A new multifactorial approach for studying intra-annual secondary growth dynamics in Mediterranean mixed forests: integrating biotic and abiotic interactions
Bibliographic record
Abstract
Studying intra-annual growth dynamics through a modelling approach has proved useful for characterizing differences in wood phenology between species and deviations from the expected tree growth due to climate change. The aim of this paper was to determine the climatic factors and the inter- and intra-specific competition traits that drive intra-annual secondary growth dynamics in three Mediterranean tree species that naturally co-exist in the Spanish Northern Plateau (Pinus pinea L., Juniperus thurifera L., and Quercus ilex subsp. ballota (Desf.) Samp.). For this purpose, we obtained girth increment data from band dendrometers installed on 58 trees from May 2012 to April 2015. Half of them were located in a high-density plot and half were in a low-density plot. These data were analysed through a nonlinear multifactorial model considering daily and cumulative climatic effects together with inter- and intra-specific competition indices. Our results provide evidence of a spatiotemporal niche separation between species. Compared with other species, Q. ilex subsp. ballota showed a wider thermal amplitude, a higher mean optimum temperature for growth, and a milder effect of drought on its secondary growth. Under current climate change projections, simulations based on our model predict a secondary growth reduction for P. pinea and a growth enhancement for Q. ilex subsp. ballota in these Mediterranean mixtures.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".