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The Rangeland Journal The Rangeland Journal Society
Journal of the Australian Rangeland Society
RESEARCH ARTICLE

The potential distribution of the woody weed Calotropis procera (Aiton) W.T. Aiton (Asclepiadaceae) in Australia

Enock O. Menge A , Alyson Stobo-Wilson A , Sofia L. J. Oliveira A and Michael J. Lawes A B
+ Author Affiliations
- Author Affiliations

A Research Institute for the Environment and Livelihoods, Charles Darwin University, NT 0909, Australia.

B Corresponding author. Email: Michael.Lawes@cdu.edu.au

The Rangeland Journal 38(1) 35-46 https://doi.org/10.1071/RJ15081
Submitted: 14 August 2015  Accepted: 11 January 2016   Published: 18 February 2016

Abstract

The potential spread of any invasive plant is a central concern in weed risk assessment. Calotropis procera is wind dispersed and forms extensive monospecific stands that reduce the productivity of pastoral land, but its potential distribution and drivers of its spread are not well known. Using maximum entropy methodology, we modelled current and future potential distributions of C. procera in Australia. Occurrence data (n = 5976 presence records) were collated from regional databases and a field survey. Of a set of ‘independent’ environmental correlates, those that best accounted for the observed distribution of C. procera in Australia were distance (km) to roads, average annual rainfall (mm), mean temperature (°C), average wind speed (km/h), beef density and vegetation type, in that order of importance. Current and potential distribution of C. procera was best explained by interactions between anthropogenic disturbance and climatic factors, all underpinned by species characteristics. Models were based on a grid cell size of 5 km × 5 km and model performance was good (mean AUC = 0.916; s.d. = 0.014; AUC = area under the curve; perfect fit = 1). The model showed that C. procera has not saturated its current potential distribution. Models of future spread derived from climate change projections, based on global circulation models in the ‘Representative Concentration Pathway 4.5 emissions scenario for 2035’, show the area suitable for C. procera will increase, increasing the risk the weed poses. Range expansion will occur into all three states surrounding the Northern Territory, but mostly into the north-eastern border regions of Western Australia and north-western Queensland. Joint management of rubber bush at a regional scale across jurisdictions, is urgently advised to avoid future spread of rubber bush and further reductions in pastoral productivity.

Additional keywords: climate change, environmental variables, habitat suitability, invasive species, range expansion, rubber bush.


References

ALA (2014). Atlas of Living Australia. Available at: www.ala.org.au (accessed 20 December 2014).

Ali, T., and Ali, S. I. (1989). Pollination biology of Calotropis procera subsp. hamiltonii (Asclepiadaceae). Phyton (Austria) 29, 175–188.

Ali, T., and Ali, S. I. (1996). Effect of sugar concentration on pollinium germination in some members of Asclepiadaceae. Pakistan Journal of Botany 28, 161–165.
| 1:CAS:528:DyaK2sXjvVyju7s%3D&md5=5bbdbc4506ea92b7063462cfc5a22f5aCAS |

Araújo, 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.
Validation of species–climate impact models under climate change.Crossref | GoogleScholarGoogle Scholar |

Bastin, G., Ludwig, J., Eager, R., Liedloff, A., Andison, R., and Cobiac, M. (2003). Vegetation changes in a semiarid tropical savanna, northern Australia: 1973–2002. The Rangeland Journal 25, 3–19.
Vegetation changes in a semiarid tropical savanna, northern Australia: 1973–2002.Crossref | GoogleScholarGoogle Scholar |

Bebawi, F. F., Campbell, S. D., and Mayer, R. J. (2015). Seed bank longevity and age to reproductive maturity of Calotropis procera (Aiton) W.T. Aiton in the dry tropics of northern Queensland. The Rangeland Journal 37, 239–247.
Seed bank longevity and age to reproductive maturity of Calotropis procera (Aiton) W.T. Aiton in the dry tropics of northern Queensland.Crossref | GoogleScholarGoogle Scholar |

BOM (2015). Bureau of Meteorology Rainfall Districts. Available at: www.bom.gov.au/climate/cdo/about/rain-districts.shtml (accessed 20 May 2015).

