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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 OpenAlex
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

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

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 N orth A merica and E urope. Method Flux data from nine deciduous broadleaf forests ( DBF ) and 13 evergreen needleleaf forests ( ENF ) across N orth A merica and E urope (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 ( R e ) alone could explain this variability, the negative relationship between annual NEP and autumn lag was due to a larger R e / 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 R e . However, annual GPP was insensitive to a longer autumn lag in ENF sites but annual R e 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.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.023
Threshold uncertainty score0.990

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.002
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