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INVASIVE SPECIES: PREDICTING GEOGRAPHIC DISTRIBUTIONS USING ECOLOGICAL NICHE MODELING
Citation:
Kluza, D A., H Lee II, AND D. Reusser. INVASIVE SPECIES: PREDICTING GEOGRAPHIC DISTRIBUTIONS USING ECOLOGICAL NICHE MODELING. Presented at AIBS 2004 Annual Meeting on Invasive Species, Washington, DC, March 16-18, 2004.
Description:
Present approaches to species invasions are reactive in nature. This scenario results in management that perpetually lags behind the most recent invasion and makes control much more difficult. In contrast, spatially explicit ecological niche modeling provides an effective solution to predicting where a species might spread following introduction to a new area allowing management and regulatory agencies to include proactive approaches towards invasive species.
To evaluate the potential geographic distributions of taxa non-indigenous to North America, we used the Genetic Algorithm for Rule-set Prediction (GARP, a machine-learning algorithm) to model the ecological niches of an aquatic plant (hydrilla, Hydrilla verticillata), a terrestrial insect (emerald ash borer Agrilus planipennis), and have begun applying the model to estuarine invaders such as the European Green crab (Carcinus maenas). Hydrilla, a relatively widespread invader (primarily Gulf Coast and eastern seaboard states), showed a potential distribution encompassing the U.S. Pacific Coast, and states east of the central Great Plains and south of the Great Lakes. Emerald ash borer, a more localized invasive (Ohio, Michigan, and Ontario), demonstrated a potential distribution that overlapped greater than 50% of the distributional area of 9 of North America=s 16 ash species (Fraxinus spp.). For both the ash borer and hydrilla, GARP predictions demonstrated the potential for further spread in North America. Application of GARP to estuarine invaders raises several challenges, such as obtaining environmental data layers at a fine enough spatial resolution, but our preliminary efforts suggest that this approach should also work for estuarine organisms. The spatially explicit nature of these predictions can help decision makers and environmental managers to make better and timelier decisions regarding the detection and control of invasive species.