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Record W1961143416 · doi:10.1111/geb.12044

Interannual variability of net ecosystem productivity in forests is explained by carbon flux phenology in autumn

2013· article· en· W1961143416 on OpenAlexafffund
Chaoyang Wu, Jing M. Chen, T. Andrew Black, David T. Price, Werner A. Kurz, Ankur R. Desai, Alemu Gonsamo, Rachhpal S. Jassal, Christopher M. Gough, Gil Bohrer, D. Dragoni, Mathias Herbst, Bert Gielen, Frank Berninger, Timo Vesala, Ivan Mammarella, Kim Pilegaard, Peter D. Blanken

Bibliographic record

VenueGlobal Ecology and Biogeography · 2013
Typearticle
Languageen
FieldEnvironmental Science
TopicPlant Water Relations and Carbon Dynamics
Canadian institutionsCanadian Forest ServiceUniversity of British ColumbiaNatural Resources CanadaUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaNordForskAcademy of Finland
KeywordsCarbon fluxPhenologyEcosystemEnvironmental scienceProductivityEcologyCarbon cyclePrimary productionForest ecologyFlux (metallurgy)Physical geographyAtmospheric sciencesGeographyBiologyGeology

Abstract

fetched live from OpenAlex

Abstract Aim To investigate the importance of autumn phenology in controlling interannual variability of forest net ecosystem productivity (NEP) and to derive new phenological metrics to explain the interannual variability of NEP. Location North America and Europe. Method Flux data from nine deciduous broadleaf forests (DBF) and 13 evergreen needleleaf forests (ENF) across North America and Europe (212 site‐years) were used to explore the relationships between the yearly anomalies of annual NEP and several carbon flux based phenological indicators, including the onset/end of the growing season, onset/end of the carbon uptake period, the spring lag (time interval between the onset of growing season and carbon uptake period) and the autumn lag (time interval between the end of the carbon uptake period and the growing season). Meteorological variables, including global shortwave radiation, air temperature, soil temperature, soil water content and precipitation, were also used to explain the phenological variations. Results We found that interannual variability of NEP can be largely explained by autumn phenology, i.e. the autumn lag. While variation in neither annual gross primary productivity (GPP) nor in annual ecosystem respiration (Re) alone could explain this variability, the negative relationship between annual NEP and autumn lag was due to a larger Re/GPP ratio in years with a prolonged autumn lag. For DBF sites, a longer autumn lag coincided with a significant decrease in annual GPP but showed no correlation with annual Re. However, annual GPP was insensitive to a longer autumn lag in ENF sites but annual Re increased significantly. Main conclusions These results demonstrate that autumn phenology plays a more direct role than spring phenology in regulating interannual variability of annual NEP. In particular, the importance of respiration may be potentially underestimated in deriving phenological indicators.

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 score0.005
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.002
GPT teacher head0.177
Teacher spread0.174 · 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

Citations187
Published2013
Admission routes2
Has abstractyes

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