Timing and duration of drought modulate tree growth response in pure and mixed stands of Scots pine and Norway spruce
Bibliographic record
Abstract
Abstract Climate change is increasing the severity and frequency of droughts around the globe, leading to tree mortality that reduces production and provision of other ecosystem services. Recent studies show that growth of mixed stands may be more resilient to drought than pure stands. The two most economically important and widely distributed tree species in Europe are Norway spruce ( Picea abies (L.) Karst) and Scots pine ( Pinus sylvestris L.), but little is known about their susceptibility to drought when coexist. This paper analyses the resilience (resistance, recovery rate and recovery time) at individual‐tree level using a network of tree‐ring collections from 22 sites along a climatic gradient from central Europe to Scandinavia. We aimed to identify differences in growth following drought between the two species and between mixed and pure stands, and how environmental variables (climate, topography and site location) and tree characteristics influence them. We found that both the timing and duration of drought drive the different responses between species and compositions. Norway spruce showed higher vulnerability to summer drought, with both lower resistance and a longer recovery time than Scots pine. Mixtures provided higher drought resistance for both species compared to pure stands, but the benefit decreases with the duration of the drought. Especially climate sensitive and old trees in climatically marginal sites were more affected by drought stress. Synthesis . Promoting Scots pine and mixed forests is a promising strategy for adapting European forests to climate change. However, if future droughts become longer, the advantage of mixed stands could disappear which would be especially negative for Norway spruce.
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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 | 0.000 | 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 | 0.000 |
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".