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Record W4378802226 · doi:10.21203/rs.3.rs-2876983/v1

Reducing continuous synchrony is an inherent property of tree growth in forests

2023· preprint· en· W4378802226 on OpenAlexaff
Qi‐Bin Zhang, Hengfeng Jia, Jiacheng Zheng, Ouya Fang, Jing Yang, Jia-Yang Langzhen, Richard J. Hebda

Bibliographic record

VenueResearch Square · 2023
Typepreprint
Languageen
FieldEnvironmental Science
TopicEcosystem dynamics and resilience
Canadian institutionsRoyal British Columbia Museum
FundersNational Natural Science Foundation of China
KeywordsJuniperClimate changePsychological resilienceTree (set theory)Plateau (mathematics)Disturbance (geology)DendrochronologyEcologyForest ecologyGeographyEcosystemEnvironmental scienceAgroforestryBiologyMathematics

Abstract

fetched live from OpenAlex

Abstract Forests play an important role in providing ecosystem services and regulating carbon balance. Modelers rely on the implicit assumption that, under the common climate drivers, trees have similar growth patterns over large spatial scale. Using ring-width sequences of 1046 juniper trees from Tibetan Plateau and 538 pine trees in the eastern China, we show that, although tree growth is influenced by climatic factors, individual trees often do not respond in a synchronous manner. After highly synchronous growth events, the number of trees exhibiting common growth change declined progressively and, notably, the patterns of decline exhibited the form of exponential function with parameter of base b ranging from 0.57-0.83 for juniper forests in Tibetan Plateau and from 0.56-0.72 for pine forests in eastern China. Our findings suggest that individual trees in forests tend to grow independently with respect to climate rather than synchronously, and that a relatively uniform pattern of growth de-synchronization is an inherent property of forest tree growth. We propose that this property is essential for trees collectively to have resilience and maintain forest stability, analogous to maintaining a diverse investment portfolio as a strategy to cope with unexpected risks. This new perspective on widespread non-synchronous radial tree growth sheds insight into fundamental ecological processes in forest resilience and can improve assessment and management of forest health risks under future climate change.

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)0.002
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 score0.112
Threshold uncertainty score0.984

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.003
Research integrity0.0000.001
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 head0.061
GPT teacher head0.348
Teacher spread0.287 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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