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

Performance of different targeting strategies at diffusing an innovation, based on simulations using the measured social network. Each strategy is simulated at four levels of effort (2, 10, 20, & 30 targeted individuals) except for ‘conservation’ and ‘leaders’ which are not simulated at n=30, because not enough of these people existed within the network. Performance is measured as the area under the diffusion curve (AUC) as percentage of the maximum possible diffusion at time t=20. Bootstrapped 95% confidence intervals are shown. The shaded area is the 95% confidence interval range for simulations on 30 randomly generated sets of targets, acting as a null comparator. If the line falls within the shaded area, its performance is within the bounds of random targeting. Colours indicate the threshold of diffusion: blue for complex contagions such as conservation behaviours, and red for simple contagions such as information. On the left are results when the communication probability (i.e., the probability of communication between two connected individuals) is low (0.2), and on the right it is high (0.8). See Table 2 for explanations of the strategies.

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.

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