The interaction of phylogeny and community structure: linking clades’ ecological structures and trait evolution
Bibliographic record
Abstract
1 Abstract Aim Community phylogenetic studies use information about species’ evolutionary relationships to understand the processes of community ecological assembly. A central premise of the field is that species’ evolution maps onto ecological patterns, and phylogeny reveals something more than species’ traits alone. We argue, therefore, that there is a need to better understand and model the interaction of phylogeny with species’ traits and community composition. Innovation We outline a new method that identifies clades with unusual ecological structures, based around partitioning the variation of species’ site occupancies ( β -diversity). Eco-phylogenetic theory would predict that these clades should also demonstrate distinct evolutionary trajectories. We suggest that modelling the evolution of independent trait data in these clades represents a strong test of whether there is an association between species’ ecological structure and evolutionary history. Main conclusions Using an empirical dataset of mammals from around the world, we identify two clades of rodents that tend not to co-occur (are phylogenetically overdispersed), and then find independent evidence of slower rates of body mass evolution in these clades. We suggest that our approach, which assumes nothing about the mode of species’ trait evolution but rather seeks to explain it using ecological information, presents a new way to examine eco-phylogenetic 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 | 0.008 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".