Using a null hypothesis framework to test expectations of disparity in an adaptive radiation
Bibliographic record
Abstract
Adaptive radiations are expected to generate striking differences in species and morphological diversity between closely related groups. Not all hypotheses in evolutionary biology, including these observed disparities, are amenable to experimental manipulation or comparative phylogenetics. In some cases, the use of null hypotheses offers a way to test these scientific conjectures such that their premise can be rejected. We examine the Hydrophis sea snakes (Hydrophiinae: Hydrophiini), a putative example of adaptive radiation based on both its species and morphological diversity. We compared its observed species richness and morphological disparity with the closely related, yet species and morphologically depauperate clade, Aipysurus-Emydocephalus . We used phylogenetic null models and a phylomorphospace approach to test for significant differences in diversification rate and in morphological disparity between these two major sea snake lineages. We found no diversification rate differences between the Hydrophis and Aipysurus-Emydocephalus groups under an equal-rates Markov model of diversification. However, Hydrophis species occupy a significantly larger region of the morphospace than the Aipysurus-Emydocephalus group, consistent with previous conclusions that ecological specialization causes increased levels of morphological disparity. While the Hydrophis and Aipysurus-Emydocephalus lineages do not significantly differ in species numbers, the significant morphological disparity among Hydrophis species further upholds its adaptive radiation.
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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 | 0.129 | 0.234 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.013 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".