Radial growth of subarctic tree and shrub species: relationships with climate and association with the greening of the forest–tundra ecotone of subarctic Québec, Canada
Bibliographic record
Abstract
Climate change has resulted in a widespread increase in primary productivity in northern regions. This “greening” can alter terrestrial ecosystems dynamics and trigger positive climate feedbacks but often exhibits spatial heterogeneity. Few studies have focused on the differential responses of plant functional groups to warming as a potential driver of heterogeneity in greening rates. Our objective was to determine whether climate–growth relationships are species-specific, and whether they can explain the heterogeneity of the subarctic greening rates. We compared climate–growth relationships and associations to normalized difference vegetation index (NDVI) of the dominant tree ( Picea mariana) and shrub ( Betula glandulosa) species at two locations in the forest–tundra ecotone in Nunavik (Québec, Canada). Correlation coefficients of the climate–growth relationship varied between species and locations, being higher for P. mariana and at the northernmost location. The lower association between NDVI and P. mariana radial growth at the northernmost location appeared to be compensated by a stronger association between NDVI and B. glandulosa radial growth. Our results show that response to climate varies spatially and between species, both of which could potentially be used to explain the heterogeneity of subarctic greening rates. This improved understanding of the species-specific response to climate change will help predict forthcoming changes in primary productivity and their potential positive feedback on climate change.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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 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".