MétaCan
Menu
Back to cohort
Record W2955396198 · doi:10.1111/ele.13330

Greater than the sum of the parts: how the species composition in different forest strata influence ecosystem function

2019· letter· en· W2955396198 on OpenAlexaff
Ya‐Huang Luo, Marc W. Cadotte, Kevin S. Burgess, Jie Liu, Shao‐Lin Tan, Jia‐Yun Zou, Kun Xu, Li D, Lian‐Ming Gao

Bibliographic record

VenueEcology Letters · 2019
Typeletter
Languageen
FieldEnvironmental Science
TopicEcology and Vegetation Dynamics Studies
Canadian institutionsUniversity of Toronto
FundersChinese Academy of SciencesNational Natural Science Foundation of China
KeywordsBiodiversityEcologySpecies richnessBiomass (ecology)EcosystemUnderstoryComplementarity (molecular biology)Forest ecologyShrubBiologySpecies diversityTraitPhylogenetic diversityPhylogenetic treeCanopy

Abstract

fetched live from OpenAlex

The mechanisms underpinning forest biodiversity-ecosystem function relationships remain unresolved. Yet, in heterogeneous forests, ecosystem function of different strata could be associated with traits or evolutionary relationships differently. Here, we integrate phylogenies and traits to evaluate the effects of elevational diversity on above-ground biomass across forest strata and spatial scales. Community-weighted means of height and leaf phosphorous concentration and functional diversity in specific leaf area exhibited positive correlations with tree biomass, suggesting that both positive selection effects and complementarity occur. However, high shrub biomass is associated with greater dissimilarity in seed mass and multidimensional trait space, while species richness or phylogenetic diversity is the most important predictor for herbaceous biomass, indicating that species complementarity is especially important for understory function. The strength of diversity-biomass relationships increases at larger spatial scales. We conclude that strata- and scale- dependent assessments of community structure and function are needed to fully understand how biodiversity influences ecosystem function.

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 score0.241
Threshold uncertainty score0.845

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 science0.0010.000
Research integrity0.0000.001
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 head0.010
GPT teacher head0.181
Teacher spread0.171 · 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

Citations116
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueEcology LettersSame topicEcology and Vegetation Dynamics StudiesFrench-language works237,207