Diversity and Evolution of Frog Visual Opsins: Spectral Tuning and Adaptation to Distinct Light Environments
Bibliographic record
Abstract
Visual systems adapt to different light environments through several avenues including optical changes to the eye and neurological changes in how light signals are processed and interpreted. Spectral sensitivity can evolve via changes to visual pigments housed in the retinal photoreceptors through gene duplication and loss, differential and coexpression, and sequence evolution. Frogs provide an excellent, yet understudied, system for visual evolution research due to their diversity of ecologies (including biphasic aquatic-terrestrial life cycles) that we hypothesize imposed different selective pressures leading to adaptive evolution of the visual system, notably the opsins that encode the protein component of the visual pigments responsible for the first step in visual perception. Here, we analyze the diversity and evolution of visual opsin genes from 93 new eye transcriptomes plus published data for a combined dataset spanning 122 frog species and 34 families. We find that most species express the four visual opsins previously identified in frogs but show evidence for gene loss in two lineages. Further, we present evidence of positive selection in three opsins and shifts in selective pressures associated with differences in habitat and life history, but not activity pattern. We identify substantial novel variation in the visual opsins and, using microspectrophotometry, find highly variable spectral sensitivities, expanding known ranges for all frog visual pigments. Mutations at spectral-tuning sites only partially account for this variation, suggesting that frogs have used tuning pathways that are unique among vertebrates. These results support the hypothesis of adaptive evolution in photoreceptor physiology across the frog tree of life in response to varying environmental and ecological factors and further our growing understanding of vertebrate visual evolution.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".