Extensive land cover change across Arctic–Boreal Northwestern North America from disturbance and climate forcing
Bibliographic record
Abstract
Abstract A multitude of disturbance agents, such as wildfires, land use, and climate‐driven expansion of woody shrubs, is transforming the distribution of plant functional types across Arctic–Boreal ecosystems, which has significant implications for interactions and feedbacks between terrestrial ecosystems and climate in the northern high‐latitude. However, because the spatial resolution of existing land cover datasets is too coarse, large‐scale land cover changes in the Arctic–Boreal region (ABR) have been poorly characterized. Here, we use 31 years (1984–2014) of moderate spatial resolution (30 m) satellite imagery over a region spanning 4.7 × 10 6 km 2 in Alaska and northwestern Canada to characterize regional‐scale ABR land cover changes. We find that 13.6 ± 1.3% of the domain has changed, primarily via two major modes of transformation: (a) simultaneous disturbance‐driven decreases in Evergreen Forest area (−14.7 ± 3.0% relative to 1984) and increases in Deciduous Forest area (+14.8 ± 5.2%) in the Boreal biome; and (b) climate‐driven expansion of Herbaceous and Shrub vegetation (+7.4 ± 2.0%) in the Arctic biome. By using time series of 30 m imagery, we characterize dynamics in forest and shrub cover occurring at relatively short spatial scales (hundreds of meters) due to fires, harvest, and climate‐induced growth that are not observable in coarse spatial resolution (e.g., 500 m or greater pixel size) imagery. Wildfires caused most of Evergreen Forest Loss and Evergreen Forest Gain and substantial areas of Deciduous Forest Gain. Extensive shifts in the distribution of plant functional types at multiple spatial scales are consistent with observations of increased atmospheric CO 2 seasonality and ecosystem productivity at northern high‐latitudes and signal continental‐scale shifts in the structure and function of northern high‐latitude ecosystems in response to climate change.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".