**The title, authors, and abstract for this completion report are provided below.  For a copy of the completion report, please contact the author at wilberg@umces.edu or via telephone at 410-326-7273. Questions? Contact the GLFC via e-mail or via telephone at 734-662-3209**



Improving sea lamprey control through the use of historical data to inform selection of sites for lampricide treatment



Michael J. Wilberg1, Jason M. Robinson1, Michael L. Jones2, Jean V. Adams3


1 Chesapeake Biological Laboratory, University of Maryland Center for Environmental Science, P.O. Box 38,

Solomons, MD, USA, wilberg@cbl.umces.edu.

2Department of Fisheries and Wildlife, Michigan State University, 13 Natural Resources Building, East Lansing,

Michigan 48824, USA

3U.S. Geological Survey, Great Lakes Science Center, 1451 Green Road, Ann Arbor, Michigan 48105, USA.


November 2012




The St. Marys River is a major producer of parasitic sea lampreys (Petromyzon marinus) to Lake Huron and northern Lake Michigan making it an important area for larval sea lamprey control. Bayluscide treatments are conducted in areas of high larval density, which requires density estimates at relatively fine spatial scales to inform treatment decisions. Density estimates are currently based only on the most recent year’s sampling data, but a long time series exists that could help inform treatment decisions. The objectives of this project were to (1) develop models to incorporate previous years’ data into St. Marys River sea lamprey assessment, (2) determine whether use of historical data improves estimates of abundance within plots in the St. Marys River, (3) determine whether current sampling effort can be better used to achieve more precise estimates of larval sea lamprey abundance, and (4) evaluate the potential for application of similar methods to other streams. We developed five methods of estimating spatially specific density and abundance that included the previous years’ data including a generalized

linear model (GLM) based on larval density, a GLM based on larval catch, a generalized additive model (GAM) based on larval density, a spatial age-structured population model, and a model averaging approach. The currently used sample-based approach that uses only the most recent year’s data was also considered. The GLMs and GAM included a categorical plot effect and a continuous years-since-treatment variable. The population model included a stock-recruitment function, spatial recruitment patterns, natural mortality, chemical treatment mortality, and larval metamorphosis. The model averaging approach consisted of the average plot-level density estimated using the GLM based on catch data and the currently used sample-based estimates. Methods were evaluated based on their ability to accurately project plot-level larval density, identify high density plots for treatment, and rank plots in order based on density resulting in high numbers of sea lampreys killed per hectare treated. Performance was variable, and no single method outperformed the others for all metrics. However, the model averaging method was the best method to inform sea lamprey control decisions in the St. Marys River due to its consistent performance. Although the population model did not outperform the other methods considered, it provided a description of sea lamprey population dynamics that are not estimated by the other methods. During 1993–2011 recruitment, larval abundance, and transformer abundance decreased by 80, 84, and 86%, respectively. Estimated natural mortality (0.09 per year) and treatment mortality (0.51 per treatment) were less than previous estimates. The population model also showed that out-of-plot areas contribute significantly to the population. Annual recruitment was variable, and an upstream shift in recruitment location was observed over time. We also considered the effect of sampling intensity on the success of the larval sea lamprey control program by explicitly modeling the tradeoff between assessment and control efforts to maximize number of larvae killed in the St. Marys River. When the tradeoff was incorporated, the sampling intensity that maximized the number of larvae killed depended on the overall budget available, with increased sampling intensities maximizing effectiveness under medium to large budgets ($0.4 to $2.0 million). Sea lamprey control actions based on assessment information outperformed those that were implemented with no assessment under all budget scenarios. Incorporating model-based approaches to larval density estimation, and explicitly considering the economic tradeoff between assessment and control should lead to a more efficient and effective treatment program in the St. Marys River. We also describe how the model-based methods developed for the St. Marys River can be applied to other lamprey producing streams throughout the Great Lakes to positively impact sea lamprey control efforts.