Cooperative Fish and Wildlife Research Units Program: Utah
Education, Research and Technical Assistance for Managing Our Natural Resources

LaPlanche, C. A. Elger, F, Santoul, G.P. Thiede, and P. Budy. 2018. Forecasting the eradication success of an exotic fish from an alpine stream. Biological Conservation 223:34-46. USGS FSP: IP-XX.


Management actions aimed at eradicating exotic fish species from riverine ecosystems can be better informed by forecasting abilities of mechanistic models. We illustrate this point with an example of the Logan River, Utah, originally populated with endemic cutthroat trout (Oncorhynchus clarkii utah), which compete with exotic brown trout (Salmo trutta). The coexistence equilibrium was disrupted by a large scale, experimental removal of the exotic species in 2009-2011 (on average, 8.2% of the stock each year), followed by a increase in the density of the native species. We built a spatially-explicit, reaction-diffusion model encompassing four key processes: habitat heterogeneity, competition, dispersal, and a management action. The model was calibrated with detailed long-term monitoring data collected along the 35-km long river main channel since 2001. Our model, although simple, did a remarkable job reproducing the system steady state prior to management. Insights gained from the model predictions are that the exotic species is more competitive; however, the native species still occupies more favorable habitat and has superior growth. Dynamic runs of the model also recreated the increase of the native species following the management action, with two possible distinct outcomes: recovery or suppression/eradication of the exotic species. The processing of available knowledge using Bayesian methods allowed us to conclude that the chance for suppression/eradication of the invader was 15.4%. We show that accessible mathematical and numerical tools can provide highly informative insights for managers (e.g., outcome of their conservation actions), identify knowledge gaps as well as provide testable theory for researchers.