Forecasting suppression of invasive sea lamprey in Lake Superior

Abstract

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.

Progress and opportunities in advancing near‐term forecasting of freshwater quality

“…Model projections entail running models into the future using a set of underlying assumptions or scenarios, thereby predicting a future predicated on specific conditions. For example, Lewandoski and Brenden (2022) developed model projections of whether continued lampricide application at historical levels would achieve invasive sea lamprey suppression targets in Lake Superior, USA, by 2040.…”

“…Model projections entail running models into the future using a set of underlying assumptions or scenarios, thereby predicting a future predicated on specific conditions. For example, Lewandoski and Brenden (2022) developed model projections of whether continued lampricide application at historical levels would achieve invasive sea lamprey suppression targets in Lake Superior, USA, by 2040. While projections can include uncertainty and provide preemptive decision‐making guidance, they cannot be used to make probabilistic statements about future events (unlike forecasts or hindcasts) since it is unknown which scenario is most likely to occur (Dietze, 2017).…”

Progress and opportunities in advancing near-term forecasting of freshwater quality

“…In comparison, model projections run models into the future using a set of underlying assumptions or scenarios, thereby predicting a future predicated on specific conditions. For example, Lewandoski & Brenden (2022) developed model projections of whether continued lampricide application at historical levels would achieve invasive sea lamprey suppression targets in Lake Superior, USA by 2040. While projections can provide preemptive decision-making guidance, they cannot be used to make probabilistic statements about future events (unlike forecasts or hindcasts) since it is unknown which scenario is most likely to occur (Dietze, 2017).…”

Research on Quantifying Sea Lamprey Ecological Impacts Based on Differential Equation Modeling and AHP-TOPSIS Framework

“…John W. Heinrich summarized the abundance of sea lamprey in Lake Superior, one of the Great Lakes, from 1957 to 1999 [3]. Sean A. Lewandoski developed a Bayesian state-space model using over 60 years of tributary data from Lake Superior since 1953 to predict the probability of suppressing sea lamprey populations below management targets [4]. However, data on the status of the sea lamprey population before the control program was scarce [5], and no mathematical model has been proposed to quantify the impact of sea lamprey population on ecosystem, taking into account changes in sex ratio.…”