Examining socio-ecological system resilience attributes in the context of Covid-19 using participatory mental-mapping

What prepared some communities for the Covid-19 pandemic more than others? The complex impacts of Covid-19, resulting from direct and indirect interactions between the social and biophysical world, occurring globally practically simultaneously, provide a rare opportunity to explore how such interactions affect societal resilience. We examined whether generalized characteristics, qualities or attributes (hereafter: resilience attributes) recognized in the literature to be associated with SE systems resilience (e.g., diversity, redundancy, social capital), actually contributed to their capacity to endure and manage the Covid-19 crisis. In this first case study, focused on the Changhua Long-Term Socio-Ecological research (LTSER) platform in Taiwan, we establish the methodology for a wider comparative study. We first performed interviews with diverse stakeholders (N=10) using causal loop diagrams (CLD) to collaboratively build individual mental models of each interviewee’s understanding of Covid-19 impacts, their contributing factors, and the relationships between them. We then aggregated the individual maps into a fuzzy cognitive map (FCM), indicating participants’ level of agreement regarding the relevance and importance of nodes and causal connections. We also collected available information for indicators of Covid-19 (e.g., morbidity, unemployment, school days lost, inequality rise) along with background socioeconomic and spatial-environmental site characteristics which relate to the resilience attributes (e.g., demographic diversity, employment diversity, connectivity/ remoteness, healthcare capacity). These two sets of data, and the aggregated FCM were then reflected back to the stakeholders in a group-meeting setting to facilitate reflection, discuss the interim findings, and obtain participant feedback. The meeting transcript was thematically analyzed to triangulate the FCM. This analysis deepened our understanding of the narratives and clarified discrepancies. Finally, the information was synthesized into key narratives, which were then analyzed to identify the emergent resilience attributes. Results reflected many of the attributes - especially system flexibility (i.e., having alternative options as well as the willingness to engage them) and modularity (or managing connectivity, i.e., for system components to be connected but not too connected). Furthermore, the study reveals the role of fear and worry, and other attributes related to human society and subjective perception, aligning with recent scholarship. Among the more salient results was the weakness of a very undiversified (resilience attribute = diversity) economic foundation of the community, which - due to its over-reliance on tourism - was profoundly and negatively affected during the pandemic. This study also emphasized the role of interrelationships between attributes, which shaped the local experiences. For example, scarce and unclear local data hampered effective monitoring (attribute = monitoring). This negatively affected trust in government (attribute = trust) and the provision of adequate crisis support (attribute = managing tight feedbacks). Engaged local leadership (attribute = leadership) and close community ties (attribute = social networks) helped bridge this gap. The extent to which each attribute manifested in relation to the impacts of Covid-19, and the dynamics among the attributes, provides a nuanced understanding of societal resilience factors. This informs future research for theory development, as well as resilience assessments and interventions for more effective practice and policy. Finally, this study emphasizes the limitations of data-driven approaches and advocates for transdisciplinary, participatory processes that integrate diverse knowledge for the benefit of both research and local stakeholders. Subsequent research will employ a comparative approach, replicating this study across diverse LTSER platforms. This approach will generate evidence regarding the resilience attributes and how they manifested in different social-ecological systems, that vary in their external conditions, characteristics and mechanisms, for the same disturbance occurring at the same time. Insights gained could inform the development of standardized observations and protocols for long-term resilience research in social-ecological systems.