Effects of biotic interactions on tropical tree performance depend on abiotic conditions
Bibliographic record
Abstract
Predicting biotic responses to environmental change requires understanding the joint effects of abiotic conditions and biotic interactions on community dynamics. One major challenge is to separate the potentially confounding effects of abiotic environmental variation and local biotic interactions on individual performance. The stress gradient hypothesis (SGH) addresses this issue directly by predicting that the effects of biotic interactions on performance become more positive as the abiotic environment becomes more stressful. It is unclear, however, how the predictions of the SGH apply to plants of differing functional strategies in diverse communities. We asked (1) how the effect of crowding on performance (growth and survival) of trees varies across a precipitation gradient, and (2) how functional strategies (as measured by two key traits: wood density and leaf mass per area, LMA) mediate average demographic rates and responses to crowding across the gradient. We built trait-based neighborhood models of growth and survival across a regional precipitation gradient where increasing precipitation is associated with reduced abiotic stress. In total, our dataset comprised ~170,000 individual trees belonging to 252 species. The effect of crowding on tree performance varied across the gradient; crowding negatively affected growth across plots and positively affected survival in the wettest plot. Functional traits mediated average demographic rates across the gradient, but we did not find clear evidence that the strength of these responses depends on species' traits. Our study lends support to the SGH and demonstrates how a trait-based perspective can advance these concepts by linking the diversity of species interactions with functional variation across abiotic gradients.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".