Collaborative Research: STAR: Insect Winners and Losers in a Variable World

Insects provide a vital role in pollinating crops, recycling nutrients, supporting food webs, and through their influence on human and animal health. Many beneficial insect groups are declining, while some harmful ones that damage crops, spread disease, or affect property are expanding into new areas. Extreme climate and weather events, such as heat waves, droughts, cold snaps, and heavy rainfall are becoming more frequent and severe. These extreme events can rapidly impact insect populations, but we still do not know which insects are most vulnerable, which may benefit, how those effects play out across regions and over time, and what the impact with be on the U.S. human population. This project will develop a new framework to understand how extreme climate and weather events affect adult insect abundances across the contiguous United States, with a focus on three groups: mosquitoes, butterflies and moths, and ground beetles. These groups are especially important from ecological and societal perspectives, and as targets for biotechnology development. By integrating multiple dimensions of extreme events with species traits, this framework moves beyond single-event studies, with the use of artificial intelligence (AI) tools, to obtain new insight and a more realistic and generalizable understanding of how environmental extremes shape biological systems. Improving the ability to anticipate these responses is critical to developing resilience strategies for conservation, food security, and One Health. The effort will combine more than 25 million insect records from monitoring networks, community science programs, and surveillance programs with 45 years of high-resolution climate data. It will also create a publicly accessible Extreme Climate and Weather Event Atlas for the contiguous U.S. that characterizes events by their frequency, intensity, duration, timing, and sequence. Using ecological theory, life history traits, advanced statistical modeling, and machine learning and AI approaches, the project will characterize and test how both single and compound extreme events influence insect abundances across latitude and climate context. The research will also examine whether prior conditions can amplify or reduce impacts, including cases such as false springs, ecological traps, and ecological bonanzas. By identifying likely insect winners and losers under increasing climate extremes, this project will improve ecological forecasting and provide tools that can be applied to other taxa and regions. The project also includes training for students and early-career scientists in data science, ecological modeling and AI, engagement with the public through webinars and events with monitoring networks and mosquito control programs, and development of freely available data and software tools for the broader research community and resource managers. 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.