Environmental and Biological Factors Influence Migratory Sea Lamprey Catchability: Implications for Tracking Abundance in the Laurentian Great Lakes
Bibliographic record
Abstract
Abstract Sea Lamprey Petromyzon marinus population trends in the Great Lakes are tracked by trapping migratory adults in tributaries and using mark-and-recapture techniques to estimate abundance. Understanding what environmental and biological factors influence Sea Lamprey capture in tributaries is crucial to developing efficient trapping methods and reliable abundance estimates. We analyzed data from trapping sites located on eight Great Lakes tributaries using Cormack–Jolly–Seber models and examined how water temperature, discharge, sex, and length influenced Sea Lamprey apparent survival and capture probability. Sea Lamprey apparent survival was negatively associated with water temperature in all tributaries. Additionally, the odds of small Sea Lamprey (≤ 45 cm) remaining available to capture were 39% less (95% CI: 63% decrease–1% increase) than large (> 45 cm) lamprey odds. We used these observed relationships to investigate if bias in abundance estimates using the pooled-Petersen estimator and Jolly–Seber models was expected to be similar across trapping locations or influenced by variable environmental conditions and biological traits. Pooled-Petersen abundance estimates had a positive bias when we generated data sets from simulated populations with empirical relationships between environmental characteristics and catchability. The degree of bias depended upon changes in stream warming patterns and was not consistent among trapping locations. Jolly–Seber models using data from either weekly batch-marked or uniquely marked individuals generated abundance estimates with low bias when data quality was high, but performed poorly in scenarios with few recaptured Sea Lamprey. This research can promote improved Sea Lamprey monitoring efforts by providing insight into the reliability of the pooled-Petersen abundance estimator as a tool for tracking Sea Lamprey populations and demonstrating the limitations of adopting more robust methods when data are sparse.
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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 | 0.000 | 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) | 0.000 | 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".