Adaptive Evolution of Nearctic Deepwater Fish Vision: Implications for Assessing Functional Variation for Conservation
Bibliographic record
Abstract
Intraspecific functional variation is critical for adaptation to rapidly changing environments. For visual opsins, functional variation can be characterized in vitro and often reflects a species' ecological niche but is rarely considered in the context of intraspecific variation or the impact of recent environmental changes on species of cultural or commercial significance. Investigation of adaptation in postglacial lakes can provide key insight into how rapid environmental changes impact functional evolution. Here, we report evidence for molecular adaptation in vision in 2 lineages of Nearctic fishes that are deep lake specialists: ciscoes and deepwater sculpin. We found depth-related variation in the dim-light visual pigment rhodopsin that evolved convergently in these 2 lineages. In vitro characterization of spectral sensitivity of the convergent deepwater rhodopsin alleles revealed blue-shifts compared with other more widely distributed alleles. These blue-shifted rhodopsin alleles were only observed in deep clear postglacial lakes with underwater visual environments enriched in blue light. This provides evidence of remarkably rapid and convergent visual adaptation and intraspecific functional variation in rhodopsin. Intraspecific functional variation has important implications for conservation, and these fishes are of conservation concern and great cultural, commercial, and nutritional importance to Indigenous communities. We collaborated with the Saugeen Ojibway Nation to develop and test a metabarcoding approach that we show is efficient and accurate in recovering the ecological distribution of functionally relevant variation in rhodopsin. Our approach bridges experimental analyses of protein function and genetics-based tools used in large-scale surveys to better understand the ecological extent of adaptive functional variation.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 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".