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Record W2114266955 · doi:10.1111/ele.12476

Species colonisation, not competitive exclusion, drives community overdispersion over long‐term succession

2015· letter· en· W2114266955 on OpenAlexafffund
Shaopeng Li, Marc W. Cadotte, Scott J. Meiners, Zheng‐shuang Hua, Lin Jiang, Wensheng Shu

Bibliographic record

VenueEcology Letters · 2015
Typeletter
Languageen
FieldEnvironmental Science
TopicEcology and Vegetation Dynamics Studies
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaNational Natural Science Foundation of China
KeywordsColonisationEcological successionEcologyBiologyPhylogenetic treeExtinction (optical mineralogy)OverdispersionCommunityCompetitive exclusionCompetition (biology)ColonizationDemographyEcosystemPopulationSociology

Abstract

fetched live from OpenAlex

Ecological communities often transition from phylogenetic and functional clustering to overdispersion over succession as judged by space-for-time substitution studies. Such a pattern has been generally attributed to the increase in competitive exclusion of closely related species with similar traits through time, although colonisation and extinction have rarely been examined. Using 44 years of uninterrupted old-field succession in New Jersey, USA, we confirmed that phylogenetic and functional clustering decreased as succession unfolded, but the transition was largely driven by colonisation. Early colonists were closely related and functionally similar to residents, while later colonists became less similar to the species present. Extirpated species were generally more distantly related to residents than by chance, or exhibited random phylogenetic/functional patterns, and their relatedness to residents was not associated with time. These results provide direct evidence that the colonisation of distant relatives, rather than extinction of close relatives, drives phylogenetic and functional overdispersion over succession.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.498
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0020.002
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0050.001

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 head0.017
GPT teacher head0.250
Teacher spread0.233 · 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 designObservational
Domainnot available
GenreCommentary

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

Citations137
Published2015
Admission routes2
Has abstractyes

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