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Prioritizing Invasive Plant Management with Multi-Criteria Decision Analysis

Published online by Cambridge University Press:  20 January 2017

Matthew G. Hohmann*
Affiliation:
U.S. Army Engineer Research and Development Center, Construction Engineering Research Laboratory, P.O. Box 9005, Champaign, IL 61826
Michael G. Just
Affiliation:
Department of Natural Resources and Environmental Sciences, W-503 Turner Hall, University of Illinois at Urbana-Champaign, Urbana, IL 61801
Peter J. Frank
Affiliation:
Invasive Species Management, Inc., 439 Rollins Road, Vass, NC 28394
Wade A. Wall
Affiliation:
U.S. Army Engineer Research and Development Center, Construction Engineering Research Laboratory, P.O. Box 9005, Champaign, IL 61826
Janet B. Gray
Affiliation:
Endangered Species Branch, Fort Bragg, NC 28310
*
Corresponding author's E-mail: [email protected]

Abstract

Prioritizing management of invasive plants is important for large land management entities, such as federal and state public land stewards, because management resources are limited and multiple land uses and management objectives are differentially impacted. Management decisions also have important consequences for the likelihood of success and ultimate cost of control efforts. We applied multi-criteria decision analysis methods in a geographic information system using natural resource and land use data from Fort Bragg, North Carolina. Landscape-scale prioritization was based on a hierarchical model designed to increase invasive plant management efficiencies and reduce the risk of impacts to key installation management goals, such as training-land management and protected species conservation. We also applied spatial sensitivity analyses to evaluate the robustness of the prioritization to perturbations of the model weights, which were used to describe the relative importance of different elements of the hierarchical model. Based on stakeholders' need for confidence in making management investments, we incorporated the results of the sensitivity analysis into the decision-making process. We identified high-priority sites that were minimally affected by the weight perturbations as being suitable for up-front management and evaluated how adopting this strategy affected management area, locations, and costs. We found that incorporating the results of the sensitivity analysis led to a reduced management area, different target locations, and lower costs for an equal area managed. Finally, we confirmed the distinctiveness of the approach by comparing this same subset of prioritized sites with locations representing species-centric strategies for three invasive plants and their aggregate distribution. By supplying pragmatic information about the localized effects of weighting uncertainty, spatial sensitivity analyses enhanced the invasive plant management decision-making process and increased stakeholder confidence.

