The role of desiccation tolerance in determining tree species distributions along the Malay–Thai Peninsula
Bibliographic record
Abstract
1 Patterns of water availability are frequently implicated in local and regional tree species distributions. A major floristic and climatic transition from aseasonal to seasonal evergreen tropical forest is the Kangar–Pattani Line (KPL) in the Indo-Sundaic region of Southeast Asia. We hypothesize that differences in species’ drought tolerance will correspond with their distribution with respect to the KPL. Using a common garden study, we assess the role of differences in physiological drought tolerance traits to geographic distributions for 24 tropical tree species in relation to rainfall seasonality. 2 Inherent differences in desiccation tolerance of seedlings were quantified as water potentials and relative water contents (RWCs) below which the plant could no longer support living tissue, and plant water relation parameters were measured using pressure–volume analysis. The relationships among these traits were examined using bivariate trait relationships and a principal components analysis (PCA). The physiological traits contributing most to lethal water potential and RWC were assessed using multiple regression analysis. 3 Distribution-related differences in all desiccation tolerance and pressure–volume traits were detected both with and without phylogenetic correction. Widespread species that occurred in seasonally dry forests were able to maintain living tissue at more negative water potentials and lower RWCs than were species restricted to aseasonal forests. Likewise, widespread species demonstrated more negative water potentials at turgor loss, more negative saturated osmotic potential, lower symplastic water fraction and higher bulk modulus of elasticity values. Turgor loss point (TLP) and bulk modulus of elasticity were the best predictors of lethal water potential while symplastic water fraction and bulk modulus of elasticity were included as predictors of lethal RWC. 4 Inherent differences in physiological traits contributing to drought tolerance are associated with differences in tropical tree species distributions in relation to rainfall seasonality. These results, combined with lack of support for hypothesized historical factors, strongly implicate climate as a determinant of tree species distributions around the KPL.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 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".