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Record W4412803864 · doi:10.1038/s43247-025-02609-2

Climate-driven patterns of global tree longevity

2025· article· en· W4412803864 on OpenAlexaff
Jiani Gao, Keyan Fang, Jing M. Chen, Jinbao Li, Sergio Rossi, Deliang Chen, Hans W. Linderholm, J. Julio Camarero, Jan Esper, Nicole Davi, Tsun Fung Au, Zhengtang Guo

Bibliographic record

VenueCommunications Earth & Environment · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicPlant Water Relations and Carbon Dynamics
Canadian institutionsUniversité du Québec à ChicoutimiUniversity of Toronto
FundersChina Postdoctoral Science FoundationNational Natural Science Foundation of China
KeywordsLongevityTree (set theory)Climate changeEnvironmental scienceGeographyEcologyBiologyMathematics

Abstract

fetched live from OpenAlex

Abstract Concerns about climate change-influenced tree growth declines and world tree mortality raise questions about potential reductions in tree longevity. However, the global influences of climate and growth patterns on tree longevity remain poorly understood. Here we analyzed 219,000 tree-ring widths from 4880 globe sites, encompassing 246 species, to investigate tree longevity patterns. Gymnosperms exhibited significantly greater average longevity (366 ± 240 years) than angiosperms (216 ± 81 years), with the oldest individual exceeded 3000 years. Globally, gymnosperm longevity was negatively correlated with precipitation. Arid-adapted trees exhibited significantly higher longevity, likely due to their conservative growth strategy, characterized by slow growth rates and enhanced drought resilience. Trees in harsh environments defined by high altitude, nutrient-poor soils, and minimal human impact were more likely to attain greater longevity. These findings highlight the impact of climate change on tree longevity and the necessity for targeted conservation strategies to protect these vital ecosystem components.

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 score0.039
Threshold uncertainty score0.456

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 science0.0010.001
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 head0.010
GPT teacher head0.228
Teacher spread0.217 · 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

Citations7
Published2025
Admission routes1
Has abstractyes

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