A multi‐platform package for the analysis of intra‐ and interspecific trait evolution
Bibliographic record
Abstract
Abstract Evolutionary forces affect the distribution of phenotypes both within and among species. Yet, at the macro‐evolutionary scale, the evolution of intraspecific variance is rarely considered. Here, we present an r and a BEAST 2 implementation that extends the JIVE (Joint inter‐ and Intraspecific Variance Evolution) model aimed at the analysis of continuous trait evolution at both inter‐ and intraspecific level. Using a hierarchical Bayesian approach, we implemented a range of models for continuous trait evolution that operate independently on species means and variances along a phylogeny. The package uses Markov chain Monte Carlo for the inference of parameters and the evaluation of model fit. JIVE is available in the bite (Bayesian Integrative models of Trait Evolution) r package, as well as in BEAST 2. The two implementations offer the same continuous trait evolutionary models, but differ in their use and types of analyses. The r implementation allows for faster analyses by taking the phylogeny as data, while providing graphical and statistical functions as part of tools for model comparison, result parsing and summary, and plotting. In the BEAST 2 implementation, the species tree is a parameter, and both its topology and divergence times are jointly estimated with trait model parameters. The bite package and the BEAST 2 implementation introduce new frameworks within comparative phylogenetics that explicitly model intraspecific variance. These tools allow users to tackle long‐standing questions in evolutionary biology, such as the identification of key evolutionary processes determining niche conservatism, niche partitioning, and life‐history strategies.
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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 | 0.008 | 0.022 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.001 | 0.006 |
| Insufficient payload (model declined to judge) | 0.144 | 0.075 |
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".