Can Environmental Variation Generate Positive Indirect Effects in a Model of Shared Predation?
Bibliographic record
Abstract
Classic models of apparent competition predict negative indirect effects between prey with a shared enemy. If predator per capita growth rates are nonlinear, then endogenously generated periodic cycles are predicted to generate less negative or even positive indirect effects between prey. Here I determine how exogenous mechanisms such as environmental variation could modify indirect effects. I find that exogenous variation can have a broader range of effects on indirect interactions than endogenously generated cycles. Indirect effects are altered by environmental variation even in simple models for which the per capita growth rate of the predator species is a linear function of population densities. Temporal variation that affects the predator attack rate or the conversion efficiency can lead to large increases or decreases in the indirect effects between prey, dependent on how prey populations co-vary with the environmental variation. Positive indirect effects can occur when the period of environmental variation is close to the natural period of the biological system and shifts in subharmonic resonance occur with the addition of the second prey. Models that include nonlinear numerical responses generally lead to indirect effects that are sensitive to environmental variation in more parameters and across a wider range of frequencies.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".