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Record W4401609868 · doi:10.1016/j.geodrs.2024.e00850

Dynamics of soil water potential as a function of stand types in a temperate forest: Emphasis on flash droughts

2024· article· en· W4401609868 on OpenAlex
Blandine Courcot, Daniel Lemire, Nicolas Bélanger

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueGeoderma Regional · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicPlant Water Relations and Carbon Dynamics
Canadian institutionsUniversité TÉLUQUniversité du Québec à Montréal
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsTemperate climateEnvironmental scienceTemperate rainforestSoil waterTemperate forestAgroforestrySoil scienceEcologyEcosystemBiology

Abstract

fetched live from OpenAlex

In the context of a changing climate and the increasing occurrences of extreme events, including droughts, field evidence, and models suggest that cases of forest decline and migration of tree species to more suitable climates will augment in the 21st century. In northeastern North America, an expansion of American beech at the expense of maples has been observed since the 1970s and has been associated to several causes. Through an analysis of time series leveraging thousands of data collected in a temperate forest in southern Quebec, Canada, dynamics of soil water potential were analyzed in interaction with soil temperature, meteorological variables and forest types, including hardwoods (mostly maple) with a large presence of beech trees (hardwood-beech stands), hardwoods (maple and birch) and mixedwoods (maple and fir). During flash drought events with a net precipitation deficit and water stress, the presence of beech led to a decrease in soil temperature and favored the maintenance of low soil water potential and faster restoration of water reserves compared to mixedwoods. Using machine learning-based approaches, distinct critical soil temperature thresholds in regard to water potential were identified for the various forest types, and the temporality in soil water regime changes was more favorable under hardwood-beech stands. The presence of beech appears to render greater resilience in regard to water stress in this forest. A greater capacity of beech to preserve and restore soil water not only offers an additional explanation for its establishment in hardwoods in the last decades, but greater water conservation in the presence of beech, assuming it remains in the landscape, could also help local plant species adapt to climate change and to the predicted increased water deficits, as well as species migrating northward to find more suitable environmental envelopes. • Hardwoods with a presence of beech have developed faster soil water potential regulation strategies. • Beech trees act to conserve soil water by maintaining cooler soils and limiting water loss during flash droughts. • Beech trees could help local plant species adapt to climate change and to the predicted increased water deficits.

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.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.019
Threshold uncertainty score0.281

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 head0.005
GPT teacher head0.194
Teacher spread0.188 · 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