Seasonal variation of behavior and brain size in a freshwater fish
Bibliographic record
Abstract
) can behaviorally respond to seasonal variation by shifting their habitat deeper and further offshore in response to warmer surface water temperatures during the summer. During cooler seasons, the use of more structurally complex nearshore zones by lake trout could increase cognitive demands and potentially result in a larger relative brain size during those periods. Yet, there is limited understanding of how such behavioral responses to a seasonally shifting environment might shape, or be shaped by, the nervous system.Here, we quantified variation in relative brain size and the size of five externally visible brain regions in lake trout, across six consecutive seasons in two different lakes. Acoustic telemetry data from one of our study lakes were collected during the study period from a different subset of individuals and used to infer relationships between brain size and seasonal behaviors (habitat use and movement rate).Our results indicated that lake trout relative brain size was larger in the fall and winter compared with the spring and summer in both lakes. Larger brains coincided with increased use of nearshore habitats and increased horizontal movement rates in the fall and winter based on acoustic telemetry. The telencephalon followed the same pattern as whole brain size, while the other brain regions (cerebellum, optic tectum, olfactory bulbs, and hypothalamus) were only smaller in the spring.These findings provide evidence that flexibility in brain size could underpin shifts in behavior, which could potentially subserve functions associated with differential habitat use during cold and warm seasons and allow fish to succeed in seasonally variable environments.
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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) | 0.001 | 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".