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Record W7109821869 · doi:10.5061/dryad.xwdbrv1p8

Risk of predation increases susceptibility to parasitism via trait-mediated indirect effects

2025· dataset· en· W7109821869 on OpenAlexaff

Bibliographic record

VenueDRYAD · 2025
Typedataset
Languageen
Field
Topic
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsPredationParasitismForagingPredatorOrganismNatural enemies

Abstract

fetched live from OpenAlex

The presence of natural enemies can cause organisms to change habitat use, foraging behaviour and/or resource allocation in response to a perceived risk; responses that may come at the cost of other fitness-related traits. Since most species encounter multiple natural enemies, defensive behaviours against one attacker may make the focal organism more vulnerable to attack by a different natural enemy. Anti-predator behaviours can lead to trait-mediated indirect effects such as an increased risk of attack by parasites, and vice versa. Few empirical studies have examined the response of a single focal species to the risk of attack by multiple species. Our experiments provided Drosophila nigrospiracula with opportunities to prioritise either anti-predator or anti-parasite behaviour at the cost of increased infection or predation, respectively. When exposed to parasites in the presence of predator cues, D. nigrospiracula experienced increased infection compared to flies without predator cues, but the presence/absence of parasite cues had no analogous effect on predation rates. We suggest that flies perceived parasitic infection to be a lesser threat and responded more strongly to predation risk at the cost of increased infection. In an ecological context, we shows how trait-mediated indirect effects could regulate community structure by increasing susceptibility to infection.

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)0.009
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.420
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.009
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.001

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.

Opus teacher head0.007
GPT teacher head0.269
Teacher spread0.262 · 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 designNot applicable
Domainnot available
GenreDataset

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

Citations0
Published2025
Admission routes1
Has abstractyes

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