3 results
Forecasting Weed Distributions using Climate Data: A GIS Early Warning Tool
- Catherine S. Jarnevich, Tracy R. Holcombe, David T. Barnett, Thomas J. Stohlgren, John T. Kartesz
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- Journal:
- Invasive Plant Science and Management / Volume 3 / Issue 4 / December 2010
- Published online by Cambridge University Press:
- 20 January 2017, pp. 365-375
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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.
Using State-and-Transition Modeling to Account for Imperfect Detection in Invasive Species Management
- Leonardo Frid, Tracy Holcombe, Jeffrey T. Morisette, Aaryn D. Olsson, Lindy Brigham, Travis M. Bean, Julio L. Betancourt, Katherine Bryan
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- Journal:
- Invasive Plant Science and Management / Volume 6 / Issue 1 / March 2013
- Published online by Cambridge University Press:
- 20 January 2017, pp. 36-47
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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.
Cross-Scale Assessment of Potential Habitat Shifts in a Rapidly Changing Climate
- Catherine S. Jarnevich, Tracy R. Holcombe, Elizabeth M. Bella, Matthew L. Carlson, Gino Graziano, Melinda Lamb, Steven S. Seefeldt, Jeffery Morisette
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- Journal:
- Invasive Plant Science and Management / Volume 7 / Issue 3 / September 2014
- Published online by Cambridge University Press:
- 20 January 2017, pp. 491-502
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We assessed the ability of climatic, environmental, and anthropogenic variables to predict areas of high-risk for plant invasion and consider the relative importance and contribution of these predictor variables by considering two spatial scales in a region of rapidly changing climate. We created predictive distribution models, using Maxent, for three highly invasive plant species (Canada thistle, white sweetclover, and reed canarygrass) in Alaska at both a regional scale and a local scale. Regional scale models encompassed southern coastal Alaska and were developed from topographic and climatic data at a 2 km (1.2 mi) spatial resolution. Models were applied to future climate (2030). Local scale models were spatially nested within the regional area; these models incorporated physiographic and anthropogenic variables at a 30 m (98.4 ft) resolution. Regional and local models performed well (AUC values > 0.7), with the exception of one species at each spatial scale. Regional models predict an increase in area of suitable habitat for all species by 2030 with a general shift to higher elevation areas; however, the distribution of each species was driven by different climate and topographical variables. In contrast local models indicate that distance to right-of-ways and elevation are associated with habitat suitability for all three species at this spatial level. Combining results from regional models, capturing long-term distribution, and local models, capturing near-term establishment and distribution, offers a new and effective tool for highlighting at-risk areas and provides insight on how variables acting at different scales contribute to suitability predictions. The combinations also provides easy comparison, highlighting agreement between the two scales, where long-term distribution factors predict suitability while near-term do not and vice versa.