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Record W2591094500 · doi:10.1038/srep42636

Parasitised caterpillars suffer reduced predation: potential implications for intra-guild predation

2017· article· en· W2591094500 on OpenAlexaff
Wenbin Chen, Liette Vasseur, Minsheng You, Jianyu Li, Chengxiang Wang, Ruo-xue Meng, Geoff M. Gurr

Bibliographic record

VenueScientific Reports · 2017
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicInsect-Plant Interactions and Control
Canadian institutionsBrock University
FundersFujian Academy of Agricultural SciencesRecruitment Program of Global ExpertsFujian Agriculture and Forestry UniversityNational Natural Science Foundation of ChinaCharles Sturt University
KeywordsGuildPredationDiamondback mothPlutellaBiologyIntraguild predationTrophic levelParasitismEcologyLarvaLepidoptera genitaliaHost (biology)PredatorHabitat

Abstract

fetched live from OpenAlex

Intra-guild predation (IGP) is an important phenomenon structuring ecological communities and affects the success of biological control. Here we show that parasitism by the koinobiont wasp Cotesia vestalis is associated with behavioural changes in its larval host (diamondback moth, Plutella xylostella) that reduce risk of IGP. Compared with unparasitised caterpillars, parasitised P. xylostella moved less frequently to new feeding patches on plants and were less likely to fall from the plant. Wolf spiders killed significantly fewer parasitised larvae. Reflecting their reduced movement and capacity to select plant tissue of optimal quality, parasitised caterpillars fed at a lower rate and exhibited delayed development suggesting a trade-off between IGP avoidance and nutrient intake by the host. This change in behaviour to reduce risk may cascade to the first trophic level and help explain the stability of IGP systems.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.266
Teacher spread0.244 · 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 source (direct Gemma or distilled Codex), 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

Citations8
Published2017
Admission routes1
Has abstractyes

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