The role of ecological factors in shaping bat cone opsin evolution
Bibliographic record
Abstract
Bats represent one of the largest and most striking nocturnal mammalian radiations, exhibiting many visual system specializations for performance in light-limited environments. Despite representing the greatest ecological diversity and species richness in Chiroptera, Neotropical lineages have been undersampled in molecular studies, limiting the potential for identifying signatures of selection on visual genes associated with differences in bat ecology. Here, we investigated how diverse ecological pressures mediate long-term shifts in selection upon long-wavelength (Lws) and short-wavelength (Sws1) opsins, photosensitive cone pigments that form the basis of colour vision in most mammals, including bats. We used codon-based likelihood clade models to test whether ecological variables associated with reliance on visual information (e.g. echolocation ability and diet) or exposure to varying light environments (e.g. roosting behaviour and foraging habitat) mediated shifts in evolutionary rates in bat cone opsin genes. Using additional cone opsin sequences from newly sequenced eye transcriptomes of six Neotropical bat species, we found significant evidence for different ecological pressures influencing the evolution of the cone opsins. WhileLwsis evolving under significantly lower constraint in highly specialized high-duty cycle echolocating lineages, which have enhanced sonar ability to detect and track targets, variation inSws1constraint was significantly associated with foraging habitat, exhibiting elevated rates of evolution in species that forage among vegetation. This suggests that increased reliance on echolocation as well as the spectral environment experienced by foraging bats may differentially influence the evolution of different cone opsins. Our study demonstrates that different ecological variables may underlie contrasting evolutionary patterns in bat visual opsins, and highlights the suitability of clade models for testing ecological hypotheses of visual evolution.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".