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Predicting the potential invasive distributions of four alien plant species in North America

Published online by Cambridge University Press:  20 January 2017

A. Townsend Peterson ,
Monica Papes  and
Daniel A. Kluza
Monica Papes
Affiliation:
Natural History Museum and Biodiversity Research Center, The University of Kansas, Lawrence, KS 66045
Daniel A. Kluza
Affiliation:
Natural History Museum and Biodiversity Research Center, The University of Kansas, Lawrence, KS 66045
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Abstract

Ecological niche modeling, a new methodology for predicting the geographic course of species' invasions, was tested based on four invasive plant species (garlic mustard, sericea lespedeza, Russian olive, and hydrilla) in North America. Models of ecological niches and geographic distributions on native distributional areas (Europe and Asia) were highly statistically significant. Projections for each species to North America—effectively predictions of invasive potential—were highly coincident with areas of known invasions. Hence, in each case, the geographic invasive potential was well summarized in a predictive sense; this methodology holds promise for development of control and eradication strategies and for risk assessment for species' invasions.

Keywords

Hydrilla, Hydrilla verticillata (L.f.) Royle HYLLIRussian olive, Elaeagnus angustifolia L. ELGANsericea lespedeza, Lespedeza cuneata (Dum.-Cours.) G. Don LESCUgarlic mustard, Alliaria petiolata (Bieb) Cavara & Grande ALAPEInvasive speciesecological niche modelingGenetic Algorithm for Rule-set Predictionprediction
Type
Weed Biology
Information
Weed Science , Volume 51 , Issue 6 , December 2003 , pp. 863 - 868
Copyright
Copyright © Weed Science Society of America 

