Uncertainty in ensemble forecasting of species distribution

Authors

  • LAËTITIA BUISSON,

    1. Laboratoire Evolution et Diversité Biologique, UMR CNRS 5174, Université Paul Sabatier, 118 route de Narbonne, 31062 Toulouse cedex 4, France
    2. Laboratoire d'Ecologie Fonctionnelle, UMR CNRS 5245, ENSAT, Avenue de l'Agrobiopole, BP 32607, Auzeville-Tolosane, 31326 Castanet-Tolosan, France
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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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  • NICOLAS CASAJUS,

    1. Université du Québec à Rimouski, 300 allée des Ursulines, Rimouski, QC, G5L 3A1, Canada
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  • SOVAN LEK,

    1. Laboratoire Evolution et Diversité Biologique, UMR CNRS 5174, Université Paul Sabatier, 118 route de Narbonne, 31062 Toulouse cedex 4, France
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  • GAËL GRENOUILLET

    1. Laboratoire Evolution et Diversité Biologique, UMR CNRS 5174, Université Paul Sabatier, 118 route de Narbonne, 31062 Toulouse cedex 4, France
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Laëtitia Buisson, tel. +33 5 61 55 69 11, fax + 33 5 61 55 67 28, e-mail: buisson@cict.fr

Abstract

Species distribution modelling has been widely applied in order to assess the potential impacts of climate change on biodiversity. Many methodological decisions, taken during the modelling process and forecasts, may, however, lead to a large variability in the assessment of future impacts. Using measures of species range change and turnover, the potential impacts of climate change on French stream fish species and assemblages were evaluated. Our main focus was to quantify the uncertainty in the projections of these impacts arising from four sources of uncertainty: initial datasets (Data), statistical methods [species distribution models (SDM)], general circulation models (GCM), and gas emission scenarios (GES). Several modalities of the aforementioned uncertainty sources were combined in an ensemble forecasting framework resulting in 8400 different projections. The variance explained by each source was then extracted from this whole ensemble of projections. Overall, SDM contributed to the largest variation in projections, followed by GCM, whose contribution increased over time equalling almost the proportion of variance explained by SDM in 2080. Data and GES had little influence on the variability in projections. Future projections of range change were more consistent for species with a large geographical extent (i.e., distribution along latitudinal or stream gradients) or with restricted environmental requirements (i.e., small thermal or elevation ranges). Variability in projections of turnover was spatially structured at the scale of France, indicating that certain particular geographical areas should be considered with care when projecting the potential impacts of climate change. The results of this study, therefore, emphasized that particular attention should be paid to the use of predictions ensembles resulting from the application of several statistical methods and climate models. Moreover, forecasted impacts of climate change should always be provided with an assessment of their uncertainty, so that management and conservation decisions can be taken in the full knowledge of their reliability.

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