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

Abstract

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 individuals 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 knowledge 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 conservation 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’.

Integrating conflict, lobbying, and compliance to predict the sustainability of natural resource use

“…Similarly, subsequent lobbying actions, i.e., external pressures, affect the decision provided by the genetic algorithm, but not the simulated selection process itself. Even though our goal-oriented decision makers in the model do not reflect the entire complexity of people in the real world, our approach represents an important step forward compared to error-free and static decisions used in more standard harvesting models (Dobson et al 2019).…”

“…Although often presented within an adaptive management framework (Kolbe et al 2017, Andrén et al 2020, these approaches tend to overlook how the implementation of management decisions can be perturbed by conflicts of interest. Predictive models that can include these governance processes are now sorely needed to guide the management of terrestrial harvesting systems (Bunnefeld et al 2011, Dobson et al 2019, in which mismanagement and overharvesting have become widespread (Díaz et al 2019).…”

Linking behaviour to dynamics of populations and communities: application of novel approaches in behavioural ecology to conservation

“…Staying on the African savannah, but focusing on the predator community, Green et al then point out how monitoring changes in the behaviour of a key species, the spotted hyaena (Crocuta crocuta), can predict the population dynamics, not only of the species itself but also of other predators [49]. Next, Dobson et al turn their attention to predicting the behaviour of people and demonstrate how innovative integration of SNA and ABM can elucidate the effectiveness of conservation interventions that depend on social relationships, in this case, sharing of information on sanctions for rule-breaking [35]. Zooming in on disease transmission, Silk et al follow on by reviewing how recent epidemiological modelling using SNA integrates demography and information on social behaviour to further our understanding of the spread of infections and thereby inform management interventions [31].…”

Mathematical Analysis of a Thermostatted Equation with a Discrete Real Activity Variable

“…The mathematical analysis presented in [5] is based on the assumption that the discrete activity variable is greater than 1. In order to model complex systems, such as social and economical systems [16][17][18][19][20], the possibility for the activity variable to also attain negative values should be planned. Accordingly, this paper is devoted to a further generalization of the existence and uniqueness of the solution and of the non-equilibrium stationary solution for a real activity variable.…”