Postdoctoral Fellowship: PRFB: Understanding human-wildlife interactions in the face of global change: the case of birds around the world

Frans, Veronica Felicia, East Lansing MI

Investigators

Abstract

This action funds an NSF Postdoctoral Research Fellowship in Biology for FY 2024, Broadening Participation of Groups Underrepresented in Biology. The Fellowship supports a research and training plan for the Fellow that will increase the participation of groups underrepresented in biology. Rapid changes around the globe are causing wildlife species to alter their geographic ranges, leading to intensified interactions between humans and wildlife. Human-wildlife interactions are complex. They have a range of outcomes and result from various societal and ecological conditions. To date, research on human-wildlife interactions is limited. Studies tend to focus on relatively small study areas, popular species, or one type of human-wildlife relationship. As a result, it is difficult to understand human-wildlife interactions in broad, predictive, or theoretical ways. The fellow’s research will enhance understanding of human-wildlife interactions by developing a new modeling framework and global database for predicting human-wildlife interactions and discovering how they are influenced under global change. The project will inform policy and decision-making at local to global levels. It will also broaden participation for underrepresented groups in biology through the fellow’s training in data science and artificial intelligence, teaching and mentoring of underrepresented undergraduate students, and engagement with underserved, faith-based communities in science communication. The postdoctoral training experience will help the fellow advance towards a professorship. This project will focus on birds around the world, integrating biological, ecological, sociological, economic, and anthropological information with data science and artificial intelligence methods. The fellow will conduct a systematic review and mine through published article text to find case studies matching eight proposed archetypes of human-wildlife interaction outcomes. The fellow will then supplement information from these articles with additional species trait, environmental, cultural, conservation, and socioeconomic data to create an open-access human-wildlife interaction database. The fellow will develop Bayesian networks to predict human-wildlife archetypes by species, traits, scale, location, and socioeconomic or sociocultural conditions in current and future years. Finally, the fellow will determine the most explanatory drivers for predicting human-wildlife interaction outcomes based on sensitivity analysis. Throughout the project, the fellow will mentor and train underrepresented undergraduate students in research. The fellow will invite these students to present posters at scientific conferences. The fellow will also partner with non-profit organizations to speak at Christian churches and outreach events to help foster trust among faith-based communities, reduce tensions around human-wildlife conflict, and increase the recruitment of underrepresented students in the sciences. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.