Diva-Gis.Org Administrative Boundary For Douala
This dataset falls under the category Planning & Policy Planning.
It contains the following data: Downloaded administrative boundary (level 2) from diva-gis.org/data on February 1, 2019. In Esri ArcMap, a new shapefile was created by selecting singleboundary containing Douala, Cameroon. Projection: WGS 84.
This dataset was scouted on 02/13/2022 as part of a data sourcing project conducted by TUMI. License information might be outdated: Check original source for current licensing. The data can be accessed using the following URL / API Endpoint: https://state-hiu.maps.arcgis.com/home/item.html?id=b33e96557b7442b0ae087a01501f5542See URL for data access and license information.

Fig 1: Administration data is used from Diva-GIS project (public domain) https://www.diva-gis.org/Data; Digital Elevation Model and Satellite image (Landsat 8) are used from USGS Earth Explorer (public domain) https://earthexplorer.usgs.gov; and point data is established by the authors. (RAR)

A. current conditions; B. RCP 2.6 scenario by 2050; C. RCP 8.5 scenario by 2050; D. RCP 2.6 scenario by 2070; E. RCP 8.5 scenario by 2070. 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)

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)

Ciudades de México

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

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

description: Liberia Railroads; abstract: Liberia Railroads

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

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

description: Railroads in Guinea; abstract: Railroads in Guinea

description: Liberia Administrative Boundaries Level 2; abstract: Liberia Administrative Boundaries Level 2

Road Network Dataset for Myanmar. The data is created in 2007, but curated on ENERGYDATA.INFO in 2016. For details please visit http://www.diva-gis.org/gdata

Aim: Factors affecting bromeliad distribution depend on the life forms of the studied species, some could grow as terrestrial, saxicolous, or epiphytic depending on the type of habitat. We analyzed the distribution patterns of the life forms of a bromeliad species on different biogeographic domains and associated them with environmental variables and vegetation types. Location: Chaquenian, Amazonian, and Seasonally Dry Tropical Forest domains; South America. Taxon: The tank bromeliad Aechmea distichantha (Bromeliaceae: Bromelioideae). Methods: We compiled records of the biogeographic distribution and the vegetation types where Aechmea distichantha occurs based on bibliographic data, digital datasets, herbaria, and personal observations. We associated the distributional records of this species with altitude, five selected bioclimatic variables, four soil variables, and with the vegetation types where it occurs. Results: Aechmea distichantha has been recorded as epiphytic, terrestrial, and saxicolous in all biogeographic domains, but showed contrasting patterns in life form proportions among them. In the Amazonian domain, characterized by evergreen tropical and subtropical forests with high precipitation, it mainly grows as epiphytic. In the Chaquenian domain, dominated by xerophytic forests with low rainfall, high soil pH, and base saturation, it mainly grows as terrestrial, whereas in the Seasonally Dry Tropical Forest domain the three life forms were recorded in similar proportions. In azonal plant communities of all domains, it mainly grows as saxicolous. Main conclusions: This tank bromeliad species can thrive in sites with contrasting habitat and environmental conditions. Its ability to survive in different environments could be associated with its high frost tolerance, the presence of the CAM photosynthetic pathway, a well-developed phytotelma, and high phenotypic plasticity. The life form prevailing in each domain is influenced by water availability (i.e. the quantity of water available during each year, the precipitation in the driest month, and the plant water supply relative to demand).

Methods Occurrences data survey

Occurrence points were obtained by extensively searching the Google Scholar and Scopus databases for literature reporting information on its appearance, as well as reports about the interaction of this species with animal or fungi species. Specimens deposited in FACEN, FCQ, PY (Paraguay), and UNR (Argentina) herbaria, and other specimens available in digital databases (GBIF, 2019; Tropicos, 2019; Flora do Brasil, 2019), as well as journal datasets (Ramos et al., 2019), were also explored. JStor Global Plants (JSTOR, 2019) and ‘Flora del Conosur’ (Zuloaga & Belgrano, 2019) websites were consulted to check types or synonyms. Given that most occurrences and literature citations mentioned only the species name ‘Aechmea distichantha’, for the present analysis we did not make any distinction between infraspecific taxa.

