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

Howe, E., J. E. Marsden, T. M. Donovan, and R. Lamberson. 2012. A life cycle approach to modeling sea lamprey population dynamics in the Lake Champlain basin to evaluate alternative control strategies. Journal of Great Lakes Research 38:101-114.


Sea lamprey (Petromyzon marinus) is a nuisance species in the Laurentian Great Lakes and Lake Champlain that has devastated native fi sh populations and hampered sport fi sheries development. We developed a modified stage-based life history matrix for sea lamprey to analyze the effects of various management efforts to suppress sea lamprey population growth in Lake Champlain. These efforts targeted different life stages of the sea lamprey life cycle. A beta distribution was used to distribute stochastic larval populations among twenty sea lamprey-bearing tributaries and five deltas to Lake Champlain, from which sea lamprey that survive through larval metamorphosis were then pooled into a lake-wide parasitic-phase population. Parasitic-phase survival to the spawning stage was evaluated based on proximity to the natal tributary and on the size of the resident larval population in each tributary. Potential control strategies were modeled at egg to emergence, larval, and spawning stages to reduce vital rates at each stage, with the goal of suppressing parasitic-phase production. Simulations indicate that control of the larval stage was essential to achieving this goal, and with supplemental effort to reduce the vital rates at early life stages and at the spawning stage, the parasitic-phase population can be further suppressed. Sensitivity simulations indicate that the life history model was sensitive to egg deposition rate, abundance of parasitic-phase sea lamprey from unknown, uncontrolled sources, and the method in which parasitic-phase sea lamprey select tributaries for spawning. Results from this model can guide management agencies to optimize future management programs.