A Spatially-Explicit Study Of Prey-Predator Interactions In Larval Fish: Assessing The Influence Of Food And Predator Abundance On Growth And Survival
Bibliographic record
Abstract
No abstracts are to be cited without prior reference to the author.We apply a coupled bio-physical model of transport to reconstruct the environmental history of larval radiated shanny in Conception Bay, Newfoundland. The model is applied to data collected during a two week period during which larvae, their food (Copepod nauplii) and their predators (capelin) were monitored in three intensive surveys. Our goal is to determine whether environmentally explicit information can be used to infer the characteristics of individual larvae which are ‘most likely to survive. Backward reconstruction is used to assess the influence of variations in the feeding environment on changes in the growth rates of individual larvae. Forward projections are used to assess the impact of predators on the cumulative density distribution of growth rates on the population of larvae in different areas of the bay. An individual’s past growth has a strong influence on the pattern of growth during the course of our study. There was relatively little influence of current feeding conditions on increment widths for larvae less than 15 days old but there was some evidence of a slight positive influence of increasing prey abundance on growth beyond this age, although this was not statistically significant. Patterns of selective mortality suggest that fast growing individuals suffered higher mortality rates, suggesting they are growing into a predator’s prey field. However, the mortality rates appeared to increase with decreasing predator abundance, based on the drift reconstructions The relationship of growth and mortality with environmental conditions suggests that short-term, small scale variations in environmental history may be difficult to describe accurately in this relatively small system (1000 km2)
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".