The many potential indirect interactions between predators that share competing prey
Bibliographic record
Abstract
Using a Lotka‐Volterra model, we explore how the indirect interactions between two predators are altered by interspecific competition between two shared prey. We identify when different indirect interactions arise between the predators, classifying interactions by predator responses to (1) slightly increased mortality in the other predator, (2) a slightly decreased population of the other predator, or (3) removal of the other predator. When interspecific prey competition is low, all methods predict negative indirect effects between predators, i.e., competitive interactions. Strong and/or highly asymmetric interspecific prey competition often produces at least one positive indirect effect between predators, i.e., mutualism or contramensalism. However, the three methods often disagree about the strength of and signs characterizing the indirect effects between predators, including cases where all three methods predict a qualitatively different interaction. These differences arise for a variety of reasons, including hydra effects (where a predator increases in abundance with increased mortality) and extinction of prey species following the removal of one predator. We also show that cyclic dynamics do not arise in our model when there is a single predator, but under strong interspecific prey competition, the indirect interactions between two predators can drive cyclic community dynamics. Similar phenomena are likely to occur in other food webs, and understanding them will be required to predict the impact of environmental change on the abundances of species in those webs.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 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) | 0.003 | 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".