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Using State-and-Transition Modeling to Account for Imperfect Detection in Invasive Species Management

Published online by Cambridge University Press:  20 January 2017

Leonardo Frid ,
Tracy Holcombe ,
Jeffrey T. Morisette ,
Aaryn D. Olsson ,
Lindy Brigham ,
Travis M. Bean ,
Julio L. Betancourt  and
Katherine Bryan
Leonardo Frid*
Affiliation:
Apex Resource Management Solutions Ltd., 1110 Lenora Rd., Bowen Island, BC, Canada, V0N 1G1
Tracy Holcombe
Affiliation:
U.S. Geological Survey, Fort Collins Science Center, Invasive Species Science Branch, 2150 Centre Ave., Building C, Fort Collins, CO 80526
Jeffrey T. Morisette
Affiliation:
DOI North Central Climate Science Center, U.S. Geological Survey, Fort Collins Science Center 2150 Centre Ave., Building C, Fort Collins, CO 80526-8118
Aaryn D. Olsson
Affiliation:
School of Earth Sciences and Environmental Sustainability, Northern Arizona University, 1298 S. Knoles Dr., Flagstaff, AZ 86011-5694
Lindy Brigham
Affiliation:
Southern Arizona Buffelgrass Coordination Center, 1955 E. 6th St., Tucson, AZ 85719
Travis M. Bean
Affiliation:
School of Natural Resources and Environment, University of Arizona, 1955 E. 6th St., Tucson, AZ 85719
Julio L. Betancourt
Affiliation:
U.S. Geological Survey, National Research Program, 1955 E. 6th St., Tucson, AZ 85719
Katherine Bryan
Affiliation:
ESSA Technologies Ltd., 600-2695 Granville St., Vancouver, BC, Canada V6H 3H4
*
Corresponding author's E-mail: leonardo.frid@apexrms.com
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Abstract

Buffelgrass, a highly competitive and flammable African bunchgrass, is spreading rapidly across both urban and natural areas in the Sonoran Desert of southern and central Arizona. Damages include increased fire risk, losses in biodiversity, and diminished revenues and quality of life. Feasibility of sustained and successful mitigation will depend heavily on rates of spread, treatment capacity, and cost–benefit analysis. We created a decision support model for the wildland–urban interface north of Tucson, AZ, using a spatial state-and-transition simulation modeling framework, the Tool for Exploratory Landscape Scenario Analyses. We addressed the issues of undetected invasions, identifying potentially suitable habitat and calibrating spread rates, while answering questions about how to allocate resources among inventory, treatment, and maintenance. Inputs to the model include a state-and-transition simulation model to describe the succession and control of buffelgrass, a habitat suitability model, management planning zones, spread vectors, estimated dispersal kernels for buffelgrass, and maps of current distribution. Our spatial simulations showed that without treatment, buffelgrass infestations that started with as little as 80 ha (198 ac) could grow to more than 6,000 ha by the year 2060. In contrast, applying unlimited management resources could limit 2060 infestation levels to approximately 50 ha. The application of sufficient resources toward inventory is important because undetected patches of buffelgrass will tend to grow exponentially. In our simulations, areas affected by buffelgrass may increase substantially over the next 50 yr, but a large, upfront investment in buffelgrass control could reduce the infested area and overall management costs.

Keywords

Buffelgrass, Pennisetum ciliare (L.) LinkBuffelgrassSouthern Arizona Buffelgrass Coordination CenterSABCCdecision support toolsTool for Exploratory Landscape Scenario Analyses (TELSA)maxentinventoryEDRR

Information

Type
Research
Information
Invasive Plant Science and Management , Volume 6 , Issue 1 , March 2013 , pp. 36 - 47
Copyright
Copyright © Weed Science Society of America 

