Early life history studies of preypredator interactions: quantifying the stochastic individual responses to environmental variability
Bibliographic record
Abstract
Laboratory evidence shows that growth and survival of larval fish are strongly affected by variations in prey and predators; field evidence, in general, does not. This discrepancy may be partly due to the mismatch of scales at which manipulative and observational studies are conducted, or perhaps field studies are somehow not detecting the variable component of the larvae or their environment. I discuss potentially important variable features of fish larvae and their environment and show how mean values can be misleading. Using data from several field studies dealing with the growth and mortality of radiated shanny (Ulvaria subbifurcata) larvae, I illustrate how observational programs can miss important variation. I show evidence of how differences among individuals may lead to varying responses to fluctuations in prey availability. I also discuss issues concerning the level of variability in environmental conditions that may be described by standard survey methods used in the study of larval fish. The examples are intended to serve as illustration of the need to better describe the underlying stochastic structure of environmental conditions to understand early life dynamics.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".