Functional traits as predictors of vulnerability from plants to climate change: a case study in brazilian semi-arid
Bibliographic record
Abstract
The ongoing global climate crisis will potentially affect the structure and functioning of ecosystems causing the extinction or reduction in the geographic range of many taxa. For the Brazilian semiarid region, an increase in temperature and a reduction in precipitation is expected, and, consequently, an increase in the water deficit in a region that has historically been subject to periods of drought. Understanding which species are most vulnerable to this increase in the severity of aridity is the first step in planning strategies that minimize the risks of extinction. Analyzes of niche modeling of species together with functional traits that are indicators of growth, water savings and dispersion capacity can be good indicators of vulnerability to climate change. In this sense, we analyzed two ecological niche models (current and future) for shrub-tree species in order to simulate the effects of climate change on their distribution. To build the species distribution models, we used data from the current climate, soil physical variables at various depths, taxon occurrence data and bioclimatic predictions from the Canadian Earth System Model for the pessimistic emission scenario (RCP8.5). To check the variation in the distribution area, we calculate the number of suitable pixels in the present and the future. Finally, we verified whether the functional attributes of water saving, dispersion and plant growth would be good predictors of the potential for expansion or retraction of the species. We found that the functional attributes can predict the vulnerability of plants in the Brazilian semiarid region. Species with a greater specific leaf area, greater leaf thickness and smaller seeds should lose more area due to climate change. These results serve as a basis for testing in future longterm studies the architecture, functioning and dynamics of populations.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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 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".