Modelling horses for novel climate courses: insights from projecting potential distributions of native and alien Australian acacias with correlative and mechanistic models

Authors

  • Bruce L. Webber,

    Corresponding author
    1. CSIRO Ecosystem Sciences and Climate Adaptation Flagship, Private Bag 5, Wembley, WA 6913, Australia
    2. School of Plant Biology, The University of Western Australia, 35 Stirling Highway, Crawley, WA 6009, Australia
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  • Colin J. Yates,

    1. Science Division, Western Australian Department of Environment and Conservation, LMB 104, Bentley Delivery Centre, Perth, WA 6983, Australia
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  • David C. Le Maitre,

    1. Natural Resources and Environment, CSIR, PO Box 320, Stellenbosch 7599, South Africa
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  • John K. Scott,

    1. CSIRO Ecosystem Sciences and Climate Adaptation Flagship, Private Bag 5, Wembley, WA 6913, Australia
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  • Darren J. Kriticos,

    1. CSIRO Ecosystem Sciences, GPO Box 1700, Canberra, ACT 2601, Australia
    2. Cooperative Research Centre for National Plant Biosecurity, Bruce, ACT 2617, Australia
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  • Noboru Ota,

    1. CSIRO Livestock Industries, Private Bag 5, P.O. Wembley, WA 6913, Australia
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  • Asha McNeill,

    1. Science Division, Western Australian Department of Environment and Conservation, LMB 104, Bentley Delivery Centre, Perth, WA 6983, Australia
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  • Johannes J. Le Roux,

    1. Centre for Invasion Biology, Department of Botany and Zoology, Stellenbosch University, Private Bag XI, Matieland 7602, South Africa
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  • Guy F. Midgley

    1. Climate Change Research Group, Kirstenbosch Research Centre, South African National Biodiversity Institute, PBag x7, Claremont, Cape Town 7735, South Africa
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Errata

This article is corrected by:

  1. Errata: Corrigendum Volume 18, Issue 1, 100, Article first published online: 6 December 2011

Bruce Webber, Climate Adaptation Flagship, CSIRO Ecosystem Sciences, Private Bag 5, PO Wembley, WA 6913, Australia.
E-mail: bruce.webber@csiro.au

Abstract

Aim  Investigate the relative abilities of different bioclimatic models and data sets to project species ranges in novel environments utilizing the natural experiment in biogeography provided by Australian Acacia species.

Location  Australia, South Africa.

Methods  We built bioclimatic models for Acacia cyclops and Acacia pycnantha using two discriminatory correlative models (MaxEnt and Boosted Regression Trees) and a mechanistic niche model (CLIMEX). We fitted models using two training data sets: native-range data only (‘restricted’) and all available global data excluding South Africa (‘full’). We compared the ability of these techniques to project suitable climate for independent records of the species in South Africa. In addition, we assessed the global potential distributions of the species to projected climate change.

Results  All model projections assessed against their training data, the South African data and globally were statistically significant. In South Africa and globally, the additional information contained in the full data set generally improved model sensitivity, but at the expense of increased modelled prevalence, particularly in extrapolation areas for the correlative models. All models projected some climatically suitable areas in South Africa not currently occupied by the species. At the global scale, widespread and biologically unrealistic projections by the correlative models were explained by open-ended response curves, a problem which was not always addressed by broader background climate space or by the extra information in the full data set. In contrast, the global projections for CLIMEX were more conservative. Projections into 2070 indicated a polewards shift in climate suitability and a decrease in model interpolation area.

Main conclusions  Our results highlight the importance of carefully interpreting model projections in novel climates, particularly for correlative models. Much work is required to ensure bioclimatic models performed in a robust and ecologically plausible manner in novel climates. We explore reasons for variations between models and suggest methods and techniques for future improvements.

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