The interaction of phylogeny and community structure: Linking the community composition and trait evolution of clades
Bibliographic record
Abstract
Abstract Aim Community phylogenetic studies use information about the evolutionary relationships of species to understand the ecological processes of community assembly. A central premise of the field is that the evolution of species maps onto ecological patterns, and phylogeny reveals something more than species traits alone about the ecological mechanisms structuring communities, such as environmental filtering, competition, and facilitation. We argue, therefore, that there is a need for better understanding and modelling of the interaction of phylogeny with species traits and community composition. Innovation We outline a new approach that identifies clades that are ecophylogenetically clustered or overdispersed and assesses whether those clades have different rates of trait evolution. Ecophylogenetic theory would predict that the traits of clustered or overdispersed clades might have evolved differently, in terms of either tempo (fast or slow) or mode (e.g., under constraint or neutrally). We suggest that modelling the evolution of independent trait data in these clades represents a strong test of whether there is an association between the ecological co‐occurrence patterns of a species and its evolutionary history. Main conclusions Using an empirical dataset of mammals from around the world, we identify two clades of rodents whose species tend not to co‐occur in the same local assemblages (are phylogenetically overdispersed) and find independent evidence of slower rates of body mass evolution in these clades. Our approach, which assumes nothing about the mode of species trait evolution but instead seeks to explain it using ecological information, presents a new way to examine ecophylogenetic structure.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".