Interaction strength and stability in stage‐structured food web modules
Bibliographic record
Abstract
There has been a long‐standing debate on what creates stability in food webs. One major finding is that weak interactions can mute the destabilizing potential of strong interactions. Considering that stage structure is common in nature, that existing studies on stability that include population stage structure point in different directions, and the recent theoretical developments in the area of stage structure, there is a need to address the effects of population stage structure in this context. Using simple food web modules, with stage structure in an intermediate consumer, we here begin to theoretically investigate the effects of stage structure on food web stability. We found a general correspondence to previous results such that strong interactions had destabilizing effects and weak interactions that result in decreased energy flux had stabilizing effects. However, we also found a number of novel results connected to stage structure. Interestingly, weak interactions can be destabilizing when they excite other interactions. We also found that cohort cycles and predator–prey cycles did not respond in the same way to increasing interactions strength. We found that the combined effects of two predators feeding on the same prey can strongly destabilize a system. Consistent with previous studies, we also found that stage‐specific feeding can create a refuge effect that leads to a lack of strong destabilization at high interaction strength. Overall, stage structure had both stabilizing and destabilizing aspects. Some effects could be explained by our current understanding of energetic processes; others need additional consideration. Additional aspects such as shunting of energy between stages, control of biomass fluxes, and interactions between lags and energy flux, should be considered.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".