Predicting occurrences and impacts of smallmouth bass introductions in north temperate lakes

TitlePredicting occurrences and impacts of smallmouth bass introductions in north temperate lakes
Publication TypeJournal Article
Vander Zanden MJ, Olden JD, Thorne JH, Mandrak NE
Type of Articlearticle
Year of Publication2004

Smallmouth bass and other warmwater littoral piscivores are presently expanding their geographic range northward into lakes across southern Canada. Smallmouth bass introduction can dramatically reduce minnow abundances, causing native lake trout to shift to low quality, invertebrate-based diets. Here we develop models to predict future occurrences and impacts of smallmouth bass in central Ontario, with the goal of identifying ``vulnerable'' lakes in order to better guide prevention efforts. Using local and regional environmental variables for 3046 central Ontario lakes, an artificial neural network was used to predict lakes that are likely to be invaded by bass. Smallmouth bass can significantly influence the occurrence and abundance of small-bodied fishes (mainly minnows), and stable isotope analysis of food webs in 18 lakes revealed that lake trout are buffered from impacts of bass on minnows in lakes containing pelagic prey fishes. In the absence of pelagic prey fishes, the trophic niche of lake trout depends on the presence of bass; lake trout feed primarily on zooplankton in the presence of bass, and minnows in the absence of bass. Of the 3046 lakes, the 788 take trout lakes in central Ontario were classified according to their vulnerability to bass invasion based on the predictability of bass occurrence and their subsequent impacts, and mapped in a Geographic Information System (GIS). Only 48 lake trout lakes (6\%) were classified as ``high vulnerability''-predicted to be invaded and impacted by bass. Another 301 lakes had a sensitive food web structure but were not predicted to support a bass population. Based on this information, efforts to prevent further impacts can be optimized by focusing on this vulnerable subset of lakes.

Journal DateFEB
Keywordsartificial neural networks
Citation KeyVanderZanden2004