Using a forest dynamics model to link community assembly processes and traits structure
Bibliographic record
Abstract
Summary Trait‐based approaches have been increasingly used to understand the role of environmental and biotic filters on species assembly. However, our understanding of the relationships between traits and community assembly processes remain limited. Indeed, various assembly processes may lead to similar functional patterns, and the effects of a given process may vary with the considered traits. Especially, competition can result in trait divergence or convergence depending on whether the trait is related to niche differences or to species’ competitive abilities. In this study, we used a process‐based forest gap‐model to explore the effects of environmental and biotic assembly processes on the functional diversity of tree communities along a productivity gradient of 11 sites across central Europe. In a simulation experiment, we (i) disentangled the effects of environmental and biotic filtering on community structure, and (ii) tested whether competition resulted in trait divergence or convergence. Our results confirmed the expected decrease in species richness with decreasing site fertility. We detected environmental filtering for traits related to both species environmental requirements and species competitive ability, highlighting that environmental trait filtering can affect all aspects of tree life‐history strategies. We observed convergence of traits related to growth and light capture resulting from competition for light, suggesting that tree species assembly is mainly driven by differences in competitive abilities. Additionally, the observed trait convergence was stronger in more productive sites than in less fertile ones, reflecting the impact of environmental conditions on competitive interactions. Synthesis . Our study shows that process‐based forest gap‐models can help to test whether functional traits composition reveal the signature of community assembly processes. This process‐based approach challenges the classical view on the links between traits and mechanisms driving community assembly. A lay summary is available for this article.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".