Acoustic communication and social network dynamics underlying collective defense in the wild

Communication connects animals across space and time, allowing information to travel rapidly through a population. For example, the presence of a predator elicits an alarm call from an individual bird, which then results in a cascade of alarm calls across a colony of nesting birds, alerting all members. How such signals spread is influenced by both the proximity of birds, but also the social relationships that have from prior interactions and communications. Understanding the factors that influence the spread of information has important implications not just for animal populations, but also human and engineered communication systems. This project will harness new acoustic and tracking technologies as well as AI systems to study the fine-scale dynamics and fitness consequences of alarm call propagation in wild birds. Mapping the spread of information across colonies in the wild can help answer questions about how and why communication might breed social relationships among neighbors, and how emergent properties of communication networks affect predator-prey dynamics. This project will contribute to the national priority in building an AI-ready workforce by training undergraduate and graduate students in designing and customizing AI engines for analyzing soundscapes and conducting automated tracking. This project will also engage the public through an interactive multi-sensory installation at a local science museum that recreates avian communication networks using motion-sensitive speaker arrays. This installation will allow the public to experience how networks play a vital role in nature. This project will use colonies of breeding red-winged blackbirds (Agelaius phoeniceus), an abundant species with exceptionally well-studied acoustic communication and social system, to address two fundamental aims: Aim 1 disentangles the interplay between acoustic communication and spatiotemporal association, and Aim 2 determines the role of signal propagation in collective defense behavior. These aims will be accomplished by leveraging new technological and analytical advances in acoustic monitoring, automated tracking, and AI/machine learning algorithms for analyzing soundscapes and radiotelemetry data. These processes will allow us to address Aim 1 by simultaneously tracking vocalizations and movements of individuals as they engage in natural interactions at breeding colonies, yielding both communication networks and spatiotemporal association networks. Aim 2 will use a field experiment to induce alarm call cascades under different interfering noise levels to study the process of signal propagation in nature. This project will deliver two groundbreaking contributions to our understanding of acoustic communication in animal systems. First, it will advance the field toward a unified conceptual framework that integrates communication networks and social networks as complementary tools for studying social systems across spatial scales. Second, it will provide the most comprehensive analysis to date of acoustic signal propagation in natural environments. This approach will also advance the use of AI and machine learning for analyzing natural soundscapes. The conceptual insights gained here have broad relevance to other domains where communication networks and signal propagation are fundamental, such as neural circuits, human communication technologies (e.g., internet and wireless systems), and robotics. 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. NSF Award ID: 2608132 | Program: 01002627DB NSF RESEARCH & RELATED ACTIVIT | Principal Investigator: Daizaburo Shizuka | Institution: University of Nebraska-Lincoln, LINCOLN, NE | Award Amount: $873,777 View on NSF Award Search: https://www.nsf.gov/awardsearch/show-award/?AWD_ID=2608132 View on Research.gov: https://www.research.gov/awardapi-service/v1/awards/2608132.html