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References

Literature Cited

Bradley, B. A., Oppenheimer, M., and Wilcove, D. S. 2009. Climate change and plant invasions: restoration opportunities ahead? Global Change Biol. 15 :1511–1521.Google Scholar
Brooks, M. L. 2008. Plant invasions and fire regimes. Pages 33–45 in: Zouhar, K., Smith, J. K., Sutherland, S., and Brooks, M. L., eds. Wildland Fire in Ecosystems: Fire and Nonnative Invasive Plants. Ogden, UT : U.S. Department of Agriculture, Forest Sevice, Rocky Mountain Research Station.Google Scholar
Brooks, M. L., D'Antonio, C. M., Richardson, D. M., Grace, J. B., Keely, J. E., DiTomaso, J. M., Hobbs, R. J., Pellant, M., and Pyke, D. 2004. Effects of invasive alien plants on fire regimes. Bioscience 54 :677–688.Google Scholar
Browning, D. M., Archer, S. R., and Byrne, A. T. 2009. Field validation of 1930s aerial photography: what are we missing? J. Arid Environ. 73 :844–853.Google Scholar
Czembor, C. A., Morris, W. K., Wintle, B. A., and Vesk, P. A. 2011. Quantifying variance components in ecological models based on expert opinion. J. Appl. Ecol. 48 :736–745.Google Scholar
Czembor, C. A. and Vesk, P. A. 2009. Incorporating between-expert uncertainty into state-and-transition simulation models for forest restoration. For. Ecol. Manag. 259 :165–175.Google Scholar
D'Antonio, C. M. and Vitousek, P. M. 1992. Biological invasions by exotic aa grasses, the grass/fire cycle, and global change. Annu. Rev. Ecol. Syst. 23 :63–87.Google Scholar
Eiswerth, M. E. and van Kooten, G. C. 2002. Uncertainty, economics, and the spread of an invasive plant species. Am. J. Agric. Econ. 84 :1317–1322.Google Scholar
Esque, T. C., Burquez-Montijo, A., Schwalbe, C. R., van Devender, T. R., Nijhuis, M. J. M., and Anning, P. P. 2002. Fire ecology of the Sonoran desert tortoise. Pages 312–333 in: van Devender, T. R., ed. The Sonoran Desert Tortoise: Natural History, Biology, and Conservation. Tucson : Arizona–Sonora Desert Museum and The University of Arizona Press.Google Scholar
Esque, T. C., Schwalbe, C. R., Haines, D. F., and Halvorson, W. L., W. L. 2004. Saguaros under siege: invasive species and fire. Desert Plants 20 :49–55.Google Scholar
ESSA Technologies Ltd. 2008. TELSA—Tool for Exploratory Landscape Scenario Analyses, Model Descritption, Version 3.6. http://essa.com/wp-content/uploads/2010/09/ModelDescription.pdf. Accessed October 3, 2012.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
Forbis, T. A., Provencher, L., Frid, L., and Medlyn, G. 2006. Great Basin land management planning using ecological modeling. Environ. Manag. 38 :62–83.Google Scholar
Franklin, K. A., Lyons, K., Nagler, P. L., Lampkin, D., Glenn, E. P., Molina-Freaner, F., Markow, T., and Huete, A. R. 2006. Buffelgrass (Penisetum ciliare) land conversion and productivity in the plains of Sonora Mexico. Biol. Conserv. 127 :62–71.Google Scholar
Frid, L., Hanna, D., Korb, N., Bauer, B., Bryan, K., Martin, B., and Holzer, B. 2013. Evaluating alternative weed management strategies for three Montana landscapes. Invasive Plant Sci. Manag 6 :48–59.Google Scholar
Frid, L. and Wilmshurst, J. F. 2009. Decision analysis to evaluate control strategies for crested wheatgrass (Agropyron cristatum) in Grasslands National Park of Canada. Invasive Plant Sci. Manag. 2 :324–336.Google Scholar
Gesch, D. B. 2007. The National Elevation Dataset, in Maune, D., ed., Digital Elevation Model Technologies and Applications: The DEM Users Manual, 2nd Edition. Bethesda, Maryland : American Society for Photogrammetry and Remote Sensing. Pp. 99–118.Google Scholar
Gesch, D., Oimoen, M., Greenlee, S., Nelson, C., Steuck, M. and Tyler, D. 2002. The National Elevation Dataset:. Photogrammetric Engineering and Remote Sensing 68 (1):5–11.Google Scholar
Hemstrom, M. A., Merzenich, J., Reger, A., and Wales, B. 2007. Integrated analysis of landscape management scenarios using state and transition models in the upper Grande Ronde River subbasin, Oregon, USA. Landsc. Urban Plann. 80 :198–211.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 :1833–1848.Google 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 Biol. 12 :2272–2281.Google Scholar
Hirzel, A. and Arlettaz, R. 2003. Modeling habitat suitability for complex species distributions by environmental-distance geometric mean. Environ. Manag. 32 :614–623.Google Scholar
Huang, C. Y. and Asner, G. 2009. Applications of remote sensing to alien invasive plant studies. Sensors 9 :4869–4889.Google Scholar
