Moonlight Drives Ocean-Scale Mass Vertical Migration of Zooplankton during the Arctic Winter
Bibliographic record
Abstract
In extreme high-latitude marine environments that are without solar illumination in winter, light-mediated patterns of biological migration have historically been considered non-existent [1]. However, diel vertical migration (DVM) of zooplankton has been shown to occur even during the darkest part of the polar night, when illumination levels are exceptionally low [2, 3]. This paradox is, as yet, unexplained. Here, we present evidence of an unexpected uniform behavior across the entire Arctic, in fjord, shelf, slope and open sea, where vertical migrations of zooplankton are driven by lunar illumination. A shift from solar-day (24-hr period) to lunar-day (24.8-hr period) vertical migration takes place in winter when the moon rises above the horizon. Further, mass sinking of zooplankton from the surface waters and accumulation at a depth of ∼50 m occurs every 29.5 days in winter, coincident with the periods of full moon. Moonlight may enable predation of zooplankton by carnivorous zooplankters, fish, and birds now known to feed during the polar night [4]. Although primary production is almost nil at this time, lunar vertical migration (LVM) may facilitate monthly pulses of carbon remineralization, as they occur continuously in illuminated mesopelagic systems [5], due to community respiration of carnivorous and detritivorous zooplankton. The extent of LVM during the winter suggests that the behavior is highly conserved and adaptive and therefore needs to be considered as "baseline" zooplankton activity in a changing Arctic ocean [6-9]. VIDEO ABSTRACT.
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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 | 0.001 |
| 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 | 0.001 | 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".