Our primary goal is to explain the mechanisms controlling the capacity of fire-prone socio-environmental systems (SESs) to cope with and adapt to intensifying disturbance regimes. Our investigation is based on two overarching axioms: 1) Social networks manage risk from disturbances by regulating feedbacks between sociocultural and environmental subsystems; 2) When the magnitude or frequency of disturbances exceed the coping capacity of the social network, the network must adaptively reorganize to sustain valued landscape productions.
We hypothesize that extant social network topology affects the ability of a system to reorganize adaptively to disturbances outside the bounds of experience. We are investigating the importance of both collaboration and coordination in wildfire management networks, with a particular focus on a) replicable building blocks of network relationships (network motifs), b) individuals or organizations who serve as bridges between communities, c) the role of leadership and, d) the elevation of trust among actors.
We emphasize the role of anticipatory thinking in aligning the spatial and temporal distribution of adaptation to the emerging spatial and temporal distribution of risk. We seek to develop a generalizable theory of adaptive capacity to changing disturbance regimes, and practical tools for enhancing it, by linking empirical studies, simulation modeling and stakeholder engagement. We are developing empirical evidence from four study areas to understand the dynamics between intensifying fire regimes and social networks, and to assess and disseminate lessons for adaptive capacity.
We are applying these findings to parameterize and validate an innovative SES computational platform linking, for the first time, adaptive social network, biophysical and agent-based simulation models. We will test the limits of different social network topologies to respond to intensifying wildfire regimes, and the potential for guided social network reorganization to maintain valued landscape productions and services. By examining adaptive responses to intensifying disturbance regimes with stakeholders, the project explores the proposition that action-oriented research linking simulation models to stakeholders’ lived experience can increase adaptive capacity.
Research Plan: Objectives and Hypotheses
We organize our research plan to fulfill two core objectives through integrated empirical studies and simulation modeling. Under each objective, we will broadly explore the interactions and feedbacks among CNH2 components. We also will test two specific hypotheses, one related to each objective, that show particular promise as pathways to increasing adaptive capacity in fire-prone SESs.
Objective 1. Through empirical studies, investigate how intensifying disturbance regimes have affected wildfire management networks, and what factors appear to increase or inhibit their capacity to reduce risk.
Objective 2. Through simulation modeling, investigate how improved signal processing and guided reorganization of network relationships can enhance adaptive capacity under intensifying disturbance regimes.
We focus our inquiries on the integrated linkages among the four SES components described above by targeting our research hypotheses toward socio-environmental interactions among those components:
H1: SESs that experience intensifying disturbance regimes and have fewer inequities among stakeholders will have greater collaborative governance potential.
H2: Social network features associated with greater collaborative governance potential will increase adaptive capacity in the face of intensifying disturbance regimes. The degree to which adaptation constrains risk will depend on how the spatial and temporal distribution of adaptation aligns with the emerging spatial and temporal distribution of risk.
Integrating empirical work and simulation modeling, our research advances understanding of interactions among SES components by explaining: 1) how landscape signals interact with wildfire management networks to set the capacity of the SES to cope with increased risk; 2) how signals from disturbances outside the range of past conditions stimulate reorganization in the SES; and 3) how targeted interventions in the social network may enhance SES adaptive capacity by improving information processing