Type
Research
Copyright
Copyright © Weed Science Society of America 

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References

Literature Cited

Cacho, O. J., Wise, R. M., Hester, S. M., and Sinden, J. A. 2008. Bioeconomic modeling for control of weeds in natural environments. Ecol. Econ. 65:559568.Google Scholar
Chen, H. and Kocaoglu, D. F. 2008. A sensitivity analysis algorithm for hierarchical decision models. Eur. J. Oper. Res. 185:266288.Google Scholar
Christen, D. C. and Matlack, G. R. 2009. The habitat and conduit functions of roads in the spread of three invasive plant species. Biol. Invasions 11:453465.Google Scholar
Coutts, S. R., van Klinken, R. D., Yokomizo, H., and Buckley, Y. M. 2011. What are the key drivers of spread in invasive plants: dispersal, demography, or landscape: and how can we use this knowledge to aid management? Biol. Invasions 13:16491661.Google Scholar
Davies, K. W. and Sheley, R. L. 2007. A conceptual framework for preventing the spatial dispersal of invasive plants. Weed Sci. 55:178184.Google Scholar
Delgado, M. and Sendra, J. B. 2004. Sensitivity analysis in multicriteria spatial decision-making: a review. Hum. Ecol. Risk Assess. 10:11731187.Google Scholar
Epanchin-Niell, R. S. and Hastings, A. 2010. Controlling established invaders: integrating economics and spread dynamics to determine optimal management. Ecol. Lett. 13:528541.Google Scholar
Feick, R. D. and Hall, G. B. 2004. A method for examining the spatial dimension of multi-criteria weight sensitivity. Int. J. Geogr. Inf. Sci. 18:815840.Google Scholar
Finnoff, D., Shogren, J. F., Leung, B., and Lodge, D. 2007. Take a risk: preferring prevention over control of biological invaders. Ecol. Econ. 62:216222.Google Scholar
Fox, A. M. and Gordon, D. R. 2009. Approaches for assessing the status of nonnative plants: a comparative analysis. Invasive Plant Sci. Manag. 2:166184.Google Scholar
Geneletti, D. 2004. A GIS-based decision support system to identify nature conservation priorities in an alpine valley. Land Use Policy 21:149160.Google Scholar
Giljohann, K. M., Hauser, C. E., Williams, N. S. G., and Moore, J. L. 2011. Optimizing invasive species control across space: willow invasion management in the Australian Alps. J. Appl. Ecol. 48:12861294.Google Scholar
Gray, J. B., Wentworth, T. R., and Brownie, C. 2003. Extinction, colonization, and persistence of rare vascular flora in the longleaf pine–wiregrass ecosystem: responses to fire frequency and population size. Nat. Areas J. 23:210219.Google Scholar
Greene, R., Luther, J. E., Devillers, R., and Eddy, B. 2010. An approach to GIS-based multiple criteria decision analysis that integrates exploration and evaluation phases: case study in a forest-dominated landscape. For. Ecol. Manag. 260:21022114.Google Scholar
Grevstad, F. 2005. Simulating control strategies for a spatially structured weed invasion: Spartina alterniflora (Loisel) in Pacific Coast estuaries. Biol. Invasions 7:665677.Google Scholar
Guikema, S. and Milke, M. 1999. Quantitative decision tools for conservation programme planning: practice, theory and potential. Environ. Conserv. 26:179189.Google Scholar
Hajkowicz, S. A. 2008. Supporting multi-stakeholder environmental decisions. J. Environ. Manage. 88:607614.Google Scholar
Heffernan, K. E., Coulling, P. P., Townsend, J. F., and Hutto, C. J. 2001. Ranking Invasive Exotic Plant Species in Virginia. Richmond, VA Virginia Department of Conservation and Recreation, Division of Natural Heritage Technical Report 01-13. 27 p.Google Scholar
Higgins, S. I., Richardson, D. M., and Cowling, R. M. 2000. Using a dynamic landscape model for planning the management of alien plant invasions. Ecol. Appl. 10:18331848.Google Scholar
Hyde, K. M., Maier, H. R., and Colby, C. B. 2005. A distance-based uncertainty analysis approach to multi-criteria decision analysis for water resource decision making. J. Environ. Manag. 77:278290.Google Scholar
Kuefler, D., Haddad, N. M., Hall, S., Hudgens, B., Bartel, B., and Hoffman, E. 2008. Distribution, population structure and habitat use of the endangered Saint Francis satyr butterfly, Neonympha mitchellii francisci . Am. Midl. Nat. 159:298320.Google Scholar
Li, X., He, H. S., Bu, R., Wen, Q., Chang, Y., Hu, Y., and Li, Y. 2005. The adequacy of different landscape metrics for various landscape patterns. Pattern Recognit. 38:26262638.Google Scholar
Ligmann-Zielinska, A. and Jankowski, P. 2008. A framework for sensitivity analysis in spatial multiple criteria evaluation, lecture notes in computer science no. 5266. Pages 217233 in Cova, T. J., Miller, H. J., Beard, K., and Frank, A. U., eds. Proceedings of 5th International Conference, GIScience 2008. Berlin and Heidelberg Springer Verlag.Google Scholar
Malczewski, J. 1999. GIS and Multicriteria Decision Analysis. New York J. Wiley. 392 p.Google Scholar
Malczewski, J. 2000. On the use of weighted linear combination method in GIS: common and best practice approaches. Trans. GIS 4:522.Google Scholar
Malczewski, J. 2006. GIS-based multicriteria decision analysis: a survey of the literature. Int. J. Geogr. Inf. Sci. 20:703726.Google Scholar
