Using functional traits and phylogenetic trees to examine the assembly of tropical tree communities
Bibliographic record
Abstract
Summary 1. Niche theory proposes that species differences underlie both coexistence within communities and the differentiation in species composition among communities via limiting similarity and environmental filtering. However, it has been difficult to extend niche theory to species‐rich communities because of the empirical challenge of quantifying niches for many species. This has motivated the development of functional and phylogeny‐based approaches in community ecology, which represent two different means of approximating niche attributes. 2. Here, we assess the utility of plant functional traits and phylogenetic relationships in predicting community assembly processes using the largest trait and phylogenetic data base to date for any set of species‐rich communities. 3. We measured 17 functional traits for all 4672 individuals of 668 tree species co‐occurring in nine tropical rain forest plots in French Guiana. Trait variation was summarized into two ordination axes that reflect species niche overlap. 4. We also generated a dated molecular phylogenetic tree based on DNA sequencing of two plastid loci (rbcLandmatK) comprising 97% of the individuals and 91% of the species in the plots. 5. We found that, on average, co‐occurring species had greater functional and, to a lesser extent, phylogenetic similarity than expected by chance. 6. We also found that functional traits and their ordination loadings showed significant, albeit weak, phylogenetic signal, suggesting that phylogenetic distance provides pertinent information on niche overlap in tropical tree communities. 7. Synthesis. We provide the most comprehensive examination to date of the relative importance of environmental filtering and limiting similarity in structuring tropical tree communities. Our results confirm that environmental filtering is the overriding influence on community assembly in these species‐rich systems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| 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) | 0.001 | 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".