Diel vertical migration of adult burbot: a dynamic trade-off among feeding opportunity, predation avoidance, and bioenergetic gain
Bibliographic record
Abstract
Diel vertical migration (DVM) of pelagic organisms is typically attributed to bioenergetic gain, foraging opportunity, predator avoidance, and multifactor hypotheses. While a number of benthic species perform nightly migrations into shallower waters, the function of these DVMs has largely been ignored in benthic fishes. We used depth and temperature sensing telemetry to investigate DVM function in burbot (Lota lota), a freshwater benthic piscivore. We modeled the influence of season, diel period, and body size on the depth, vertical activity, migration probability, and thermal experience of 47 adult burbot over 2 years in a reservoir in British Columbia, Canada. Burbot were found to occupy significantly shallower water at night than during the day. Our results, which showed elevated nightly activity and a seasonal size-structured depth distribution during DVMs, suggest these migrations likely provide a feeding opportunity “window” for this nocturnal predator, constrained by predation or cannibalism threats to smaller individuals. The observed thermal experience patterns suggest DVM may also provide a seasonal bioenergetic advantage. Our detection of within-individual plasticity in migration strategy is indicative of a partial migration. Taken together, our results suggest a multifactor DVM hypothesis: a dynamic trade-off among bioenergetic advantage, foraging opportunity, and predation threat.
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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".