Hostname: page-component-cd9895bd7-dzt6s Total loading time: 0 Render date: 2024-12-29T10:47:44.016Z Has data issue: false hasContentIssue false
  • English
  • Français

Forecasting Weed Distributions using Climate Data: A GIS Early Warning Tool

Published online by Cambridge University Press:  20 January 2017

Catherine S. Jarnevich ,
Tracy R. Holcombe ,
David T. Barnett ,
Thomas J. Stohlgren  and
John T. Kartesz
Catherine S. Jarnevich*
Affiliation:
U.S. Geological Survey, Fort Collins Science Center, 2150 Centre Ave., Building C, Fort Collins, CO 80526-8118
Tracy R. Holcombe
Affiliation:
U.S. Geological Survey, Fort Collins Science Center, 2150 Centre Ave., Building C, Fort Collins, CO 80526-8118
David T. Barnett
Affiliation:
Natural Resource Ecology Laboratory, Colorado State University, Campus Mail 1499, Fort Collins, CO 80523-1499
Thomas J. Stohlgren
Affiliation:
U.S. Geological Survey, Fort Collins Science Center, 2150 Centre Ave., Building C, Fort Collins, CO 80526-8118
John T. Kartesz
Affiliation:
Biota of North America Program, 9319 Bracken Lane, Chapel Hill, NC 27516
*
Corresponding author's E-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

The number of invasive exotic plant species establishing in the United States is continuing to rise. When prevention of exotic species from entering into a country fails at the national level and the species establishes, reproduces, spreads, and becomes invasive, the most successful action at a local level is early detection followed by eradication. We have developed a simple geographic information system (GIS) analysis for developing watch lists for early detection of invasive exotic plants that relies upon currently available species distribution data coupled with environmental data to aid in describing coarse-scale potential distributions. This GIS analysis tool develops environmental envelopes for species based upon the known distribution of a species thought to be invasive and represents the first approximation of its potential habitat while the necessary data are collected to perform more in-depth analyses. To validate this method we looked at a time series of species distributions for 66 species in Pacific Northwest and northern Rocky Mountain counties. The time series analysis presented here did select counties that the invasive exotic weeds invaded in subsequent years, showing that this technique could be useful in developing watch lists for the spread of particular exotic species. We applied this same habitat-matching model based upon bioclimatic envelopes to 100 invasive exotics with various levels of known distributions within continental U.S. counties. For species with climatically limited distributions, county watch lists describe county-specific vulnerability to invasion. Species with matching habitats in a county would be added to that county's list. These watch lists can influence management decisions for early warning, control prioritization, and targeted research to determine specific locations within vulnerable counties. This tool provides useful information for rapid assessment of the potential distribution based upon climate envelopes of current distributions for new invasive exotic species.

Keywords

Exotic speciesgeographic information systeminvasive speciesiterative samplingmodelingrapid assessmentweeds
Type
Research
Information
Invasive Plant Science and Management , Volume 3 , Issue 4 , December 2010 , pp. 365 - 375
Copyright
Copyright © Weed Science Society of America 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Literature Cited

