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Snow cover manipulations alter survival of early life stages of cold‐temperate tree species

2012· article· en· W2023038544 on OpenAlexaff
Michael Drescher, Sean C. Thomas

Bibliographic record

VenueOikos · 2012
Typearticle
Languageen
FieldEnvironmental Science
TopicEcology and Vegetation Dynamics Studies
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsTemperate climateSnowOverwinteringBiologyEcologyGerminationHabitatSnow coverRange (aeronautics)Vegetation (pathology)AgronomyGeography

Abstract

fetched live from OpenAlex

Projections of future climate suggest increases in global temperatures that are especially pronounced in winter in cold‐temperate regions. Thermal insulation provided by snow cover to litter, soil, and overwintering plants will likely be affected by changing winter temperatures and might influence future species composition and ranges. We investigated effects of changing snow cover on seed germination and sapling survival of several cold‐temperate tree species using a snow manipulation approach. Post‐winter seed germination increased or decreased with increasing snow cover, depending on species; decreased seed germination was found in species that characteristically disperse seed in summer or fall months prior to snowfall. Post‐winter sapling survival increased with increasing snow cover for all species, though some species benefitted more from increased snow cover than others. Sapling mortality was associated with root exposure, suggesting the possibility that soil frost heaving could be an important mechanism for observed effects. Our results suggest that altered snow regimes may cause re‐assembly of current species habitat relationships and may drive changes in species’ biogeographic range. However, local snow regimes also vary with associated vegetation cover and topography, suggesting that species distribution patterns may be strongly influenced by spatial heterogeneity in snow regimes and complicating future projections.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.022
Threshold uncertainty score0.999

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)0.0020.000

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 head0.026
GPT teacher head0.249
Teacher spread0.223 · 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.

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
Published2012
Admission routes1
Has abstractyes

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