Boutraa, T. (2010). Growth performance and biomass partitioning of the desert shrub Calotropis Procera under water stress conditions. Research Journal of Agriculture and Biological Sciences 6, 20–26.

Cabral, J. S., Kreft, H., and Higgins, S. (2012). Linking ecological niche, community ecology and biogeography: insights from a mechanistic niche model. Journal of Biogeography 39, 2212–2224.
Linking ecological niche, community ecology and biogeography: insights from a mechanistic niche model.Crossref | GoogleScholarGoogle Scholar |

Catford, J. A., Daehler, C. C., Murphy, H. T., Sheppard, A. W., Hardesty, B. D., Westcott, D. A., Rejmánek, M., Bellingham, P. J., Pergl, J., Horvitz, C. C., and Hulme, P. E. (2012). The intermediate disturbance hypothesis and plant invasions: Implications for species richness and management. Perspectives in Plant Ecology, Evolution and Systematics 14, 231–241.
The intermediate disturbance hypothesis and plant invasions: Implications for species richness and management.Crossref | GoogleScholarGoogle Scholar |

Csurhes, S. (2009). ‘Weed Risk Assessment: Calotrope–Calotropis procera.’ (Biosecurity Queensland, Queensland Primary Industries and Fisheries: Brisbane.)

Csurhes, S., and Edwards, R. (1998). ‘Potential Environmental Weeds in Australia.’ (The Director of the National Parks and Wildlife: Canberra.)

Davies, W. K., and Sheley, R. (2007). Influence of neighbouring vegetation height on seed dispersal: implications for invasive plant management. Weed Science 55, 626–630.
Influence of neighbouring vegetation height on seed dispersal: implications for invasive plant management.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD2sXhtlKktrrO&md5=30e26d15d45adeb3e72b91b807d3b8e5CAS |

Davis, M. A., Grime, P. J., and Thompson, K. (2000). Fluctuating resources in plant communities: a general theory of invasibility. Journal of Ecology 88, 528–534.
Fluctuating resources in plant communities: a general theory of invasibility.Crossref | GoogleScholarGoogle Scholar |

Dormann, C. F., Schymanski, S. J., Cabral, J., Chuine, I., Graham, C., Hartig, F., Kearney, M., Morin, X., Römermann, C., Schröder, B., and Singer, A. (2012). Correlation and process in species distribution models: bridging a dichotomy. Journal of Biogeography 39, 2119–2131.
Correlation and process in species distribution models: bridging a dichotomy.Crossref | GoogleScholarGoogle Scholar |

Dudík, M. (2007). Maximum entropy density estimation and modelling geographic distributions of species. PhD Thesis, Princeton University, Princeton, NJ, USA.

Eisikowitch, D. (1986). Morpho-ecological aspects on the pollination of Calotropis procera (Asclepiadaceae) in Israel. Plant Systematics and Evolution 152, 185–194.
Morpho-ecological aspects on the pollination of Calotropis procera (Asclepiadaceae) in Israel.Crossref | GoogleScholarGoogle Scholar |

Elith, J., Graham, C. H., Anderson, R. P., Dudík, M., Ferrier, S., Guisan, A., Hijmans, R. J., Huettmann, F., Leathwick, J. R., Lehmann, A., Li, J., Lohmann, L. G., Loiselle, B. A., Manion, G., Moritz, C., Nakamura, M., Nakazawa, Y., Overton, J. M., Peterson, A. T., Phillips, S. J., Richardson, K. S., Scachetti-Pereira, R., Schapire, R. E., Soberón, J., Williams, S., Wisz, M. S., and Zimmermann, N. E. (2006). Novel methods improve prediction of species’ distributions from occurrence data. Ecography 29, 129–151.

ESRI (2011). ‘ArcMap ver. 10.2.1.’ (Environmental Systems Research Institute: Redlands, CA.)

Everitt, B. S. (2002). ‘Cambridge Dictionary of Statistics.’ 2nd edn. (Cambridge University Press: Cambridge, UK.)

