Integrating spatially explicit habitat projections into extinction risk assessments: a reassessment of Amazonian avifauna incorporating projected deforestation

Aim We aimed to complete the first systematic assessment of extinction risk based on projected population declines derived from spatially explicit habitat projections for any taxonomic group at a regional scale, to use the outputs to ascertain the efficacy of an existing protected area network in covering species of conservation concern, and identify gaps therein. Location This study focused on Amazonia; an area of exceptional biodiversity, currently experiencing the highest absolute rate of forest loss globally but where the proportion of species assessed as ‘threatened’ on the International Union for the Conservation of Nature (IUCN) Red List in the region is below global averages. Methods For all forest‐dependent Amazonian bird species (814), we revised extinction risk estimates by combining data from a spatially explicit deforestation model with generation length estimates. By overlaying distribution maps for these revised threatened species, we identified crisis areas (areas of projected deforestation supporting the highest numbers of threatened species), refugia (areas projected to retain forest supporting the highest numbers of threatened species) and areas of high irreplaceability: short‐ and long‐term priorities for new protected areas (PAs). Results The number of species qualifying as threatened rose substantially from 24 (3%) to 64–92 (8–11%). Areas of particular concern are the crisis and highly irreplaceable areas within the ‘arc of deforestation’ in the southern Brazilian Amazon states of Rondônia, Mato Grosso and Pará. Main conclusions Through a novel application of the IUCN Red List criteria, we present a spatially accurate rendering of the extinction risks of Amazonian birds. Important areas in the Amazon are not secure. We identify priorities for expansion of the PAs network and key locations where protection should be enforced. We recommend a collaborative approach employing our methods to repeat this process for other taxonomic groups.