Climate, Environment, and Disturbance History Govern Resilience of Western North American Forests
Bibliographic record
Abstract
Resilience and resistance concepts have broad application to ecology and society. Resilience is an emergent property that reflects the amount of disruption a system can withstand before its structure or organization uncharacteristically shift. Resistance is a component of resilience. Before the advent of intensive forest management and fire suppression, western North American forests exhibited a naturally occurring resilience to wildfires and other disturbances. Using evidence from ten ecoregions, spanning forests from Canada to Mexico, we review the properties of these forests that reinforced those qualities. We show examples of multi-level landscape resilience, of feedbacks within and among levels, and how conditions have changed under climatic and management influences. We highlight geographic similarities and differences in the structure and organization of historical landscapes, their forest types, and in the conditions that have changed resilience and resistance to abrupt or large-scale disruptions. We discuss the regional climates’ role in episodically or abruptly reorganizing plant and animal biogeography, and forest resilience and resistance to disturbances. We give clear examples of these changes and suggest that managing for resilient forests is a construct that is strongly dependent on scale and social values. It involves human community adaptations that work with the ecosystems they depend on and the processes that shape them. It entails actively managing factors and exploiting mechanisms that drive dynamics at each level as means of adapting landscapes, species, and human communities to climate change, and maintaining core ecosystem functions, processes, and services. Finally, it compels us to prioritize management that incorporates ongoing disturbances and anticipated effects of climatic changes, to support dynamically shifting patchworks of forest and nonforest. Doing so will make these shifting forest conditions and wildfire regimes more gradual and less disruptive to individuals and society.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".