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Does Host Value Influence Female Aggressiveness, Contest Outcome and Fitness Gain in Parasitoids?

2007· article· en· W2104033823 on OpenAlexaff
Marlène Goubault, Anne Marie Cortesero, Denis Poinsot, Éric Wajnberg, Guy Boivin

Bibliographic record

VenueEthology · 2007
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant and animal studies
Canadian institutionsAgriculture and Agri-Food Canada
Fundersnot available
KeywordsPteromalidaeAgonistic behaviourBiologyIntraspecific competitionHost (biology)CONTESTScramble competitionParasitoidCompetition (biology)AnthomyiidaeParasitismEcologyZoologyAggressionSocial psychologyPsychologyLarva

Abstract

fetched live from OpenAlex

Abstract Intraspecific competition for resources is common in animals and may lead to physical contests. Contest outcomes and aggressiveness can be influenced by the resource holding potential of contestants but also by their perception of the resource value (RV). Competitors may assess resource quality directly (real RV) but may also estimate it according to their physiological status and their experience of the habitat quality (subjective RV). In this article, we studied contests between females of the solitary parasitoid Pachycrepoideus vindemmiae Rondani (Hymenoptera: Pteromalidae) when exploiting simultaneously a host, a Delia radicum L. (Diptera: Anthomyiidae) pupa. We tested the effect of factors modifying host value on the occurrence of agonistic behaviours, contest outcomes and host exploitation. The factors tested were: the quality of the previous habitat experienced by females, female egg load, host parasitism status and the stage reached by the owner female in her behavioural oviposition sequence. Females successfully protected their host against intruders during its exploitation, but not after oviposition, and their aggressiveness did not seem to be influenced by their perception of the RV. The fact that the host is subsequently parasitized by the opponent females appears to mainly depend on the host selectiveness of females.

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 score0.100
Threshold uncertainty score0.615

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 head0.041
GPT teacher head0.289
Teacher spread0.248 · 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

Citations36
Published2007
Admission routes1
Has abstractyes

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