Excoffier, L., Foll, M., and Petit, R. J. (2009). Genetic consequences of range expansions. Annual Review of Ecology Evolution and Systematics 40, 481–501.
Genetic consequences of range expansions.Crossref | GoogleScholarGoogle Scholar |

Fourcade, Y., Engler, J. O., Rödder, D., and Secondi, J. (2014). Mapping species distributions with MAXENT using a geographically biased sample of presence data: a performance assessment of methods for correcting sampling bias. PLoS One 9, e97122.
Mapping species distributions with MAXENT using a geographically biased sample of presence data: a performance assessment of methods for correcting sampling bias.Crossref | GoogleScholarGoogle Scholar | 24818607PubMed |

Fox, J. W. (2013). The intermediate disturbance hypothesis should be abandoned. Trends in Ecology & Evolution 28, 86–92.
The intermediate disturbance hypothesis should be abandoned.Crossref | GoogleScholarGoogle Scholar |

Gelbard, J. L., and Belnap, J. (2003). Roads as conduits for exotic lant invasions in a semiarid landscape. Conservation Biology 17, 420–432.
Roads as conduits for exotic lant invasions in a semiarid landscape.Crossref | GoogleScholarGoogle Scholar |

Gioria, M., and Osborne, B. A. (2014). Resource competition in plant invasions: emerging patterns and research needs. Frontiers in Plant Science 5, art. no. 501.
Resource competition in plant invasions: emerging patterns and research needs.Crossref | GoogleScholarGoogle Scholar |

Gleckler, P. J., Gleckler, P. J., Taylor, K. E., and Doutriaux, C. (2008). Performance metrics for climate models. Journal of Geophysical Research 113, D06104.
Performance metrics for climate models.Crossref | GoogleScholarGoogle Scholar |

Gordon, D. R., Onderdonk, D. A., Fox, A. M., and Stocker, R. K. (2008). Consistent accuracy of the Australian weed risk assessment system across varied geographies. Diversity & Distributions 14, 234–242.
Consistent accuracy of the Australian weed risk assessment system across varied geographies.Crossref | GoogleScholarGoogle Scholar |

Gordon, D. R., Mitterdorfer, B., Pheloung, P. C., Ansari, S., Buddenhaggen, C., Chimera, C., Daehler, C. C., Dawson, W., Denslow, J. S., LaRosa, A., Nishida, T., Onderdonk, D. A., Panetta, D. F., Pyšek, P., Randall, R. P., Richardson, D. M., Tshidada, N. J., Virtue, J. G., and Williams, P. A. (2010). Guidance for addressing the Australian Weed Risk Assessment questions. Plant Protection Quarterly 25, 56–74.

Grace, B. S. (2006). The biology of Australian weeds 45. Calotropis procera (Aiton) W.T.Aiton. Plant Protection Quarterly 21, 152–160.

Grice, A. C., and Martin, T. G. (2005). ‘The Management of Weeds and their Impact on Biodiversity in the Rangelands.’ (The CRC for Australian Weed Management: Townsville, Qld.)

Guisan, A., and Thuiller, W. (2005). Predicting species distribution: offering more than simple habitat models. Ecology Letters 8, 993–1009.
Predicting species distribution: offering more than simple habitat models.Crossref | GoogleScholarGoogle Scholar |

Harte, J., and Newman, E. A. (2014). Maximum information entropy: a foundation for ecological theory. Trends in Ecology & Evolution 29, 384–389.
Maximum information entropy: a foundation for ecological theory.Crossref | GoogleScholarGoogle Scholar |

Hijmans, R. J., and Graham, C. H. (2006). The ability of climate envelope models to predict the effect of climate change on species distributions. Global Change Biology 12, 2272–2281.
The ability of climate envelope models to predict the effect of climate change on species distributions.Crossref | GoogleScholarGoogle Scholar |

Hijmans, R. J., Cameron, S. E., Parra, J. L., Jones, P. G., and Jarvis, A. (2005). Very high resolution interpolated climate surfaces for global land areas. International Journal of Climatology 25, 1965–1978.
Very high resolution interpolated climate surfaces for global land areas.Crossref | GoogleScholarGoogle Scholar |

Hirzel, A. H., and Le Lay, G. (2008). Habitat suitability modelling and niche theory. Journal of Applied Ecology 45, 1372–1381.
Habitat suitability modelling and niche theory.Crossref | GoogleScholarGoogle Scholar |

Hughes, L. (2003). Climate change and Australia: trends, projections and impacts. Austral Ecology 28, 423–443.
Climate change and Australia: trends, projections and impacts.Crossref | GoogleScholarGoogle Scholar |

