Abstract
Species Distribution Models (SDMs) have been increasingly used in biodiversity conservation tasks, especially for rare and threatened species. In this study, we selected Silene marizii, a near-threatened endemic species distributed in the central and north-western Iberian Peninsula. We used a total of 88 occurrences, reduced to 67 after accounting for spatial correlation, obtained from field surveys, herbaria and GBIF. As potential predictors, we used 34 variables (23 bioclimatic, 7 edaphic, and 4 topographic) in Maxent. The Maxent model performed well, demonstrating high predictive accuracy. The predictors that contributed most to the model were precipitation seasonality, precipitation amount of the driest month, pH index, weight percentage of the sand particles, mean monthly precipitation amount of the coldest quarter, mean diurnal air temperature range and mean daily mean air temperature of the driest quarter. Our results suggested the following areas for further exploration of new populations and, if necessary, for reintroductions and translocation efforts: northern half of Portugal, southern Galicia, the northwest of Zamora province and several mountain ranges in the Central System. In conclusion, robust predictive models are now fundamental for conducting more efficient fieldwork, ultimately improving conservation and management plans for rare and threatened species such as S. marizii.
Published Version
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