Time-series maps reveal widespread change in plant functional type cover across Arctic and boreal Alaska and Yukon
Bibliographic record
Abstract
Abstract Widespread changes in the distribution and abundance of plant functional types (PFTs) are occurring in Arctic and boreal ecosystems due to the intensification of disturbances, such as fire, and climate-driven vegetation dynamics, such as tundra shrub expansion. To understand how these changes affect boreal and tundra ecosystems, we need to first quantify change for multiple PFTs across recent years. While landscape patches are generally composed of a mixture of PFTs, most previous moderate resolution (30 m) remote sensing analyses have mapped vegetation distribution and change within land cover categories that are based on the dominant PFT; or else the continuous distribution of one or a few PFTs, but for a single point in time. Here we map a 35 year time-series (1985–2020) of top cover (TC) for seven PFTs across a 1.77 × 106 km2 study area in northern and central Alaska and northwestern Canada. We improve on previous methods of detecting vegetation change by modeling TC, a continuous measure of plant abundance. The PFTs collectively include all vascular plants within the study area as well as light macrolichens, a nonvascular class of high importance to caribou management. We identified net increases in deciduous shrubs (66 × 103 km2), evergreen shrubs (20 × 103 km2), broadleaf trees (17 × 103 km2), and conifer trees (16 × 103 km2), and net decreases in graminoids (−40 × 103 km2) and light macrolichens (−13 × 103 km2) over the full map area, with similar patterns across Arctic, oroarctic, and boreal bioclimatic zones. Model performance was assessed using spatially blocked, nested five-fold cross-validation with overall root mean square errors ranging from 8.3% to 19.0%. Most net change occurred as succession or plant expansion within areas undisturbed by recent fire, though PFT TC change also clearly resulted from fire disturbance. These maps have important applications for assessment of surface energy budgets, permafrost changes, nutrient cycling, and wildlife management and movement analysis.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".