Non-stationary Response of Tree Growth to Climate Trends Along the Arctic Margin
Bibliographic record
Abstract
Climate change modulates cold-marginal forest ecosystems through changing growth constraints. Understanding spatiotemporal variations in climate–growth relationships is essential to project forest ecosystem dynamics, and climate–environmental feedbacks. We explored variations in growth and climate–growth relationships, along the Arctic margin in north-western Europe, using Scots pine radial growth chronologies, climate data and links between the geographical origin of dominant air masses and growth-controlling variables. Analyses covered nineteenth century to early twenty - first century, with emphasis on two separate warming periods (early twentieth century, and late twentieth to early twenty - first century) and the intervening cooling period. The analyses revealed spatiotemporally unstable growth responses to climate along the Arctic margin. Spatial growth patterns were most similar during the cooling period. However, climate trends (warming, cooling) were weak drivers of growth-limiting climate variables. Instead, a transition in growth-limiting variables occurred throughout the analysed period. A wide range of growing season and non-growing season climate variables limited growth during the early twentieth century. Thereafter the number of growth-limiting variables progressively decreased. This change was accompanied by a contraction in the spatial correspondence between growth and climate, and by a shift in the geographical origin of dominant air masses. This was particularly emphasized close to the Atlantic during recent warming period. The weak association between growth-limiting variables and climate trends question projections of future ecosystem dynamics based on climate variables identified during specific periods (for example, recent warming period). Such projections may be misleading as the diversity of climate conditions constraining cold-marginal forests will be underestimated.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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; both teacher heads agree on what is shown here.
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".