Skipped Spawning in Fishes: More Common than You Might Think
Bibliographic record
Abstract
Abstract The traditional view of iteroparity in fishes is one of an annual reproductive cycle that culminates each year in spawning. More recently, a more flexible view of fish reproduction has been adopted, including the potential for mature fish to skip spawning. Here, we review the abundance of recent research on skipped spawning, covering a broad range of fishes with diverse life history strategies. Evidence for skipped spawning has been collected by use of traditional histological techniques as well as modern technological advances, such as satellite tags and the ability to track fish movements based on elemental and isotope signatures. Skipped spawning is most commonly attributed to deficient diet and poor nutritional condition. Advances made in this field of study in recent years include descriptions of hormonal changes that precede and perhaps initiate skipped spawning, the development of life history models that incorporate the potential for skipped spawning, and estimates of the degree to which skipped spawning influences the reproductive potential of fish populations. In addition to summarizing this new research, we attempt to advance current knowledge by (1) providing the first review discussion of skipped spawning in males, (2) exploring skipped spawning in anadromous fishes by using the Atlantic salmon Salmo salar as an example, and (3) discussing the potential for and difficulties in identifying skipped spawning in species with indeterminate fecundity.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".