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 admin2 file of Kenya

DIVA-GIS's admin0 file of Gabon

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

Ciudades de México

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

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

DIVA-GIS's admin1 file of Zambia

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

DIVA-GIS's admin4 file of Kenya

Shapefiles for Ethiopia's Administrative boundaries: Regions, Zones and Woredas

A. current conditions; B. RCP 2.6 by 2050; C. RCP 8.5 by 2050; D. RCP 2.6 by 2070; E. RCP 8.5 by 2070. Warm areas: suitable; Cold areas: unsuitable, for tiger mosquito. The maps were built using the free and open source QGIS software version 3.10.11 (https://www.qgis.org/en/site/about/index.html) and shapefiles were obtained from the free and open source DIVA-GIS site (https://www.diva-gis.org/gdata). (ZIP)

Trans-Saharan migrants often spend a large proportion of their annual cycle wintering in the Sahel. Advances in fieldwork and tracking technology have greatly enhanced our ability to study their ecology in these areas. Using GPS-tracking we aimed to investigate the little-known non-breeding movements of the lesser kestrel Falco naumanni in sub-Saharan Africa. We segment non-breeding tracks (n = 79 tracks by 54 individuals) into staging events (131± 25 days per non-breeding cycle), itinerant movements between staging sites (11 ± 10 days), and non-directed exploratory movements (6 ± 5 days). We then describe timing and directionality of itinerant movements by male and female kestrels throughout the non-breeding season. Regardless of sex, lesser kestrels spent on average 89% of the non-breeding season staging at 2 (range = 1–4) sites in West Africa. At the end of September, kestrels arrived along a broad front throughout the northern Sahel. By December, however, they congregated into two distinct clusters in Senegal and along the Malian-Mauritanian border. The birds stayed for longer periods and showed greater daily activity in the latter areas, compared to their first and intermediate ones. Among 24 individuals tracked along multiple annual cycles, 20 individuals consistently used the Senegalese or Malian-Mauritanian cluster. The remaining four birds used these clusters only after 2-3 years of tracking or switched between clusters across years. The eastward and westward itinerant movements of lesser kestrels during the non-breeding season, coupled with their tendency to cluster geographically towards the end, differ from the southward movements of other insectivorous raptors in West Africa. While 31% of Spanish lesser kestrels converged in Senegal, where roosts of > 20,000 birds are known, 68% moved into the Malian-Mauritanian border region where more groundwork is needed. Methods Fieldwork was conducted in Spain during the breeding seasons of 2016–2020. A total of 216 adults were captured near the colony using balchatri or mist nets. They were also captured within the nest (such as nestboxes or other cavities) before egg laying, at the end of the incubation period or during the chick-rearing phase. We used two models of solar GPS-UHF biologgers from different manufacturers (GPSminiDatalogger, Microsensory LS, Córdoba, Spain; and NanoFix GEO+RF, Pathtrack Ltd., Leeds, UK.). The GPS-UHF loggers weighing 5.5 g (including harness, ~3.8 % of the mean weight at capture, males = 146.0 g ± 35 SD; females = 148.0 g ± 29) were attached as backpacks with a Teflon harness. Locations were stored on-board and downloaded via a UHF base station placed in the vicinity of the colony. Overall, we analysed 79 non-breeding tracks from the 54 adult birds (25 males and 29 females) from 20 breeding sites All data analyses were conducted in R (V 4.2.3), and all figures were produced with ggplot2. The full data was resampled to a 1-h interval, allowing deviations of up to 20 min. We calculated movement metrics using the R package ‘fossil’. All the mixed linear models were implemented using the ‘lme4’ package. We determined daily sunrise/sunset times using the "StreamMetabolism" package. In addition, we used third-party public data from:

Country borders via: https://www.naturalearthdata.com/downloads/50m-cultural-vectors/

Topography: alt_30s_bil via https://geodata.ucdavis.edu/climate/worldclim/1_4/grid/cur/

The main inland water (e.g. rivers and deltas) present in Senegal, Gambia, Mauritania and Mali via: https://www.diva-gis.org/

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

Roads in Somalia from the Digital Chart of the World.

A set of global climate layers (climate grids) with a spatial resolution of about 1 square kilometer. The data can be used for mapping and spatial modeling in a GIS or with other computer programs. If you are not familiar with such programs, you can try DIVA-GIS or the R raster package.

DIVA-GIS's admin0 file of Lesotho

Roads in Guinea

The market for GIS Mapping Tools is projected to reach a value of $XX million by 2033, growing at a CAGR of XX% during the forecast period (2025-2033). The market growth is attributed to the increasing adoption of GIS mapping tools by various industries, including government, utilities, and telecom, for a wide range of applications such as geological exploration, water conservancy projects, and urban planning. The convergence of GIS with other technologies such as artificial intelligence (AI) and the Internet of Things (IoT) is further driving market growth, as these technologies enable GIS mapping tools to provide more accurate and real-time data analysis. The market is segmented by type (cloud-based, web-based), application (geological exploration, water conservancy projects, urban planning, others), and region (North America, Europe, Asia Pacific, Middle East & Africa). North America is expected to remain the largest market for GIS mapping tools throughout the forecast period, due to the early adoption of these technologies and the presence of leading vendors such as Esri, MapInfo, and Autodesk. Asia Pacific is expected to experience the highest growth rate during the forecast period, due to the increasing adoption of GIS mapping tools in emerging economies such as China and India. Key industry players include Golden Software Surfer, Geoway, QGIS, GRASS GIS, Google Earth Pro, CARTO, Maptive, Shenzhen Edraw Software, MapGIS, Oasis montaj, DIVA-GIS, Esri, MapInfo, Autodesk, BatchGeo, Cadcorp, Hexagon, Mapbox, Trimble, and ArcGIS.

Railroads in Guinea