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Record W4297206790 · doi:10.1139/cjfr-2022-0114

Greater tree species diversity and lower intraspecific competition attenuate impacts from temperature increases and insect epidemics in boreal forests of western Quebec, Canada

2022· article· en· W4297206790 on OpenAlexaffvenueabout
Raphaël D. Chavardès, Lorena Balducci, Yves Bergeron, Pierre Grondin, Véronique Poirier, Hubert Morin, Fabio Gennaretti

Bibliographic record

VenueCanadian Journal of Forest Research · 2022
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicTree-ring climate responses
Canadian institutionsMinistère des Ressources naturelles et des ForêtsUniversité du Québec à ChicoutimiUniversité du Québec à MontréalUniversité du Québec en Abitibi-Témiscamingue
Fundersnot available
KeywordsIntraspecific competitionTaigaCompetition (biology)EcologyBorealBiologyClimate changeGeography

Abstract

fetched live from OpenAlex

We investigated how the surrounding environment influences the growth of dominant trees and their responses to temperature and insect epidemics in boreal forests of eastern Canada. We focused on 82 black spruce and jack pine focal trees in stands spanning a double gradient of species diversity and soil texture within a 36 km 2 area of western Quebec. For these trees, we compared their diameter at breast height, growth rates, temperature–growth relations, and growth during insect defoliator epidemics. We used linear models to study how surrounding tree attributes and soil properties affected the growth of focal trees. Models showed that tree growth responses and responses to temperature and insect epidemics were generally negative with higher intraspecific competition and positive with greater tree species diversity. Growth of both species benefitted from lower soil sand content. Our research offers novel insights on the potential role of the surrounding environment, notably tree competition and species diversity, in mitigating the vulnerability of eastern Canada's boreal trees to anthropogenic climate change and insect epidemics.

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)0.000
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 score0.006
Threshold uncertainty score0.430

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.043
GPT teacher head0.241
Teacher spread0.198 · 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

Citations10
Published2022
Admission routes3
Has abstractyes

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