Determining forest degradation, ecosystem state and resilience using a standard stand stocking measurement diagram: theory into practice
Bibliographic record
Abstract
Forest degradation is a major issue for policy-makers that is exacerbated by no clear and globally accepted definition of the term. For forest managers, a loss of forest productive capacity is one form of forest degradation. We present a quantitative method to assess forest degradation from a productivity perspective. Our method uses a standard stocking chart and calculation methods based on standard forest inventory data, to derive a clear threshold value for stocking, below which a forest should be considered degraded. The method is illustrated using the example of a self-regenerating Nothofagus production forest type from Chile. For that forest type, we determined that harvesting trees to below a specific basal area relative to site type, resulted in a loss of resilience, an unpredictable shift in ecosystem state, and a degraded condition. Our method illustrates how over-harvesting can degrade the long-term productivity of a stand and forest resilience. Nevertheless, it is important to consider that forests can also be degraded from other perspectives, such as loss of biodiversity, carbon, or protective functions as a result of excessive disturbances. Ecosystem management requires that managers consider degradation from a range of perspectives. We see the quantified approach used here as a way to provide practitioners with, in part, a transition from sustained yield to ecosystem management with an ultimate objective of providing a pathway towards adaptive management of complex systems and avoiding degradation.
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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 | 0.009 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".