Relationship between post‐fire regeneration and leaf economics spectrum in Mediterranean woody species
Bibliographic record
Abstract
1 Recent work has identified global-scale relationships between key leaf traits (leaf economics spectrum). However, it is important to determine whether this approach can be applied at local scale with smaller subsets of species facing similar environments. Since fire is a key process in Mediterranean shrubland dynamics we analyze whether fire-related life-history traits influence the pattern of correlation between the leaf economic spectrum and leaf moisture traits. 2 Using structural equation modelling and exploratory path analysis, we developed alternative models to test how interspecific leaf traits are related to the seasonal variation of water content (leaves and shoots) and to the type of post-fire regeneration of Mediterranean woody species. 3 This study demonstrates that for these species seasonal variation in water content and fuel moisture would be better predicted by the presence or absence of a trait describing post-fire seedling establishment than by the leaf economic spectrum traits. However, leaf dry matter content (LDMC) is influenced by both the leaf economic spectrum and the post-fire regenerative type. 4 Seeder species (those that recruit via seeds immediately after fire) present lower LDMC and higher relative seasonal variation of relative water content (RWCrsv) than non-seeders. We hypothesize that since seeder species mostly evolved under the Mediterranean climate, they developed a particular strategy of drought tolerance (without causing an effect to the relation between the volume occupied by cytoplasm relative to the volume occupied by cell walls), which is the cause of the observed relation between LDMC and RWCrsv. 5 This study suggests that the leaves of Mediterranean woody species would follow the general leaf economics spectrum (Wright et al. 2004) but that specific selective forces, such as disturbance regime, acting at regional scale also play a relevant role to explain leaf traits related to water content.
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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 | 0.000 | 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 | 0.001 |
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; 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".