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Habitat selection by predators and prey in communities with asymmetrical intraguild predation

2001· article· en· W2002601840 on OpenAlexafffund
Michael R. Heithaus

Bibliographic record

VenueOikos · 2001
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant and animal studies
Canadian institutionsSimon Fraser University
FundersNatural Sciences and Engineering Research Council of CanadaSimon Fraser UniversityNational Science Foundation
KeywordsIntraguild predationPredationEcologyGuildBiologyHabitatCompetition (biology)Predator

Abstract

fetched live from OpenAlex

Competition and predation have broad ecological consequences as they may influence individual behavior and community structure. In some cases, they are linked and predator and prey are also competitors (intraguild predation). I present a game theoretic model of habitat use by predators and prey under conditions of asymmetrical intraguild predation. This model predicts that when the diet of intraguild predators is restricted to intraguild prey and the resource for which predators and prey compete (the basal resource), co‐occurrence is only stable when dietary overlap is low and productivity of the basal resource is not high. The addition of alternative resources for predators results in co‐occurrence under all conditions. Variation in alternative resource productivity produces a continuum of intraguild prey distributions from matching relative habitat safety, to one that reflects both food and predation risk. When there is a substantial alternative resource for predators, the distribution of predators matches that of alternative resource availability while the distribution of prey is influenced by both habitat riskiness and food availability. The density and distribution of the predator's alternative resource thus influence habitat selection by the intraguild prey. This stresses the importance of indirect interactions in structuring habitat use in communities and the need to view habitat selection in a community context.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.023
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.024
GPT teacher head0.201
Teacher spread0.177 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations130
Published2001
Admission routes2
Has abstractyes

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