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Record W2497502450 · doi:10.1111/gcb.13420

Persistent and pervasive compositional shifts of western boreal forest plots in Canada

2016· article· en· W2497502450 on OpenAlexafffundabout
Eric B. Searle, Han Y. H. Chen

Bibliographic record

VenueGlobal Change Biology · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicFire effects on ecosystems
Canadian institutionsLakehead University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsDeciduousEcological successionTaigaClimate changeBorealEcologyBiodiversityGlobal warmingGeographyEnvironmental scienceBiology

Abstract

fetched live from OpenAlex

Abstract Species compositional shifts have important consequences to biodiversity and ecosystem function and services to humanity. In boreal forests, compositional shifts from late‐successional conifers to early‐successional conifers and deciduous broadleaves have been postulated based on increased fire frequency associated with climate change truncating stand age‐dependent succession. However, little is known about how climate change has affected forest composition in the background between successive catastrophic fires in boreal forests. Using 1797 permanent sample plots from western boreal forests of Canada measured from 1958 to 2013, we show that after accounting for stand age‐dependent succession, the relative abundances of early‐successional deciduous broadleaves and early‐successional conifers have increased at the expense of late‐successional conifers with climate change. These background compositional shifts are persistent temporally, consistent across all forest stand ages and pervasive spatially across the region. Rising atmospheric CO 2 promoted early‐successional conifers and deciduous broadleaves, and warming increased early‐successional conifers at the expense of late‐successional conifers, but compositional shifts were not associated with climate moisture index. Our results emphasize the importance of climate change on background compositional shifts in the boreal forest and suggest further compositional shifts as rising CO 2 and warming will continue in the 21st century.

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 score0.073
Threshold uncertainty score0.241

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 science0.0000.000
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 head0.016
GPT teacher head0.216
Teacher spread0.200 · 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

Citations65
Published2016
Admission routes3
Has abstractyes

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