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Record W2337861808 · doi:10.1111/ele.12600

Functional identity is the main driver of diversity effects in young tree communities

2016· article· en· W2337861808 on OpenAlexafffund
Cornelia M. Tobner, Alain Paquette, Dominique Gravel, Peter B. Reich, Laura Williams, Christian Messier

Bibliographic record

VenueEcology Letters · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicEcology and Vegetation Dynamics Studies
Canadian institutionsUniversité du Québec en OutaouaisMcGill UniversityUniversité de SherbrookeUniversité du Québec à Montréal
FundersNatural Sciences and Engineering Research Council of CanadaFonds Québécois de la Recherche sur la Nature et les Technologies
KeywordsEcologyDiversity (politics)Identity (music)Functional diversityTree (set theory)BiodiversityBiologyGeographySociologyAnthropologyAesthetics

Abstract

fetched live from OpenAlex

Two main effects are proposed to explain biodiversity-ecosystem functioning relationships: niche complementarity and selection effects. Both can be functionally defined using the functional diversity (FD) and functional identity (FI) of the community respectively. Herein, we present results from the first tree diversity experiment that separated the effect of selection from that of complementarity by varying community composition in high-density plots along a gradient of FD, independent of species richness and testing for the effects of FD and community weighted means of traits (a proxy for FI) on stem biomass increment (a proxy for productivity). After 4 years of growth, most mixtures did not differ in productivity from the averages of their respective monocultures, but some did overyield significantly. Those positive diversity effects resulted mostly from selection effects, primarily driven by fast-growing deciduous species and associated traits. Net diversity effect did not increase with time over 4 years.

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 score0.089
Threshold uncertainty score0.953

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)0.0010.000

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 head0.009
GPT teacher head0.200
Teacher spread0.191 · 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

Citations245
Published2016
Admission routes2
Has abstractyes

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