Lunar synchrony and reproductive strategies of intertidal-breeding fishes
Bibliographic record
Abstract
Abstract When is the best time to breed? This is an important question, especially for fishes that breed in the intertidal zone, a dynamic habitat where conditions change rapidly and cyclically. Many intertidal fishes reproduce during the spring tides (during the new and full moons when tidal fluctuations are strongest). However, we use long-term field data to present a counter example, the toadfish, Porichthys notatus, which spawns more often during the neap tides (during the quarter moons when tidal fluctuations are weakest). We hypothesize that if a species’ reproduction involves time-consuming tasks, such as courtship, mate selection, nest preparation, and prolonged egg-laying, and if these activities must occur underwater, then such species will align their reproduction with neap tides rather than spring tides. To examine the prevalence of neap tide spawning, we conducted a comprehensive literature review to explore the diversity of reproductive strategies and timings in intertidal fishes. Because some species must leave the intertidal zone or find refuge when the tides recede, whereas others exhibit amphibious lifestyles and can even breathe air, we paid specific attention to different species’ requirements for submersion to perform their reproductive behaviours. We gathered data on 131 fish species and ultimately highlight a scarcity in data on reproductive timing in intertidal fishes. Our literature survey provides preliminary support for our hypothesis, and we now call on researchers to directly examine lunar synchrony of reproduction in intertidal fishes to better understand how reproductive strategies are shaped by the tides.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".