Linking the fine‐scale social environment to mating decisions: a future direction for the study of extra‐pair paternity
Bibliographic record
Abstract
Variation in extra-pair paternity (EPP) among individuals of the same population could result from stochastic demography or from individual differences in mating strategies. Although the adaptive value of EPP has been widely studied, much less is known about the characteristics of the social environment that drive the observed patterns of EPP. Here, we demonstrate how concepts and well-developed tools for the study of social behaviour (such as social network analysis) can enhance the study of extra-pair mating decisions (focussing in particular on avian mating systems). We present several hypotheses that describe how characteristics of the social environment in which individuals are embedded might influence the levels of EPP in a socially monogamous population. We use a multi-level social approach (Hinde, 1976) to achieve a detailed description of the social structure and social dynamics of individuals in a group. We propose that the pair-bond, the direct (local) social environment and the indirect (extended) social environment, can contribute in different ways to the variation observed in the patterns of EPP, at both the individual and the population level. A strength of this approach is that it integrates into the analysis (indirect) interactions with all potential mates in a population, thus extending the current framework to study extra-pair mating behaviour. We also encourage the application of social network methods such as temporal dynamic analysis to depict temporal changes in the patterns of interactions among individuals in a group, and to study how this affects mating behaviour. We argue that this new framework will contribute to a better understanding of the proximate mechanisms that drive variation in EPP within populations in socially monogamous species, and might ultimately provide insights into the evolution and maintenance of mating systems.
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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 | 0.007 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".