Social network differences underlie phenotypic divergence between stickleback ecotypes
Neumann, Kevin (2022), Social network differences underlie phenotypic divergence between stickleback ecotypes , Dryad, Dataset, https://doi.org/10.5061/dryad.tdz08kq38
Elucidating the mechanisms underlying differentiation between populations is essential to our understanding of ecological and evolutionary processes. While social network analysis has yielded numerous insights in behavioral ecology in recent years, it has rarely been applied to questions about population differentiation. Here, we use social network analysis to assess the potential role of social behavior in the recent divergence between two three-spined stickleback ecotypes, “whites” and “commons”. These ecotypes differ significantly in their social behavior and mating systems as adults, but it is unknown when or how differences in social behavior develop. We found that as juveniles, the white ecotype was bolder and more active than the common ecotype. Furthermore, while there was no evidence for assortative shoaling preferences, the two ecotypes differed in social network structure. Specifically, groups of the white ecotype had a lower clustering coefficient than groups of the common ecotype, suggesting that groups of the white ecotype were characterized by the formation of smaller subgroups, or ‘cliques’. Interestingly, ecotypic differences in clustering coefficient were not apparent in mixed groups composed of whites and commons. The formation of cliques could contribute to population divergence by restricting the social environment that individuals experience, potentially influencing future mating opportunities and preferences. These findings highlight the insights that social network analysis can offer into our understanding of population divergence and reproductive isolation.
Dataset is social behavior data on stickleback using automated video tracking technolgy (idTracker - https://www.idtracker.es/).
All data was analyzed in R.
National Science Foundation, Award: DGE 21-46756