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Record W4224228484 · doi:10.1111/ele.14010

Net plant interactions are highly variable and weakly dependent on climate at the global scale

2022· review· en· W4224228484 on OpenAlexaff
Xuejun Yang, Lorena Gómez‐Aparicio, Christopher J. Lortie, Miguel Verdú, Lohengrin A. Cavieres, Zhenying Huang, Ruiru Gao, Rong Liu, Yonglan Zhao, Johannes H. C. Cornelissen

Bibliographic record

VenueEcology Letters · 2022
Typereview
Languageen
FieldAgricultural and Biological Sciences
TopicPlant and animal studies
Canadian institutionsYork University
FundersNational Natural Science Foundation of China
KeywordsPlant communityEcologyBiomeFacilitationCompetition (biology)MicroclimateClimate changeBiologyEnvironmental scienceEcosystemEcological succession

Abstract

fetched live from OpenAlex

Although plant-plant interactions (i.e. competition and facilitation) have long been recognised as key drivers of plant community composition and dynamics, their global patterns and relationships with climate have remained unclear. Here, we assembled a global database of 10,502 pairs of empirical data from the literature to address the patterns of and climatic effects on the net outcome of plant interactions in natural communities. We found that plant interactions varied among plant performance indicators, interaction types and biomes, yet competition occurred more frequently than facilitation in plant communities worldwide. Unexpectedly, plant interactions showed weak latitudinal pattern and were weakly related to climate. Our study provides a global comprehensive overview of plant interactions, highlighting competition as a fundamental mechanism structuring plant communities worldwide. We suggest that further investigations should focus more on local factors (e.g. microclimate, soil and disturbance) than on macroclimate to identify key environmental determinants of interactions in plant communities.

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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0030.004
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.049
GPT teacher head0.244
Teacher spread0.195 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations66
Published2022
Admission routes1
Has abstractyes

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