Climate and atmospheric circulation related to frost-ring formation in Picea mariana trees from the Boreal Plains, interior North America
Bibliographic record
Abstract
Earlier spring and earlier onset of growth, as a consequence of climate change, may expose trees and crops to increased risk of exposure to frost damage. In this study, we compare the frequency of frost rings in three regions [Porcupine Provincial Forest (PPF; north-latitude); Duck Mountain Provincial Forest (DMPF; mid-latitude) and Riding Mountain National Park (RMNP, south-latitude)] located in the Boreal Plains of interior North America. In each of PPF and DMPF, twenty upland black spruce [Picea mariana (Mill.) BSP] trees were sampled using stem analysis and others were sampled at breast height or below. In RMNP, multiple coniferous tree species were sampled at breast height or below to allow comparison among species. Results from stem analysis indicated that frost rings were more frequent in DMPF than PPF (north of DMPF). Frost rings identified up to a height of 16 m and were formed predominantly in the early cambial age zone. As a general pattern, frost rings recorded in PPF occurred more abundantly in years with warm April temperatures and this association was less prevalent in DMPF. Frost rings were formed following extreme frost events in late May - early June and frost-ring years corresponded to years with cooler June temperatures. A significant positive association was found between frost-ring frequency and the El Niño Southern Oscillation. In the absence of an early spring, black spruce trees were less affected by frost damages. Stem analysis provided a better record of spring frosts than solely sampling at breast height or below. The multi-species approach used in RMNP revealed many years with synchronous frost rings among species. The development of a large network of frost-ring chronologies from tree species of various age classes and/or from stem analysis will help with assessing the impact of late spring frosts on forest dynamics and to document large-scale climate anomalies in areas with low climate data coverage and/or prior to instrumental records.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".