Species prioritization for recovery potential estimation. Case of study: Seasonally dry tropical forest at an inter Andean valley of Cauca River, South America

Seasonally dry tropical forest (SDTF) at the Colombian inter-Andean valley of Cauca River (IVCR) has been under constant transformation. Following the current SDTF’s global distribution, it has remained as small and sparse fragments embedded in an anthropogenic landscape. Information regarding the known species composition is a basic input in any modelling scheme. Environmental data is also needed to understand its influence as an explanatory variable for the species occurrences. Knowledge about the biomes and ecoregions where they have been registered may help to understand the way in which the multiple species have been distributed through environmental gradients. In such a way we could recognize the relevance that IVCR plays for the conservation of SDTF plant species in the long term. From multiple datasets, a database with 1725 plant species was built. After applying different criteria set (endangered, endemic, conservation status, at national and regional level), different species subsets were obtained. For a first subset with the species prioritized, Maxent Algorithm on R Studio has been applied to produce predictive habitat suitability models to support the detection of potential areas for restoration. Restoration scenarios will be built for each subset of prioritized species which can be used for landscape planning purposes.