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Record W4414006745 · doi:10.1016/j.foreco.2025.123129

Black spruce growth under climate extremes: Genetic insights for managing a key resource production species

2025· article· en· W4414006745 on OpenAlexafffundabout
Étienne Robert, P. Lenz, Yves Bergeron, Nathalie Isabel, Martin P. Girardin

Bibliographic record

VenueForest Ecology and Management · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicForest ecology and management
Canadian institutionsUniversité du Québec en Abitibi-TémiscamingueUniversité du Québec à MontréalNatural Resources Canada
FundersNatural Resources CanadaCanadian Forest ServiceNatural Sciences and Engineering Research Council of Canada
KeywordsKey (lock)Resource (disambiguation)Production (economics)EcologyBiologyAgroforestryEnvironmental resource managementEnvironmental scienceComputer scienceEconomics

Abstract

fetched live from OpenAlex

Understanding the influence of intraspecific genetic variation on the response of tree species to stress events—like heatwaves, droughts, and growing season frost—is crucial. This genetic variation is linked to species adaptive potential and plays a pivotal role in shaping the resilience and long-term adaptability of species to climate change. Furthermore, genetic variation can affect populations’ responses to stress events, thereby influencing forest productivity and carbon sequestration potential. We combined dendroecological and genomic approaches to analyze the growth response of 61 black spruce ( Picea mariana ) populations, grown for over 40 years in four common gardens, to daily extreme vapor pressure, soil moisture deficits, and growing season frosts. Our objectives were to 1) assess the effects of stress events defined from physiological thresholds on annual biomass production and 2) explore the potential influence of standing genetic variation on trees’ responses to stress events at the population scale. The growth response of black spruce to those events was site-specific but with an important influence of soil or atmospheric drought at most sites. This response was also nonlinear, with a negative effect on growth occurring only after several stress events a year. Additionally, an interaction between this response and the geographic origin of the population indicated an influence of standing genetic variation, notably linked to local adaptation. These results may provide insight into tree resilience to stress events and help estimate the adaptive potential of the Canadian boreal forest. Moreover, this knowledge can help guiding forest management, for example assisted gene flow. • Growth response of black spruce to extreme weather events is site-specific. • Extreme weather events only impact biomass after a threshold of several events. • Standing genetic variation linked to local adaptation influences growth response.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.025
Threshold uncertainty score0.050

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.008
GPT teacher head0.205
Teacher spread0.197 · 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 source (direct Gemma or distilled Codex), 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

Citations2
Published2025
Admission routes3
Has abstractyes

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