Ten lessons for controlling invasive species: Wisdom from the long-standing sea lamprey control program on the Laurentian Great Lakes
Bibliographic record
Abstract
) control in the Laurentian Great Lakes of North America is among the largest and most successful control programs of an invasive species anywhere on the planet. The effort began more than 75 years ago; it unites multiple nations, states, and provinces with the common goal of controlling this invasive species and protecting a valuable fishery. The science-based control program is administered by the Great Lakes Fishery Commission (GLFC), a body arising from a treaty signed by the United States and Canada. In the present article, we share 10 lessons learned from decades of successful sea lamprey control with the hopes of informing ongoing and future control programs targeting biological invasions. The 10 lessons we identified are to act boldly in times of crisis, to maintain the social license, to invest in capacity building, to break down the silos, to support fundamental science, to diversify your portfolio of control measures, to strive for continuous improvement, to confront the trade-off between information and action, to keep your foot on the gas, and to keep your eyes on the prize. The GLFC has long fostered a framework that uses some military strategy and verbiage that extends across the lessons (e.g., know your enemy). Other lessons are more nascent as the GLFC reenvisions its relationship with Indigenous peoples and governments in a path to reconciliation where two-eyed seeing is being embraced. Through adaptive management, horizon scanning methods, and embracing implementation science, the lessons learned about sea lamprey control will continue to evolve, which is itself a lesson. We submit that the lessons shared in the present article will help guide invasive species control programs spanning taxa, ecosystems, and regions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.017 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.007 | 0.017 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".