Promoting the use of phylogenetic multinomial generalised mixed-effects model to understand the evolution of discrete traits
Bibliographic record
Abstract
Phylogenetic comparative methods (PCMs) are fundamental tools for understanding trait evolution across species. While linear models are widely used for continuous traits in ecology and evolution, their application to discrete traits - particularly ordinal and nominal traits - remains limited. Researchers sometimes recategorise such traits into binary traits (0 or 1 data) to make them more manageable. However, this risks distorting the original data structure and meaning, potentially reducing the information it initially contained. This paper promotes the use of phylogenetic generalised linear mixed-effects models (PGLMMs) as a flexible framework for analysing the evolution of discrete traits. We introduce the theoretical foundations of PGLMMs and demonstrate how univariate and multivariate versions of binary PGLMMs, which might be more familiar to evolutionary biologists, can be conceptually extended to model ordinal and nominal traits. Specifically, we describe ordered and unordered multinomial PGLMMs for ordinal and nominal traits, respectively. We then explain how to interpret regression coefficients and (co)variance components, including associated statistics (e.g., phylogenetic heritability and correlation) from PGLMMs for discrete traits. Using real-world examples from avian datasets, we illustrate the practical implementation of PGLMMs to reveal evolutionary patterns in discrete traits. We also provide online tutorials to guide researchers through the application of these models using Bayesian implementations in R. By making complex models more accessible, we aim to facilitate a more precise and insightful understanding of the evolution and function of discrete traits, which has received relatively limited attention in evolutionary biology so 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 | 0.014 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".