Enhancing Species Distribution Modelling through novel approaches to monitor an endemic species

Species Distribution Models (SDMs) are statistical tools that relate species-occurrence data to environmental variables. In general, species distributions are influenced by three groups of factors: abiotic, biotic, and movement. However, SDMs typically neglect biotic. This doctoral research addresses that limitation by incorporating biotic variables into SDMs, using Crocus etruscus Parl., an endemic species of central Italy, as the target species. A functional study of C. etruscus and its plant communities was carried out. Intraspecific trait variability was investigated by analysing three leaf functional traits and by examining the ecological strategy of the target species in relation to soil properties and surrounding communities. On the basis of the environmental variables that influenced this trait variability, an abiotic SDM was produced to outline the potential distribution of C. etruscus. Field-validation activities followed, designed to assess the predictive performance of the model under real conditions and to refine current knowledge of the species’ range, including undocumented populations. Lastly, two biotic variables were identified as proxies for interspecific interactions, according to the ecological analysis: Normalised Difference Vegetation Index (NDVI) and Land Cover. These variables were incorporated into the modelling process, together with the abiotic predictors, to estimate the potential distribution of C. etruscus. The inclusion of biotic layers improved both the accuracy and the ecological relevance of the SDM, highlighting the importance of biotic factors in refining species-distribution predictions.