Pattern and Process in the Comparative Study of Convergent Evolution
Bibliographic record
Abstract
Understanding processes that have shaped broad-scale biodiversity patterns is a fundamental goal in evolutionary biology. The development of phylogenetic comparative methods has yielded a tool kit for analyzing contemporary patterns by explicitly modeling processes of change in the past, providing neontologists tools for asking questions previously accessible only for select taxa via the fossil record or laboratory experimentation. The comparative approach, however, differs operationally from alternative approaches to studying convergence in that, for studies of only extant species, convergence must be inferred using evolutionary process models rather than being directly measured. As a result, investigation of evolutionary pattern and process cannot be decoupled in comparative studies of convergence, even though such a decoupling could in theory guard against adaptationist bias. Assumptions about evolutionary process underlying comparative tools can shape the inference of convergent pattern in sometimes profound ways and can color interpretation of such patterns. We discuss these issues and other limitations common to most phylogenetic comparative approaches and suggest ways that they can be avoided in practice. We conclude by promoting a multipronged approach to studying convergence that integrates comparative methods with complementary tests of evolutionary mechanisms and includes ecological and biogeographical perspectives. Carefully employed, the comparative method remains a powerful tool for enriching our understanding of convergence in macroevolution, especially for investigation of why convergence occurs in some settings but not others.
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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 | 0.017 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.002 | 0.012 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".