How a growing organismal perspective is adding new depth to integrative studies of morphological evolution
Bibliographic record
Abstract
Over the past half century, the field of Evolutionary Developmental Biology, or Evo-devo, has integrated diverse fields of biology into a more synthetic understanding of morphological diversity. This has resulted in numerous insights into how development can evolve and reciprocally influence morphological evolution, as well as generated several novel theoretical areas. Although comparative by default, there remains a great gap in our understanding of adaptive morphological diversification and how developmental mechanisms influence the shape and pattern of phenotypic variation. Herein we highlight areas of research that are in the process of filling this void, and areas, if investigated more fully, that will add new insights into the diversification of morphology. At the centre of our discussion is an explicit awareness of organismal biology. Here we discuss an organismal framework that is supported by three distinct pillars. First, there is a need for Evo-devo to adopt a high-resolution phylogenetic approach in the study of morphological variation and its developmental underpinnings. Secondly, we propose that to understand the dynamic nature of morphological evolution, investigators need to give more explicit attention to the processes that generate evolutionarily relevant variation at the population level. Finally, we emphasize the need to address more thoroughly the processes that structure variation at micro- and macroevolutionary scales including modularity, morphological integration, constraint, and plasticity. We illustrate the power of these three pillars using numerous examples from both invertebrates and vertebrates to emphasize that many of these approaches are already present within the field, but have yet to be formally integrated into many research programs. We feel that the most exciting new insights will come where the traditional experimental approaches to Evo-devo are integrated more thoroughly with the principles of this organismal framework.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.025 |
| Scholarly communication | 0.007 | 0.024 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".