Density‐dependent growth and survival in salmonids: Quantifying biological mechanisms and methodological biases
Bibliographic record
Abstract
Abstract Understanding the complex variation in patterns of density‐dependent individual growth and survival across populations is critical to adaptive fisheries management, but the extent to which this variation is caused by biological or methodological differences is unclear. Consequently, we conducted a correlational meta‐analysis of published literature to investigate the relative importance of methodological and biological predictors on the shape and strength of density‐dependent individual growth and survival in salmonids. We obtained 160 effect sizes from 75 studies of 12 species conducted between 1977 and 2019 that differed in experimental approach (sensu Ecological Monographs , 54 , 187–211; 65 laboratory experiments, 60 observational field studies, and 35 field experiments). The experimental approach was the strongest factor influencing the strength of density dependence across studies: density‐dependent survival was stronger than growth in field observational studies, whereas laboratory experiments detected stronger density‐dependent growth than survival. The difference between density‐dependent growth and survival was minimal in field experiments, and between lotic and lentic habitats. The shape of density dependence (logarithmic, linear, exponential or density‐independent) could be predicted with 66.7% accuracy based solely on the experimental approach and the density gradient (highest/lowest*100) of the study. Overall, the strength and shape of density dependence were primarily influenced by methodological predictors, while biological factors (predator presence, food abundance, and species) had predictable but modest effects. For both empirical studies and adaptive fisheries management, we recommend using field experiments with a density gradient of at least 470% to detect the proper shape of the density‐dependent response, or accounting for potential biases if observational or laboratory studies are conducted.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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 teacher head, 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".