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Record W3176293445 · doi:10.1111/geb.13340

Regionwide temporal gradients of carbon allocation allow for shoot growth and latewood formation in boreal black spruce

2021· article· en· W3176293445 on OpenAlexafffundabout
Valentinà Buttò, Siddhartha Khare, Guillaume Drolet, Jean‐Daniel Sylvain, Fabio Gennaretti, Annie Deslauriers, Hubert Morin, Sergio Rossi

Bibliographic record

VenueGlobal Ecology and Biogeography · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicPlant Water Relations and Carbon Dynamics
Canadian institutionsUniversité du Québec en Abitibi-TémiscamingueMinistère des Ressources naturelles et des ForêtsMcGill UniversityUniversité du Québec à Chicoutimi
FundersCanadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of CanadaCanada Foundation for InnovationMinistère des Forêts, de la Faune et des Parcs
KeywordsPhenologyBlack spruceTaigaBorealEvergreenEcologyEnvironmental sciencePhysical geographyClimatologyBiologyGeographyGeology

Abstract

fetched live from OpenAlex

Abstract Aim In boreal ecosystems, phenological events display seasonal patterns. These patterns allow for the development of tissues during the short time window available for growth in cold climates. Primary and secondary growth, two expensive processes for plants, are supposedly modulated in time to optimize allocation of carbon to bud and woody tissues. We aimed to assess the phenology of primary and secondary meristems, testing their relationship over the closed black spruce stands of the commercially exploited forest region in Quebec, Canada. Location Quebec, Canada. Time period 2002–2016. Major taxa studied Gymnospermae. Methods We combined weekly scaled field observations with Moderate Resolution Imaging Spectroradiometer (MODIS) time series of the normalized difference vegetation index (NDVI) to extract timings of photosynthesis and meristem growth in five black spruce [Picea mariana (Mill.) B.S.P.] stands located along a latitudinal gradient and to assess their relationship. We then tested empirical models based on geographical position and seasonal temperatures to predict wood phenology (i.e., the onset and ending of earlywood and latewood formation), and compared its spatial patterns with existing predictions of bud phenology for the same study area. Results Photosynthesis started at the beginning of May, 3 weeks before bud reactivation and the onset of wood growth. Latewood formation started in mid‐July, after shoot elongation was completed. For wood phenology models, the residual standard error ranged from 1 week to 12 days. Growth dynamics spatialized across the boreal forest of Quebec varied with the transition between the subarctic and humid continental climate. Main conclusions Shoot elongation and latewood formation were temporally separated, providing evidence of a trade‐off in structural carbon allocation between primary and secondary growth in trees. Spatial patterns of wood phenology predicted for the black spruce polygons are consistent with spatial patterns of bud phenology, demonstrating synchronized temporal dynamics of meristems at the regional scale.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.130
Threshold uncertainty score0.259

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.005
GPT teacher head0.193
Teacher spread0.188 · 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".

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Citations19
Published2021
Admission routes3
Has abstractyes

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