr.tif = Hotspot model for Wesselsbron, Sindbis and Middelburg viruses for 2015 2. Are there multiple versions of the dataset? no C SHARING/ACCESS INFORMATION 1. Was data derived from another source?: Yes, https://doi.org/10.60507/FK2/LA6LJW References for data sources used to produce these data: 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: Creative Commons 4.0 International License 3. Links to publications that cite or use the data : D METHODOLOGICAL INFORMATION 1. Description of methods used for collection/generation of data: Methods are described at https://doi.org/10.60507/FK2/LA6LJW 2. Methods for processing the data: Models are the result of modeling in R version 4.4.1 using "kuenm", an R package for detailed development of ecological niche models using Maxent. 3. Instrument- and/or software-specific information needed to interpret the data: Models are raster files in ASCII and GeoTiff formats and can be visualized using any geoinformation software or program. 4. People involved in sample collection, processing, analysis and/or submission: 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: 1. Variable list including full names and definitions of column headings for tabular data: 2. Units of measurement used: 3. Missing data codes/symbols: 4. Specialized formats or other abbreviations used: