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Prey, predators, parasites: intraguild predation or simpler community modules in disguise?

2010· article· en· W1494908648 on OpenAlexaff
Michael Sieber, Frank M. Hilker

Bibliographic record

VenueJournal of Animal Ecology · 2010
Typearticle
Languageen
FieldEnvironmental Science
TopicParasite Biology and Host Interactions
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsPredationIntraguild predationEcologyBiologyPredator

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.045
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.022
GPT teacher head0.339
Teacher spread0.317 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations38
Published2010
Admission routes1
Has abstractyes

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