Ensemble forecasting of potential habitat for three invasive fishes

Helen M. Poulos; Barry Chernoff; Pam L. Fuller; David Butman

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USGS · 70009631

Ensemble forecasting of potential habitat for three invasive fishes

Helen M. Poulos·Barry Chernoff·Pam L. Fuller·David Butman

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Abstract

Aquatic invasive species pose major ecological and economic threats to aquatic ecosystems worldwide via displacement, predation, or hybridization with native species and the alteration of aquatic habitats and hydrologic cycles. Modeling the habitat suitability of alien aquatic species through spatially explicit mapping is an increasingly important risk assessment tool. Habitat modeling also facilitates identification of key environmental variables influencing invasive species distributions. We compared four modeling methods to predict the potential continental United States distributions of northern snakehead Channa argus (Cantor, 1842), round goby Neogobius melanostomus (Pallas, 1814), and silver carp Hypophthalmichthys molitrix (Valenciennes, 1844) using maximum entropy (Maxent), the genetic algorithm for rule set production (GARP), DOMAIN, and support vector machines (SVM). We used inventory records from the USGS Nonindigenous Aquatic Species Database and a geographic information system of 20 climatic and environmental variables to generate individual and ensemble distribution maps for each species. The ensemble maps from our study performed as well as or better than all of the individual models except Maxent. The ensemble and Maxent models produced significantly higher accuracy individual maps than GARP, one-class SVMs, or DOMAIN. The key environmental predictor variables in the individual models were consistent with the tolerances of each species. Results from this study provide insights into which locations and environmental conditions may promote the future spread of invasive fish in the US.

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Helen M. Poulos, Barry Chernoff, Pam L. Fuller, David Butman. 2012. Ensemble forecasting of potential habitat for three invasive fishes. https://pubs.usgs.gov/publication/70009631

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