Within and between Population Variation in Epidermal Club Cell Investment in a Freshwater Prey Fish: A Cautionary Tale for Evolutionary Ecologists
Bibliographic record
Abstract
Many prey fishes possess large club cells in their epidermis. The role of these cells has garnered considerable attention from evolutionary ecologists. These cells likely form part of the innate immune system of fishes, however, they also have an alarm function, releasing chemical cues that serve to warn nearby conspecifics of danger. Experiments aimed at understanding the selection pressures leading to the evolution of these cells have been hampered by a surprisingly large intraspecific variation in epidermal club cell (ECC) investment. The goal of our current work was to explore the magnitude and nature of this variation in ECC investment. In a field survey, we documented large differences in ECC investment both within and between several populations of minnows. We then tested whether we could experimentally reduce variation in mean ECC number by raising fish under standard laboratory conditions for 4 weeks. Fish from different populations responded very differently to being held under standard laboratory conditions; some populations showed an increase in ECC investment while others remained unchanged. More importantly, we found some evidence that we could reduce within population variation in ECC investment through time, but could not reduce among-population variation in mean ECC investment. Given the large variation we observed in wild fish and our limited ability to converge mean cell number by holding the fish under standard conditions, we caution that future studies may be hard pressed to find subtle effects of various experimental manipulations; this will make elucidating the selection pressures leading to the evolution of the cells challenging.
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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 | 0.013 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".