Comparison and utility of different size-based metrics of fish communities for detecting fishery impacts
Bibliographic record
Abstract
The use of fish community indicators based on size spectra has become popular in the development of an ecosystem approach to fisheries. Size spectrum theory arose from basic ecological work on energy flow, predatorprey interactions, and biomass standing stock and was later applied to fish communities as lengthfrequency analysis. A multitude of size spectrum indicators have resulted, but it is not clear if they all present similar information. Here we develop a simple framework describing what four size spectra indicators suggest about fish communities, their likely response to fisheries exploitation, their ecological interpretation, and some of their biases. We examined indicators for scientific survey data from six exploited North Atlantic fish communities for the information that they reveal about each community. Each indicator revealed different information and had different biases. Combining indicators for the most impacted system (owing to fisheries and environmental change), the eastern Scotian Shelf, revealed a pattern analogous to Holling's ecological cycle of exploitation, conservation, release, and reorganisation. If this analogy is generally valid, then it suggests that collapsed fish communities are more susceptible to chance events, and recovery is not directly reversible and may not be recoverable (to previous known state) at all if the system moves to an alternative cycle.
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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 | 0.009 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.009 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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 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".