The assembly of tropical tree communities – the advances and shortcomings of phylogenetic and functional trait analyses
Bibliographic record
Abstract
Tropical tree communities present one of the most challenging systems for studying the processes underlying community assembly. Most community assembly hypotheses consider the relative importance of the ecological similarity of co‐occurring species. Quantifying this similarity is a daunting and potentially impossible task in species‐rich assemblages. During the past decade tropical tree ecologists have increasingly utilized phylogenetic trees and functional traits to estimate the ecological similarity of species in order to test mechanistic community assembly hypotheses. A large amount of work has resulted with many important advances having been made along the way. That said, there are still many outstanding challenges facing those utilizing phylogenetic and functional trait approaches to study community assembly. Here I review the conceptual background, major advances and major remaining challenges in phylogenetic‐ and trait‐based approaches to community ecology with a specific focus on tropical trees. I argue that both approaches tremendously improve our understanding of tropical tree community ecology, but neither approach has fully reached its potential thus far.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".