Geographical distributions in tropical trees: can geographical range predict performance and habitat association in co‐occurring tree species?
Bibliographic record
Abstract
Abstract Aim A major floristic and climatic transition from aseasonal to seasonal evergreen tropical forest (the Kangar–Pattani Line; KPL) exists in the Indo‐Sundaic region of Southeast Asia. Mechanisms constraining species distribution here are at present poorly understood, but it is hypothesized that species differ in their tolerances of abiotic factors, in particular water availability. Under this hypothesis, we anticipate differences in performance or habitat preferences, or both, of species differing in distribution with respect to the KPL. The aim of this study is to test whether geographical distributions can be used to explain variation in growth, mortality and habitat preferences in co‐occurring tree species differing in their distribution in relation to the KPL. Location Pasoh Forest Reserve, Negeri Sembilan, Malaysia; south of the KPL. Methods All tree species within a 50‐ha forest dynamics plot were classified as widespread or southern based upon their distributions in relation to the KPL and as habitat specialists or generalists based on spatial association with soil‐based habitat categories. Growth and mortality rates, variation in growth and mortality with respect to soil type, and levels of habitat association were quantified for species with different geographical distributions. Results Differences existed in species performance based upon geographical distributions. Specifically, widespread species had lower growth rates than did species restricted to the aseasonal forests. Mortality rates did not differ as a function of geographical distribution. The growth responses of species to soil habitats also diverged, such that differences in performance of widespread species among soil types were more conservative than those of species restricted in their distribution to the aseasonal forests. However, the proportion of species showing positive habitat associations did not differ significantly between widespread and southern species. Main conclusions Distribution‐based differences in species performance and response to soil type support the hypothesis that species tolerant of wider climatic variation perform less well in any given environment due to limitations on plasticity. These performance differences provide quantitative evidence of the role of climatic transitions in determining tree species distributions in relation to the Kangar–Pattani Line in the Indo‐Malay region. Such differences in performance have important implications for our understanding of biodiversity gradients and responses of Indo‐Sundaic forests to climate change.
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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 | 0.001 | 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 | 0.001 |
| 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".