Forest ecosystem management in North America: From theory to practice
Bibliographic record
Abstract
Forest ecosystem management (EM) in North America has evolved from a theoretical concept to operational practice over the last two decades, but its implementation varies greatly among regions. This paper attempts to evaluate (1) if and how emulation of natural disturbances (END) is being used as a conceptual bases for implementing EM, and more particularly, what strategies are used to define the natural forest of reference, and (2) what temporal and spatial scale strategies are being considered for seven important retention elements (downed woody debris, snags, green trees, corridors, riparian buffers, large patches and old forest)? To conduct this evaluation, five guides from four geographically well-distributed regions in North America are compared. Although END is the central conceptual foundation underlying four of the five guides, a natural forest of reference is not always clearly identified and none of the guides consider future impacts due to global change. The major weakness common to all five guides is the lack of consideration of long-term forest dynamics, particularly the lack of clear strategies for retention elements at a temporal scale longer than a single rotation. Generally, the spatial scales chosen for retention elements are not well-justified ecologically and targets for each retention element are not identified at different spatial scales. We stress that strong efforts have been made to develop forest management that incorporates some elements of natural variability and which considers societal needs, but further improvements are required. We conclude by presenting some suggestions to improve the approach. For example, creating more realistic guidelines in integrating current and future forest dynamics with pre-settlement information and planning rotation lengths that are inspired by the dominant natural disturbance.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.022 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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 source (direct Gemma or distilled Codex), 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".