“…Such gap analysis approaches have been applied to a range of species and varying ecosystems to guide conservation efforts, e.g. seahorses ( 14 ), cacti ( 15 ), and rivers ( 16 ). Species or ecosystem representation and human pressures within existing protected areas provide another lens for conservation assessments ( 17 – 19 ).…”
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.
“…Such gap analysis approaches have been applied to a range of species and varying ecosystems to guide conservation efforts, e.g. seahorses ( 14 ), cacti ( 15 ), and rivers ( 16 ). Species or ecosystem representation and human pressures within existing protected areas provide another lens for conservation assessments ( 17 – 19 ).…”
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.
“…This is supported by numerous field‐based surveys that show that the cumulative effect on biodiversity of multiple drivers can be more (e.g., Chen et al, 2015 ), less (e.g., Reich, 2009 ), or equivalent to the additive impact of independent drivers (e.g., Mora et al, 2007). It is also supported by recent analyses in which field‐based observations were used to construct a framework to assess extinction risk from multiple simultaneous human activities (Lum et al., 2020 ; Zhang & Vincent, 2019 ). Furthermore, our model results suggest that species not currently identified as threatened with extinction but that experience multiple, small, and persistent risks may experience a large long‐term extinction risk over long periods because the extinction risk from many low‐level persistent drivers are approximately additive and the risks increase through time.…”
Many species may face multiple distinct and persistent drivers of extinction risk, yet theoretical and empirical studies tend to focus on the single largest driver. This means that existing approaches potentially underestimate and mischaracterize future risks to biodiversity. We synthesized existing knowledge on how multiple drivers of extinction can interact to influence a species’ overall extinction probability in a probabilistic model of extinction risk that incorporated the impacts of multiple drivers of extinction risk, their interactions, and their accumulative effects through time. We then used this model framework to explore how different threats, interactions between them, and time trends may affect a species’ overall extinction probability. Multiple small threats together had potential to pose a large cumulative extinction risk; for example, 10 individual threats posed a 1% extinction risk each and cumulatively posed a 9.7% total extinction risk. Interactions among drivers resulted in escalated risk in some cases, and persistent threats with a small (1%) extinction risk each decade ultimately posed large extinction risk over 100 (9.6% total extinction risk) to 200 years (18.2% total extinction risk). By estimating long‐term extinction risk posed by several different factors and their interactions, this approach provides a framework to identify drivers of extinction risk that could be proactively targeted to help prevent species currently of least concern from becoming threatened with extinction.
“…While the BLM is calibrated to find the best value that strikes a balance between area compactness and cost (as the value of the BLM increases, the algorithm will prefer a 'single large' design over a 'several small' designs, or increase 'connectivity'). To compare the spatial arrangement within and between scenarios, we used the kappa statistic 58 . Figure 1 Workflow for identifying priority conservation areas in Sulawesi under three protection scenarios.…”
As more ambitious protected area (PA) targets for the post-2020 global biodiversity framework are set beyond Aichi Target 11, renew thinking into spatial prioritisation is required to enable PA expansion that maximises environmental values. Our study focuses on the biodiverse and forest-rich Indonesian island of Sulawesi, which has a terrestrial PA network that covers 10% of the island. We used Marxan to investigate trade-offs in the design of an expanded PA network that prioritised different conservation features (biodiversity, forest cover, carbon stock, karst and valuable metal-rich areas) under varying island-wide coverage targets (17%, 30%, and 50%). Our first scenario, which required existing PAs to be selected, required larger areas to meet these coverage targets, in contrast to our second scenario, which allowed for any part of the island to be chosen, irrespective of PA status. The vast Mekongga and Bangkiriang Landscapes, and Gorontalo corridor were consistently identified as a high priority for protection under all scenarios. To meet our conservation targets through expanding current PAs, creating new PAs, and creating corridors that connect existing PAs, we used a spatially explicit three-phase approach. Our findings identified 26,508 km2 of priority areas to be included in the current PA network, potentially assisting Indonesia in meeting its post-2020 GBF target, if our approach is replicated across Indonesia as a national or sub-national analysis. We discuss various land management options through other effective area-based conservation measures (OECMs) and the costs to deliver this strategy.
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