Natural disturbance regimes for implementation of ecological forestry: a review and case study from Nova Scotia, Canada
Bibliographic record
Abstract
Ecological forestry is based on the idea that forest patterns and processes are more likely to persist if harvest strategies produce stand structures, return intervals, and severities similar to those from natural disturbances. Taylor et al. (2020) reviewed forest natural disturbance regimes in Nova Scotia, Canada, to support implementation of ecological forestry. In this follow-up paper, we (i) review the use of natural disturbance regimes to determine target harvest rotations, age structures, and residual stand structures; and (ii) describe a novel approach for use of natural disturbance regimes in ecological forestry developed for Nova Scotia. Most examples of ecological forestry consider only the local, dominant disturbance agent, such as fire in boreal regions. Our approach included: (i) using current ecological land classification to map potential natural vegetation (PNV) community types; (ii) determining cumulative natural disturbance effects of all major disturbances, in our case fire, hurricanes, windstorm, and insect outbreaks for each PNV; and (iii) using natural disturbance regime parameters to derive guidelines for ecological forestry for each PNV. We analyzed disturbance occurrence and return intervals based on low, moderate, and high severity classes (<30, 30–60, and >60% of biomass of living trees killed, respectively), which were used to determine mean annual disturbance rates by severity class. Return intervals were used to infer target stand age-class distributions for high, moderate, and low severity disturbances for each PNV. The range of variation in rates of high severity disturbances among PNVs was from 0.28%·year–1 in Tolerant Hardwood to 2.1%·year–1 in the Highland Fir PNV, equating to return intervals of 357 years in Tolerant Hardwood to 48 years in Highland Fir PNVs. As an example, this return interval for the Tolerant Hardwood PNV resulted in target rotation lengths of 200 years for 35% of the PNV area, 500 years for 40%, and 1000 years for 25%. The proposed approach of determining natural disturbance regimes for PNV communities and calculating target disturbance rates and corresponding harvest rotation lengths or entry times appears to be a feasible method to guide ecological forestry in any region with a strong ecological land classification system and multiple disturbance agents.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".