Size, growth, temperature and the natural mortality of marine fish
Bibliographic record
Abstract
Abstract The natural mortality of exploited fish populations is often assumed to be a species‐specific constant independent of body size. This assumption has important implications for size‐based fish population models and for predicting the outcome of size‐dependent fisheries management measures such as mesh‐size regulations. To test the assumption, we critically review the empirical estimates of the natural mortality,M(year−1), of marine and brackish water fish stocks and model them as a function of von Bertalanffy growth parameters,L(cm) andK(year−1), temperature (Kelvin) and length,L(cm). Using the Arrhenius equation to describe the relationship betweenMand temperature, we findMto be significantly related to length,LandK, but not to temperature (R2 = 0.62,P < 0.0001,n = 168). Temperature andKare significantly correlated and whenKis removed from the model the temperature term becomes significant, but the resulting model explains less of the total variance (R2 = 0.42,P < 0.0001,n = 168). The relationships betweenM,L,L,Kand temperature are shown to be in general accordance with previous theoretical and empirical investigations. We conclude that natural mortality is significantly related to length and growth characteristics and recommend to use the empirical formula: ln(M) = 0.55 − 1.61ln(L) + 1.44ln(L) + ln(K), for estimating the natural mortality of marine and brackish water fish.
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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 | 0.001 | 0.003 |
| 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 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".