Dataset for: Mating competition and adult sex ratio in wild Trinidadian guppies
Bibliographic record
Abstract
Most experimental tests of mating systems theory have been conducted in the laboratory, using operational sex ratios (ratio of ready-to-mate male to ready-to-mate female) that are often not representative of natural conditions. Here, we first measured the range of adult sex ratio (proportion of adult males to adult females; ASR) in two populations of Trinidadian guppies (Poecilia reticulata) differing in ambient predation risk (high vs. low). We then explored, under semi-wild conditions, the effect of ASR (i.e. 0.17, 0.50, 0.83) on mating competition patterns in these populations. ASR in the wild was female-biased and did not significantly differ between the two populations. The range of ASR in our experiment was representative of natural ASRs. As expected, we observed an increase in intrasexual aggression rates in both sexes as the relative abundance of competitors increased. In support of the risky competition hypothesis, all measured behaviors had lower rates in a high vs. low predation-risk population, likely due to the costs of predation. In terms of mating tactics, a male-biased ASR did not lead males to favor forced mating over courtship, indicating that males did not compensate for the cost of competition by switching to a less costly alternative mating tactic. Overall, this study highlights the need for field experiments using natural ranges of ASRs to test the validity of mating systems theory in a more complex, ecologically relevant context.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.089 | 0.018 |
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".