Critical phenomena in human-environment systems

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In our era of environmental challenges, it is becoming increasingly clear that human populations and environmental systems mutually influence one another, such that we may conceptualize them as a single, coupled human-environment system. The study of coupled human-environment systems is growing rapidly, but important questions remain unanswered for many study systems. For instance, when are human-environment systems pushed beyond tipping points into dramatically different social or environmental states? When are those new states harmful or beneficial? Is it possible to anticipate these tipping points by monitoring data from human or environmental systems and looking for certain indicators? My research program will take steps to address some of these questions. The methodology will develop mathematical and computer models of four human-environment study systems: (1) grey wolf population dynamics and culling of wolves by humans, (2) forest pest outbreaks and control through camp firewood movement restrictions, (3) measles outbreaks and control through vaccination, and (4) dynamics of the climate change debate on social networks. These models will be tested against data from both natural and human systems. The models will then be carefully analyzed to understand the interactions between human and environmental systems that they predict, as well as the predicted response to interventions such as vaccination for measles, or hunting for grey wolves. The models will be examined to see if and when they exhibit tipping points, and whether those tipping points can be detected in advance through early warning signals. A particular focus will be the spatial aspects of these systems, including analyzing the social networks in online social media through which ideas, opinions and sentiments about environmental systems propagate. We will also use data science approaches like machine learning algorithms to investigate online social media data and Internet search data, allowing us to see whether the early warning signals predicted by the models are present for any of the real-world systems. These efforts will be supplemented by novel approaches that use computer algorithms to "discover" models directly from data, which contrasts with the usual way that models are developed. This research will improve our understanding of how human-environment systems respond to interventions, when they are near tipping points, and whether we can obtain advance warning of those tipping points. The real-world significance and impact of the research will come from improving the health of humans and their environments in Canada and beyond. Being able to detect heightened risk of incipient tipping points for human-environment systems by monitoring online social media would enable us to take steps to prevent or mitigate tipping points with negative consequences for sustainability and health, or accelerate positive tipping points that lead to positive environmental states.