Comparison of social networks derived from ecological data: implications for inferring infectious disease dynamics
Article first published online: 21 MAY 2009
© 2009 The Authors. Journal compilation © 2009 British Ecological Society
Journal of Animal Ecology
Volume 78, Issue 5, pages 1015–1022, September 2009
How to Cite
Perkins, S. E., Cagnacci, F., Stradiotto, A., Arnoldi, D. and Hudson, P. J. (2009), Comparison of social networks derived from ecological data: implications for inferring infectious disease dynamics. Journal of Animal Ecology, 78: 1015–1022. doi: 10.1111/j.1365-2656.2009.01557.x
- Issue published online: 29 JUL 2009
- Article first published online: 21 MAY 2009
- Received 28 August 2008; accepted 6 April 2009 Handling Editor: Andy White
- social network analysis;
1. Social network analyses tend to focus on human interactions. However, there is a burgeoning interest in applying graph theory to ecological data from animal populations. Here we show how radio-tracking and capture–mark–recapture data collated from wild rodent populations can be used to generate contact networks.
2. Both radio-tracking and capture–mark–recapture were undertaken simultaneously. Contact networks were derived and the following statistics estimated: mean-contact rate, edge distribution, connectance and centrality.
3. Capture–mark–recapture networks produced more informative and complete networks when the rodent density was high and radio-tracking produced more informative networks when the density was low. Different data collection methods provide more data when certain ecological characteristics of the population prevail.
4. Both sets of data produced networks with comparable edge (contact) distributions that were best described by a negative binomial distribution. Connectance and closeness were statistically different between the two data sets. Only betweenness was comparable. The differences between the networks have important consequences for the transmission of infectious diseases. Care should be taken when extrapolating social networks to transmission networks for inferring disease dynamics.