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Biological Invasions

, Volume 17, Issue 11, pp 3197–3210 | Cite as

Buffel grass and climate change: a framework for projecting invasive species distributions when data are scarce

  • Tara G. MartinEmail author
  • Helen Murphy
  • Adam Liedloff
  • Colette Thomas
  • Iadine Chadès
  • Garry Cook
  • Rod Fensham
  • John McIvor
  • Rieks D. van Klinken
Original Paper

Abstract

Invasive species pose a substantial risk to native biodiversity. As distributions of invasive species shift in response to changes in climate so will management priorities and investment. To develop cost-effective invasive species management strategies into the future it is necessary to understand how species distributions are likely to change over time and space. For most species however, few data are available on their current distributions, let alone projected future distributions. We demonstrate the benefits of Bayesian Networks (BNs) for projecting distributions of invasive species under various climate futures, when empirical data are lacking. Using the introduced pasture species, buffel grass (Cenchrus ciliaris) in Australia as an example, we employ a framework by which expert knowledge and available empirical data are used to build a BN. The framework models the susceptibility and suitability of the Australian continent to buffel grass colonization using three invasion requirements; the introduction of plant propagules to a site, the establishment of new plants at a site, and the persistence of established, reproducing populations. Our results highlight the potential for buffel grass management to become increasingly important in the southern part of the continent, whereas in the north conditions are projected to become less suitable. With respect to biodiversity impacts, our modelling suggests that the risk of buffel grass invasion within Australia’s National Reserve System is likely to increase with climate change as a result of the high number of reserves located in the central and southern portion of the continent. In situations where data are limited, we find BNs to be a flexible and inexpensive tool for incorporating existing process-understanding alongside bioclimatic and edaphic variables for projecting future distributions of species invasions.

Keywords

BN Bayesian belief network Expert judgement Expert elicitation Invasive species Exotic pasture Cenchrus ciliaris Species distribution models 

Notes

Acknowledgments

This project was funded through the Department of Environment, Water, Heritage and the Arts and some initial findings of this research were published as a report (Martin et al. 2012b). This project would not have been possible without the generous contribution of time and expertise from scientists and natural resource managers across the country: Gary Bastin, Kerrie Bennison, John Clarkson, Keith Ferdinands, Margaret Friedel, David Gobbett, Tony Grice, Ben Lawson, Neil Macleod, Cam McDonald, Samantha Setterfield, Stephen VanLeeuwen, Wayne Vogler, and Dick Williams. We thank our colleagues involved in the wider National Reserve System project: Michael Dunlop, Simon Ferrier, Tom Harwood, David Hilbert, Alan House, Suzanne Prober, Anita Smyth and Kristen Williams. Finally, our gratitude to Jennifer Firn, John Dwyer and two anonymous reviewers for providing helpful comments on this manuscript.

Supplementary material

10530_2015_945_MOESM1_ESM.docx (23 kb)
Supplementary material 1 (DOCX 22 kb)

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Tara G. Martin
    • 1
    Email author
  • Helen Murphy
    • 2
  • Adam Liedloff
    • 3
  • Colette Thomas
    • 4
  • Iadine Chadès
    • 1
  • Garry Cook
    • 3
  • Rod Fensham
    • 5
    • 6
  • John McIvor
    • 1
  • Rieks D. van Klinken
    • 1
  1. 1.CSIRO, Ecosciences PrecinctDutton ParkAustralia
  2. 2.CSIROAthertonAustralia
  3. 3.CSIRODarwinAustralia
  4. 4.Catchment to Reef Research Group, TropWATER, Australian Tropical Science and Innovation Precinct, Building 145James Cook UniversityDouglasAustralia
  5. 5.Department of Environment and Heritage Protection, Queensland HerbariumThe Queensland GovernmentMt Coot-thaAustralia
  6. 6.School of Biological SciencesThe University of QueenslandBrisbaneAustralia

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