Evaluation of consensus methods in predictive species distribution modelling

Authors

  • Mathieu Marmion,

    Corresponding author
    1. Department of Geography,
    2. Thule Institute, University of Oulu, PO Box 3000, FIN-90014 Oulu, Finland,
      *Correspondence: Mathieu Marmion, Department of Geography, University of Oulu, PO Box 3000, FIN-90014 Oulu, Finland. E-mail: mathieu.marmion@oulu.fi
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  • Miia Parviainen,

    1. Department of Geography,
    2. Thule Institute, University of Oulu, PO Box 3000, FIN-90014 Oulu, Finland,
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  • Miska Luoto,

    1. Department of Geography,
    2. Thule Institute, University of Oulu, PO Box 3000, FIN-90014 Oulu, Finland,
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  • Risto K. Heikkinen,

    1. Finnish Environment Institute, Research Program for Biodiversity, PO Box 140, FIN-00251 Helsinki, Finland,
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  • Wilfried Thuiller

    1. Laboratoire d’Ecologie Alpine, UMR CNRS 5553, Université Joseph Fourier, BP 53, 38041 Grenoble Cedex 9, France
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*Correspondence: Mathieu Marmion, Department of Geography, University of Oulu, PO Box 3000, FIN-90014 Oulu, Finland. E-mail: mathieu.marmion@oulu.fi

ABSTRACT

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.

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