How Systematic Age Underestimation Can Impede Understanding of Fish Population Dynamics: Lessons Learned from a Lake Superior Cisco Stock
Bibliographic record
Abstract
Abstract Systematic underestimation of fish age can impede understanding of recruitment variability and adaptive strategies (like longevity) and can bias estimates of survivorship. We suspected that previous estimates of annual survival ( S ; range = 0.20‐0.44) for Lake Superior ciscoes Coregonus artedi developed from scale ages were biased low. To test this hypothesis, we estimated the total instantaneous mortality rate of adult ciscoes from the Thunder Bay, Ontario, stock by use of cohort‐based catch curves developed from commercial gill‐net catches and otolith‐aged fish. Mean S based on otolith ages was greater for adult females (0.80) than for adult males (0.75), but these differences were not significant. Applying the results of a study of agreement between scale and otolith ages, we modeled a scale age for each otolith‐aged fish to reconstruct catch curves. Using modeled scale ages, estimates of S (0.42 for females, 0.36 for males) were comparable with those reported in past studies. We conducted a November 2005 acoustic and midwater trawl survey to estimate the abundance of ciscoes when the fish were being harvested for roe. Estimated exploitation rates were 0.085 for females and 0.025 for males, and the instantaneous rates of fishing mortality were 0.089 for females and 0.025 for males. The instantaneous rates of natural mortality were 0.131 and 0.265 for females and males, respectively. Using otolith ages, we found that strong year‐classes at large during November 2005 were caught in high numbers as age‐1 fish in previous annual bottom trawl surveys, whereas weak or absent year‐classes were not. For decades, large‐scale fisheries on the Great Lakes were allowed to operate because ciscoes were assumed to be short lived and to have regular recruitment. We postulate that the collapse of these fisheries was linked in part to a misunderstanding of cisco biology driven by scale‐ageing error.
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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 | 0.001 | 0.001 |
| 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".