DOI: 10.5061/dryad.djh9w0w2t
title: Socio-ecological gap analysis to forecast species range contractions for conservation

description: 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.
description: 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
 5km<sup style="font-family:'Times New Roman;">2</sup>. 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:
 ACC<sub style="font-family:'Times New Roman' , serif;">j</sub> represents the global level as the total capacity gap for species <em style="font-family:'Times New Roman' , serif;font-size:12pt;">j</em> where <em style="font-family:'Times New Roman' , serif;font-size:12pt;">R </em>is the sum of normalized resources values and <em style="font-family:'Times New Roman' , serif;font-size:12pt;">T</em> is the sum of normalized threat values across <em style="font-family:'Times New Roman' , serif;font-size:12pt;">n</em> 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 <em style="font-family:'Times New Roman' , serif;font-size:12pt;">R</em> and <em style="font-family:'Times New Roman' , serif;font-size:12pt;">T</em> by<em style="font-family:'Times New Roman' , serif;font-size:12pt;">x</em><sub style="font-family:'Times New Roman' , serif;">ij</sub> and <em style="font-family:'Times New Roman' , serif;font-size:12pt;">y</em><sub style="font-family:'Times New Roman' , serif;">ij</sub> represent the number of resources and threats included, respectively. ACC<sub style="font-family:'Times New Roman' , serif;">i</sub> were mapped for each 5 km<sup style="font-family:'Times New Roman' , serif;">2</sup> 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. ACC<sub style="font-family:'Times New Roman' , serif;">i</sub> values that resulted in differences between resources and threats of &lt;|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 (ACC<sub>j</sub>) and for each individual cell within a species range (ACC<sub>i</sub>).
description: Files include Microsoft Word and Excel files as well as TIFF
 raster files for ArcMap, Google Earth Engine, or QGIS