Prey, predators, parasites: intraguild predation or simpler community modules in disguise?
Bibliographic record
Abstract
1. Competition and predation are at the heart of community ecology. The theoretical concept of intraguild predation (IGP) combines these key interactions in a single community module. Because IGP is believed to be ubiquitous in nature, it has been subject to extensive research, and there exists a well-developed theoretical framework. 2. We show that a general class of IGP models can be transformed to simpler, but equivalent community structures. This rather unexpected simplification depends critically on the property of 'indiscriminate predation', which we define broadly as the top-predator not distinguishing between its two different prey species. 3. In a broader context, the great importance of IGP and of the simplifying transformation we report here is enhanced by the recent insight that the basic IGP structure extends naturally to host-parasitoid and host-pathogen communities. We show that parasites infecting prey (predators) tend to render IGP effectively into exploitative competition (tritrophic food chain, respectively). 4. The equivalence between the original and simplified community module makes it possible to take advantage from already existing insights. We illustrate this by means of an eco-epidemiological IGP model that is strikingly similar to a classical exploitative competition model. 5. The change of perspective on certain community modules may contribute to a better understanding of food web dynamics. In particular, it may help explain the interactions in food webs that include parasites. Given the ubiquity of parasitism, food webs may appear in a different light when they are transformed to their simplified analogue.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.002 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".