Coexistence of Tropical Forest Tree Species Along the Demographic Buffering Spectrum
Bibliographic record
Abstract
Organisms have evolved diverse adaptive strategies to cope with environmental fluctuations. Slow-growing long-lived species tend to exhibit low temporal variability in population growth (strongly buffered demographically), whereas fast-growing short-lived species optimize growth in favorable years (weakly buffered). These patterns set up the expectation that differentiation in demographic buffering may reduce disparities in long-term fitness among species, enhancing the potential for coexistence in variable environments. Yet, this expectation has never been empirically tested for trees. Here, we quantified differences in long-term population growth among 204 co-occurring tropical trees spanning a life-history spectrum from strongly to weakly buffered. We found that interspecific differences in demographic buffering reduced disparities in long-term population fitness at low densities, highlighting demographic differentiation as a key mechanism promoting coexistence in fluctuating environments. However, simulated increases in temperature, precipitation, and drought variability produced divergent fitness responses among species and exacerbated interspecific fitness disparities. Together, these findings provide a novel perspective on the mechanisms that underpin the astounding tree diversity 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".