Density-dependent habitat use and growth of an estuarine fish
Bibliographic record
Abstract
Density dependence can stabilize or destabilize population size through negative or positive feedback controls operating over different spatial and temporal scales. While many species have been shown to exhibit density dependence, the topic has received little attention in estuaries where environmental variability and larval supply are often considered to be the primary drivers of population dynamics. We used multiple long-term, fishery-independent data sets and a unique modeling approach to test the hypothesis that juvenile red drum ( Sciaenops ocellatus ) exhibit density-dependent habitat use and growth rates in estuaries in North Carolina, USA. Age-1 red drum exhibited density-dependent habitat use after accounting for environmental and landscape variables, disproportionately increasing northward and coastward in the study area at high abundance. Apparent individual growth rates of age-0 and age-1 red drum were generally negatively related to the abundance of their own age classes, but evidence of density-dependent growth rates for age-2 red drum was weak to nonexistent. Changes in spatial distribution of red drum when overall abundance was high did not overcome density-dependent effects on individual growth rates. Thus, density-dependent effects have potential negative feedbacks on population growth in estuaries and should not be ignored in future theoretical or empirical estuarine studies.
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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 | 0.002 |
| 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 | 0.001 |
| 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".