Observatorio de I+D+i UPM

Memorias de investigación
Artículos en revistas:
Modelling species distributions with penalised logistic regressions: A comparison with maximum entropy models
Año:2011
Áreas de investigación
  • Ecología
Datos
Descripción
An important aspect of species distribution modelling is the choice of the modelling method because a suboptimal method may have poor predictive performance. Previous comparisons have found that novel methods, such as Maxent models, outperform well-established modelling methods, such as the standard logistic regression. These comparisons used training samples with small numbers of occurrences per estimated model parameter, and this limited sample size may have caused poorer predictive performance due to overfitting. Our hypothesis is that Maxent models would outperform a standard logistic regression because Maxent models avoid overfitting by using regularisation techniques and a standard logistic regression does not. Regularisation can be applied to logistic regression models using penalised maximum likelihood estimation. This estimation procedure shrinks the regression coefficients towards zero, causing biased predictions if applied to the training sample but improving the accuracy of new predictions. We used Maxent and logistic regression (standard and penalised) to analyse presence/pseudo-absence data for 13 tree species and evaluated the predictive performance (discrimination) using presence¿absence data. The penalised logistic regression outperformed standard logistic regression and equalled the performance of Maxent. The penalised logistic regression may be considered one of the best methods to develop species distribution models trained with presence/pseudo-absence data, as it is comparable to Maxent. Our results encourage further use of the penalised logistic regression for species distribution modelling, especially in those cases in which a complex model must be fitted to a sample with a limited size.
Internacional
Si
JCR del ISI
Si
Título de la revista
ECOLOGICAL MODELLING
ISSN
0304-3800
Factor de impacto JCR
1,871
Información de impacto
Volumen
222
DOI
10.1016/j.ecolmodel.2011.04.015
Número de revista
13
Desde la página
2037
Hasta la página
2041
Mes
MAYO
Ranking
Esta actividad pertenece a memorias de investigación
Participantes
  • Autor: Aitor Gaston Gonzalez (UPM)
  • Autor: Juan Ignacio Garcia Viñas (UPM)
Grupos de investigación, Departamentos, Centros e Institutos de I+D+i relacionados
  • Creador: Grupo de Investigación: Ecología y Gestión Forestal Sostenible
  • Departamento: Producción Vegetal: Botánica y Protección Vegetal
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