Linking infidelity with personality and behaviour in social networks in birds

Infidelity is common among many taxa with prevailing social monogamy, but we still do not know what shapes variation in and drives the evolution of, extra-pair behaviour. Males are expected to reap fitness benefits from siring extra-pair offspring because extra-pair fathers do not expend resources on costly parental care. This is, however, not the case for females who raise the resulting extra-pair young, posing the question of why females take part in extra-pair matings. The indirect benefits hypothesis explains female infidelity, where females benefit indirectly from better, or more compatible genes for their offspring. However, this hypothesis is not well supported empirically, evidenced by two contradictory meta-analyses on the topic, and ongoing discussion in the field, suggesting that this hypothesis does not satisfactorily explain why females cheat. The recently suggested novel, testable hypotheses provide a fresh perspective. These hypotheses explain female infidelity with intra- and intersexual antagonistic pleiotropy, and remain largely untested. This project aims to empirically test these hypotheses by using the powerful combination of long-term data from a wild population, state-of-the-art social network analysis and manipulative experiments on captive birds. This project will reap the benefits from long-term data in the wild, where precise fitness data and a genetic pedigree allow fitness costs and benefits to be measured, and quantitative genetic analyses. Given the long-standing conundrum of female extra-pair behaviour, this project has the potential forward this field significantly. Methodologically, using social network analysis to test hypotheses in evolutionary biology is not straightforward, because data points are relational and thus not independent. This studentship will explore recent suggestions of randomization for social network analysis in behavioural ecology, and develop respective tools for quantitative genetic analyses. A good understanding of numerical analyses, linear mixed models, and randomizations is thus required. Data collection will take place using automatical RFID tags, in a captive and in a wild population of house sparrows. Thus, this studentship will also require some skills and enthusiasm for working with animals.
This project is in collaboration with András Gyorgy from the Faculty of Engineering