TY - JOUR

T1 - Integrating models of human behaviour between the individual and population levels to inform conservation interventions

AU - Dobson, Andrew D.M.

AU - de Lange, Emiel

AU - Keane, Aidan

AU - Ibbett, Harriet

AU - Milner-Gulland, EJ

PY - 2019/9/16

Y1 - 2019/9/16

N2 - Conservation takes place within social – ecological systems, and many conservation interventions aim to influence human behaviour in order to push these systems towards sustainability. Predictive models of human behaviour are potentially powerful tools to support these interventions. This is particularly true if the models can link the attributes and behaviour of indi- viduals with the dynamics of the social and environmental systems within which they operate. Here we explore this potential by showing how combining two modelling approaches (social network analysis, SNA, and agent-based modelling, ABM) could lead to more robust insights into a particular type of conservation intervention. We use our simple model, which simulates knowl- edge of ranger patrols through a hunting community and is based on empirical data from a Cambodian protected area, to highlight the complex, context- dependent nature of outcomes of information-sharing interventions, depending both on the configuration of the network and the attributes of the agents. We conclude by reflecting that both SNA and ABM, and many other modelling tools, are still too compartmentalized in application, either in ecology or social science, despite the strong methodological and conceptual parallels between their uses in different disciplines. Even a greater sharing of methods between disciplines is insufficient, however; given the impact of conserva- tion on both the social and ecological aspects of systems (and vice versa), a fully integrated approach is needed, combining both the modelling approaches and the disciplinary insights of ecology and social science. This article is part of the theme issue ‘Linking behaviour to dynamics of populations and communities: application of novel approaches in behavioural ecology to conservation’.

AB - Conservation takes place within social – ecological systems, and many conservation interventions aim to influence human behaviour in order to push these systems towards sustainability. Predictive models of human behaviour are potentially powerful tools to support these interventions. This is particularly true if the models can link the attributes and behaviour of indi- viduals with the dynamics of the social and environmental systems within which they operate. Here we explore this potential by showing how combining two modelling approaches (social network analysis, SNA, and agent-based modelling, ABM) could lead to more robust insights into a particular type of conservation intervention. We use our simple model, which simulates knowl- edge of ranger patrols through a hunting community and is based on empirical data from a Cambodian protected area, to highlight the complex, context- dependent nature of outcomes of information-sharing interventions, depending both on the configuration of the network and the attributes of the agents. We conclude by reflecting that both SNA and ABM, and many other modelling tools, are still too compartmentalized in application, either in ecology or social science, despite the strong methodological and conceptual parallels between their uses in different disciplines. Even a greater sharing of methods between disciplines is insufficient, however; given the impact of conserva- tion on both the social and ecological aspects of systems (and vice versa), a fully integrated approach is needed, combining both the modelling approaches and the disciplinary insights of ecology and social science. This article is part of the theme issue ‘Linking behaviour to dynamics of populations and communities: application of novel approaches in behavioural ecology to conservation’.

KW - agent-based model

KW - conservation

KW - information-sharing

KW - law enforcement

KW - predictive modelling

KW - social network analysis

U2 - 10.1098/rstb.2018.0053

DO - 10.1098/rstb.2018.0053

M3 - Article

C2 - 31352880

VL - 374

JO - Philosophical Transactions of the Royal Society B

JF - Philosophical Transactions of the Royal Society B

SN - 0962-8436

IS - 1781

M1 - 20180053

ER -