Origins and evolution of biological novelty
Bibliographic record
Abstract
Understanding the origins and impacts of novel traits has been a perennial interest in many realms of ecology and evolutionary biology. Here, we build on previous evolutionary and philosophical treatments of this subject to encompass novelties across biological scales and eco-evolutionary perspectives. By defining novelties as new features at one biological scale that have emergent effects at other biological scales, we incorporate many forms of novelty that have previously been treated in isolation (such as novelty from genetic mutations, new developmental pathways, new morphological features, and new species). Our perspective is based on the fundamental idea that the emergence of a novelty, at any biological scale, depends on its environmental and genetic context. Through this lens, we outline a broad array of generative mechanisms underlying novelty and highlight how genomic tools are transforming our understanding of the origins of novelty. Lastly, we present several case studies to illustrate how novelties across biological scales and systems can be understood based on common mechanisms of change and their environmental and genetic contexts. Specifically, we highlight how gene duplication contributes to the evolution of new complex structures in visual systems; how genetic exchange in symbiosis alters functions of both host and symbiont, resulting in a novel organism; and how hybridisation between species can generate new species with new niches.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.011 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".