Functional traits and the growth–mortality trade‐off in tropical trees
Bibliographic record
Abstract
A trade-off between growth and mortality rates characterizes tree species in closed canopy forests. This trade-off is maintained by inherent differences among species and spatial variation in light availability caused by canopy-opening disturbances. We evaluated conditions under which the trade-off is expressed and relationships with four key functional traits for 103 tree species from Barro Colorado Island, Panama. The trade-off is strongest for saplings for growth rates of the fastest growing individuals and mortality rates of the slowest growing individuals (r2 = 0.69), intermediate for saplings for average growth rates and overall mortality rates (r2 = 0.46), and much weaker for large trees (r2 < or = 0.10). This parallels likely levels of spatial variation in light availability, which is greatest for fast- vs. slow-growing saplings and least for large trees with foliage in the forest canopy. Inherent attributes of species contributing to the trade-off include abilities to disperse, acquire resources, grow rapidly, and tolerate shade and other stresses. There is growing interest in the possibility that functional traits might provide insight into such ecological differences and a growing consensus that seed mass (SM), leaf mass per area (LMA), wood density (WD), and maximum height (H(max)) are key traits among forest trees. Seed mass, LMA, WD, and H(max) are predicted to be small for light-demanding species with rapid growth and mortality and large for shade-tolerant species with slow growth and mortality. Six of these trait-demographic rate predictions were realized for saplings; however, with the exception of WD, the relationships were weak (r2 < 0.1 for three and r2 < 0.2 for five of the six remaining relationships). The four traits together explained 43-44% of interspecific variation in species positions on the growth-mortality trade-off; however, WD alone accounted for > 80% of the explained variation and, after WD was included, LMA and H(max) made insignificant contributions. Virtually the full range of values of SM, LMA, and H(max) occurred at all positions on the growth-mortality trade-off. Although WD provides a promising start, a successful trait-based ecology of tropical forest trees will require consideration of additional traits.
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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 | 0.001 |
| 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 | 0.000 |
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".