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Improving bioregional frameworks for conservation by including mammal distributions



Large identifiable landscape units, such as ecoregions, are used to prioritize global and continental conservation efforts, particularly where biodiversity knowledge is inadequate. Setting biodiversity representation targets using coarse large-scale biogeographic boundaries, can be inefficient and under-representative. Even when using fine-scale biodiversity data, representation deficiencies can occur through misalignment of target distributions with such prioritization frameworks. While this pattern has been recognized, quantitative approaches highlighting misalignments have been lacking, particularly for assemblages of mammal species. We tested the efficacy of Australia's bioregions as a spatial prioritization framework for representing mammal species, within protected areas, in New South Wales. We produced an approach based on mammal assemblages and assessed its performance in representing mammal distributions. Substantial spatial misalignment between New South Wales's bioregions and mammal assemblages was revealed, reflecting deficiencies in the representation of more than half of identified mammal assemblages. Using a systematic approach driven by fine-scale mammalian data, we compared the efficacy of these two frameworks in securing mammalian representation within protected areas. Of the 61 species, 38 were better represented by the mammalian framework, with remaining species only marginally better represented when guided by bioregions. Overall, the rate at which mammal species were incorporated into the protected area network was higher (5.1% ± 0.6 sd) when guided by mammal assemblages. Guided by bioregions, systematic conservation planning of protected areas may be constrained in realizing its full potential in securing representation for all of Australia's biodiversity. Adapting the boundaries of prioritization frameworks by incorporating amassed information from a broad range of taxa should be of conservation significance.