Hulme, P. E. (2009). Trade, transport and trouble: managing invasive species pathways in an era of globalization. Journal of Applied Ecology 46, 10–18.
Trade, transport and trouble: managing invasive species pathways in an era of globalization.Crossref | GoogleScholarGoogle Scholar |

Kearney, M., and Porter, W. (2009). Mechanistic niche modelling: combining physiological and spatial data to predict species’ ranges. Ecology Letters 12, 334–350.
Mechanistic niche modelling: combining physiological and spatial data to predict species’ ranges.Crossref | GoogleScholarGoogle Scholar | 19292794PubMed |

Kriticos, D. J., Sutherst, R. W., Brown, J. R., Adkins, S. W., and Maywald, G. F. (2003). Climate change and the potential distribution of an invasive alien plant: Acacia nilotica ssp. indica in Australia. Journal of Applied Ecology 40, 111–124.
Climate change and the potential distribution of an invasive alien plant: Acacia nilotica ssp. indica in Australia.Crossref | GoogleScholarGoogle Scholar |

Kumar, S., and Stohlgren, T. J. (2009). MaxEnt modeling for predicting suitable habitat for threatened and endangered tree Canacomyrica monticola in New Caledonia. Journal of Ecology and Natural Environment 1, 94–98.

Leal, L. C., Maiado, M. V., Lopes, A. V., and Leal, I. R. (2013). Germination responses of the invasive Calotropis procera (Ait.) R.Br. (Apocynaceae): comparisons with seeds from two ecosystems in northeastern Brazil. Annals of the Brazilian Academy of Sciences 85, 1025–1034.
Germination responses of the invasive Calotropis procera (Ait.) R.Br. (Apocynaceae): comparisons with seeds from two ecosystems in northeastern Brazil.Crossref | GoogleScholarGoogle Scholar |

Leroux, S. J., Larrivée, M., Boucher-Lalonde, V., Hurford, A., Zuloaga, J., Kerr, J. T., and Lutscher, F. (2013). Mechanistic models for the spatial spread of species under climate change. Ecological Applications 23, 815–828.
Mechanistic models for the spatial spread of species under climate change.Crossref | GoogleScholarGoogle Scholar | 23865232PubMed |

MacDougall, A. S., and Turkington, R. (2005). Are invasive species the drivers or passengers of change in degraded ecosystems? Ecology 86, 42–55.
Are invasive species the drivers or passengers of change in degraded ecosystems?Crossref | GoogleScholarGoogle Scholar |

Mägi, M., Semchenko, M., Kalamees, R., and Zobel, K. (2011). Limited phenotypic plasticity in range-edge populations: a comparison of co-occurring populations of two Agrimonia species with different geographical distributions. Plant Biology 13, 177–184.
Limited phenotypic plasticity in range-edge populations: a comparison of co-occurring populations of two Agrimonia species with different geographical distributions.Crossref | GoogleScholarGoogle Scholar | 21143739PubMed |

Mark, R. N., Simberloff, D., Lonsdale, W. M., Evans, H., Clout, M., and Bazzaz, F. (2000). Biotic invasions: causes, epidemiology, global consequences and control. Issues in Ecology 5, 1–22.

MaxEnt (2015). Maxent software for species habitat modeling version 3.3.3k. Available at: www.cs.princeton.edu/~schapire/maxent/ (accessed 10 July 2015).

Merow, C., Smith, M. J., and Silander, J. A. (2013). A practical guide to MaxEnt for modeling species’ distributions: what it does, and why inputs and settings matter. Ecography 36, 1058–1069.
A practical guide to MaxEnt for modeling species’ distributions: what it does, and why inputs and settings matter.Crossref | GoogleScholarGoogle Scholar |

Minister for Agriculture and Food W. A. (2013). Biosecurity and Agriculture Management (Declared Pests) Declaration 2013. GoW Australia, Perth.

