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Socio-ecological gap analysis to forecast species range contractions for conservation

Cite this dataset

Harris, Nyeema et al. (2023). Socio-ecological gap analysis to forecast species range contractions for conservation [Dataset]. Dryad.


Conservation requires both a needs assessment and prioritization scheme for planning and implementation. Range maps are critical for understanding and conserving biodiversity, but current range maps often omit content, negating important metrics of variation in populations and places. Here, we integrate a myriad of conditions that are spatially explicit across distributions of carnivores to identify gaps in capacity necessary for their conservation. Expanding on traditional gap analyses that focus almost exclusively on quantifying discordance in protected area coverage across a species’ range, our work aggregates threat layers (e.g., drought, human pressures) with resources layers (e.g., protected areas, cultural diversity) to identify gaps in available conservation capacity (ACC) across ranges for 91 African carnivores. Our model indicated that all species have some portion of their range at risk of contraction, with an average of 15 percentage range loss. We found that the ACC differed based on body size and taxonomy. Results deviated from current perceptions of extinction risks for species with an International Union for Conservation of Nature (IUCN) threat status of Least Concern and yielded insights for species categorized as Data Deficient. Our socio-ecological gap analysis presents a geospatial approach to inform decision-making and resource allocation in conservation. Ultimately, our work advances forecasting dynamics of species’ ranges that are increasingly vital in an era of great socio-ecological change to mitigate human–wildlife conflict and promote inclusive carnivore conservation across geographies.


We obtained a species list from the IUCN Red List of 91 extant terrestrial African carnivores excluding Otariidae and Phocidae species. Threat layers included human modification, drought, and hunting pressure. Resource layers included habitat, protected area, biodiversity, and cultural diversity (Table S3). Because the spatial data obtained for threat and resource variables varied widely in format, resolution and spatial projection, we completed several pre-processing steps prior to analysis that depended on the format of the data. Data stored as polygons (e.g., PA) were processed to be represented in a numerical raster format, specifying the cell size of the output to be 5km2. The dataset of threat and resource variables had a wide range of values including continuous and binary classification. To facilitate comparison and calculation of the available conservation capacity index, all variables were normalized to scale from 0-1. To achieve this, we clipped each resource and threat raster file to the extent and geometry of each species range and then normalized the values of each variable at the clipped extent. Normalization was performed at the clipped extent, rather than at the continental scale to better capture the localized variability in resource and threat values occurring at the scale relevant to the species in question. The ACC index represents the difference between the resources available and threats occurring in a spatially explicit manner. For each species, the ACC was calculated for each grid cell within a species’ geographic range as well as at global level as an aggregated total (Eq 1). We assigned equal weight to each variable, although future analysis could scale particular variables based on their ecological importance for a given species or group of species, if this information is known.

 Eq 1:

ACCj represents the global level as the total capacity gap for species j where R is the sum of normalized resources values and T is the sum of normalized threat values across n locations of a species’ geographic range. Because all resource and threat variables may not be present at each location and to make that all variables that were present are weighted equally, we divided R and T byxij and yij represent the number of resources and threats included, respectively. ACCi were mapped for each 5 km2 grid cell across the species range. Positive values indicate a surplus of available resources that presumably can combat threats across landscapes, while negative values signal a deficit of resources and raise concerns for the local persistence of species. ACCi values that resulted in differences between resources and threats of <|0.01| were deemed negligible and assigned 0 as the functional value. In summary, the mean difference of averaged normalized resources and threats values were calculated to derive the ACC at the global scale as a single value (ACCj) and for each individual cell within a species range (ACCi).

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Files include Microsoft Word and Excel files as well as TIFF raster files for ArcMap, Google Earth Engine, or QGIS