Tree rings reveal mixtures of aspen and spruce exhibit greater drought resilience in a planted field experiment
Bibliographic record
Abstract
Boreal forests provide a wealth of ecosystem services, many of which are linked to forest productivity. Climate change is expected to increase the frequency, duration, and severity of drought, and undermine the productivity of western boreal forests. Favouring species mixtures has emerged as a potential strategy to increase forest resilience to drought. To test the hypothesis that mixtures of trembling aspen and white spruce were more productive than pure stands because they were more resistant and resilient to drought, we analyzed community and species responses to drought utilizing measurements of annual growth obtained from stem disc samples from a planted field experiment. The experiment, established in 1999, included pure aspen, aspen dominated mixtures, equal mixtures, spruce dominated mixtures, and pure spruce in a randomized block design. In 2020, trees were harvested, and annual basal area increment was estimated from measurements of stem discs. We retrospectively studied the response of productivity to strong droughts in 2009 and 2015 by defining pre- and post-drought growth periods, and calculating drought resistance (drought / pre-drought) and resilience (post-drought / pre-drought). Our results support the hypothesis that mixtures are both more productive and resilient to drought than pure stands. Composition significantly influenced the three resilience components for spruce, but effects on aspen were limited. Community mortality was greatest in pure spruce, followed by pure aspen. For mixtures, community productivity increased with the proportion of spruce; and pure spruce exhibited the lowest basal area, with less than half the basal area value than the other compositions. These findings indicate that overall favouring mixed stands is a more suitable strategy for enhancing forest resilience to drought. These results provide insight into the importance of managing aspen-spruce mixtures under expected drought as a result of climate change. • We analyzed species and community responses to drought in a field experiment. • Productivity in aspen-spruce mixtures was greater than in pure stands. • Mixtures generally performed better than pure stands during drought periods. • Mortality was greatest in pure stands. • The study supports mixed-species planting for climate change adaptation.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".