Assessing the cues required for mate choice copying in the plainfin midshipman fish
Bibliographic record
Abstract
When choosing a mate, females can rely on their own judgements of male quality or use social information from other females' choices. The use of social information to inform mating decisions is called mate choice copying. Theory predicts that mate choice copying should be strongest in species where females have few mates over the course of their life span because each mating constitutes a greater proportion of the female's expected reproductive value; however, most research on mate choice copying has thus far focused on species with highly promiscuous females. In this study, we use the plainfin midshipman, Porichthys notatus , a toadfish in which females typically choose one mate per year, to investigate whether females mate-choice copy and, if they do, which cues influence their decision making. We show that in the wild, plainfin midshipman females co-occur in nests more often than expected under random female choice. Additionally, we found that females in the laboratory did not base their mating decisions on the mere presence of another female or previously laid eggs; however, females were more likely to choose a male they had observed spawning with another female. Taken together, our results indicate that female plainfin midshipman do mate-choice copy, but only when they observe a spawning event. Understanding how different mating systems affect the strength of mate choice copying and which cues are necessary to elicit mate choice copying will help elucidate more broadly how this behaviour evolved and is maintained.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".