A whole-lake density reduction to assess compensatory responses of gizzard shad<i>Dorosoma cepedianum</i>
Bibliographic record
Abstract
We used a fishery-induced density reduction of gizzard shad Dorosoma cepedianum at a previously unharvested lake to evaluate compensatory density dependence in recruitment processes. We also studied gizzard shad populations at two nearby unharvested lakes to provide contrast with the harvested population. Gizzard shad spawner biomass was reduced by 72% at the harvested lake after 2 years of gill-net removals, although variation in total shad biomass was more modest. We evaluated responses by gizzard shad to the range of biomasses present among the three lakes and 5 years of the study. Annual growth increments varied little over 5 years and were not related to population density across the three lakes. Length-at-maturity differed among lakes and years, but was not related to population density. Despite the range in spawner biomass among the lakes during the study, annual recruitment estimates showed little relationship to the size of the spawner population, suggesting density-dependent prerecruit survival. A spawner–recruit analysis on pooled data from the three lakes indicated that prerecruit survival was negatively related to spawner biomass. Our study provides a rare glimpse of fish compensatory responses following exploitation of a previously unharvested population and has implications for population dynamics theory and fisheries management.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".