Nathan, R., and Muller-Landau, H. C. (2000). Spatial patterns of seed dispersal, their determinants and consequences for recruitment. Trends in Ecology & Evolution 15, 278–285.
Spatial patterns of seed dispersal, their determinants and consequences for recruitment.Crossref | GoogleScholarGoogle Scholar |

Nathan, R., Katul, G. G., Bohrer, G., Kuparinen, A., Soons, M. B., Thompson, S. E., Trakhtenbrot, A., and Horn, H. S. (2011). Mechanistic models of seed dispersal by wind. Theoretical Ecology 4, 113–132.
Mechanistic models of seed dispersal by wind.Crossref | GoogleScholarGoogle Scholar |

Parsons, W. T., and Cuthbertson, E. G. (2001). ‘Noxious Weeds of Australia.’ (CSIRO Publishing: Melbourne.)

Pheloung, P. C., Williams, P. A., and Halloy, S. R. (1999). A weed risk assessment model for use as a biosecurity tool evaluating plant introductions. Journal of Environmental Management 57, 239–251.
A weed risk assessment model for use as a biosecurity tool evaluating plant introductions.Crossref | GoogleScholarGoogle Scholar |

Phillips, S. J., and Dudik, M. (2008). Modeling of species distributions with Maxent: new extensions and a comprehensive evaluation. Ecography 31, 161–175.
Modeling of species distributions with Maxent: new extensions and a comprehensive evaluation.Crossref | GoogleScholarGoogle Scholar |

Phillips, S. J., Dudik, M., and Schapire, R. E. (2004). A maximum entropy approach to species distribution modelling. In: ‘21st International Conference on Machine Learning, 2004’. pp. 655–662. (ACM Press: New York.)

Phillips, S. J., Anderson, R. P., and Schapire, R. E. (2006). Maximum entropy modeling of species geographic distributions. Ecological Modelling 190, 231–259.
Maximum entropy modeling of species geographic distributions.Crossref | GoogleScholarGoogle Scholar |

Pierce, D. W., Barnett, T. P., Santer, B. D., Gleckler, P. J., and Thiemens, M. H. (2009). Selecting global climate models for regional climate change studies. Proceedings of the National Academy of Sciences of the United States of America 106, 8441–8446.
Selecting global climate models for regional climate change studies.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD1MXnt1Sru74%3D&md5=9ead15865dc8854748de15ff75668ae4CAS | 19439652PubMed |

Pimentel, D. L., Zuniga, R., and Morrison, D. (2000). Environmental and economic costs of non-indigenous species in the United States. Bioscience 50, 53–65.
Environmental and economic costs of non-indigenous species in the United States.Crossref | GoogleScholarGoogle Scholar |

Scheldeman, X., and van Zonneveld, M. (2010). ‘Training Manual on Spatial Analysis of Plant Diversity and Distribution.’ (Biodiversity International: Rome, Italy.)

Simberloff, D., and Von Holle, B. (1999). Positive interactions of nonindigenous species: invasional meltdown? Biological Invasions 1, 21–32.
Positive interactions of nonindigenous species: invasional meltdown?Crossref | GoogleScholarGoogle Scholar |

Sobrinho, M. S., Tabatinga, G. M., Machado, I. C., and Lopes, A. V. (2013). Reproductive phenological pattern of Calotropis procera (Apocynaceae), an invasive species in Brazil: annual in native areas; continuous in invaded areas of caatinga. Acta Botanica Brasílica 27, 456–459.
Reproductive phenological pattern of Calotropis procera (Apocynaceae), an invasive species in Brazil: annual in native areas; continuous in invaded areas of caatinga.Crossref | GoogleScholarGoogle Scholar |

Sparrow, A. D., Friedel, M. H., and Tongway, D. J. (2003). Degradation and recovery processes in arid grazing lands of central Australia Part 3: implications at landscape scale. Journal of Arid Environments 55, 349–360.
Degradation and recovery processes in arid grazing lands of central Australia Part 3: implications at landscape scale.Crossref | GoogleScholarGoogle Scholar |

Stone, L. M., and Byrne, M. (2011). Comparing the outputs of five weed risk assessment models implemented in Australia: are there consistencies across models? Plant Protection Quarterly 26, 29–35.