Manly, B. F. J. 2007. Randomization, Bootstrap and Monte Carlo Methods in Biology. 3rd ed. Boca Raton, FL Chapman and Hall/CRC, Taylor & Francis Group. 455 p.Google Scholar
Melbourne, B. A. and Hastings, A. 2009. Highly variable spread rates in replicated biological invasions: fundamental limits to predictability. Science 325:15361539.Google Scholar
Mendoza, G. A. and Martins, H. 2006. Multi-criteria decision analysis in natural resource management: a critical review of methods and new modeling paradigms. For. Ecol. Manag. 230:122.Google Scholar
Moody, M. E. and Mack, R. N. 1988. Controlling the spread of plant invasions: the importance of nascent foci. J. Appl. Ecol. 25:10091021.Google Scholar
Noel, J. M., Platt, W. J., and Moser, E. B. 1998. Structural characteristics of old- and second-growth stands of longleaf pine (Pinus palustris) in the Gulf Coastal region of the U.S.A. Conserv. Biol. 12:533548.Google Scholar
[NCNHP] North Carolina Natural Heritage Program. 2009. North Carolina Natural Heritage Program Biennial Protection Plan: List of Significant Natural Heritage Areas. http://www.ncnhp.org/Images/priority_list%202009.pdf. Accessed March 10, 2010.Google Scholar
Panetta, D. F. and Cacho, O. J. 2012. Beyond fecundity control: which weeds are most containable? J. Appl. Ecol. 49:311321.Google Scholar
Parshall, D. and Kral, T. 1989. A new subspecies of (French) Satyridae from North Carolina. J. Lepid. Soc. 43:114119.Google Scholar
Pimental, 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. 52:273288.Google Scholar
Prato, T. 1999. Multiple attribute decision analysis for ecosystem management. Ecol. Econ. 30:207222.Google Scholar
Regan, H. M., Davis, F. W., Andelman, S. J., Widyanata, A., and Freese, M. 2007. Comprehensive criteria for biodiversity evaluation in conservation planning. Biodivers. Conserv. 16:27152728.Google Scholar
Roura-Pascual, N., Krug, R. M., Richardson, D. M., and Hui, C. 2010. Spatially-explicit sensitivity analysis for conservation management: exploring the influence of decisions in invasive alien plant management. Divers. Distrib. 16:426438.Google Scholar
Roura-Pascual, N., Richardson, D. M., Krug, R. M., Brown, A., Chapman, R. A., Forsyth, G. G., Le Maitre, D. C., Robertson, M. P., Stafford, L., Van Wilgen, B. W., Wannenburgh, A., and Wessels, N. 2009. Ecology and management of alien plant invasions in South African fynbos: accommodating key complexities in objective decision making. Biol. Conserv. 142:15951604.Google Scholar
Saaty, T. L. 1977. A scaling method for priorities in hierarchical structures. J. Math. Psychol. 15:234281.Google Scholar
Saaty, T. L. 1980. The Analytic Hierarchy Process: Planning, Priority Setting, Resource Allocation. New York McGraw-Hill. 437 p.Google Scholar
Sharov, A. A. 2004. Bioeconomics of managing the spread of exotic pest species with barrier zones. Risk Anal. 24:879892.Google Scholar
Shea, K., Possingham, H. P., Murdoch, W. W., and Roush, R. 2002. Active adaptive management in insect pest and weed control: intervention with a plan for learning. Ecol. Appl. 12:927936.Google Scholar
Simberloff, D. 2003. How much information on population biology is needed to manage introduced species? Conserv. Biol. 17:8392.Google Scholar
Skurka Darin, G. M., Schoenig, S., Barney, J. N., Panetta, F. D., and DiTomaso, J. M. 2011. WHIPPET: a novel tool for prioritizing invasive plant populations for regional eradication. J. Environ. Manag. 92:131139.Google Scholar
Sorrie, B. A., Gray, J. B., and Crutchfield, P. J. 2006. The vascular flora of the longleaf pine ecosystem of Fort Bragg and Weymouth Woods, North Carolina. Castanea 71:129161.Google Scholar
Theoharides, K. A. and Dukes, J. S. 2007. Plant invasion across space and time: factors affecting nonindigenous species success during four stages of invasion. New Phytol. 176:256273.Google Scholar
Trani, K. M. and Giles, R. H. 1999. An analysis of deforestation: metrics used to describe pattern. For. Ecol. Manag. 114:459470.Google Scholar
Triantaphyllou, E. and Sánchez, A. 1997. A sensitivity analysis approach for some deterministic multi-criteria decision-making methods. Decis. Sci. 28:151194.Google Scholar
[USFWS] U.S. Fish and Wildlife Service. 2003. Recovery Plan for the Red-cockaded Woodpecker (Picoides borealis): Second Revision. Atlanta, GA U.S. Fish and Wildlife Service. 296 p.Google Scholar
Vilà, M., Espinar, J. L., Hejda, M., Hulme, P. E., Jarošik, J., Maron, J. L., Pergl, J., Schaffner, U., Sun, Y., and Pyšek, P. 2011. Ecological impacts of invasive alien plants: a meta-analysis of their effects on species, communities and ecosystems. Ecol. Lett. 14:702708.Google Scholar
Ware, S., Frost, C., and Doerr, P. D. 1993. Southern mixed hardwood forest: the former longleaf pine forest. Pages 447493 in Martin, W. H., Boyce, S. G., and Echternacht, A. C., eds. Biodiversity of the Southeastern United States: Lowland Terrestrial Communities. New York J. Wiley.Google Scholar
Yager, L. Y. and Smith, M. 2009. Use of GIS to prioritize cogongrass (Imperata cylindrica) control on Camp Shelby Joint Forces Training Center, Mississippi. Invasive Plant Sci. Manag. 2:7482.Google Scholar