Ecological Classification in Forest Ecosystem Management: Links Between Current Practices and Future Climate Change in a Québec Case Study
Bibliographic record
Abstract
Abstract Climate change is expected to profoundly impact boreal forests, ranging from changes in forest composition and productivity to modifications in disturbance regimes. These climate-induced changes represent a major challenge for forest ecosystem management, as information based on ecological classification may no longer provide a straightforward guide for attaining management goals in the future. In this chapter, we examine how climate change could influence the use of ecological classification and by what means this approach can continue to be relevant for guiding the ongoing development of management practices. We address these questions by first describing ecological classification, using the example of Québec’s classification system, and then showing its importance in forest ecosystem management. Using a forest landscape in Québec as a case study, we then look at how climate change could affect boreal forest ecosystems by presenting a detailed, multistep analysis that considers climate analogs, habitat suitability, and changes in forest composition. We show that at the end of the century, the vegetation of the Abies-Betula western subdomain will not change sufficiently to resemble that of its climate analog, currently located ~500 km to the south. Changes in fire frequency and severity could significantly modify forest dynamics and composition. Consequently, the potential vegetation and the successional pathways defined under the current climate could change and follow new successional trajectories. This possible reality forces us to question some fundamental aspects of ecological classification. However, we argue that ecological classification can still provide a valuable framework for future forest management, particularly in continuing to recognize the various types of ecosystems present along toposequences. Given the changes expected in forest vegetation composition and dynamics, future variability and uncertainty must be integrated into the current stable classification units and predictable successional trajectories of ecological classification.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".