Alaskan coastal Indigenous communities face severe, urgent, and complex social and infrastructural challenges resulting from environmental changes. However, the magnitude and significance of impacts are unclear; as is how local communities will respond to resulting disruptions and disasters. This POLARIS project investigates how interconnected environmental stressors and infrastructure disruptions are affecting coastal Alaskan communities and identifies important social, environmental, infrastructural, and institutional assets to help them adapt and become more resilient to climate-related changes. The POLARIS project has identified three convergent and interconnected research pillars to help communities adapt: environmental hotspots of disruption to communities and infrastructure, food in complex adaptive systems, and migration and community relocation.

The ultimate goal of this integrated research project is to enable communities to become more resilient with both stronger societies, civic culture, and improved infrastructure needed as the new Arctic continues to emerge. In addition to introducing the POLARIS project, this talk will highlight one of its studies—the COVID-19 pandemic impacts on fishing communities in Alaska.

Bristol Bay in Alaska is home to the world’s largest commercial salmon fishery. During an average fishing season, the population of the Bristol Bay region more than doubles as thousands of workers from out of state converge on the fishery. In the months leading up to 2020 commercial fishery opening, as the COVID-19 pandemic exploded worldwide, great uncertainty existed about the health risks of opening the fishery. Bristol Bay residents had not yet experienced any cases of COVID-19, yet the livelihoods of most were closely tied to the commercial fishery opening. To better understand how COVID-19 risk perceptions affected decisions to participate in the fishery, we administered an online survey to community members and fishery participants. We collected standard socioeconomic data and posed questions to gauge risk perceptions related to COVID-19. We find that COVID-19 risk perceptions vary across race/ethnic groups by residency and income. People with below median income who are members of minority groups—notably, non-resident Hispanic workers and resident Alaska Native respondents—reported the highest risk perceptions related to COVID-19. It is also the same demographic group who had the highest participation in the fishing season. This study highlights the important linkages among risk perceptions, socioeconomic characteristics, and employment decisions during an infectious disease outbreak.

Learn More about Dr. Guangqing Chi

Guangqing Chi is a Professor of Rural Sociology and Demography and Director of the Computational and Spatial Analysis Core at The Pennsylvania State University. His research seeks to understand the interactions between human populations and the built and natural environments and to identify important social, environmental, infrastructural, and institutional assets to help vulnerable populations adapt and become resilient to environmental changes. His research has been supported by more than $50 million grants, including the $3 million multi-institutional transdisciplinary POLARIS project (https://arcticpolaris.org) funded by the National Science Foundation, to investigate environmental migration and food security in response to climate change. He has published over 140 publications including more than 80 peer-reviewed journal articles, contributing to foundational advances in environmental demography and population-infrastructure nexus. Chi’s work has led to innovative methods for identifying and measuring human–environment hotspots relating to land developability, population stress, wildfire–population corridors, ecosystems–development stress areas, rural land vulnerable to abandonment, critical riparian zones, and urban areas with high heat risks. He also led the development of spatiotemporal regression methods and applied them in his research on migration, poverty, and fertility. Chi is lead author of the textbook Spatial Regression Models for the Social Sciences (SAGE 2019). His work in applied demography has led to state-of-the-art spatial methods for population forecasting. His current methodological focus is to build an infrastructure for collecting, integrating, and analyzing multi-dimensional and multi-scale data, including big social data (60+ TB; Twitter, Facebook, mobile phones, credit cards, web scraping). He currently leads an NSF project to study the (mis)representativeness of Twitter data and to develop weights to generalize the data, which will create myriad opportunities for social scientists to take advantage of rich social media data. His work is often collaborative and transdisciplinary, aiming to create significant impacts through the integration of research, education, community engagement and outreach, and sometimes international collaboration. For more information, refer to: https://theedenresearch.org/