MétaCan
Menu
Back to cohort
Record W2756387275 · doi:10.1111/ele.12831

No consistent pollinator‐mediated impacts of alien plants on natives

2017· review· en· W2756387275 on OpenAlexafffund
Julia A. Charlebois, Risa D. Sargent

Bibliographic record

VenueEcology Letters · 2017
Typereview
Languageen
FieldAgricultural and Biological Sciences
TopicPlant and animal studies
Canadian institutionsUniversity of Ottawa
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPollinatorAlienEcologyBiologyIntroduced speciesGeographyPollinationPopulationDemography

Abstract

fetched live from OpenAlex

The introduction of an alien plant is widely assumed to have negative consequences for the pollinator-mediated fitness of nearby natives. Indeed, a number of studies, including a highly cited meta-analysis, have concluded that the trend for such interactions is competitive. Here we provide evidence that publication bias and study design have obscured our ability to assess the pollinator-mediated impacts of alien plants. In a meta-analysis of 76 studies, we demonstrate that alien/native status does not predict the outcome of pollinator-mediated interactions among plants. Moreover, we found no evidence that similarity in floral traits or phylogenetic distance between species pairs influences the outcome of pollinator-mediated interactions. Instead, we report that aspects of study design, such as distance between the control and nearest neighbour, and/or the arrangement of study plants better predict the impact of a neighbour than does alien/native status. Our study sheds new light on the role that publication bias and experimental design play in the evaluation of key patterns in ecology. We conclude that, due to the absence of clear, generalisable pollinator-mediated impacts of alien species, management schemes should base decisions on community-wide assessments of the impacts of individual alien plant species, and not solely on alien/native status itself.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.865
Threshold uncertainty score0.476

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.138
GPT teacher head0.303
Teacher spread0.165 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations65
Published2017
Admission routes2
Has abstractyes

Explore more

Same venueEcology LettersSame topicPlant and animal studiesFrench-language works237,207