As there could be many sources of potential errors when using large online datasets (Maldonado et al., 2015; Zizka et al., 2019, Zizka et al., 2020), the dataset was compiled and filtered by comparing recorded distributions with areas noted in the literature, as well as with the field experience of the authors. Obvious distribution outliers were checked and deleted when necessary and cultivated specimens were excluded from the analysis. For specimens lacking georeferenced data, coordinates were estimated only for records with accurate locality level spatial data (e. g. municipality or town name, station, farm, finca, estancia or mountain location, roads or rivers intersections, park, reserve or forested area, etc.). We performed manual georeferencing by meticulous interpretation of site descriptions. When available, we checked the original field notes, specimen labels, etc. to improve georeferencing precision and reduce spatial error. To assign the coordinates of each occurrence record we analyzed the site location, classified the type of locality, and then georeferenced it by using the point or point-radius methods in Google Maps and/or Google Earth respectively, following Chapman & Wieczorek (2020) Georeferencing Best Practices.

Records with unambiguous life form information were classified according to the presence of this species on the canopy (epiphytic), on the soil (terrestrial), and on rocky outcrops (saxicolous; Zotz, 2016). A final dataset of 1232 occurrences of A. distichantha was compiled, containing information either on life form, geographic coordinates, or biogeographic regions (provided or inferred).

Environmental data survey

For those records with vegetation description, vegetation types were identified for each biogeographic domain based on community structure description or from its floristic composition (DRYFLOR, 2016; Oliveira-Filho & Fontes, 2000; Prado, 2000). For the Amazonian domain (sensu Cabrera & Willink, 1980) we classified the records into wet forests, savannas, and azonal communities. For the Seasonally Dry Tropical Forest domain (sensu Prado, 2000; Särkinen, Iganci, Linares-Palomino, Simon, & Prado, 2011), we classified the vegetation as mesophytic forests or azonal communities. Finally, for the Chaquenian domain (sensu Prado, 1993a, b), we recognized the following vegetation types: tall xerophytic forests, low xerophytic forests, savannas, and azonal communities. We did not include in the vegetation dataset those records that corresponded to transitions between different domains (N=83; i.e. 63 transitions Chaquenian-SDTF domains, and 20 transitions SDTF-Amazonian domains). For the present contribution, we consider that other bioregionalization schemes (e.g. Morrone, 2014) are not suitable because they do not take into account the unique identity of the SDTF in South America (sensu DRYFLOR, 2016), to which the studied species shows an important association.

We selected altitude and five bioclimatic variables based on the effects that they could have on the growth and survival of a facultative epiphytic bromeliad, and therefore on its distribution (Males & Griffiths, 2017; Males, 2018). Mean Annual Precipitation (MAP, mm) was considered as a proxy for the absolute quantity of water available during each year (Males, 2018). Precipitation in the driest month (Pdry, mm) was a proxy for the absolute degree of water limitation during the dry season (Males, 2018). Precipitation Seasonality (Pseas, %) was used as a proxy for the severity of the dry season relative to the remainder of the year (Males, 2018). Aridity Index (AI, mm mm-1) is measured as MAP/MAE, where MAE is Mean Annual Evapotranspiration, and hence is affected by precipitation, potential evaporation, and temperature. It was used as a proxy for the degree of dryness, where higher AI values denote lower dryness (Zomer et al., 2007). Actual Evapotranspiration/Potential Evapotranspiration (AET/PET, mm mm−1) was used as a proxy for plant water supply relative to demand (Males, 2018).

As terrestrial bromeliad distribution could also be affected by soil features (Benzing, 2000; Barberis et al., 2014), we selected four soil variables (i.e. pH, Percentage of Clay (Clay, %), Cation Exchange Capacity (CEC, cmolc kg-1), and Base Saturation (BSAT, %)) that are known to vary widely among biomes (Rubio et al., 2019).

Altitude, MAP, Pdry, and Pseas, were taken from Worldclim version 2.0 (Fick & Hijmans, 2017, available in http://www.diva-gis.org/), at 30 seconds spatial resolution (~1 km2). Aridity Index, Actual Evapotranspiration (AET), and Potential Evapotranspiration (PET) layers were obtained at the same resolution from the CGIAR-CSI portal (Zomer et al., 2007). The selected soil variables were taken from The Soil and Terrain database for Latin America and the Caribbean (SOTERLAC), version 2.0, at a scale 1:5 million (available in http://www.isric.org/). DIVA GIS v7.5 (Hijmans et al., 2012) was used to extract the environmental information associated with each record.

