Thinning increases climatic resilience of red pine
Bibliographic record
Abstract
Forest management techniques such as intermediate stand-tending practices (e.g., thinning) can promote climatic resiliency in forest stands by moderating tree competition. Residual trees gain increased access to environmental resources (i.e., soil moisture, light), which in turn has the potential to buffer trees from stressful climatic conditions. The influences of climate (temperature and precipitation) and forest management (thinning method and intensity) on the productivity of red pine (Pinus resinosa Ait.) in Michigan were examined to assess whether repeated thinning treatments were able to increase climatic resiliency (i.e., maintaining productivity and reduced sensitivity to climatic stress). The cumulative productivity of each thinning treatment was determined, and it was found that thinning from below to a residual basal area of 14 m 2 ·ha −1 produced the largest average tree size but also the second lowest overall biomass per acre. On the other hand, the uncut control and the thinning from above to a residual basal area of 28 m 2 ·ha −1 produced the smallest average tree size but also the greatest overall biomass per acre. Dendrochronological methods were used to quantify sensitivity of annual radial growth to monthly and seasonal climatic factors for each thinning treatment type. Climatic sensitivity was influenced by thinning method (i.e., thinning from below decreased sensitivity to climatic stress more than thinning from above) and by thinning intensity (i.e., more intense thinning led to a lower climatic sensitivity). Overall, thinning from below to a residual basal area of 21 m 2 ·ha −1 represented a potentially beneficial compromise to maximize tree size, biomass per acre, and reduced sensitivity to climatic stress, and, thus, the highest level of climatic resilience.
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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 | 0.001 | 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) | 0.001 | 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".