Using phylogenetic trees to test for character displacement: a model and an example from a desert mammal community
Bibliographic record
Abstract
The distribution of traits within communities is thought to provide information on the evolutionary and ecological forces that structure community composition. Ecological character displacement (evolutionary divergence among populations of interacting species within a community), as well as community‐wide character displacement (even dispersion of species traits within a community via species sorting), have been widely reported and are typically interpreted as evidence for interspecific competition. However, defining an appropriate null distribution with which to assess the observed distribution of traits within a community has proved controversial. Phylogenetic methods provide an alternative approach to evaluating community structure, but such methods also require an appropriate null and have typically overlooked the potential for evolutionary dynamics within communities. Here, we present a novel phylogenetic framework that uses evolutionary expectations to generate a simple null model of the expected distribution of traits among co‐occurring species. Using a stochastic Brownian motion model of trait change, we illustrate that the expected community‐wide dispersion of traits varies with phylogenetic tree shape. We then use data on body mass for mammals to evaluate the accuracy with which phylogeny can predict the empirical distribution of traits and find a strong correlation between predicted and observed trait distributions. We suggest that deviations from phylogenetic expectations may therefore provide a useful tool for evaluating the role of competition in shaping community structure. Finally, we demonstrate the utility of our approach using empirical data on body mass and a phylogeny for a small community of terrestrial mammals in Yotvata, Israel, and reveal evidence consistent with ecological and community‐wide character displacement. Our method unites ecological and evolutionary approaches, and it provides a novel framework for exploring community 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 | 0.010 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".