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Record W3041081237 · doi:10.1111/jvs.12921

Effects of climate change on treeline trees in Sagarmatha (Mt. Everest, Central Himalaya)

2020· article· en· W3041081237 on OpenAlexaff
Sudip Pandey, Paolo Cherubini, Matthias Saurer, Marco Carrer, Giai Petit

Bibliographic record

VenueJournal of Vegetation Science · 2020
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicTree-ring climate responses
Canadian institutionsUniversity of British Columbia
FundersUniversità degli Studi di PadovaFondazione Cassa di Risparmio di Padova e Rovigo
Keywordsδ13CPrecipitationClimate changeAltitude (triangle)EcologyTranspirationδ18OEnvironmental scienceDendrochronologyStable isotope ratioPhysical geographyAtmospheric sciencesBiologyGeographyPhotosynthesisGeologyBotany

Abstract

fetched live from OpenAlex

Abstract Question Tree growth at high altitude in the Himalayan region is limited by cold temperatures and also strongly influenced by the seasonality of the Asian monsoon. Understanding whether the ongoing increase in temperatures and changes in precipitation regimes in the Himalayan region can stimulate or limit tree growth is of particular importance to predict the local treeline dynamics. Location Altitudinal treeline (~4000 m a.s.l.) in the Sagarmatha (Mt. Everest) National Park (Central Himalaya, Eastern Nepal). Methods We assessed the relationships between ring widths and monthly precipitations and mean temperatures, and analysed cellulose stable isotopes (δ13C and δ18O) and their derived C discrimination (Δ13C), and intrinsic water use efficiency (iWUE) in Abies spectabilis and Betula utilis at the Himalayan treeline. Results Growth of A. spectabilis strongly depended on summer temperatures, whereas that of B. utilis on spring precipitation. δ13C and iWUE increased with time in both species, especially in A. spectabilis . The long‐term decrease in Δ13C was accompanied by an increase in δ18O in both species, thus suggesting an increase in photosynthetic efficiency rather than a stronger stomatal control of transpiration. Conclusions Climate change is progressively reducing the physiological limitations due to low temperatures and low spring precipitations at the Central Himalayan treeline, thus potentially facilitating a further altitudinal forest advance.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.131
Threshold uncertainty score0.331

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.032
GPT teacher head0.271
Teacher spread0.239 · 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

Citations24
Published2020
Admission routes1
Has abstractyes

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