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Mônica Canaan Carvalho Lucas Rezende Gomide Rubens Manoel dos Santos José Roberto Soares Scolforo Luís Marcelo Tavares de Carvalho José Márcio de Mello


Modeling of the ecological niche of vegetal species is useful for understanding the species-environment relationship, for prediction of responses to climate changes and for correct reforestation programs and establishment of plantation’s recommendation. The objective of this work was to establish a model for the distribution of four tree species (Casearia sylvestris, Copaifera langsdorffii, Croton floribundus and Tapirira guianensis), widely used in reforestation projects in the state of Minas Gerais, Brazil. In addition, we analyzed the relationship between environmental characteristics and the occurrence of species and tested the performance of Random Forest and Artificial Neural Networks as modeling methods. These methods were evaluated by their overall accuracy, sensitivity, specificity, Kappa, true skill statistic and the area under the receiver operating curve. The results showed the species Casearia sylvestris, Copaifera langsdorffii and Tapirira guianensis widely occurring in the state of Minas Gerais, including a broad range of environmental variables. Croton floribundus had restricted occurrence in the southern state, showing narrow environmental variation. The resulting algorithms demonstrated greater performance when modeling restricted geographic and environmental species, as well as species occurring with high prevalence in data. The algorithm Random Forest performed better for distribution modeling of all species, although the results varied for each metric and species. The maps generated had acceptable metrics and are supported by and ecological information obtained from other sources, constituting a useful tool to understand the ecology and biogeography of the target species.

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CARVALHO, Mônica Canaan et al. MODELING ECOLOGICAL NICHE OF TREE SPECIES IN BRAZILIAN TROPICAL AREA. CERNE, [S.l.], v. 23, n. 2, p. 229-240, june 2017. ISSN 2317-6342. Available at: <>. Date accessed: 23 aug. 2017.
Artificial Neural Networks; Phytogeography; Random Forest