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Assessing the efficiency of protected areas to represent biodiversity: a small island case study

  • Articles in SCI Journals
  • Apr, 2016

Vergílio, M., Fonseca, C., Calado, H., Borges, P.A.V., Elias, R.B., Gabriel, R., Martins. A.F. de, Azevedo, E.B. & Cardoso, P. (2016) Assessing the efficiency of protected areas to represent biodiversity: a small island case study.

Environmental Conservation, 43(4), 337-349. DOI:10.1017/S037689291600014X (IF2016 1,826; Q1 Biodiversity Conservation)
Summary:

Protected areas (PAs) have been selected using either subjective or objective criteria applied to an extremely limited subset of biodiversity. Improved availability of species distribution data, better statistical tools to predict species distributions and algorithms to optimize spatial conservation planning allow many impediments to be overcome, particularly on small islands. This study analyses whether 219 species are adequately protected by PAs on Pico Island (the Azores, Portugal), and if they are as efficient as possible, maximizing species protection while minimizing costs. We performed distribution modelling of species’ potential distributions, proposed individual conservation targets (considering the context of each species in the archipelago and their current conservation status) to determine the efficiency of current PAs in meeting such targets and identify alternative or complementary areas relevant for conservation. Results showed that current PAs do not cover all taxa, leaving out important areas for conservation. We demonstrate that by using optimization algorithms it is possible to include most species groups in spatial conservation planning in the Azores with the current resources. With increasing availability of data and methods, this approach could be readily extended to other islands and regions with high endemism levels.


https://www.cambridge.org/core/journals/environmental-conservation/article/assessing-the-efficiency-of-protected-areas-to-represent-biodiversity-a-small-island-case-study/F97DC18ABB676A42D75FEE255C3DA104