Comparing machine learning classifiers in potential distribution modelling

Species` potential distribution modelling consists of building a representation of the fundamental ecological requirements of a species from biotic and abiotic conditions where the species is known to occur. Such models can be valuable tools to understand the biogeography of species and to support the prediction of its presence/absence considering a particular environment scenario. This paper investigates the use of different supervised machine learning techniques to model the potential distribution of 35 plant species from Latin America. Each technique was able to extract a different representation of the relations between the environmental conditions and the distribution profile of the species. The experimental results highlight the good performance of random trees classifiers, indicating this particular technique as a promising candidate for modelling species` potential distribution. (C) 2010 Elsevier Ltd. All rights reserved.

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

FAPESP (Fundacao de Amparo a Pesquisa do Estado de Sao Paulo)

CNPq (Conselho Nacional de Desenvolvimento Cientifico e Tecnologico)

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)