Combining simulation and empirical data to explore the scope for social network interventions in conservation

Conservationists can use social network analysis to improve targeting for behaviour-change interventions, selecting individuals to target who will go on to inform or influence others. However, collecting sociometric data is expensive. Using empirical data from a case study in Cambodia and simulations we examine the conditions under which collecting this data is cost-effective. Our results show that targeting interventions using sociometric data can lead to greater dissemination of information and adoption of new behaviours. However, these approaches are not cost-effective for small interventions implemented in only a few communities, and it is an order of magnitude cheaper to achieve the same results by simply targeting more individuals in each community at random. For interventions across multiple communities, network data from one community could inform rules-of-thumb that can be applied to boost the effectiveness of interventions. In rural Cambodia, this approach is worthwhile if it can inform interventions covering at least 21 villages. Our findings provide a framework for understanding how insights from network sciences, such as targeting clusters of individuals for interventions that aim to change behaviour, can make a practical contribution to conservation.