Barnett, D. T., Stohlgren, T. J., Jarnevich, C. S., Chong, G. W., Ericson, J. A., Davern, T. R., and Simonson, S. E. 2007. The art and science of weed mapping. Environ. Monit. Assess 132:235–252.Google Scholar
Brotons, L., Thuiller, W., Araujo, M. B., and Hirzel, A. H. 2004. Presence–absence versus presence-only modelling methods for predicting bird habitat suitability. Ecography 27:437–448.Google Scholar
Caley, P. and Kuhnert, P. 2006. Application and evaluation of classification trees for screening unwanted plants. Austral. Ecol 31:647–655.CrossRefGoogle Scholar
Crooks, J. A. 2005. Lag times and exotic species: the ecology and management of biological invasions in slow-motion. Ecoscience 12:316–329.Google Scholar
Crossman, N. D. and Bass, D. A. 2008. Application of common predictive habitat techniques for post-border weed risk management. Divers. Distrib 14:213–224.CrossRefGoogle Scholar
Daly, C., Taylor, G. H., Gibson, W. P., Parzybok, T. W., Johnson, G. L., and Pasteris, P. A. 2000. High-quality spatial climate data sets for the United States and beyond. Trans. ASAE (Am. Soc. Agric. Eng.) 43:1957–1962.CrossRefGoogle Scholar
Elith, J., Graham, C. H., Anderson, R. P., Dudik, 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., Scachetti-Pereira, R., Schapire, R. E., Soberon, 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.Google Scholar
Evangelista, P., Kumar, S., Stohlgren, T. J., Jarnevich, C. S., Crall, A. W., Norman, J. B. III, and Barnett, D. 2008. Modelling invasion for a habitat generalist and a specialist plant species. Divers. Distrib 14:808–817.Google Scholar
Ficetola, G. F., Thuiller, W., and Miaud, C. 2007. Prediction and validation of the potential global distribution of a problematic alien invasive species—the American bullfrog. Divers. Distrib 13:476–485.Google Scholar
Fielding, A. H. and Bell, J. F. 1997. A review of methods for the assessment of prediction errors in conservation presence/absence models. Environ. Conserv 24:38–49.CrossRefGoogle Scholar
Gibson, L., Barrett, B., and Burbidge, A. 2007. Dealing with uncertain absences in habitat modelling: a case study of a rare ground-dwelling parrot. Divers. Distrib 13:704–713.CrossRefGoogle Scholar
Hijmans, R. J. 2006. Worldclim. http://worldclim.org/bioclim.htm. Accessed: February 29, 2008.Google Scholar
Hirzel, A. H., Helfer, V., and Metral, F. 2001. Assessing habitat-suitability models with a virtual species. Ecol. Model 145:111–121.CrossRefGoogle Scholar
Hortal, J., Jimenez-Valverde, A., Gomez, J. F., Lobo, J. M., and Baselga, A. 2008. Historical bias in biodiversity inventories affects the observed environmental niche of the species. Oikos 117:847–858.CrossRefGoogle Scholar
Kartesz, J. T. 2004. Biota of North America Program County Weed Database. Biota of North America Program. Chapel Hill, NC North Carolina Botanical Garden.Google Scholar
Kartesz, J. T. 2007. Biota of North America Program County Weed Database. Biota of North America Program. Chapel Hill, NC North Carolina Botanical Garden.Google Scholar
Krivanek, M. and Pysek, P. 2006. Predicting invasions by woody species in a temperate zone: a test of three risk assessment schemes in the Czech Republic (Central Europe). Divers. Distrib 12:319–327.Google Scholar
Levine, J. M. and D'Antonio, C. M. 2003. Forecasting biological invasions with increasing international trade. Conserv. Biol 17:322–326.Google Scholar
Lodge, D., Williams, S., MacIsaac, H. J., Hayes, K. R., Leung, B., Reichard, S. H., Mack, R. N., Moyle, P. B., Smith, M., Andow, D. A., Carlton, J. T., and McMichael, A. 2006. Biological invasions: recommendations for U.S. policy and management. Ecol. Appl 16:2035–2054.Google Scholar
Loiselle, B. A., Jorgensen, P. M., Consiglio, T., Jimenez, I., Blake, J. G., Lohmann, L. G., and Montiel, O. M. 2008. Predicting species distributions from herbarium collections: does climate bias in collection sampling influence model outcomes? J. Biogeogr 35:105–116.CrossRefGoogle Scholar
Mack, R. N., Simberloff, D., Lonsdale, W. M., Evans, H., Clout, M., and Bazzaz, F. A. 2000. Biotic invasions: causes, epidemiology, global consequences, and control. Ecol. Appl 10:689–710.CrossRefGoogle Scholar
National Institute of Invasive Species Science 2008. National Institute of Invasive Species Science. http://www.niiss.org. Accessed: February 29, 2008.Google Scholar
Nix, H. 1986. A biogeographic analysis of Australian elapid snakes. Pages 4–15. In Longmore, R. ed. Atlas of Elapid Snakes of Australia. Canberra Australian Government Publishing Service.Google Scholar
North American Weed Management Association 2002. North American invasive plant mapping standards. http://www.nawma.org. Accessed: November 1, 2007.Google Scholar
Pimentel, D., Zuniga, R., and Morrison, D. 2005. Update on the environmental and economic costs associated with alien-invasive species in the United States. Ecol. Econ 52:273–288.CrossRefGoogle Scholar
PRISM Climate Group, Oregon State University 2007. PRISM Climate Group. http://www.prism.oregonstate.edu/. Accessed: February 2008.Google Scholar
Rejmanek, M. and Pitcairn, M. J. 2002. When is eradication of exotic pest plants a realistic goal?. Pages 249–253. In Veitch, C. R. and Clout, M. N. eds. Turning the tide: the eradication of invasive species. Gland, Switzerland, and Cambridge, UK IUCN Species Survival Commission Invasive Species Specialist Group.Google Scholar
Rice, P. M. 2006. INVADERS Database System. http://invader.dbs.umt.edu. Accessed: August 29, 2006.Google Scholar
Richardson, D. M. and Thuiller, W. 2007. Home away from home—objective mapping of high-risk source areas for plant introductions. Divers. Distrib 13:299–312.CrossRefGoogle Scholar
Rouget, M., Richardson, D. M., Milton, S. J., and Polakow, D. 2001. Predicting invasion dynamics of four alien Pinus species in a highly fragmented semi-arid shrubland in South Africa. Plant Ecol 152:79–92.Google Scholar
Stohlgren, T. J., Barnett, D. T., Jarnevich, C. S., Flather, C., and Kartesz, J. 2008. The myth of plant species saturation. Ecol. Lett 11:313–326.Google Scholar
Stohlgren, T. J. and Schnase, J. L. 2006. Risk analysis for biological hazards: what we need to know about invasive species. Risk Anal 26:163–173.Google Scholar
University of Conneticut 2007. Invasive Plant Atlas of England. Available at: http://nbii-nin.ciesin.columbia.edu/ipane/. Accessed November 10, 2007.Google Scholar
Vitousek, P. M., D'Antonio, R. C., Loope, L., and Westbrooks, R. 1996. Biological invasions as global environmental change. Am. Sci 84:468–478.Google Scholar
Wilcove, D. S., Rothstein, D., Dubow, J., Phillips, A., and Losos, E. 1998. Quantifying threats to imperiled species in the United States. Bioscience 48:607–615.Google Scholar
Work, T. T., McCullough, D. G., Cavey, J. F., and Komsa, R. 2005. Arrival rate of nonindigenous insect species into the United States through foreign trade. Biol. Invasions 7:323–332.Google Scholar
Zaniewski, A. E., Lehmann, A., and Overton, J. M. C. 2002. Predicting species spatial distributions using presence-only data: a case study of native New Zealand ferns. Ecol. Model 157:261–280.Google Scholar