Why Do Some Lineages Radiate While Others Do Not? Perspectives for Future Research on Adaptive Radiations
Bibliographic record
Abstract
Understanding the processes that drive phenotypic diversification and underpin speciation is key to elucidating how biodiversity has evolved. Although these processes have been studied across a wide array of clades, adaptive radiations (ARs), which are systems with multiple closely related species and broad phenotypic diversity, have been particularly fruitful for teasing apart the factors that drive and constrain diversification. As such, ARs have become popular candidate study systems for determining the extent to which ecological features, including aspects of organisms and the environment, and inter- and intraspecific interactions, led to evolutionary diversification. Despite substantial past empirical and theoretical work, understanding mechanistically how ARs evolve remains a major challenge. Here, we highlight a number of understudied components of the environment and of lineages themselves, which may help further our understanding of speciation and AR. We also outline some substantial remaining challenges to achieving a detailed understanding of adaptation, speciation, and the role of ecology in these processes. These major challenges include identifying factors that have a causative impact in promoting or constraining ARs, gaining a more holistic understanding of features of organisms and their environment that interact resulting in adaptation and speciation, and understanding whether the role of these organismal and environmental features varies throughout the radiation process. We conclude by providing perspectives on how future investigations into the AR process can overcome these challenges, allowing us to glean mechanistic insights into adaptation and speciation.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.006 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".