Multiple mating reveals complex patterns of assortative mating by personality and body size
Bibliographic record
Abstract
Understanding patterns of non-random mating is central to predicting the consequences of sexual selection. Most studies quantifying assortative mating focus on testing for correlations among partners' phenotypes in mated pairs. Few studies have distinguished between assortative mating arising from preferences for similar partners (expressed by all or a subset of the population) vs. from phenotypic segregation in the environment. Also, few studies have assessed the robustness of assortative mating against temporal changes in social conditions. We tracked multiple matings by stream water striders (Aquarius remigis) across variable social conditions to investigate mating patterns by both body size and behavioural type (personality). We documented temporal changes in partner availability and used a mixed model approach to analyse individual behaviours and changes in mating status recorded on an hourly basis. We assessed whether all or only a subset of individuals in the population expressed a tendency to mate with similar phenotypes. Our analyses took into account variation in the level of competition and in the phenotypes of available partners. Males and females exhibited significant assortative mating by body size: the largest males and females, and the smallest males and females mated together more often than random. However, individuals of intermediate size were equally likely to mate with small, intermediate or large partners. Individuals also displayed two contrasting patterns of assortative mating by personality (activity level). Individuals generally mated preferentially with partners of similar activity level. However, beyond that general trend, individuals with more extreme personalities tended to exhibit disassortative mating: the most active males mated disproportionately with less active females and the least active males tended to mate with more active females. Our analyses thus revealed multiple, distinct patterns of nonrandom mating. These mating patterns did not arise from differences in partner availability among individuals and were robust to temporal changes in social conditions. Hence, mating patterns likely reflect mate preferences or arise from male-male competition coupled with sexual conflict. Our study also stresses the importance of accounting for variation in partner availability and demonstrates the influence of behavioural variation on mating patterns.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".