Post-harvest regeneration is driven by ecological factors rather than wood procurement intensity in eastern Canadian forests
Bibliographic record
Abstract
Abstract Biomass from surplus forest growth that is not harvested for wood supply of conventional industries can be an important source of feedstock for bioenergy. Its procurement can be integrated with little effort into current harvest operations. However, the increasing harvesting intensity to meet greater demand for biomass procurement can impact forest ecosystem functions because of its direct and indirect effects on woody debris and the regeneration of next-rotation stands. In this context, we aimed to determine the relationships between wood procurement intensity, woody debris inputs, and regeneration success over 2 years after harvesting. We tested four treatments of increasing wood procurement intensity using a randomized block design within six experimental sites along a gradient of varying forest characteristics of boreal and temperate forests. We assessed stand characteristics in terms of standing trees, woody debris, and regeneration pre- and post-harvest. We used mixed effects models to evaluate (i) the effects of wood procurement intensity and pre-harvest stand characteristics on the volume and cover of woody debris and (ii) the specific influence of woody debris on the presence of suitable planting microsites post-harvest. Furthermore, we used principal component regressions to explore the relationships between harvesting intensity and the presence of natural regeneration and competing vegetation as a function of pre- and post-harvest stand characteristics (iii). Our results showed that increasing wood procurement intensity reduced the volume of post-harvest woody debris while having a limited effect on regeneration. Increasing harvesting intensity had a negligible effect on suitable planting microsites in hardwood-dominated stands but it increased their presence in conifer-dominated stands. Natural regeneration and competing vegetation were mainly related to stand characteristics, and only broadleaf regeneration was sensitive to harvest intensity. We conclude that the relationships between wood procurement intensity and regeneration are complex and rely mainly on stand characteristics rather than wood procurement needs.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".