Increased complementarity in water‐limited environments in Scots pine and European beech mixtures under climate change
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Management of mixedwoods is advocated as an effective adaptation strategy to increase ecosystem resiliency in the context of climate change. Although mixedwoods have been shown to have greater resource use efficiency relative to pure stands, considerable uncertainty remains with respect to the underlying ecological processes. We explored species interactions in Scots pine/European beech mixedwoods with the process‐based model FORECAST Climate. The model was calibrated for two contrasting forests in the southwestern Pyrenees (northern Spain): a wet Mediterranean site at 625 m.a.s.l. and a subalpine site at 1335 m.a.s.l. Predicted mixedwood yield was higher than that for beech stands but lower than pine stands. When simulating climate change, mixedwood yield was reduced at the Mediterranean site (−33%) but increased at the subalpine site (+11%). Interaction effects were enhanced as stands developed. Complementarity dominated the Mediterranean stand but neutral or net competition dominated the subalpine stand, which had higher stand density and water availability. Reduced water demand and consumption, increased canopy interception, and improved water‐use efficiency in mixtures compared to beech stands, suggest a release of beech intraspecific competition. Beech also facilitated pine growth through better litter quality, nonsymbiotic nitrogen fixation, and above‐ and belowground stratification, leading to higher foliar nitrogen content and deeper canopies in pines. In conclusion, mixtures may improve water availability and use efficiency for beech and light interception for pine, the main limiting factors for each species, respectively. Encouraging pine–beech mixtures could be an effective adaptation to climate change in drought‐prone sites in the Mediterranean region.
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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 | 0.000 |
| 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 it