Evaluation of consensus methods in predictive species distribution modelling
Article first published online: 21 MAY 2008
© 2008 The Authors. Journal compilation © 2008 Blackwell Publishing Ltd
Diversity and Distributions
Volume 15, Issue 1, pages 59–69, January 2009
How to Cite
Marmion, M., Parviainen, M., Luoto, M., Heikkinen, R. K. and Thuiller, W. (2009), Evaluation of consensus methods in predictive species distribution modelling. Diversity and Distributions, 15: 59–69. doi: 10.1111/j.1472-4642.2008.00491.x
- Issue published online: 8 DEC 2008
- Article first published online: 21 MAY 2008
- Distribution modelling;
- machine learning methods;
- model selection;
- predictive accuracy;
- regression and classification methods
Aim Spatial modelling techniques are increasingly used in species distribution modelling. However, the implemented techniques differ in their modelling performance, and some consensus methods are needed to reduce the uncertainty of predictions. In this study, we tested the predictive accuracies of five consensus methods, namely Weighted Average (WA), Mean(All), Median(All), Median(PCA), and Best, for 28 threatened plant species.
Location North-eastern Finland, Europe.
Methods The spatial distributions of the plant species were forecasted using eight state-of-the-art single-modelling techniques providing an ensemble of predictions. The probability values of occurrence were then combined using five consensus algorithms. The predictive accuracies of the single-model and consensus methods were assessed by computing the area under the curve (AUC) of the receiver-operating characteristic plot.
Results The mean AUC values varied between 0.697 (classification tree analysis) and 0.813 (random forest) for the single-models, and from 0.757 to 0.850 for the consensus methods. WA and Mean(All) consensus methods provided significantly more robust predictions than all the single-models and the other consensus methods.
Main conclusions Consensus methods based on average function algorithms may increase significantly the accuracy of species distribution forecasts, and thus they show considerable promise for different conservation biological and biogeographical applications.