Have some landscapes in the eastern Canadian boreal forest moved beyond their natural range of variability?
Bibliographic record
Abstract
In the context of ecosystem management, the present study aims to compare the natural and the present-day forested landscapes of a large territory in Quebec (Canada). Using contemporary and long-term fire cycles, each natural forest landscape is defined according to the variability of its structure and composition, and compared to the present-day landscape. This analysis was conducted to address the question of whether human activities have moved these ecosystems outside the range of natural landscape variability. The study encompassed a forested area of 175 000 km2 divided into 14 landscapes. Using a framework that integrates fire cycles, age structure and forest dynamics, we characterized the forest composition and age structures that resulted from three historical fire cycles (110, 140, and 180 years) representative of the boreal forest of eastern Canada. The modeled natural landscapes were compared with present-day landscapes in regard to the proportion of old-growth forests (landscape level) and the proportion of late-successional forest stands (landscape level and potential vegetation type). Four landscapes (39%) remain within their natural range of variability. In contrast, nine landscapes (54%) show a large gap between natural and present-day landscapes. These nine are located in the southern portion of the study area, and are mainly associated with Abies-Betula vegetation where human activities have contributed to a strong increase in the proportion of Populus tremuloides stands (early-successional stages) and a decrease of old-growth forest stands (more than 100 years old). A single landscape (7%), substantially changed from its potential natural state, is a candidate for adaptive-based management. Comparison of corresponding natural (reference conditions) and present-day landscapes showed that ten landscapes reflecting an important shift in forest composition and age structure could be considered beyond the range of their natural variability. The description of a landscape’s natural variability at the scale of several millennia can be considered a moving benchmark that can be re-evaluated in the context of climate change. Focusing on regional landscape characteristics and long-term natural variability of vegetation and forest age structure represents a step forward in methodology for defining reference conditions and following shifts in landscape over time.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".