Numerical changes in stream-resident brown trout (<i>Salmo trutta</i>): uncovering the roles of density-dependent and density-independent factors across space and time
Bibliographic record
Abstract
The objective was to uncover the roles of environmental, density-independent, and intrinsic, density-dependent factors as drivers of population size in stream-resident brown trout (Salmo trutta) across space and time. The study was based on census data and environmental variables collected every fourth month over 1986–2006 at 12 sites selected along four Rio Esva tributaries (northwestern Spain). Density-dependent hypotheses involving the operation of current and lagged densities on recruitment and subsequent spawner density were analyzed at three increasingly larger geographical scales (site, stream, and river drainage). No evidence supported these hypotheses, suggesting an overall lack of negative feedbacks among year classes. Stream discharge in March when trout emerge and site depth explained substantial amounts of spatiotemporal variation in recruitment. The effects of these two factors on recruitment were sufficiently strong to be detected in year-class mortality and subsequent spawner density. Discharge and site depth determine the amount of spatial habitat suitable for sized individuals during their lifetime and, hence, the maximum number of individuals across space and time. Recruitment dependence, as a delayed density-dependent factor, adjusts the numbers in excess to the numbers established by spatial habitat. These findings differ markedly from the prediction of the most recent theories of population dynamics and are discussed in this context.
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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 | 0.000 | 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) | 0.000 | 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".