Jarnevich, C. and Reynolds, L. V. 2010. Challenges of predicting the potential distribution of a slow-spreading invader: a habitat suitability map for Russian olive (Elaeagnus angustifolia) in the western United States. Biolol. Invasions 13 :153–163.Google Scholar
Leung, B., Lodge, D. M., Finnoff, D., Shogren, J. F., Lewis, M. A., and Lamberti, G. 2002. An ounce of prevention or a pound of cure: bioeconomic risk analysis of invasive species. Proc. R. Soc. Lond. B Biol. Sci. 269 :2407–2413.Google Scholar
Lonsdale, W. M. 1993. Rates of spread of an invading species—Mimosa pigra in northern Australia. J. Ecol. 81 :513–521.Google Scholar
Marshall, V. M., Lewis, M. M., and Ostendorf, B. 2011. Buffel grass (Cenchrus ciliaris) as an invader and threat to biodiversity in arid envjronments: a review. J. Arid Environ. 78 :1–12.Google Scholar
Maxwell, B. D., Lehnhoff, E., and Rew, L. J. 2009. The rationale for monitoring invasive plant populations as a crucial step for management. Invasive Plant Sci. Manag. 2 :1–9.Google Scholar
McDonald, C. J. and McPherson, G. R. 2011. Fire behavior characteristics of buffelgrass-fueled fires and native plant community composition in invaded patches. J. Arid Environ. 75 :1147–1154.Google Scholar
Miller, G., Friedel, M., Adam, P., and Chewings, V. 2010. Ecological impacts of buffel grass (Cenchrus ciliaris L.) invasion in central Australia—does field evidence support a fire-invasion feedback? Rangeland J. 32 :353–365.Google Scholar
Moody, M. E. and Mack, R. N. 1998. Controlling the spread of plant invasions: the importance of nascent foci. J. Appl. Ecol. 25 :1009–1021.Google Scholar
Morales, J. M. and Carlo, T. A. 2006. The effects of plant distribution and frugivore density on the scale and shape of dispersal kernels. Ecology 87 :1489–1496.Google Scholar
Morales-Romero, D. and Molina-Freaner, F. 2008. Influence of buffelgrass pasture conversion on the regeneration and reproduction of the columnar cactus, Pachycereus pecten-aboriginum, in northwestern Mexico. J. Arid Environ. 73 :26–32.Google Scholar
Olsson, A., Betancourt, J. L., Marsh, S., and Crimmins, M. 2012a. Constancy of local spread rates for buffelgrass (Pennisetum ciliare L.) in the Arizona upland of the Sonoran Desert. 2012. J. Arid Environ. 87 :136–143.Google Scholar
Olsson, A. D., Betancourt, J., McClaran, M. P., and Marsh, S. E. 2012b. Sonoran desert ecosystem transformation by a C4 grass without the grass/fire cycle. Divers. Distrib. 18 :10–21.Google Scholar
Phillips, S. J., Anderson, R. P., and Schapire, R. E. 2006. Maximum entropy modeling of species geographic distributions. Ecol. Model. 190 :231–259.Google Scholar
Provencher, L., Forbis, T. A., Frid, L., and Medlyn, G. 2007. Comparing alternative management strategies of fire, grazing, and weed control using spatial modeling. Ecol. Model. 209 :249–263.Google Scholar
Regan, T. J., Chades, I., and Possingham, H. P. 2011. Optimally managing under imperfect detection: a method for plant invasions. J. Appl. Ecol. 48 :76–85.Google Scholar
Regan, T. J., McCarthy, M. A., Baxter, P. W. J., Dane Panetta, F., and Possingham, H. P. 2006. Optimal eradication: when to stop looking for an invasive plant. Ecol. Lett. 9 :759–766.Google Scholar
Schmid, M. K. and Rogers, G. F. 1988. Trends in fire occurrence in the Arizona upland subdivision of the Sonoran Desert, 1955–1983. Southwest. Nat. 33 :437–444.Google Scholar
Stevens, J. M. and Fehmi, J. S. 2009. Competitive effect of two nonnative grasses on a native grass in southern Arizona. Invasive Plant Sci. Manag. 2 :379–385.Google Scholar
Strand, E. K., Vierling, L. A., and Bunting, S. C. 2009. A spatially explicit model to predict future landscape composition of aspen woodlands under various management scenarios. Ecol. Model. 220 :175–191.Google Scholar
Swets, J. A. 1988. Measuring the accuracy of diagnostic systems. Science 240 :1285–1293.Google Scholar
Turner, R. M. and Brown, D. E. 1994. Sonoran desertscrub. Pages 181–221 in: Brown, D. E., ed. Biotic Communities: Southwestern United States and Northwestern Mexico. Salt Lake City : University of Utah Press.Google Scholar
Wadsworth, R. A., Collingham, Y. C., Willis, S. G., Huntley, B., and Hulme, P. E. 2000. Simulating the spread and management of alien riparian weeds: are they out of control? J. Appl. Ecol. 37 :28–38.Google Scholar
Westoby, M., Walker, B., and Noy-Meir, I. 1989. Oportunistic management for rangelands not at equilibrium. J. Range Manag. 42 :266–274.Google Scholar