Abstract
AbstractAimHuman land‐use and climate change are two of the main threats affecting biodiversity, especially in arid/semiarid regions. The most effective way to protect the species in these ecosystems against these threats is through the delimitation of protected areas (PAs). However, such PAs need to be targeted cost‐efficiently and consider future climate change. We identify priority areas to preserve small mammal species in the Caatinga in the present and in a future of climate changes. We also evaluate how well these priority areas are protected by currently PAs and identify ways forward to improve their protection.LocationThe Caatinga Dry Forest, Northeast Brazil.MethodsWe use ecological niche models and Zonation spatial prioritisation software to identify the top 30% priority areas to preserve small mammal species under current climate and land use scenarios, besides considering optimistic and pessimistic scenarios of future climate change. We also evaluate how much these priority areas are covered by current PAs, identify ways to further improve their protection using hierarchical mask analysis, and by evaluating species mean distribution coverage.ResultsThe consequences of climate change will not hugely impact the distribution of priority areas for species conservation in the Caatinga. Around 13% of the identified priority areas overlap with current PAs, and planning the expansion of PAs considering integral protection areas increases the coverage of priority areas to more than 18% and captures more than 72% of species suitable area.Main ConclusionsOur prioritisations take into account climate change and provide low risk if conducted as a ‘no‐regrets’ conservation action. These priority areas are poorly supported by the Brazilian PA system, and need of further protection. One cost‐effective option could be to upgrade some sustainable use PAs into more restrictive ones. Securing these priority areas helps preserve the long‐term ecosystem functioning and to prevent biodiversity loss in a changing world.
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