Predicting potential distribution of Quercus suber in Italy based on ecological niche models: Conservation insights and reforestation involvements - Vessella, Federico; Schirone, Bartolomeo (Forest Ecology and Management, 2013)
Different statistical techniques have been used to model species potential distribution related to environ- mental variables. This paper provides a comprehensive assessments of GARP and MaxEnt methods, and investigates for the first time the probability of occurrence of cork oak (Quercus suber L.) in Italy based on ecological niche modelling approaches. A detailed distribution of the species was achieved during a 3- year National Project (SuberItalia) and 17 environmental layers were employed to obtain the potential distribution of cork oak. The performance of the models were measured using the receiver operating characteristic (ROC) approach and Cohen’s Kappa statistic. Results achieved by GARP and MaxEnt showed as the drought and the cold stresses are the main factors affecting cork oak occurrence in Italy. Moreover, the accuracy of the obtained prediction maps were compared to a specifically calibrated geo-statistical method at regional scale, pointing out a preliminary geographical assessment of the suitable surfaces to set apart for cork oak forest expansion in Italy, thereby useful to address reforestation and conserva- tion concerns to face the ongoing area reduction of these forests.
Keywords: Conservation and reforestation areas; Distribution patterns; GARP; Maxent; Quercus suber

Dealing with vagueness in complex forest landscapes: A soft classification approach through a niche-based distribution model - Amici, Valerio (Ecological Informatics, 2011)
The increasing interest in biodiversity conservation has led to the development of new approaches to facilitate ecologically based conservation policies and management plans. In this context, the development of effective methods for the classiﬁcation of forest types constitutes a crucial issue as forests represent the most widespread vegetation structure and play a key role in ecosystem functioning. In this study a maximum entropy approach (Maxent) to forest type classiﬁcation in a complex Mediterranean area, has been investigated. Maxent, a niche-based model of species/habitat distribution, allowed researchers to estimate the potential distribution of four forest types: Holm oak, Mixed oak, Mixed broadleaved and Riparian forests. The Maxent model's internal tests have proved a powerful tool for estimating the model's accuracy and analyzing the effects of the most important variables in the produced models. Moreover the comparison with a spectral response-based fuzzy classiﬁcation, showed a higher accuracy in the Maxent outputs, demonstrating how the use of environmental variables, combined with spectral information in the classiﬁcation of natural or seminatural land cover classes, improves map accuracies. The modeling approach followed by this study, taking into account the uncertainty proper of the natural ecosystems and the use of environmental variables in land cover classiﬁcation, can represent a useful approach to making more efﬁcient and effective ﬁeld inventories and to developing effective conservation policies.
The following values have no corresponding Zotero field: publisher: Elsevier B.V.
Keywords: classification uncertainty; Forecasting forests; Forest cover map; Fuzzy set; Maxent; Remote sensing