I have now officially completed my PhD! My thesis, completed under the wonderful supervision of Dr Aidan Keane (Edinburgh) and Prof E.J. Milner-Gulland (Oxford), was entitled ‘Improving environmental interventions by understanding social networks’. A full copy can be found HERE. Scroll down to read the abstract and a lay summary.

Lay Summary

Consider some of the major factors causing deforestation or the extinction of wildlife; clearing of forests for agriculture, over-hunting of wildlife, or logging for wood. All these factors result from people’s actions. So, to conserve habitats and wildlife, we need to understand why people behave as they do. One of the most important influences on people’s behaviour is the behaviour of the people they communicate and interact with on a regular basis – their social networks. Understanding social networks – how and from whom people get information on different topics – can therefore help us to more effectively influence their behaviour, such as by working with influential ‘opinion leaders’ who are connected to many people. In this thesis, I explore how this might work in a conservation context.

I started by reviewing the published literature from other disciplines, such as public health and sociology, and considered the relevance of the approaches they use to conservation. Then, in the rest of the thesis I looked at the role of social networks in an intervention aiming to reduce wildlife poisoning in Cambodia. First, I used a variety of research methods to better understand wildlife poisoning. I found that some residents are poisoning wildlife for food, particularly young men, and some children. But most residents in the area are strongly against wildlife poisoning. To help local efforts against poisoning, I therefore worked with a local NGO, WCS Cambodia, to develop and test a strategy for promoting the use of a hotline for reporting poisoning in one village.

To look at how the village social network might affect the success of these efforts, I used a survey to gather information from everyone in the village about their social relations, enabling me to map the social network in the village. I then used surveys to measure residents’ behaviour and knowledge at three time points, before and after the intervention. I used dynamic network models to determine how these changes relate to the social network. WCS invited a group of 41 people to the promotion event, but I found that information from the event spread through the village, so at least 144 people had received some information after six months. Most of this spread occurred within households. After two weeks, people throughout the village reported being more likely to report poisoning. But this was not a result of them learning about the hotline. Instead, it seems they were influenced by their peers who attended the event. After six months, this peer influence also played a role in people reverting to their previous level of behaviour.

With information about the social network, WCS may be able to better spread information about the hotline, or target people who can persuade others to use it. I use computer simulations to see how information about the hotline, or intention to use the hotline, might spread through the network depending on who WCS targets to receive information. I find that targeting individuals that are highly connected in the network is much more effective than targeting people based on other characteristics, such as wealthy people or those in leadership positions. However, this increase in effectiveness is not large enough to justify the costs of collecting and analysing network data. It would be more cost-effective to target a greater number of randomly chosen people. If WCS are promoting the hotline in many villages, they might be able to analyse the social network of one village to identify some rules-of-thumb about what sorts of people are well connected, which they can then apply elsewhere. For example, perhaps wealthy households tend to be better connected. But I find that rules-of-thumb identified in other studies do not apply here and are probably quite context-specific.

Overall, this thesis highlights how important it is to take social networks into account when designing a behaviour-change strategy. We find that social relationships can help to spread information but can also reinforce existing behaviours and prevent behaviour change. Understanding the structure of a social network can suggest targeting strategies that could overcome this barrier, and interventions should try to use social influences wherever possible. For example, once some residents adopt a new behaviour, they can be a valuable resource for influencing others.  

Abstract

Interventions to conserve biodiversity often aim to change human behaviour. Social relations and interactions, or social networks, have a strong influence on the information people receive and on their behaviour. Thus, the interactions between social networks and behaviour have been the subject of intense research effort in countless domains, and practitioners in fields such as public health have developed a range of strategies which account for relational processes in their interventions. This thesis seeks to integrate these insights into conservation and explore their practical implications. I begin by synthesising the literature and discussing the relevance of social network interventions for conservation. The remainder of the thesis examines the role of social networks in a case study intervention aiming to reduce wildlife poisoning in Northern Cambodia. I first use a mixed-method approach to better understand wildlife poisoning. I find that it is widespread, occurring in eight of the ten villages studied, but generally low prevalence, and often carried out by young men or children. However, most residents hold negative attitudes towards poisoning. With the Wildlife Conservation Society (WCS) Cambodia, I develop and pilot a social marketing intervention to promote the use of a hotline for reporting incidences of poisoning. I then use longitudinal data on behaviour and dynamic social network models to unpick the role of information flow and social influence in this intervention. I find that information from the intervention flowed widely through the village social networks, particularly within households, reaching an audience three-times larger than originally targeted. Having a knowledgeable household member doubled the probability that an individual would become knowledgeable. I also find that intention to report poisoning increases throughout the village in the short-term but returns to baseline levels in the long term. These changes are not driven by knowledge of the intervention. Instead, individuals are influenced by the intentions of network peers. One way to more effectively produce behavioural change that exploits these social influences is to target interventions at influential individuals identified using sociometric data. Using diffusion simulations, I explore the cost-effectiveness of these approaches within the study village. I find that network-informed targeting could result in uptake of the hotline more than double other targeting strategies, but that the relatively high cost of collecting network data makes it cost-ineffective. A more feasible strategy for large-scale interventions might be to conduct network research to identify general rules-of-thumb that can be used to select influential individuals. However, I find that rules-of-thumb identified in other contexts do not apply in Cambodia. Overall, my findings highlight the critical importance of social relations in shaping the outcomes of conservation interventions and illustrate some possible strategies for exploiting them in intervention.