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RESEARCH ARTICLE
Contents Vol 35(3)

The toad ahead: challenges of modelling the range and spread of an invasive species

Benjamin L. Phillips A D , Joseph D. Chipperfield B and Michael R. Kearney C
+ Author Affiliations
- Author Affiliations

A School of Biological Sciences A08, University of Sydney, NSW 2006, Australia.

B UKPopNet, Department of Biology, University of York, North Yorkshire, YO10 5YW, United Kingdom.

C Department of Zoology, University of Melbourne, Vic. 3010, Australia.

D Corresponding author. Email: bphi4487@mail.usyd.edu.au

Wildlife Research 35(3) 222-234 https://doi.org/10.1071/WR07101
Submitted: 25 July 2007  Accepted: 24 December 2007   Published: 20 May 2008

Abstract

An ability to predict the rate at which an organism spreads its range is of growing importance because the process of spread (during invasion by an exotic species) is almost identical to that occurring at the expanding range margins of a native species undergoing range shifts in response to climate change. Thus, the methods used for modelling range spread can also be employed to assess the distributional implications of climate change. Here we review the history of research on the spread of cane toads in Australia and use this case study to broadly examine the benefits and pitfalls of various modelling approaches. We show that the problems of estimating the current range, predicting the future range, and predicting the spread rate are interconnected and inform each other. Generally, we argue that correlative approaches to range-prediction are unsuitable when applied to invasive species and suggest that mechanistic methods are beginning to look promising (despite being more difficult to execute), although robust comparisons of correlative versus mechanistic predictions are lacking. Looking to the future, we argue that mechanistic models of range advance (drawing from both population ecology and environmental variation) are the approaches most likely to yield robust predictions. The complexity of these approaches coupled with the steady rise in computing power means that they have only recently become computationally tractable. Thus, we suggest that the field is only recently in a position to incorporate the complexity necessary to robustly model the rate at which species shift their range.


Acknowledgements

This review would not have been possible without the support of Rick Shine, or without travel supported by a grant from the Environmental Futures Network (to BLP). We thank Barbara Anderson for organising a workshop (funded by UKPopNet), which prompted the review of the methods outlined in the latter parts of the paper. We thank Adnan Moussalli, Stuart Baird, Arnaud Estoup, Jason Kolbe and Mark Urban for lively discussions around all these issues. Arnaud Estoup and Jason Kolbe also provided access to unpublished manuscripts for which we are grateful. Three anonymous reviewers greatly improved the manuscript. Bob Sutherst kindly reran his 1995 CLIMEX prediction on a new, high-resolution climate surface for the construction of Fig. 2. Additional funding was provided by the ARC (MRK, BLP) and UKPopNet (JDC).


References

Araujo, M. B. , Pearson, R. G. , Thuiller, W. , and Erhard, M. (2005). Validation of species–climate impact models under climate change. Global Change Biology 11, 1504–1513.
| Crossref | GoogleScholarGoogle Scholar | Borchers D., Buckland S. T., and Zucchini W. (2002). ‘Estimating Animal Abundance.’ (Springer: London.)

Broennimann, O. , Treier, U. A. , Muller-Scharer, H. , Thuiller, W. , Peterson, A. T. , and Guisan, A. (2007). Evidence of climatic niche shift during biological invasion. Ecology Letters 10, 701–709.
| Crossref | GoogleScholarGoogle Scholar | PubMed | Busby J. R. (1991). BIOCLIM – A bioclimate analysis and prediction system. In ‘Nature Conservation: Cost Effective Biological Surveys and Data Analysis’. (Eds C. R. Margules and M. P. Austin.) pp. 64–68. (CSIRO: Melbourne.)

Campbell G. S., and Norman J. M. (1998). ‘Environmental Biophysics.’ (Springer: New York.)

Carpenter, G. , Gillison, A. N. , and Winter, J. (1993). DOMAIN: a flexible modelling procedure for mapping potential distributions of plants and animals. Biodiversity and Conservation 2, 667–680.
| Crossref | GoogleScholarGoogle Scholar | Elith J., and Burgman M. A. (2003). Habitat models for population viability analysis. In ‘Population Viability in Plants: Conservation, Management and Modeling of Rare Plants’. (Eds C. A. Brigham and M. W. Schwartz.) pp. 203–235. (Springer-Verlag: Berlin.)

Elith, J. , Graham, C. H. , Anderson, R. P. , Dudik, M. , and Ferrier, S. , et al. (2006). Novel methods improve prediction of species’ distributions from occurrence data. Ecography 29, 129–151.
| Crossref | GoogleScholarGoogle Scholar | Elton C. S. (1958). ‘The Ecology of Invasions by Animals and Plants.’ (Methuen: London.)

