Life history scaling in a tropical forest
Bibliographic record
Abstract
Abstract Both tree size and life history variation drive forest structure and dynamics, but little is known about how life history frequency changes with size. We used a scaling framework to quantify ontogenetic size variation and assessed patterns of abundance, richness, productivity and light interception across life history strategies from >114,000 trees in a primary, neotropical forest. We classified trees along two life history axes: a fast–slow axis characterized by a growth–survival trade‐off, and a stature–recruitment axis with tall, long‐lived pioneers at one end and short, short‐lived recruiters at the other. Relative abundance, richness, productivity and light interception follow an approximate power law, systematically shifting over an order of magnitude with tree size. Slow saplings dominate the understorey, but slow trees decline to parity with rapidly growing fast and long‐lived pioneer species in the canopy. Like the community as a whole, slow species are the closest to obeying the energy equivalence rule (EER)—with equal productivity per size class—but other life histories strongly increase productivity with tree size. Productivity is fuelled by resources, and the scaling of light interception corresponds to the scaling of productivity across life history strategies, with slow and all species near solar energy equivalence. This points towards a resource‐use corollary to the EER: the resource equivalence rule. Fitness trade‐offs associated with tree size and life history may promote coexistence in tropical forests by limiting niche overlap and reducing fitness differences. Synthesis . Tree life history strategies describe the different ways trees grow, survive and recruit in the understorey. We show that the proportion of trees with a pioneer life history strategy increases steadily with tree size, as pioneers become relatively more abundant, productive, diverse and capture more resources towards the canopy. Fitness trade‐offs associated with size and life history strategy offer a mechanism for coexistence in tropical forests.
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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 | 0.002 |
| 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 | 0.001 |
| 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".