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Record W2911551514 · doi:10.1111/geb.12884

Responses of nitrogen concentrations and pools to multiple environmental change drivers: A meta‐analysis across terrestrial ecosystems

2019· article· en· W2911551514 on OpenAlexaff
Kai Yue, Yan Peng, Dario Fornara, Koenraad Van Meerbeek, Lars Vesterdal, Wanqin Yang, Changhui Peng, Bo Tan, Wei Zhou, Zhenfeng Xu, Xiangyin Ni, Li Zhang, Fuzhong Wu, Jens‐Christian Svenning

Bibliographic record

VenueGlobal Ecology and Biogeography · 2019
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric chemistry and aerosols
Canadian institutionsUniversité du Québec à Montréal
FundersNational Natural Science Foundation of ChinaVillum Fonden
KeywordsEcologyEcosystemTerrestrial ecosystemEnvironmental changeEnvironmental scienceGeographyClimate changeBiology

Abstract

fetched live from OpenAlex

Abstract Aim We sought to understand how the individual and combined effects of multiple environmental change drivers differentially influence terrestrial nitrogen (N) concentrations and N pools and whether the interactive effects of these drivers are mainly antagonistic, synergistic or additive. Location Worldwide. Time period Contemporary. Major taxa studied Plants, soil, and soil microbes in terrestrial ecosystems. Methods We synthesized data from manipulative field studies from 758 published articles to estimate the individual, combined and interactive effects of key environmental change drivers (elevated CO2, warming, N addition, phosphorus addition, increased rainfall and drought) on plant, soil, and soil microbe N concentrations and pools using meta‐analyses. We assessed the influences of moderator variables on these effects through structural equation modelling. Results We found that (a) N concentrations and N pools were significantly affected by the individual and combined effects of multiple drivers, with N addition (either alone or in combination with another driver) showing the strongest positive effects; (b) the individual and combined effects of these drivers differed significantly between N concentrations and N pools in plants, but seldom in soils and microbes; (c) additive effects of driver pairs on N concentrations and pools were much more common than synergistic or antagonistic effects across plants, soils and microbes; and (d) environmental and experimental factors were important moderators of the individual, combined and interactive effects of these drivers on terrestrial N. Main conclusions Our results indicate that terrestrial N concentrations and N pools, especially those of plants, can be significantly affected by the individual and combined effects of environmental change drivers, with the interactive effects of these drivers being mostly additive. Our findings are important because they contribute to the development of models to better predict how altered N availability affects ecosystem carbon cycling under future environmental changes.

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)0.015
metaresearch head score (Gemma)0.018
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.015
Threshold uncertainty score0.081

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.018
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0060.030
Bibliometrics0.0040.004
Science and technology studies0.0010.001
Scholarly communication0.0030.001
Open science0.0020.002
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.019
GPT teacher head0.233
Teacher spread0.214 · 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 designMeta-analysis
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

Citations64
Published2019
Admission routes1
Has abstractyes

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