DIVA-GIS's admin2 file of Kenya

Fig 5: Administration data is used from Diva-GIS project (public domain) https://www.diva-gis.org/Data; Digital Elevation Model is used from USGS Earth Explorer (public domain) https://earthexplorer.usgs.gov. (RAR)

DIVA-GIS's admin0 file of Gabon

DIVA-GIS's admin4 file of Kenya

Downloaded from http://www.diva-gis.org/

Source: http://www.diva-gis.org/

Ciudades de México

Roads in Somalia from the Digital Chart of the World.

Madagascar Digital Elevation Model, downloaded from DIVA GIS in March 2012 (CGIAR-SRTM data aggregated to 30 seconds).

description: Liberia Water Lines; abstract: Liberia Water Lines

description: Liberia Railroads; abstract: Liberia Railroads

DIVA-GIS's admin1 file of Zambia

Roads in Guinea

DIVA-GIS has published Administrative Areas Level 3 in Guinea on their website in support of the Ebola crisis.

description: Railroads in Guinea; abstract: Railroads in Guinea

description: This data was created by the DIVA-GIS for Liberia water data.; abstract: This data was created by the DIVA-GIS for Liberia water data.

IntroductionClimate change forms one of the most dangerous problems that disturb the earth today. It not only devastates the environment but also affects the biodiversity of living organisms, including fungi. Macrophomina phaseolina (Tassi) Goid. is one of the most pervasive and destructive soil-borne fungus that threatens food security, so predicting its current and future distribution will aid in following its emergence in new regions and taking precautionary measures to control it.MethodsThroughout this work, there are about 324 records of M. phaseolina were used to model its global prevalence using 19 environmental covariates under several climate change scenarios for analysis. Maximum Entropy (MaxEnt) model was used to predict the spatial distribution of this fungus throughout the world while algorithms of DIVA-GIS were chosen to confirm the predicted model.ResultsBased on the Jackknife test, minimum temperature of coldest month (bio_6) represented the most effective bioclimatological parameter to fungus distribution with a 52.5% contribution. Two representative concentration pathways (RCPs) 2.6 and 8.5 of global climate model (GCM) code MG, were used to forecast the global spreading of the fungus in 2050 and 2070. The area under curve (AUC) and true skill statistics (TSS) were assigned to evaluate the resulted models with values equal to 0.902 ± 0.009 and 0.8, respectively. These values indicated a satisfactory significant correlation between the models and the ecology of the fungus. Two-dimensional niche analysis illustrated that the fungus could adapt to a wide range of temperatures (9 °C to 28 °C), and its annual rainfall ranges from 0 mm to 2000 mm. In the future, Africa will become the low habitat suitability for the fungus while Europe will become a good place for its distribution.DiscussionThe MaxEnt model is potentially useful for predicting the future distribution of M. phaseolina under changing climate, but the results need further intensive evaluation including more ecological parameters other than bioclimatological data.

45 data sources of hydrological/hydromet, water quality, water resource, environmental, agro-environmental and development indicators. Datasets include: Achieving National Development Strategy in Tajikistan (Nurek), Water Transition, Central Asia Hydrometeorology Modernization Project, Lake Levels, Night Lights, Landscan Population Density, Satellite Precipitation, Solar Energy Data, Earth Wind Map, Land Cover Comparison, Earth Engine NDVI Analysis, Kyrgyz Republic DRM Portal, Climate Adaptation and Mitigation Program for Aral Sea Basin, Croplands, Watershed Mapper, Forest Cover, Kyrgyz Republic Hydromet Portal, World Water Quality, Human Footprint, Glacier Inventory, MODIS layers, Cropping Extent, Fire Data, Surface Water Explorer, Human Influence Index, Development Data, GADAS (Agriculture) Wind Potential, ESRI Water Balance, Air Quality, Tajikistan Hydromet Website, Open Street Map Data, Land-Water Changes, Himawari, GEOGRLAM RAPP, Google Earth Data, GEOSS Portal, USGS Global Visualization Viewer (GloVis), STRM Topography Data, UNEP Database, DIVA GIS Country Boundaries, ARCGIS Hub- Water Bodies, ARCGIS Hub- World Cities, WUEMoCA, World Bank Climate Change Portal

DIVA-GIS's admin0 file of Lesotho

DIVA-GIS's admin0 file of Central African Republic