REFERENCES

Barberis, I. M., Torres, P. S., Batista, W. B., Magra, G., Galetti, L., & Lewis, J. P. (2014). Two bromeliad species with contrasting functional traits partition the understory space in a Southamerican xerophytic forest: correlative evidence of environmental control and limited dispersal. Plant Ecology, 215, 143-153. https://doi.org/10.1007/s11258-013-0261-3

Benzing, D. H. (2000). Bromeliaceae. Profile of an adaptive radiation. Cambridge, England: Cambridge University Press.

Cabrera, A. & Willink, A. (1980). Biogeografía de América Latina. Washington, USA: Secretaría General de la Organización de los Estados Americanos.

Chapman, A. D., & Wieczorek, J. (2020). Georeferencing best practices. Copenhagen, Denmark: Global Biodiversity Information Facility.

DRYFLOR, Banda, K., Delgado-Salinas, A., Dexter, K. G., Linares-Palomino, R., Oliveira-Filho, A., … Pennington, R. T. (2016). Plant diversity patterns in neotropical dry forests and their conservation implications. Science, 353, 1383-1387. https://doi.org/10.1126/science.aaf5080

Fick, S. E., & Hijmans, R. J. (2017). Worldclim 2: New 1-km spatial resolution climate surfaces for global land areas. International Journal of Climatology, 37, 4302–4315. https://doi.org/10.1002/joc.5086

Flora do Brasil (2019). Jardim Botânico do Rio de Janeiro. Retrieved from: http://floradobrasil.jbrj.gov.br/ (accessed 12 July 2019].

GBIF (2019). GBIF - Global Biodiversity Information Facility. Retrieved from: https://www.gbif.org (accessed 12 July 2019).

Hijmans, R., Guarino, L., Bussink, C., Mathur, P., Cruz, M., Barrentes, I., & Rojas, E. (2012). DIVA-GIS: A geographic information system for the analysis of species distribution data. Version 7, 476-486.

JSTOR (2019). JSTOR Global Plants. Retrieved from: http://plants.jstor.org (accessed 12 July 2019).

Maldonado, C., Molina, C. I., Zizka, A., Persson, C.,

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.

Burundi rivers. Source (DIVA-GIS).

Presence locations used for SDM computingDatafile (csv) includes the selected occurrence records used for conducting the SDMs. The file has three columns including: species (species epithet), x, and y geographic coordinates (WGS decimal degree). Based on this file bioclimatic information could be extracted from DIVA-GIS or equivalent programs and used for computing SDMs (like Maxent as done here)Dryad-Engler et al. JEB - occurrences for SDM.csv

In this study, we used the monthly time series of medium spatial resolution remote sensing images (2003 to 2023) operated by the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor. All images were processed on the Google Earth Engine platform (https://code.earthengine.google.com). GEE is a scalable, cloud-based geospatial retrieval and processing platform that offers free in-cloud data access, processing, and administration. The MODIS product used for burned area assessment was from collection 6.1. The images of the burned area (MCD64A1) and active fire (MCD14DL and NRT) were computed from https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MCD64A1 and https://developers.google.com/earth-engine/datasets/catalog/FIRMS, In this study, over a 20-year study period (2003 to 2023), 252 images were processed at a rate of one image per month for the burned area through the GEE platform when setting up the code. Based on this information, a technique for processing, evaluating, and displaying geodata on forest fires and their consequences was created. The approach consists of four (4) main parts in this study, which are: acquiring and loading input data from publicly available geographic information web services (http://diva-gis.org/datadown)(Step1); pre-processing (filtering) of multi-channel satellite images, computation of indexed images (Step2); thresholding and saving the original data and outcomes in cloud storage (Step3) and then exportation in GIS for geoprocessing (Step3). At the end of the processing, the metadata is composed of data in shapefile format of the monthly and annual burnt areas over the period 2003-2023 as well as those of the active fires. All the data has been re-structured in an Excel file with the appropriate area and number of active points. Description of downloadable files: The single files for burned area mBA[year] contain the characteristics of the fire intensity and the occurrence. mBA[year]_Dissolve contain only one information about the area. The supplemental material contains the statistics of the burned area and active fire.

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/