A δ18O and δ2H stable water isotope analysis of subalpine forest water sources under seasonal and hydrological stress in the Canadian Rocky Mountains
Bibliographic record
Abstract
Subalpine forests are hydrologically important to the function and health of mountain basins. Identifying the specific water sources and the proportions used by subalpine forests is necessary to understand potential impacts to these forests under a changing climate. The recent ‘Two Water Worlds’ hypothesis suggests that trees can favour tightly bound soil water instead of readily available free-flowing soil water. Little is known about the specific sources of water used by subalpine trees Abies lasiocarpa (Subalpine fir) and Picea engelmannii (Engelmann spruce) in the Canadian Rocky Mountains. In this study, stable water isotope (δ18O and δ2H) samples were obtained from Subalpine fir and Engelmann spruce trees at three points of the growing season in combination with water sources available at time of sampling (snow, bound soil water, saturated soil water, precipitation). Using the Bayesian Mixing Model, MixSIAR, relative source water proportions were calculated. In the drought summer examined, there was a net loss of water via evapotranspiration from the system. Results highlighted the importance of tightly bound soil water to subalpine forests, providing insights of future health under sustained years of drought and net loss in summer growing seasons. This work builds upon concepts from the ‘Two Water Worlds’ hypothesis, showing that subalpine trees can draw from different water sources depending on season and availability. In our case, water use was largely driven by a tension gradient within the soil allowing trees to utilize tightly bound soil water and saturated soil water at differing points of the growing season.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.002 | 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".