Geographic variation in density, demography, and life history traits of a harvested, sex-changing, temperate reef fish
Bibliographic record
Abstract
Geographic variation in ecological and environmental factors may lead to intraspecific differences among populations. For the California sheephead ( Semicossyphus pulcher ), an important predator in kelp forests and a target of commercial and recreational fisheries, we evaluated the degree to which different populations exhibited variation in density, demography, and life history traits. We assessed biogeographic patterns of abundance through underwater visual census at 39 sites spanning a major portion of the species range (southern California, USA, to Baja California, Mexico) and made collections from seven focal sites to investigate geographic differences in demography and life histories. California sheephead densities were significantly greater in the southern part of their range and at offshore islands than along the mainland coast. At the focal sites, we found significant spatial variation in density, fecundity, size structure, growth rates, annual survivorship, and the timing of maturation and sex change. Density- and temperature-dependent effects helped to explain the intraspecific differences in these parameters. Studies such as this one will allow for demographic plasticity to be incorporated into future stock assessments. Management of temperate reef fishes may best be achieved on smaller spatial scales as we increase our understanding of geographic variation among populations.
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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 | 0.000 | 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 | 0.000 | 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".