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Record W2156897527 · doi:10.1086/647903

Dominant Species and Diversity: Linking Relative Abundance to Controls of Species Establishment

2009· article· en· W2156897527 on OpenAlexafffund
Benjamin Gilbert, Roy Turkington, Diane S. Srivastava

Bibliographic record

VenueThe American Naturalist · 2009
Typearticle
Languageen
FieldEnvironmental Science
TopicEcology and Vegetation Dynamics Studies
Canadian institutionsUniversity of British Columbia
FundersUniversity of British Columbia
KeywordsBiologyEcologyAbundance (ecology)Species diversityUnderstoryBiomass (ecology)Relative species abundancePlant communityCompetition (biology)Complementarity (molecular biology)Competitive exclusionSpecies richness

Abstract

fetched live from OpenAlex

Ecological theories make divergent predictions about whether extant species inhibit or promote the establishment of new species and which aspects of community composition determine these interactions; diversity, individual dominant species, and neutral interactions have all been argued to be most important. We experimentally tested these predictions by removing plant biomass (0%, 7%, 100%) from boreal forest understory communities. The 7% removals were restricted to the numerically dominant species, the second most dominant species, or many low-abundance species, thereby separating the effects of species composition from those of biomass. We tested the effects of all removal treatments on seedling establishment. Competitive effects were driven by one dominant species and were inconsistent with resource complementarity, neutral, or competition-colonization models. Facilitative effects were apparent only following removal of all vegetation, of which the most dominant species comprised more than 80%. Our results indicate that numerically dominant species in a community can influence the establishment of new species more than species diversity, but the direction of interaction can shift from facilitative to competitive as community density increases.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.108
Threshold uncertainty score0.356

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 studies0.0000.001
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 head0.009
GPT teacher head0.231
Teacher spread0.222 · 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 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

Citations59
Published2009
Admission routes2
Has abstractyes

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