Predator-dependent functional responses and interaction strengths in a natural food web
Bibliographic record
Abstract
Predator-dependent functional responses decouple predation mortality from fluctuations in predator abundance and therefore can prevent strong "top-down" interaction strengths in food webs. We evaluated whether contrasts in the functional response of Baltic Sea cod (Gadus morhua) were consistent with the contrasting population dynamics of two prey species, herring (Clupea harengus) and sprat (Sprattus sprattus): sprat abundance increased nearly threefold following a sharp decline in the cod population (a strong interaction), whereas herring abundance failed to increase (a weak interaction). We found striking differences in the functional response of cod on alternative prey, and these were consistent with the observed patterns in interaction strengths. Cod predation was the dominant source of mortality for age-1 and age-2 sprat but was only important for age-1 herring. Moreover, the magnitude of predation mortality on age-1 and age-2 sprat was highly sensitive to cod biomass, whereas predation mortality on herring was only moderately sensitive to cod biomass. These analyses suggest the possibility that food webs are comprised of linkages that vary with respect to the magnitude and importance of predation mortality and how this mortality varies with changes in predator abundance.
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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) | 0.001 | 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".