Nonadditive interactions between trophic levels bias the appraisal of the strength of mortality factors
Bibliographic record
Abstract
Abstract The role of bottom‐up and top‐down factors in determining the abundance of organisms is still often evaluated in terms of the nominal (i.e., apparent) cause of death. To determine whether estimates of mortality can be influenced by nonadditive multitrophic interactions, we generated both marginal (i.e., the entirety of mortality attributable to a given factor, including its obscured fraction) and nominal estimates of the trophic forces acting on an herbivore. This was accomplished by comparing mortality rates of the willow leaf beetle Phratora vulgatissima on willows affected or relatively unaffected by local growing conditions, in the presence or absence of natural enemies. Marginal estimates indicated that bottom‐up and top‐down mortality factors interacted in a synergistic or compensatory way, with the nature of the interaction varying in response to the time elapsed since the last harvest of willow plantations. In contrast, nominal estimates could not discriminate synergistic and compensatory interactions from top‐down influence. Combined with recent evidence of compensatory mortality between trophic forces in another system, this study suggests that nonadditive effects between bottom‐up and top‐down mortality factors may be common, offering an explanation through which the contrasting evidences previously presented by proponents of the bottom‐up and top‐down views can be understood.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".