Role of male spatial distribution and condition-dependent colouration on female spawning behaviour and reproductive success in bluegills
Bibliographic record
Abstract
Female choice for male ornamental colouration has been demonstrated in a number of different taxa. Among fishes, most studies have been conducted in a laboratory setting and show that females prefer more colourful male ornaments. In this study, we observed female bluegills (Lepomis macrochirus) spawning in their natural environment and compared spawning behaviours to male traits and position within a colony. We observed spawning activities of 76 parental males in Lake Opinicon, Ontario. We captured each male and used reflectance spectrometry to objectively quantify the colour of six body regions and measured morphological characteristics. Our results show that female spawning behaviours did not significantly differ between central and peripheral males, although egg scores were higher in central nests. During spawning, females appeared to enter the nests of parental males haphazardly. However, our results suggest that male cheek colouration influenced the number of females spawning, the number of eggs they released, and the amount of time they spent in the nest. Moreover, male breast colouration significantly predicted reproductive success as quantified through egg scores. Together, our findings suggest that females may use male cheek and breast colouration, condition-dependent sexual ornaments, as key traits on which to base their mate choice decisions.
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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 | 0.001 | 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".