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Record W6911040289 · doi:10.5061/dryad.g085g05

Data from: Mechanisms underlying predator-driven biotic resistance against introduced barnacles on the Pacific coast of Hokkaido, Japan

2019· dataset· en· W6911040289 on OpenAlexaff

Bibliographic record

VenueData Archiving and Networked Services (DANS) · 2019
Typedataset
Languageen
Field
Topic
Canadian institutionsSt. Francis Xavier University
Fundersnot available
KeywordsPredationPredatorResistance (ecology)Context (archaeology)BarnacleBiotic component

Abstract

fetched live from OpenAlex

Introduced species are a major threat to coastal ecosystems worldwide. Thus, understanding biotic resistance (i.e. the ability of native species to limit introduced species) is a central goal of invasion biology. This paper examines mechanisms underlying biotic resistance. Consumption can limit introduced prey provided that native predators prefer such prey. Furthermore, predator nonconsumptive effects (NCEs), mediated through predator-released cues perceived by prey, can limit prey recruitment, a key demographic process for prey populations. However, information on predator NCEs is largely absent in the context of recruitment in introduced prey. Working on the Pacific coast of Hokkaido (Japan), we addressed this knowledge gap using native predatory dogwhelks (Nucella lima) that prey on native barnacles (Chthamalus dalli) and introduced barnacles (Balanus glandula). We experimentally examined dogwhelk preferences for barnacles in the laboratory and dogwhelk NCEs on barnacle recruitment in the field. We found that N. lima preferred B. glandula over C. dalli, likely as B. glandula prey is more profitable, as suggested by previous findings in congeneric dogwhelks. Moreover, we found that N. lima NCEs limited recruit density in C. dalli and B. glandula, likely as barnacle larvae moved away from dogwhelk cues to reduce future predation risk. Our study suggests that predator prey preferences and predator nonconsumptive limitation of prey recruitment are two mechanisms that can contribute to predator-driven biotic resistance against introduced prey.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Open science
Consensus categoriesMeta-epidemiology (narrow), Open science
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.151
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0170.011
Research integrity0.0000.002
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 head0.075
GPT teacher head0.287
Teacher spread0.212 · 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; both teacher heads agree on what is shown here.

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

Citations1
Published2019
Admission routes1
Has abstractyes

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