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References

Literature Cited

Anderson, R. P., Gomez, M., and Peterson, A. T. 2002a. Geographical distributions of spiny pocket mice in South America: insights from predictive models. Glob. Ecol. Biogeogr. Lett 11:131–141.CrossRefGoogle Scholar
Anderson, R. P., Laverde, M., and Peterson, A. T. 2002b. Using niche-based GIS modeling to test geographic predictions of competitive exclusion and competitive release in South American pocket mice. Oikos 93:3–16.CrossRefGoogle Scholar
Anderson, R. P., Lew, D., and Peterson, A. T. 2003. Evaluating predictive models of species' distributions: criteria for selecting optimal models. Ecol. Model 162:211–232.CrossRefGoogle Scholar
CONABIO. 2002. Red Mexicana de la Información de la Biodiversidad. www.conabio.gob.mx/.Google Scholar
Egbert, S. L., Martinez-Meyer, E., Ortega-Herta, M. A., and Peterson, A. T. 2002. Use of datasets derived from time-series AVHRR imagery as surrogates for land cover maps in predicting species' distributions. Pages 2337–2339 in Proceedings IEEE 2002 International Geoscience and Remote Sensing Symposium (IGARSS). Volume 4. Toronto, Canada.Google Scholar
ESRI. 2001. ArcView. Redlands, CA: Environmental Systems Research Institute.Google Scholar
Feria, T. P. and Peterson, A. T. 2002. Using point occurrence data and inferential algorithms to predict local communities of birds. Divers. Distrib 8:49–56.Google Scholar
Godown, M. E. and Peterson, A. T. 2000. Preliminary distributional analysis of U.S. endangered bird species. Biodivers. Conserv 9:1313–1322.CrossRefGoogle Scholar
Grinnell, J. 1917. Field tests of theories concerning distributional control. Am. Nat 51:115–128.CrossRefGoogle Scholar
Higgins, S. I., Richardson, D. M., Cowling, R. M., and Trinder-Smith, T. H. 1999. Predicting the landscape-scale distribution of alien plants and their threat to plant diversity. Conserv. Biol 13:303–313.Google Scholar
Holt, J. S. and Boose, A. B. 2002. Potential for spread of Abutilon theophrasti in California. Weed Sci 48:43–52.CrossRefGoogle Scholar
Iguchi, K., Matsuura, K., McNyset, K., Peterson, A. T., Scachetti-Pereira, R., Vieglais, D. A., Wiley, E. O., and Yodo, T. 2003. Predicting invasions of bass in Japan. J. Am. Fish. Soc. In press.Google Scholar
IPCC. 2001. Climate Data Archive. www.ipcc.ch/.Google Scholar
Jones, P. G. and Gladkov, A. 1999. FloraMap: A Computer Tool for Predicting the Distribution of Plants and Other Organisms in the Wild. Cali, Colombia: Centro Internacional de Agricultura Tropical. 99 p.Google Scholar
Joseph, L. and Stockwell, D. R. B. 2000. Temperature-based models of the migration of Swainson's flycatcher (Myiarchus swainsoni) across South America: a new use for museum specimens of migratory birds. Proc. Acad. Nat. Sci. Phila 150:293–300.Google Scholar
Peterson, A. T. 2001. Predicting species' geographic distributions based on ecological niche modeling. Condor 103:599–605.CrossRefGoogle Scholar
Peterson, A. T., Ball, L. G., and Cohoon, K. C. 2002. Predicting distributions of tropical birds. Ibis 144:e27–e32.Google Scholar
Peterson, A. T. and Cohoon, K. C. 1999. Sensitivity of distributional prediction algorithms to geographic data completeness. Ecol. Model 117:159–164.Google Scholar
Peterson, A. T., Egbert, S. L., Sánchez-Cordero, V., and Price, K. P. 2000. Geographic analysis of conservation priorities using distributional modelling and complementarity: endemic birds and mammals in Veracruz, Mexico. Biol. Conserv 93:85–94.Google Scholar
Peterson, A. T. and Robins, C. R. 2003. When endangered meets invasive: ecological niche modeling predicts double trouble for spotted owls, Strix occidentalis . Conserv.: Biol. In press.Google Scholar
Peterson, A. T., Sánchez-Cordero, V., Soberón, J., Bartley, J., Buddemeier, R. H., and Navarro-Siguenza, A. G. 2001. Effects of global climate change on geographic distributions of Mexican Cracidae. Ecol. Model 144:21–30.Google Scholar
Peterson, A. T., Soberón, J., and Sánchez-Cordero, V. 1999. Conservatism of ecological niches in evolutionary time. Science 285:1265–1267.CrossRefGoogle ScholarPubMed
Peterson, A. T. and Vieglais, D. A. 2001. Predicting species invasions using ecological niche modeling. Bioscience 51:363–371.CrossRefGoogle Scholar
Scachetti-Pereira, R. 2001. Desktop GARP. www.lifemapper.org/desktopgarp.Google Scholar
Scott, J. M., Heglund, P. J., and Morrison, M. L. eds. 2002. Predicting Species Occurrences: Issues of Accuracy and Scale. Washington, DC: Island. 840 p.Google Scholar
Scott, J. M., Tear, T. H., and Davis, F. W. eds. 1996. Gap Analysis: A Landscape Approach to Biodiversity Planning. Bethesda, MD: American Society for Photogrammetry and Remote Sensing. 320 p.Google Scholar
Stockwell, D. R. B. 1999. Genetic algorithms II. Pages 123–144 in Fielding, A. H. ed. Machine Learning Methods for Ecological Applications. Boston, MA: Kluwer.CrossRefGoogle Scholar
Stockwell, D. R. B. and Noble, I. R. 1992. Induction of sets of rules from animal distribution data: a robust and informative method of analysis. Math. Comp. Simul 33:385–390.CrossRefGoogle Scholar
Stockwell, D. R. B. and Peters, D. P. 1999. The GARP modelling system: problems and solutions to automated spatial prediction. Int. J. Geogr. Informat. Syst 13:143–158.CrossRefGoogle Scholar
Stockwell, D. R. B. and Peterson, A. T. 2002. Effects of sample size on accuracy of species distribution models. Ecol. Model 148:1–13.Google Scholar
USDA. 2002. Plants Database. www.plants.usda.gov/.Google Scholar
USGS. 2001. HYDRO1k Elevation Derivative Database. www.edcdaac.usgs.gov/gtopo30/hydro/.Google Scholar
USGS. 2002. Nonindigenous Aquatic Species. www.nas.er.usgs.gov/.Google Scholar
Vieglais, D. A. 2000. The Species Analyst. www.speciesanalyst.net/.Google Scholar
Walker, P. A. and Cocks, K. D. 1991. HABITAT: a procedure for modelling a disjoint environmental envelope for a plant or animal species. Glob. Ecol. Biogeogr. Lett 1:108–118.Google Scholar
Zalba, S. M., Sonaglioni, M. I., and Belenguer, C. J. 2000. Using a habitat model to assess the risk of invasion by an exotic plant. Biol. Conserv 93:203–208.CrossRefGoogle Scholar