Can Retention Harvest Maintain Natural Structural Complexity? A Comparison of Post-Harvest and Post-Fire Residual Patches in Boreal Forest
Bibliographic record
Abstract
Variable retention harvest promotes biodiversity conservation in managed boreal forests by ensuring forest continuity and structural complexity. However, do post-harvest and post-fire patches maintain the same structural complexity? This study compares post-harvest and post-fire residual patches and proposes retention modalities that can maintain the same structural complexity as in natural forests, here considering both continuous forest stands and post-fire residual patches. In boreal black spruce forests, 41 post-fire residual patches, and 45 post-harvest retention patches of varying size and ages (exposure time to disturbed matrix) and 37 continuous forest stands were classified into six diameter structure types. Types 1 (inverted-J) and 2 (trunked-unimodal) characterized stands dominated by small trees. The abundance of small trees decreased and the abundance of large trees increased from Type 1 to Type 6. Type 6 had the most irregular structure with a wide range of diameters. This study indicates that: (1) old post-harvest residual retentions maintained the range of structural complexity found in natural stands; (2) Types 1 and 2 were generally associated with young post-fire patches and post-harvest retention clumps; (3) the structure of residual patches containing only small trees was usually younger (in terms of the age of the original forest from which residual patches were formed) than those with larger trees. To avoid the risk of simplifying the structure, retention patches should be intentionally oriented towards Types 3–6, dominated by intermediate and large trees.
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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 | 0.001 |
| 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".