Below canopy evapotranspiration in four different variable retention harvesting treatments in a red pine plantation forest
Bibliographic record
Abstract
Abstract Variable retention harvest (VRH) is a silvicultural approach that retains differing proportions and patterns of canopy trees across a harvested area to emulate natural disturbance effects on stand structure and enhance the resilience of the regenerating stand to abiotic and biotic stresses. Four VRH treatments were applied to an 83‐year‐old red pine ( Pinu s resinosa Ait.) plantation forest in the Mixedwood Plains Ecozone of Canada that included 55% aggregate retention (55A), 55% dispersed retention (55D), 33% aggregate retention (33A), 33% dispersed retention (33D) and an unharvested control (CN). In the sixth growing season after harvest, tree stem sap flow and eddy covariance flux measurements were used to examine the impacts of VRH on the dominant components of total stand evapotranspiration (ET), i.e., canopy transpiration (T C ) and water flux from the understory vegetation and soil (ET U ) as well as understory and canopy water use efficiency (WUE). A positive relationship was found between harvest intensity and the growth of understory vegetation and ET U . The contribution of ET U to ET was higher in the dispersed compared to the aggregate VRH treatments. Canopy transpiration contributed 83% of ET in the CN plot and 58%, 55%, 30% and 23% in the 55D, 55A, 33A and 33D treatments, respectively. Overall, VRH treatments resulted in increased canopy WUE but little comparative effect on understory WUE. Our results suggest that the dispersed retention pattern led to higher ET and productivity than the aggregate pattern of the same retention level. Where carbon sequestration and climate change mitigation is the primary management objective, higher retention levels such as 55D might be used to favour stand level carbon storage while accepting slower rates of understory development. Our findings on the effects of VRH on productivity and WUE of the canopy and understory will help forest managers to better employ VRH as an option to meet multiple objectives and adapt forests to a warmer, more variable climate.
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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".