Evaluating harvest‐based control of invasive fish with telemetry: performance of sea lamprey traps in the Great Lakes
Bibliographic record
Abstract
Physical removal (e.g., harvest via traps or nets) of mature individuals may be a cost-effective or socially acceptable alternative to chemical control strategies for invasive species, but requires knowledge of the spatial distribution of a population over time. We used acoustic telemetry to determine the current and possible future role of traps to control and assess invasive sea lampreys, Petromyzon marinus, in the St. Marys River, the connecting channel between Lake Superior and Lake Huron. Exploitation rates (i.e., fractions of an adult sea lamprey population removed by traps) at two upstream locations were compared among three years and two points of entry to the system. Telemetry receivers throughout the drainage allowed trap performance (exploitation rate) to be partitioned into two components: proportion of migrating sea lampreys that visited trap sites (availability) and proportion of available sea lampreys that were caught by traps (local trap efficiency). Estimated exploitation rates were well below those needed to provide population control in the absence of lampricides and were limited by availability and local trap efficiency. Local trap efficiency estimates for acoustic-tagged sea lampreys were lower than analogous estimates regularly obtained using traditional mark-recapture methods, suggesting that abundance had been previously underestimated. Results suggested major changes would be required to substantially increase catch, including improvements to existing traps, installation of new traps, or other modifications to attract and retain more sea lampreys. This case study also shows how bias associated with telemetry tags can be estimated and incorporated in models to improve inferences about parameters that are directly relevant to fishery management.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| 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) | 0.001 | 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".