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Record W2951444473 · doi:10.1111/jzo.12555

Nonconsumptive predator effects on prey demography: dogwhelk cues decrease benthic mussel recruitment

2018· article· en· W2951444473 on OpenAlexafffundabout
Sonja M. Ehlers, Ricardo A. Scrosati, Julius A. Ellrich

Bibliographic record

VenueJournal of Zoology · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine Bivalve and Aquaculture Studies
Canadian institutionsSt. Francis Xavier University
FundersNatural Sciences and Engineering Research Council of CanadaDeutscher Akademischer AustauschdienstCanada Foundation for Innovation
KeywordsPredationMusselBiologyIntertidal zoneBenthic zoneEcologyPredatorHabitatFisheryPopulationPelagic zoneDemography

Abstract

fetched live from OpenAlex

Abstract Predators often have nonconsumptive effects ( NCE s) on prey behaviour, but the demographic consequences for prey remain poorly known. This is important to understand because demography influences the functional role of a species in its community. We used an intertidal predator–prey system to investigate predator NCE s on prey recruitment, a key demographic process for population persistence. Pelagic mussel larvae are known to avoid waterborne cues from dogwhelks, which prey on intertidal mussels. Through a field experiment done in Atlantic Canada, we manipulated the presence of dogwhelks in intertidal habitats during the mussel ( Mytilus spp.) recruitment season. We measured mussel recruitment in collectors that could be reached by waterborne dogwhelk cues but not by dogwhelks themselves. We found that the nearby presence of dogwhelks significantly decreased mussel recruit density. A previous study done in the same habitats under the same experimental conditions showed that dogwhelk cues also limit the recruitment of barnacles, another prey item for dogwhelks. However, such NCE s were stronger than those observed for mussel recruitment, possibly because mussels have more opportunities to escape predation during their benthic existence. Thus, basic features of natural history might be useful to predict the intensity of predator NCE s on prey recruitment.

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 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 score0.032
Threshold uncertainty score1.000

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)0.0010.000

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 head0.023
GPT teacher head0.289
Teacher spread0.266 · 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

Citations11
Published2018
Admission routes3
Has abstractyes

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