Suarez, A. V., and Tsutsui, N. D. (2008). The evolutionary consequences of biological invasions. Molecular Ecology 17, 351–360.
The evolutionary consequences of biological invasions.Crossref | GoogleScholarGoogle Scholar | 18173507PubMed |

Tezara, W., Colombo, R., Coronel, I., and Marín, O. (2011). Water relations and photosynthetic capacity of two species of Calotropis in a tropical semi-arid ecosystem. Annals of Botany 107, 397–405.
Water relations and photosynthetic capacity of two species of Calotropis in a tropical semi-arid ecosystem.Crossref | GoogleScholarGoogle Scholar | 21149276PubMed |

Thuiller, W., Lafourcade, B., Engler, R., and Araujo, M. B. (2009). BIOMOD – a platform for ensembe forecasting of species distributions. Ecography 32, 369–373.
BIOMOD – a platform for ensembe forecasting of species distributions.Crossref | GoogleScholarGoogle Scholar |

Tingley, R., Vallinoto, M., Sequeira, F., and Kearney, M. R. (2014). Realized niche shift during a global biological invasion. Proceedings of the National Academy of Sciences of the United States of America 111, 10233–10238.
Realized niche shift during a global biological invasion.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC2cXhtVOit7nL&md5=8609e968c642a5d2f77e5f8fa5624b60CAS | 24982155PubMed |

Tropical Data Hub (2015). CliMAS: climate change and biodiversity in Australia. Available at: https://research.jcu.edu.au/tdh (accessed 10 February 2015).

US Forest Service (2011). Pacific Island ecosystems at risk. Available at: www.hear.org/pier/species/calotropis_procera.htm (accessed 30 April 2015).

van Klinken, R. D., Lawson, B. E., and Zalucki, M. P. (2009). Predicting invasions in Australia by a Neotropical shrub under climate change: the challenge of novel climates and parameter estimation. Global Ecology and Biogeography 18, 688–700.
Predicting invasions in Australia by a Neotropical shrub under climate change: the challenge of novel climates and parameter estimation.Crossref | GoogleScholarGoogle Scholar |

Vincke, C., Diedhiou, I., and Grouzis, M. (2010). Long term dynamics and structure of woody vegetation in the Ferlo (Senegal). Journal of Arid Environments 74, 268–276.
Long term dynamics and structure of woody vegetation in the Ferlo (Senegal).Crossref | GoogleScholarGoogle Scholar |

Vitelli, J., Madigan, B., Wilkinson, P., and van Haaren, P. (2008). Calotrope (Calotropis procera) control. The Rangeland Journal 30, 339–348.
Calotrope (Calotropis procera) control.Crossref | GoogleScholarGoogle Scholar |

VSN International (2013). ‘Genstat for Windows version 16.’ (VSN International: Hemel Hempstead, UK.) Available at: www.genstat.co.uk

Ward, S. (2006). Genetic analysis of invasive plant populations at different spatial scales. Biological Invasions 8, 541–552.
Genetic analysis of invasive plant populations at different spatial scales.Crossref | GoogleScholarGoogle Scholar |

Warren, D. L., and Seifert, S. N. (2011). Ecological niche modeling in Maxent: the importance of model complexity and the performance of model selection criteria. Ecological Applications 21, 335–342.
Ecological niche modeling in Maxent: the importance of model complexity and the performance of model selection criteria.Crossref | GoogleScholarGoogle Scholar | 21563566PubMed |

Whitney, K. D., and Gabler, C. A. (2008). Rapid evolution in introduced species, ‘invasive traits’ and recipient communities: challenges for predicting invasive potential. Diversity & Distributions 14, 569–580.
Rapid evolution in introduced species, ‘invasive traits’ and recipient communities: challenges for predicting invasive potential.Crossref | GoogleScholarGoogle Scholar |

Williams, P. A., and Clout, M. N. (2009). ‘Invasive Species Management: A Handbook of Principles and Techniques.’ (Oxford University Press: Oxford, UK.)

Willmer, P. (2011). ‘Pollination and Floral Ecology.’ 1st edn. (Princeton University Press: Princeton, NJ.)

Wilson, P. D., Downey, P. O., Gallagher, R. V., O’Donell, J., Leishman, M. R., and Hughes, L. (2011). ‘Modelling climate suitability for exotic plants in Australia under future climate.’ Final Report on the potential impact of climate change on the distribution of national priority weeds in Australia. (Macquarie University and New South Wales Office of Environment and Heritage: Sydney, NSW.)