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Record W4321253278 · doi:10.3390/f14020412

Evidence for 40 Years of Treeline Shift in a Central Alpine Valley

2023· article· en· W4321253278 on OpenAlexaff
Esther R. Frei, Ignacio Barbeito, Lisa M. Erdle, Elisabeth Leibold, Peter Bebi

Bibliographic record

VenueForests · 2023
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicTree-ring climate responses
Canadian institutionsUniversity of British Columbia
FundersAgroParisTech
KeywordsClimate changeShrubVegetation (pathology)EcosystemEnvironmental scienceEcologyDisturbance (geology)Physical geographyPermafrostGeographyGeology

Abstract

fetched live from OpenAlex

Alpine treeline ecosystems are generally expected to advance with increasing temperatures and after land-use abandonment. Multiple interacting factors modify this trend. Understanding the long-term processes underlying treeline advance is essential to predict future changes in structure and function of mountain ecosystems. In a valley in the Central Swiss Alps, we re-assessed a 40-year-old survey of all treeline trees (>0.5 m height) and disentangled climate, topographical, biotic, and disturbance (land use and avalanche risk) factors that have led to treeline advance with a combination of ground-based mapping, decision tree, and dendroecological analyses. Between the first ground survey in 1972/73 and the resurvey in 2012, treeline advanced on average by 10 meters per decade with a maximum local advance of 42 meters per decade. Larch consistently advanced more on south-facing slopes, while pine advance was greater on north-facing slopes. Newly established spruce mostly represented infilling below the previous treeline. The forefront of treeline advance above 2330 m a.s.l. occurred mainly on favorable microsites without competing dwarf shrub vegetation. At slightly lower elevations, treeline advanced mainly on sites that were used for agriculture at the beginning of the 20th century. This study indicates that although treeline advances under the effect of climate warming, a combination of additional ecological factors controls this advance at regional and local scales.

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)0.001
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 score0.093
Threshold uncertainty score0.922

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0000.000

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 head0.062
GPT teacher head0.300
Teacher spread0.238 · 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
Published2023
Admission routes1
Has abstractyes

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