Planning from scratch: a new modelling approach for designing protected areas in remote, data-poor regions.


Human pressure on ecosystems has strongly increased over the last decades and now impacts even the most remote regions. To help mitigate these impacts, it is crucial to designate protected areas in regions that retain a high level of ecological integrity. However, ecological data remain scarce for many such areas, making the systematic design of new protected zones challenging. Following a request from local managers, we developed an original methodological approach to help design new zoning for a pre-existing protected area in a remote, data-poor Sahelian wetland of southern Chad, a vast area rich in biodiversity and exploited by diverse human activities. The method involved first collecting extensive aerial survey data (6252 records) on birds and mammals and then analysing this through a combination of distance sampling and density surface modelling. The biodiversity data, combined with ecological predictors, helped model species distribution layers that were then incorporated with socio-economic constraints into the systematic conservation planning tool Marxan. This approach produced an array of protected zoning options that met three levels of conservation objectives set by experts, corresponding to proportions of individuals from given species to protect in the proposed protected area. Frequent exchanges with local managers allowed the analyses to be refined, resulting in seven potential scenarios to be considered for conservation purposes. Synthesis and applications. In a context of high data scarcity, lack of access and short-term conservation objectives, this combined approach that optimizes newly obtained data via a suite of modelling tools can facilitate identifying and protecting natural areas in regions most in need of urgent conservation policy.

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