Abstract
Reproductive success is often highly skewed in animal populations. Yet the processes leading to this are not always clear. Similarly, connections in animal social networks are often nonrandomly distributed, with some individuals with many connections and others with few, yet whether there are simple explanations for this pattern has not been determined. Numerous social interactions involve dyads embedded within a wider network. As a result, it may be possible to model which individuals accumulate social interactions through a more general understanding of the social network's structure, and how this structure changes over time. We analysed fighting and mating interactions across the breeding season in a population of wild field crickets under surveillance from a network of video cameras. We fitted stochastic actor-oriented models to determine the dynamic process by which networks of cricket fighting and mating interactions form, and how they co-influence each other. We found crickets tended to fight those in close spatial proximity to them and those possessing a mutual connection in the fighting network, and heavier crickets fought more often. We also found that crickets that mated with many others tended to fight less in the following time period. This demonstrates that a mixture of spatial constraints, characteristics of individuals and characteristics of the immediate social environment are key for determining social interactions. The mating interaction network required very few parameters to understand its growth and thus its structure; only homophily by mating success was required to simulate the skew of mating interactions seen in this population. This demonstrates that relatively simple, but dynamic, processes can give highly skewed distributions of mating success.
Original language | English |
---|---|
Pages (from-to) | 179-188 |
Number of pages | 10 |
Journal | Animal Behaviour |
Volume | 155 |
Early online date | 14 Aug 2019 |
DOIs | |
Publication status | Published - Sept 2019 |
Bibliographical note
AcknowledgementsWe thank Paul Hopwood, Alex Thornton, Andrew Jackson and two anonymous referees for comments that improved this manuscript. We also thank Luke Meadows and Carlos Rodríguez del Valle for assistance with data collection. This work was supported by the Natural Environment Research Council (NERC, U.K.); studentship:NE/H02249X/1; standard grants: NE/E005403/1, NE/H02364X/1,NE/L003635/1, NE/R000328/1.
Keywords
- dynamic analysis
- Gryllus
- individual-based model
- reproductive skew
- social network analysis