In this study, we used a maximum entropy (MaxEnt) approach to model the distribution of the rare European amphibian Pelobates fuscus insubricus, with the final goal of identifying suitable areas for its conservation. We generated the model starting from a dataset of all locations where this species’ presence was confirmed for the region of piedmont in 2004–2010, which consisted of only 15 occurrence records. To verify the working hypothesis that population survival is higher in areas where Maxent identifies higher distribution probability values, we used suitability indexes generated by the model to compare the “historical” (before 1980) and “recent” (1980–1996) distributions of P. f. insubricus populations in the piedmont region. The average area-under-the-curve value (0.878, s = 0.075) of the Maxent model proved significantly informative. Using the Bonferroni confidence interval, we demonstrated that surviving populations occupy geographic areas characterized by significantly higher potential suitability (p < 0.05), and we selected areas accordingly. We therefore conclude that, in our case study, modelling the distribution of rare species may represent a useful strategy to select areas where these species are likely to persist. To further evaluate this approach, we suggest testing it on the study of other rare species.
Identifying priority areas for conservation of spadefoot toad, Pelobates fuscus insubricus using a maximum entropy approach
GIOVANNINI, ANDREA;SEGLIE, DANIELE;GIACOMA, Cristina
2014-01-01
Abstract
In this study, we used a maximum entropy (MaxEnt) approach to model the distribution of the rare European amphibian Pelobates fuscus insubricus, with the final goal of identifying suitable areas for its conservation. We generated the model starting from a dataset of all locations where this species’ presence was confirmed for the region of piedmont in 2004–2010, which consisted of only 15 occurrence records. To verify the working hypothesis that population survival is higher in areas where Maxent identifies higher distribution probability values, we used suitability indexes generated by the model to compare the “historical” (before 1980) and “recent” (1980–1996) distributions of P. f. insubricus populations in the piedmont region. The average area-under-the-curve value (0.878, s = 0.075) of the Maxent model proved significantly informative. Using the Bonferroni confidence interval, we demonstrated that surviving populations occupy geographic areas characterized by significantly higher potential suitability (p < 0.05), and we selected areas accordingly. We therefore conclude that, in our case study, modelling the distribution of rare species may represent a useful strategy to select areas where these species are likely to persist. To further evaluate this approach, we suggest testing it on the study of other rare species.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.