A Trait-based approach for forest ecology and management: tools for theoretical and applied ecology
Bibliographic record
Abstract
Global change threats affecting forests require better understanding of mechanisms driving species environmental responses, but also species effects on ecosystems, to identify optimal management strategies for safeguarding the services they provide. Thus, this thesis serves on trait ecology and tools as Species Distribution Modelling, Remote Sensing, and Climate Change Modelling to explore ecological concepts that prove useful for determining specific management actions. Specifically we hypothesised about three main aspects: 1) the capacity of several traits to explain the characteristics of species niche in the Mediterranean; 2) the influence of trait diversity on the temporal stability of forest productivity in Temperate and Boreal zones; and 3) current and future effects of climate on the mean traits of Mediterranean forest communities. Forest inventories from Spain and Quebec (eastern Canada) were used to obtain species distribution and community composition whereas species traits values were retrieved from the literature. Main results showed that: 1) Specific Leaf Area (SLA) can be used to accurately represent species aridity limits in the Mediterranean; 2) trait diversity provides stability in Temperate forests; and 3) climate change may reduce SLA values of forest communities suggesting notable impacts on ecosystem functioning in the Mediterranean. The thesis suggests that management strategies should be based on trait ecology in order to best adapt to global change.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".