Skip to main content

Forecasting suppression of invasive Sea Lamprey in Lake Superior: data and code for Bayesian forecast model

Cite this dataset

Lewandoski, Sean (2022). Forecasting suppression of invasive Sea Lamprey in Lake Superior: data and code for Bayesian forecast model [Dataset]. Dryad.


Resource managers frequently are tasked with mitigating or reversing adverse effects of invasive species through management policies and actions.  In Lake Superior, of the Laurentian Great Lakes, invasive sea lamprey populations are suppressed to protect valuable fish stocks.  However, the relationship between choice of long-term control strategy and the future chance of achieving the suppression target is unclear.

Using a 60+ year time-series of suppression effort and monitoring data from 50 assessment sites located on Lake Superior tributaries, we developed a Bayesian state-space model to forecast the probability of suppressing lamprey below the suppression target.

With annual application of lampricide (i.e., lamprey-specific pesticide) at historical mean levels, we forecasted a 15% chance of achieving the Lake Superior sea lamprey suppression target in 2040.

Increasing lampricide effort and/or supplementing lampricide control with age-1 recruitment reduction increased suppression chance.  Annual application of the maximum historical lampricide effort resulted in a 50% predicted chance of achieving the target, annual application of the mean historic lampricide effort plus a 40% reduction in recruitment resulted in a 54% chance, and the maximum amount of effort considered (maximum historic lampricide and 60% reduction in recruitment) resulted in a 94% chance.

Policy implications. We developed a simulation model from a robust, long-term monitoring dataset that improves understanding of why long-term sea lamprey suppression objectives have been difficult to achieve in Lake Superior.  Furthermore, the model provides a means to gauge efficacy of sea lamprey control policy and action scenarios based on forecasted chance of achieving the suppression target. Creating processes for iteratively refining our forecasting model with stakeholder and technical-expert input and integration with a decision analysis framework could strengthen the link between ecological knowledge obtained from long-term monitoring and invasive sea lamprey management.


Records of adult sea lamprey catch (LakeSuperiorCatchData.csv), associated metadata for catch records (catchMetaData.csv), and control effort records (LampricideEffort.csv, eGEffort.csv, EWNumberOpp.csv, BarrierkmBlocked.csv, and YearAfterTreatCov.csv) were obtained from the US Fish and Wildlife Service Sea Lamprey Control database and annual reports archived by the Great Lakes Fishery Commission (  The km blocked values for the barrier effort was calculated using an upstream trace of the National Hydrology Dataset from the constructed barrier to stream headwaters or next known blocking structure.  The R code and Stan file used to run the state-space model are included, with annotations in the R code describing data processing steps.