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Record W3158685676 · doi:10.1111/nph.17416

Late spring freezes coupled with warming winters alter temperate tree phenology and growth

2021· article· en· W3158685676 on OpenAlexaff
C. J. Chamberlain, E. M. Wolkovich

Bibliographic record

VenueNew Phytologist · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicPlant Water Relations and Carbon Dynamics
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsPhenologyChilling requirementSpring (device)BiologyTemperate climateShrubClimate changeEnvironmental scienceAnnual growth cycle of grapevinesShootHorticultureAgronomyEcologyDormancyGermination

Abstract

fetched live from OpenAlex

Spring phenology is advancing with warming but late spring freezes may not advance at the same rate, potentially leading to an increase in freezes after trees initiate budburst. Research suggests warming winters may delay budburst through reduced chilling, which may cause plants to leafout more slowly, thus decreasing spring freeze tolerance. Here, we assessed the effects of late spring freezes and reduced over-winter chilling on sapling phenology, growth and tissue traits, across eight temperate tree and shrub species in a laboratory experiment. We found that spring freezes delayed leafout - extending the period of greatest risk for freeze damage - increased damage to the shoot apical meristem, and decreased leaf toughness and leaf thickness. Longer chilling accelerated budburst and leafout, even under spring freeze conditions. Thus, chilling compensated for the adverse effects of late spring freezes on phenology. Despite the effects of spring freezes and chilling on phenology, we did not see any major reordering in the sequence of species leafout. Our results suggest climate change may impact forest communities not through temporal reassembly, but rather through impacts on phenology and growth from the coupled effects of late spring freezes and decreased over-winter chilling under climate change.

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 score0.168
Threshold uncertainty score0.377

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 head0.009
GPT teacher head0.202
Teacher spread0.193 · 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

Citations62
Published2021
Admission routes1
Has abstractyes

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