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Record W2060448599 · doi:10.1371/journal.pone.0088344

Colonization Rates in a Metacommunity Altered by Competition

2014· article· en· W2060448599 on OpenAlexafffund
Shajini Jeganmohan, Caroline M. Tucker, Marc W. Cadotte

Bibliographic record

VenuePLoS ONE · 2014
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant and animal studies
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMetacommunityInterspecific competitionColonizationBiologyCompetition (biology)ColonisationEcologyMicrocosmBrachionus calyciflorusBiological dispersalStorage effectRotifer

Abstract

fetched live from OpenAlex

Competition and colonization are two mechanisms that are important for determining coexistence and species diversity in spatially structured habitats. However, these mechanisms may not be independent as species can exhibit behavioral or physiological changes in response to competition that alters their colonization rates. This study examines the effect of interspecific interactions on the colonization rates of four microscopic species (three ciliates and a rotifer) in aquatic microcosms. Two species showed significant reductions in the time to colonize patches when confronted with a competitor, one was a good disperser (Colpidium striatum) and the other was the slowest disperser (Philodina spp.). These results indicate that species' colonization rates in a metacommunity can vary depending on the presence of competitors. Thus, we suggest that predictions based on heuristic tradeoffs between competition and colonization should consider effects of common biotic interactions such as competition.

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)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
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 head0.089
GPT teacher head0.207
Teacher spread0.119 · 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

Citations4
Published2014
Admission routes2
Has abstractyes

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