Satellite observations reveal stable forest limits and shrub expansion across the Canadian forest-tundra ecotone
Bibliographic record
Abstract
Abstract Climate change at high latitudes is expected to increase the cover of woody vegetation across the forest-tundra ecotone. However, there is still uncertainty concerning the nature and magnitude of these changes. In this study, we used open access satellite remote sensing data from ICESat-2 and Landsat to model change in vegetation structure across 183 million hectares of the Canadian forest-tundra ecotone from 1985 to 2021. We used Random Forests models to predict canopy presence and height across six time periods at 30 m spatial resolution. Change between time periods was used to classify nine stable and transitional vegetation types. We used these data to map advance and retreat in the northernmost forest limit and linked change types to disturbance history. Over the study period, the extent of forested area increased by 0.9% and the forest limit warmed by 1.08 °C, receiving 25 mm more annual precipitation. However, large parts of the forest limit remained stable over time despite favorable climate conditions. Our mapping also revealed divergent patterns in forest and shrub expansion across the ecotone, with shrubs exhibiting more widespread and diffuse expansion above the forest limit. Increasing vegetation structure across the ecotone was strongly associated with fire history as 80% of mapped vegetation changes occurred in disturbed areas. The majority of forest growth and new forest expansion occurred in fires that burned over 40 years ago. These findings highlight the importance of disturbance-recovery dynamics in structural vegetation change over decadal time periods.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".