Genetic control of invasive sea lamprey in the Great Lakes
Bibliographic record
Abstract
The invasive sea lamprey was a significant factor in the collapse of fish stocks in the Great Lakes, and it continues to threaten the multi-billion-dollar fishing industry. Thus, substantial resources are invested annually on sea lamprey control. Current control strategies have reduced sea lamprey populations by up to 90%, but they are expensive and have some limitations, e.g., lamprey-specific biocides applied to larval habitat impact native lampreys, and physical barriers that block adult lamprey access to spawning habitat impede migration of other fishes. Therefore, genetic control options which offer a theoretically powerful and effective pest control tool are being explored, although they have uncertain sociopolitical support, especially given the need to protect sea lamprey in their native range in Atlantic drainages. Here, we present an overview of genetic approaches with potential for application to sea lamprey control in the Great Lakes. We classify these approaches into two major categories: self-limiting (heritable sex ratio ratchet, Trojan gene, split gene drive) and self-sustaining (gene drive-based sex ratio distortion, homing suppression gene drive, toxin-antidote gene drives, and modification-type gene drives to aid suppression). We describe the technical aspects, challenges, and potential application of each method, focusing on gene drives, a fast-evolving research area that was only a distant option for sea lamprey control in previous reviews. We conclude that, given the risk of undesired spread of deleterious alleles from the Great Lakes, self-limiting genetic control options and confined gene drives will likely be preferred over unconfined gene drive options for sea lamprey control.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".