Relative effects of biotic and abiotic factors during early life history on recruitment dynamics: a case study
Bibliographic record
Abstract
The relative effects of biotic and abiotic factors, and the life-history stages upon which they act to affect fish recruitment, vary among species and ecosystems. We compared the effects of spawning stock biomass, and factors operating at early-term (encompassing the egg, yolk-sac larval, and first few days of swim-up larval stages), middle-term (including the swim-up larval and pelagic juvenile stages), and late-term (over the benthic juvenile stage) on recruitment by yellow perch (Perca flavescens) in the western basin of Lake Erie between 1999 and 2013. Variation of recruitment was mainly driven by middle-term effects. Then, abiotic factors, such as warming rate and wind speed, more strongly affected recruitment than did biotic factors. Among middle-term biotic factors, the top-down effect of yearling walleye (Sander vitreus) abundance was stronger than the bottom-up effect of zooplankton abundance. Similar to marine species, physical processes appear to strongly affect recruitment dynamics of Lake Erie yellow perch over its pelagic larval and juvenile stages, demonstrating the importance of physical and biological processes in understanding fish population dynamics in large lakes.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".