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Record W4400526337 · doi:10.1111/ele.14469

Plant diversity decreases greenhouse gas emissions by increasing soil and plant carbon storage in terrestrial ecosystems

2024· review· en· W4400526337 on OpenAlexaff
Pengfei Dang, Miaomiao Zhang, Michel Loreau, J. Emmett Duffy, Xine Li, Shuyue Wen, Xiaoqing Han, Lechen Liao, Tiantian Huang, Chenxi Wan, Xiaoliang Qin, Kadambot H. M. Siddique, Bernhard Schmid

Bibliographic record

VenueEcology Letters · 2024
Typereview
Languageen
FieldAgricultural and Biological Sciences
TopicSoil Carbon and Nitrogen Dynamics
Canadian institutionsUniversity of Alberta
FundersNational Natural Science Foundation of China
KeywordsMonocultureGreenhouse gasEnvironmental scienceSoil carbonEcosystemTerrestrial ecosystemTerrestrial plantBiodiversityPlant diversityCarbon dioxideCarbon fibersCarbon sequestrationAgronomySpecies richnessEcologySoil waterBiologySoil scienceMaterials science

Abstract

fetched live from OpenAlex

Abstract The decline in global plant diversity has raised concerns about its implications for carbon fixation and global greenhouse gas emissions (GGE), including carbon dioxide (CO 2 ), nitrous oxide (N 2 O) and methane (CH 4 ). Therefore, we conducted a comprehensive meta‐analysis of 2103 paired observations, examining GGE, soil organic carbon (SOC) and plant carbon in plant mixtures and monocultures. Our findings indicate that plant mixtures decrease soil N 2 O emissions by 21.4% compared to monocultures. No significant differences occurred between mixtures and monocultures for soil CO 2 emissions, CH 4 emissions or CH 4 uptake. Plant mixtures exhibit higher SOC and plant carbon storage than monocultures. After 10 years of vegetation development, a 40% reduction in species richness decreases SOC content and plant carbon storage by 12.3% and 58.7% respectively. These findings offer insights into the intricate connections between plant diversity, soil and plant carbon storage and GGE—a critical but previously unexamined aspect of biodiversity–ecosystem functioning.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.850
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.025
GPT teacher head0.231
Teacher spread0.207 · 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 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

Citations26
Published2024
Admission routes1
Has abstractyes

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