Maximum entropy modeling of geographic distributions of the flea beetle species endemic in Italy (Coleoptera: Chrysomelidae: Galerucinae: Alticini)

Maximum entropy modeling for predicting the potential suitability distribution of species using presence-only occurrence records and associated environmental factors is one of the most widely used tools in ecology and biogeography. The aim of this study is to identify the potential suitable habitat for 17 Italian endemic species of flea beetles (Coleoptera, Chrysomelidae) by assessing which environmental factors are mostly responsible for current distributions. Potential species distributions were reconstructed by using Maxent application with nineteen bioclimatic variables and three topographic factors. We evaluated the model accuracy by AUC values based on test data, training data and total records, highlighting the high power of Maxent to create potential habitat distribution. Presence records were used to build the final habitat map to obtain the best estimate of the species distribution. To distinguish unsuitable from suitable areas, a reclassification of the probability maps was performed using a 10th percentile training presence logistic threshold. The results of our analysis indicate that species occurrences are significantly associated with bioclimatic factors mainly characterized by annual trend, such as "temperature seasonality" (BIO4) and "precipitation seasonality" (BIO15), and to a lesser extent by variables with quarterly intervals, such as the "mean temperature of driest quarter" (BIO9). Furthermore we performed a cluster analysis finding significant correlation between the distribution of the species considered in the Maxent models and the bioclimatic and topographic variables used for the predictions. Potential suitability distribution modeling of endemic flea beetle species is an appropriate method to identify particular environmental situations that require maximum attention both for the conservation of particular species and the protection of their habitat.