Quantifying Upslope Treeline Advancement in the Mountainous Areas of the Cariboo Natural Resource Region, British Columbia, from 1985 to 2020
Bibliographic record
Abstract
Upslope treeline advancement has been occurring globally over the last several decades, leading to displacement and fragmentation of alpine habitats and declines in species richness. Examining past and current treeline migration can provide insight into potential future conditions and can be used to improve ecosystem management. There is no long-term data on treeline advancement in Central British Columbia (B.C.) and the response to climate change is poorly understood. To address this, we explored treeline advancement in the Cariboo Natural Resource Region over a 35-year period (1985-2020) using Landsat imagery. Changes to the rate of advancement within the Engelmann-Spruce Subalpine Fir (ESSF) Biogeoclimatic Zone (BEC) were investigated in five-year increments using a common greenness index and the effects of slope and aspect were analysed. The most significant increase in advancement was observed between 2005 to 2015, with 190 km2 more advancement occurring in the 2010-2015 period compared to the 2005-2010 period. This large increase could be a result of temperature warming in the region beginning in the 1990s. Unexpectedly, we found steep sloped regions (> 30 degrees) exhibited greater treeline advancement throughout the study period, compared to gentle slopes (< 30 degrees). One possible reason for this is that steep slopes are less prone to cold sinks and trees favour warm temperatures for establishment. Aspect had no effect on treeline advancement, likely due to greater moisture on north-facing slopes but warmer temperatures on south-facing slopes. Additionally, significant variability throughout the study region was found, illustrating the complexity of treeline advancement and the challenges that exist when examining large geographic regions, as well as the importance of local ecosystem knowledge for future treeline modelling.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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 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".