Snow cover manipulations and passive warming affect post-winter seed germination: a case study of three cold-temperate tree species
Bibliographic record
Abstract
CR Climate Research Contact the journal Facebook Twitter RSS Mailing List Subscribe to our mailing list via Mailchimp HomeLatest VolumeAbout the JournalEditorsSpecials CR 60:175-186 (2014) - DOI: https://doi.org/10.3354/cr01237 Snow cover manipulations and passive warming affect post-winter seed germination: a case study of three cold-temperate tree species Michael Drescher* Faculty of Environment, University of Waterloo, 200 University Avenue West, Waterloo, Ontario N2L 3G1, Canada *Corresponding author: mdresche@uwaterloo.ca ABSTRACT: Climate change is leading to increased temperatures globally, which may be especially pronounced in cold-temperate regions. During winter, this may cause changes to thermal insulation provided by snow cover to the ground and lead to altered soil and litter layer temperature regimes, affecting plant regeneration and species' ranges through frost damage. I investigated the effects of changing snow cover and litter temperature regimes on post-winter seed germination of 3 cold-temperate tree species, using snow manipulation and passive warming approaches. Snow manipulation and passive warming led to modest but complex changes in litter layer temperature regimes and caused responses in post-winter seed germination, increasing or remaining constant depending on species and treatment. Despite the modest differences in snow cover and litter temperature among treatments, post-winter seed germination varied up to 3-fold. The results suggest that tree seeds may be susceptible to modest changes in winter conditions as expected in the intermediate term under climate change and may be affecting future forest regeneration and species composition. The mechanisms underlying the observed seed germination response are currently unknown, but possible hypotheses are presented. If confirmed, these mechanisms may be involved in the re-assembly of future species-habitat relationships and control of species' biogeographic ranges. KEY WORDS: Climate change · Eastern white cedar · Forest composition · Green ash · Regeneration · Sugar maple Full text in pdf format NextCite this article as: Drescher M (2014) Snow cover manipulations and passive warming affect post-winter seed germination: a case study of three cold-temperate tree species. Clim Res 60:175-186. https://doi.org/10.3354/cr01237 Export citation RSS - Facebook - Tweet - linkedIn Cited by Published in CR Vol. 60, No. 3. Online publication date: August 05, 2014 Print ISSN: 0936-577X; Online ISSN: 1616-1572 Copyright © 2014 Inter-Research.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".