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Record W4224276062 · doi:10.1029/2021jg006709

Seasonal Variations in Leaf Maximum Photosynthetic Capacity and Its Dependence on Climate Factors Across Global FLUXNET Sites

2022· article· en· W4224276062 on OpenAlexaff
Xiaoping Wang, Jing M. Chen, Weimin Ju, Yongguang Zhang

Bibliographic record

VenueJournal of Geophysical Research Biogeosciences · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicPlant Water Relations and Carbon Dynamics
Canadian institutionsUniversity of Toronto
FundersNational Natural Science Foundation of China
KeywordsFluxNetAtmospheric sciencesEnvironmental scienceEddy covarianceEvergreenPhotosynthesisBiomePhotosynthetic capacityBiometeorologyPhotosynthetically active radiationEcosystemLeaf area indexSpecific leaf areaSoil waterShortwave radiationCanopyBotanySoil scienceEcologyBiologyPhysicsRadiation

Abstract

fetched live from OpenAlex

Abstract The maximum carboxylation rate (Vcmax) is an important parameter affecting the photosynthesis rate of plant leaves. In terrestrial ecosystem models (TEMs), Vcmax at 25°C (Vm25°) is often assigned as constants according to plant functional types (PFTs), while its variations with leaf temperature and nutrient contents are described using empirical functions. However, Vm25° could itself vary seasonally due to changes in the leaf physiological state that cannot be described by the empirical functions, potentially causing large uncertainties in simulated water and carbon fluxes. So far, the seasonal variation in Vm25° has not been systematically studied. Here, we generated a Vm25° data set of eight main biomes from 2000 to 2020 using eddy covariance (EC) measurements at globally distributed 176 sites. The boreal ecosystem productivity simulator was combined with a light response curve model (BEPS‐LRC) to invert Vm25° from EC data. We investigated seasonal variations of Vm25° and analyzed how different environmental and physiological factors, such as physiological (leaf chlorophyll content, LCC and Rubisco or RuBP) and climatic environment factors, including air temperature (Ta), solar shortwave radiation, CO 2 concentration (CO 2 ), and soil water content (SWC), influence this parameter. Vm25° values derived from flux data using BEPS‐LRC correlate well with Vm25° of the reference data set ( R 2 = 0.74, slope = 0.77, and root mean square error = 24.45 μmol m −2 s −1 , p < 0.001). Leaf Vm25° has strong seasonal variations in all PFTs except for in evergreen broadleaf forests, but its seasonal variation patterns differ greatly among eight biomes. Air temperature (Ta) is the most important determinant of Vm25°, followed by SWC. The interactive effects of Ta and SWC on Vm25° vary among different biomes. The seasonal variation of Vm25° was strongly dependent on LCC. After the correction of temperature effect, the contribution of LCC to the seasonal variation of Vm25° averaged 26% among eight biomes. These findings provide useful information for better parameterization of Vm25° in TEMs.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.018
Threshold uncertainty score0.036

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
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 head0.040
GPT teacher head0.311
Teacher spread0.272 · 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 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

Citations29
Published2022
Admission routes1
Has abstractyes

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