Estoup, A. , Beaumont, M. , Sennedot, F. , Moritz, C. , and Cornuet, J.-M. (2004). Genetic analysis of complex demographic scenarios: spatially expanding populations of the cane toad, Bufo marinus. Evolution 58, 2021–2036.
| Crossref | GoogleScholarGoogle Scholar | PubMed | Floyd R. B., Boughton W. C., Easteal S., Sabath M. D., and van Beurden E. K. (1981). The distribution records of the marine toad (Bufo marinus). 1. Australia. Griffith University, Brisbane.

Fortin, M.-J. , Keitt, T. H. , Maurer, B. A. , Taper, M. L. , Kaufman, D. M. , and Blackburn, T. M. (2005). Species’ geographic ranges and distributional limits: pattern analysis and statistical issues. Oikos 108, 7–17.
| Crossref | GoogleScholarGoogle Scholar | Gaston K. J. (2003). ‘The Structure and Dynamics of Geographic Ranges.’ (Oxford University Press: Oxford.)

Gaston, K. J. , Quinn, R. M. , Wood, S. , and Arnold, H. R. (1996). Measures of geographic range size: the effects of sample size. Ecography 19, 259–268.
| Crossref | GoogleScholarGoogle Scholar | Gates D. M. (1980). ‘Biophysical Ecology.’ (Springer Verlag: New York.)

Goss-Custard, J. , Burton, N. H. K. , Clark, N. A. , Ferns, P. N. , and McGrorty, S. , et al. (2006). Test of a behaviour-based individual-based model: response of shorebird mortality to habitat loss. Ecological Applications 16, 2215–2222.
| Crossref | GoogleScholarGoogle Scholar | PubMed | Grimm V., and Railsback S. F. (2005). ‘Individual-based Modeling and Ecology.’ (Princeton University Press: Oxford.)

Grimm, V. , Berger, U. , Bastiansen, F. , Eliassen, S. , and Ginot, V. , et al. (2006). A standard protocol for describing individual-based and agent-based models. Ecological Modelling 198, 115–126.
| Crossref | GoogleScholarGoogle Scholar | Hengeveld R. (1989). ‘Dynamics of Biological Invasions.’ (Chapman and Hall: New York.)

Hickling, R. , Roy, D. B. , Hill, J. K. , and Thomas, C. D. (2005). A northward shift of range margins in British Odonata. Global Change Biology 11, 502–506.
| Crossref | GoogleScholarGoogle Scholar | Holt R. D., Barfield M., and Gomulkiewicz R. (2006). Theories of niche conservatism and evolution. Could exotic species be potential tests? In ‘Species Invasions: Insights into Ecology, Evolution, and Biogeography’. (Eds D. F. Sax, J. J. Stachowicz and S. D. Gaines.) (Sinauer Associates: Sunderland, MA.)

Hooten, M. B. , Wikle, C. K. , Dorazio, R. M. , and Royle, J. A. (2007). Hierarchical spatio-temporal matrix models for characterizing invasions. Biometrics 63, 558–567.
| Crossref | GoogleScholarGoogle Scholar | PubMed | McCarthy M. A. (2007). ‘Bayesian Methods for Ecology.’ (Cambridge University Press: Cambridge.)

Meynard, C. N. , and Quinn, J. F. (2007). Predicting species distributions: a critical comparison of the most common statistical models using artificial species. Journal of Biogeography 34, 1455–1469.
| Crossref | GoogleScholarGoogle Scholar | Nix H. A. (1986). biogeographic analysis of the Australian elapid snakes. In ‘Atlas of Elapid Snakes’. (Ed. R. Longmore.) pp. 4–15. (Australian Government Publishing Service: Canberra.)

O’Rourke J. (1998). ‘Computational Geometry in C.’ (Cambridge University Press: Cambridge.)

Parmesan, C. (2006). Ecological and evolutionary responses to recent climate change. Annual Review of Ecology and Systematics 37, 637–669.
| Crossref | GoogleScholarGoogle Scholar | Shigesada N., and Kawasaki K. (1997). ‘Biological Invasions: Theory and Practice.’ (Oxford University Press: Oxford.)

Simberloff, D. (2003). How much information on population biology is needed to manage introduced species? Conservation Biology 17, 83–92.
| Crossref | GoogleScholarGoogle Scholar | Williamson M. (1996). ‘Biological Invasions.’ (Chapman and Hall: London.)

Worton, B. J. (1995). Using Monte Carlo simulation to evaluate kernel-based home range estimators. Journal of Wildlife Management 59, 794–800.
| Crossref | GoogleScholarGoogle Scholar |

Yamamura, K. , Moriya, S. , Tanaka, K. , and Shimizu, T. (2007). Estimation of the potential speed of range expansion of an introduced species: characteristics and applicability of the gamma model. Population Ecology 49, 51–62.
| Crossref | GoogleScholarGoogle Scholar |