Carbon isotope discrimination and water stress in trembling aspen following variable retention harvesting
Bibliographic record
Abstract
Variable retention harvesting (VRH) has been proposed as a silvicultural practice to maintain biodiversity and ecosystem integrity. No previous study has examined tree carbon isotope discrimination to provide insights into water stress that could lead to dieback and mortality of trees following VRH. We measured and compared the carbon isotope ratios (delta(13)C) in stem wood of trembling aspen (Populus tremuloides Michx.) before and after VRH. Eight trees were sampled from isolated residual, edge and control (interior of unharvested stand) positions from each of seven plots in three regions (Calling Lake and Drayton Valley, Alberta and Lac Duparquet, Québec). After VRH, the general trend in mean delta(13)C was residual > edge > control trees. Although this trend is indicative of water stress in residual trees, it also suggests that edge trees received some sheltering effect, reducing their stress compared with that of residuals. A strong inverse relationship was found between the delta(13)C values and the mean annual precipitation in each region. The trend in mean delta(13)C signature was Calling Lake > Drayton Valley > Lac Duparquet trees. These results suggest that residual or edge trees in drier regions are more likely to suffer water stress following VRH. We also observed a trend of greater delta(13)C in stout trees compared with slender trees, both before and after VRH. The evidence of greater water stress in stout trees likely occurred because of a positive relationship between stem diameter and crown volume per basal area. Our results provide evidence that water stress could be the driving mechanism leading to dieback and mortality of residual trees shortly after VRH. Additionally, the results from edge trees indicate that leaving hardwood residuals in larger patches or more sheltered landscape positions could reduce the water stress to which these trees are subjected, thereby reducing dieback and mortality.
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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) | 0.000 | 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) | 0.000 | 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 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".