White spruce growth sensitivity to climate variability in pure and mixedwood stands
Bibliographic record
Abstract
It is prudent to understand how tree growth responds to climate variability to better project their growth in the current and future changes in climate in boreal forests. In this thesis, I studied how climate variables influence individual white spruce trees (Picea glauca (Moench) Voss) over short and intermediate periods in pure and mixedwood stands in northeastern British Columbia. In Chapter 2, I studied the importance and the influence of annual, seasonal, and monthly microclimate variables on the annual growth of white spruce trees in pure and mixedwood stands. In Chapter 3, I studied the importance and the influence of microclimate variables on sap flow of white spruce trees through different time scales in these two stand types. My key finding in these two chapters is that stand composition and structure are essential determinants of how spruce radial growth and sap flow respond to fluctuations in climate variables, and how they will respond to projected future climate scenarios. A combination of warmer temperatures and drought during summer will negatively affect white spruce trees growth in pure and mixedwood stands in the studied region. Spruce sap flow in both stand types is likely to increase as the climate warms, increasing the demand for soil water. As this resource becomes less available, white spruce in both stand types are likely to respond with processes that can compromise their physiological integrity. White spruce growing in mixedwood stands might be more sensitive to drought stress than in pure stands due to the higher competition for limiting resources (primarily water). This thesis provides information of expected changes in tree growth to climate variability and demonstrates the importance of appropriate site selection to plant spruce trees and management of pure and mixedwood stands.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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 teacher head, 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".