Metabolism, population growth, and the fast‐slow life history continuum of marine fishes
Bibliographic record
Abstract
Abstract The maximum intrinsic rate of population increase (rmax) represents a population's maximum capacity to replace itself and is central to fisheries management and conservation. Species with lower rmax typically have slower life histories compared to species with faster life histories and higher rmax. Here, we posit that metabolic rate is related to the fast–slow life history continuum and the connection may be stronger for maximum metabolic rate and aerobic scope compared to resting metabolic rate. Specifically, we ask whether variation in rmax or any of its component life‐history traits – age‐at‐maturity, maximum age, and annual reproductive output – explain variation in resting and maximum metabolic rates and aerobic scope across 84 shark and teleost species, while accounting for the effects of measurement temperature, measurement body mass, ecological lifestyle, and evolutionary history. Overall, we find a strong connection between metabolic rate and the fast‐slow life history continuum, such that species with faster population growth (higher rmax) generally have higher maximum metabolic rates and broader aerobic scopes. Specifically, rmax is more important in explaining variation in maximum metabolic rate and aerobic scope compared to resting metabolic rate, which is best explained by age‐at‐maturity (out of the life history traits examined). In conclusion, teleosts and sharks share a common fast–slow physiology/life history continuum, with teleosts generally at the faster end and sharks at the slower end, yet with considerable overlap. Our work improves our understanding of the diversity of fish life histories and may ultimately improve our understanding of intrinsic sensitivity to overfishing.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".