2015
DOI: 10.1016/j.jcz.2015.08.002
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Maximum entropy modeling of geographic distributions of the flea beetle species endemic in Italy (Coleoptera: Chrysomelidae: Galerucinae: Alticini)

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Cited by 42 publications
(27 citation statements)
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“…The SDMs for the two target species and for all the different time-frames were built through the implementation of the ‘biomod2’ package 47 in R environment 39 . Species Distribution Models and cartographic tools are more and more used in recent years to interpret biogeographic processes 36 , 48 , 49 , conservation and biodiversity issues 50 53 , giving interesting results when combined 54 58 . This package permits to obtain Ensemble Models, a powerful technique which proportionally combines models obtained from different processes into one single prediction.…”
Section: Methodsmentioning
confidence: 99%
“…The SDMs for the two target species and for all the different time-frames were built through the implementation of the ‘biomod2’ package 47 in R environment 39 . Species Distribution Models and cartographic tools are more and more used in recent years to interpret biogeographic processes 36 , 48 , 49 , conservation and biodiversity issues 50 53 , giving interesting results when combined 54 58 . This package permits to obtain Ensemble Models, a powerful technique which proportionally combines models obtained from different processes into one single prediction.…”
Section: Methodsmentioning
confidence: 99%
“…The set of environmental predictors used comprises: a) nineteen bioclimatic variables (BIO1-BIO19) [ 39 ] and b) elevation data (ALT), downloaded from the Worldclim database ( http://www.worldclim.com/current/ ); c) two topographic variables, namely SLOPE, representing the incline of the surface, and ASPECT, representing the “exposure”, which is the compass direction that a topographic slope faces [ 40 ]. The latter two variables are expressed in degrees and were derived from a Digital Elevation Model originating from the elevation data, using the “surface tool” in ArcGis Spatial Analyst.…”
Section: Methodsmentioning
confidence: 99%
“…For each model based on the different GCMs used, the Area Under the Curve (AUC) of the Receiver Characteristics Operator (ROC) was used as a weight in the Multivariate Environmental Dissimilarity Index (MEDI) algorithm (Iannella et al, 2017), which proportionally combines different GCMs projections, starting with the MESS (Elith et al, 2010) maps produced by Maxent, which down-weights the extrapolations of the models. Both current and future predicted scenarios were binarized using the 10 th percentile of training presences, which is a reliable and conservative threshold when data are collected at different times by several observers (Freeman & Moisen, 2008;Rebelo & Jones, 2010;Urbani et al, 2015Urbani et al, , 2017.…”
Section: Ecological Niche Modellingmentioning
confidence: 99%