Recent greening driven by species-specific shrub growth characteristics in Nunatsiavut, Labrador, Canada
Bibliographic record
Abstract
Satellite remote sensing is a popular approach for identifying vegetation change in northern environments; however, disentangling ecological processes causing variability in spectral indices remains a challenge. Here, we aim to determine how shrub characteristics differ between low and rapidly greening areas near Nain, Nunatsiavut, Canada. Using a cross-scale approach, we combined remotely sensed spectral greening trends (Normalized Difference Vegetation Index; Landsat Collection 1, 1985–2018) with shrub dynamics derived from ring-widths of green alder (Alnus alnobetula (Ehrh.) K. Koch) and dwarf birch (Betula glandulosa Michx.). Differentiation of spectral greening classes appears to be driven by the distribution of shrub species. Alder were taller, grew faster, had more recent stem initiation than dwarf birch, and were dominant in rapid greening subplots. In low greening subplots, alder were co-dominant with dwarf birch, whose dominant stems initiated more gradually, were shorter, and had lower rates of vertical growth. The radial growth of both shrub species was favoured by warm winter temperatures and precipitation, whereas rapid greening alder was also favoured by warm summer temperatures. Further shrub growth will likely be enhanced under continued climate warming if moisture does not become limiting. This research demonstrates the importance of species identity in determining rates of spectral greening in northern environments.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".