Applied ecology in Canada’s boreal: a holistic view of the mitigation hierarchy and resilience theory
Bibliographic record
Abstract
Canada’s boreal biome is a mosaic of forests and peatlands. These ecosystems have developed dynamically, periodically affected by disturbance events of significant spatial extent and variable severity, reducing ecosystem biomass. The same ecosystem types typically regenerate from biological legacies. However, concern is growing about the impact of these different anthropogenic disturbances, particularly compound disturbances including climate change, which open the door to shifts to alternate stable states. One strategy promoted to regulate anthropogenic disturbance is the “mitigation hierarchy” for development projects, where impacts on ecosystems are avoided, mitigated, restored, or compensated. This practical approach is not yet integrated into disturbance and resilience theory. Here, I develop an integrated view of the mitigation hierarchy, as well as resilience and disturbance theory, in a boreal context using ecosystem services to measure ecosystem state in a two-step process that first models loss of ecosystem function and then integrates the mitigation hierarchy and resilience theory. The application of this model is discussed in the context of restoration studies after different types of catastrophic anthropogenic disturbance. These studies, some of which are published in this special issue, highlight the important role of bryophytes and understory plants in setting restoration targets and developing criteria and indicators of success.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.015 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".