Data from: Parallel shifts in trout feeding morphology suggest rapid adaptation to alpine lake environments
Bibliographic record
Abstract
Abstract Eco-evolutionary interactions following ecosystem change provide critical insight into the ability of organisms to adapt to shifting resource landscapes. Here we explore evidence for the rapid parallel evolution of trout feeding morphology following eco-evolutionary interactions with zooplankton in alpine lakes stocked at different points in time in the Wind River Range (Wyoming, USA). In this system, trout predation has altered the zooplankton species community and driven a decrease in average zooplankton size. In some lakes that were stocked decades ago, we find shifts in gill raker traits consistent with the hypothesis that trout have rapidly adapted to exploit available smaller-bodied zooplankton more effectively. We explore this morphological response in multiple lake populations across two species of trout (cutthroat trout, Oncorhynchus clarkii, and golden trout Oncorhynchus aguabonita) and examine the impact of resource availability on morphological variation in gill raker number among lakes. Furthermore, we present genetic data to provide evidence that historically stocked cutthroat trout populations likely derive from multiple population sources, and incorporate variation from genomic relatedness in our exploration of environmental predictors of feeding morphology. These findings describe rapid adaptation and eco-evolutionary interactions in trout and document an evolutionary response to novel, contemporary ecosystem change.
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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 | 0.000 | 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) | 0.003 | 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".