Data for Modeling the potential distribution of Wesselsbron, Sindbis, and Middelburg viruses and their vectors in Africa under future climatic and land-use changes

Citation Metadata

Title title

Data for Modeling the potential distribution of Wesselsbron, Sindbis, and Middelburg viruses and their vectors in Africa under future climatic and land-use changes

Author author

1. Author Name

   Nabatanzi, Maureen

   Author Affiliation

   University of Bonn

   Author Identifier Scheme

   ORCID

   Author Identifier

   Type Name

   authorIdentifier

   Multiple

   False

   Type Class

   primitive

   Value

   https://orcid.org/0000-0002-9801-4210

   Expandedvalue

   Person Name

   Nabatanzi, Maureen

   @id

   https://orcid.org/0000-0002-9801-4210

   Scheme

   ORCID

   @type

   https://schema.org/Person

Dataset Contact datasetContact

1. Dataset Contact Name

   Nabatanzi, Maureen

   Dataset Contact Affiliation

   University of Bonn

   Dataset Contact Email

   s65mnaba@uni-bonn.de

Ds Description dsDescription

1. Ds Description Value

   This dataset comprises ecological variables and species presence points used to model the current and future potential distribution of 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.

   Ds Description Date

   2025-04-09

Subject subject

1. Agricultural Sciences
2. Earth and Environmental Sciences
3. Medicine, Health and Life Sciences

Free Keyword freeKeyword

1. Free Keyword Value

   Arbovirus

2. Free Keyword Value

   vector

3. Free Keyword Value

   species distribution

4. Free Keyword Value

   land-use

5. Free Keyword Value

   climate-change

6. Free Keyword Value

   model

Language language

1. English

Contributor contributor

1. Contributor Type

   Data Collector

   Contributor Name

   Selina Graff

Grant Number grantNumber

1. Grant Number Agency

   German Research Foundation (DFG)

   Grant Number Value

   Project number 458328858

Depositor depositor

Nabatanzi, Maureen

Date Of Deposit dateOfDeposit

2025-04-09

Time Period Covered timePeriodCovered

1. Time Period Covered Start

   1954-01-01

   Time Period Covered End

   2024-09-19

Date Of Collection dateOfCollection

1. Date Of Collection Start

   1954-01-01

   Date Of Collection End

   2024-09-19

Kind Of Data kindOfData

1. Coordinates
2. Raster images

Software software

1. Software Name

   R

   Software Version

   4.4.1

Data Sources dataSources

1. Global land use for 2015–2100 at 0.05° resolution under diverse socioeconomic and climate scenarios. doi:10.1038/s41597-020-00669-x
2. Coupled Model Intercomparison Project Phase 6 (CMIP6), Future climate, 30 seconds spatial resolution. https://www.worldclim.org/data/cmip6/cmip6_clim30s.html
3. MODIS/Terra Vegetation Indices Monthly L3 Global 1km SIN Grid V061. Fick, S. E., & Hijmans, R. J. (2017).
4. 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
5. MODIS/Terra+Aqua Land Cover Type Yearly L3 Global 500m SIN Grid V061. https://doi.org/10.5067/MODIS/MCD12Q1.061
6. Global Human Settlement Layer: Population and Built-Up Estimates, and Degree of Urbanization Settlement Model Grid. https://doi.org/10.7927/h4154f0w
7. GLW 4: Gridded Livestock Density (Global - 2015 - 10 km) [mapDigital]. https://data.apps.fao.org/catalog/iso/15f8c56c-5499-45d5-bd89-59ef6c026704
8. GLW 4: Gridded Livestock Density (Global - 2020 - 10 km) [mapDigital]. https://data.apps.fao.org/catalog/iso/9d1e149b-d63f-4213-978b-317a8eb42d02
9. Global maps of cropland extent and change show accelerated cropland expansion in the twenty-first century. doi:10.1038/s43016-021-00429-z
10. Wang, X., Meng, X., & Long, Y. (2022). Projecting 1 km-grid population distributions from 2020 to 2100 globally under shared socioeconomic pathways. In: figshare.
11. 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.

Access To Sources accessToSources

Free to access and download