Scientific Area
Abstract Detail
Nº613/1404 - A Species Distribution Model to elucidate the occurrence of an endemic species
Format: ORAL
Authors
Paola De Giorgi1, Gabriel Cainelli1, Daniela Ciccarelli1,2, Gianni Bedini1,2
Affiliations
1 University of Pisa, Pisa, Italy
2 Centro interdipartimentale per lo studio degli effetti del cambiamento climatico (CIRSEC), Pisa, Italy
Abstract
The presence of species in ecological communities mainly depends on abiotic factors, biotic factors, and their dispersal ability. Species Distribution Models serve as valuable statistical tools to clarify the distribution of a species considered the relationships among multiple species and their interaction with the environment. Our aim is to determine how biotic and abiotic factors influence the spatial distribution of Crocus etruscus Parl., an endemic geophyte of Central Italy. We constructed a hierarchical mixed-effects model using the lme4 package in R programming language, with the presence of C. etruscus as the response variable. We conducted vegetation surveys to identify the dominant tree species coexisting with C. etruscus, considering them as biotic factors. Subsequently, we combined these factors with the classes of Corine Land Cover to incorporate random effects into our analysis. Functional leaf traits linked to the Leaf Economic Spectrum (leaf area, specific leaf area, leaf dry matter content) represented additional biotic variables. The leaf traits of C. etruscus were determined on samples collected during the vegetation surveys. Then the fixed-effect explanatory variables were the abiotic variables, sourced from the online databases WorldClim and SoilGrids, and the biotic variables, represented by the functional leaf traits of C. etruscus. This study provides an assessment of the influence of abiotic and biotic factors on the occurrence of C. etruscus. Such insights provide valuable guidance for the management of this species and aid in the identification of key areas for further exploration, thereby contributing to a comprehensive understanding of its distribution range.