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Record W2800319480 · doi:10.1111/gcb.14288

Spring phenology at different altitudes is becoming more uniform under global warming in Europe

2018· article· en· W2800319480 on OpenAlexaff
Lei Chen, Jian‐Guo Huang, Qianqian Ma, Heikki Hänninen, Sergio Rossi, Shilong Piao, Yves Bergeron

Bibliographic record

VenueGlobal Change Biology · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicRemote Sensing in Agriculture
Canadian institutionsUniversité du Québec en Abitibi-TémiscamingueUniversité du Québec à Chicoutimi
FundersChinese Academy of SciencesNational Natural Science Foundation of China
KeywordsPhenologyAltitude (triangle)Global warmingElevation (ballistics)Climate changeEnvironmental scienceForcing (mathematics)Temperate climateEffects of high altitude on humansClimatologyAtmospheric sciencesSpring (device)EcologyBiologyGeographyMeteorologyGeology

Abstract

fetched live from OpenAlex

Abstract Under current global warming, high‐elevation regions are expected to experience faster warming than low‐elevation regions. However, due to the lack of studies based on long‐term large‐scale data, the relationship between tree spring phenology and the elevation‐dependent warming is unclear. Using 652k records of leaf unfolding of five temperate tree species monitored during 1951–2013 in situ in Europe, we discovered a nonlinear trend in the altitudinal sensitivity ( S A , shifted days per 100 m in altitude) in spring phenology. A delayed leaf unfolding (2.7 ± 0.6 days per decade) was observed at high elevations possibly due to decreased spring forcing between 1951 and 1980. The delayed leaf unfolding at high‐elevation regions was companied by a simultaneous advancing of leaf unfolding at low elevations. These divergent trends contributed to a significant increase in the S A (0.36 ± 0.07 days 100/m per decade) during 1951–1980. Since 1980, the S A started to decline with a rate of −0.32 ± 0.07 days 100/m per decade, possibly due to reduced chilling at low elevations and improved efficiency of spring forcing in advancing the leaf unfolding at high elevations, the latter being caused by increased chilling. Our results suggest that due to both different temperature changes at the different altitudes, and the different tree responses to these changes, the tree phenology has shifted at different rates leading to a more uniform phenology at different altitudes during recent decades.

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 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 score0.013
Threshold uncertainty score0.932

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.041
GPT teacher head0.286
Teacher spread0.245 · 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 teacher head, 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

Citations98
Published2018
Admission routes1
Has abstractyes

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