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Global Ecology and Biogeography

Testing species distribution models across space and time: high latitude butterflies and recent warming

Authors

  • Anne Eskildsen,

    Corresponding author
    1. Wildlife Ecology Group Department of Bioscience, Aarhus University, Rønde, Denmark
    2. Ecoinformatics and Biodiversity Group, Department of Bioscience, Aarhus University, Aarhus, Denmark
    • Correspondence: Anne Eskildsen, Department of Bioscience, Aarhus University, Grenåvej 14, DK‐8410 Rønde, Denmark. E‐mail: aes@dmu.dk

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  • Peter C. le Roux,

    1. Department of Geosciences and Geography, University of Helsinki, Finland
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  • Risto K. Heikkinen,

    1. Finnish Environment Institute, Natural Environment Centre, Helsinki, Finland
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  • Toke T. Høye,

    1. Wildlife Ecology Group Department of Bioscience, Aarhus University, Rønde, Denmark
    2. Ecoinformatics and Biodiversity Group, Department of Bioscience, Aarhus University, Aarhus, Denmark
    3. Arctic Research Centre, Aarhus University, Aarhus, Denmark
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  • W. Daniel Kissling,

    1. Ecoinformatics and Biodiversity Group, Department of Bioscience, Aarhus University, Aarhus, Denmark
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  • Juha Pöyry,

    1. Finnish Environment Institute, Natural Environment Centre, Helsinki, Finland
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  • Mary S. Wisz,

    1. Arctic Research Centre, Aarhus University, Aarhus, Denmark
    2. Greenland Climate Research Centre, Greenland Institute of Natural Resources, Nuuk, Greenland
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  • Miska Luoto

    1. Department of Geosciences and Geography, University of Helsinki, Finland
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  • Editor: Jeremy Kerr

Abstract

Aim

To quantify whether species distribution models (SDMs) can reliably forecast species distributions under observed climate change. In particular, to test whether the predictive ability of SDMs depends on species traits or the inclusion of land cover and soil type, and whether distributional changes at expanding range margins can be predicted accurately.

Location

Finland

Methods

Using 10‐km resolution butterfly atlas data from two periods, 1992–99 (t1) and 2002–09 (t2), with a significant between‐period temperature increase, we modelled the effects of climatic warming on butterfly distributions with boosted regression trees (BRTs) and generalized additive models (GAMs). We evaluated model performance by using the split‐sample approach with data from t1 (‘non‐independent validation’), and then compared model projections based on data from t1 with species' observed distributions in t2 (‘independent validation’). We compared climate‐only SDMs to SDMs including land cover, soil type, or both. Finally, we related model performance to species traits and compared observed and predicted distributional shifts at northern range margins.

Results

SDMs showed fair to good model fits when modelling butterfly distributions under climate change. Model performance was lower with independent compared with non‐independent validation and improved when land cover and soil type variables were included, compared with climate‐only models. SDMs performed less well for highly mobile species and for species with long flight seasons and large ranges. When forecasting changes at northern range margins, correlations between observed and predicted range shifts were predominantly low.

Main conclusions

SDMs accurately describe current distributions of most species, yet their performance varies with species traits and the inclusion of land cover and soil type variables. Moreover, their ability to predict range shifts under climate change is limited, especially at the expanding edge. More tests with independent validations are needed to fully understand the predictive potential of SDMs across taxa and biomes.

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