Career: a mechanistic trait-driven framework of avian migration phenology

The National Science Foundation Division of Environmental Biology awarded $681,036 to the University of Texas Rio Grande Valley on September 1, 2026, under the Biological Sciences program (CFDA 47.074) to develop a mechanistic trait-driven framework of avian migration phenology. Work continues through August 31, 2031, at the recipient's campus in Edinburg, Texas.

The project integrates a trait-based framework with high-resolution, species-specific acoustic data to advance understanding of mechanisms driving variation in migration timing across the United States. The research addresses how species' functional traits determine responses to environmental variability and responds to gaps in existing migration studies that overlook behavioral plasticity and context-dependent responses across broad temporal and spatial scales. The award funds a course-based undergraduate research experience on signal processing in avian ecology and immersive workshops on bioacoustics designed so students directly contribute to data collection and analysis. The project provides research training in avian ecology, biostatistics, and computer programming to students in Southern Texas.

Abstract

Every year, billions of birds undertake seasonal migrations, traveling vast distances across north america. These movements require physical adaptations and reflect fine-tuned responses to ecological signals such as resource availability and optimal timing for reproduction. Avian migration has substantial implications for human well-being, including ecosystem service provision, impacts on food security, and the potential to spread pathogens. However, despite the relevance of large-scale seasonal avian movement, relatively few studies have explicitly sought to understand how changes in migratory patterns emerge from ecological principles and these studies remain difficult due to several challenges. First, migration data across species are often sparse or inconsistent, and existing approaches frequently overlook behavioral plasticity and context-dependent responses. Second, these limitations stem in part from the fact that migration occurs across broad time and spatial scales, making direct field observations difficult to obtain. To overcome these challenges, this project integrates a trait-based framework with high-resolution, species-specific acoustic data to advance understanding of the mechanisms driving variation in migration timing across the united states. The project is designed to benefit students in southern texas and will focus on providing training in avian ecology, biostatistics, and computer programming. Students will receive research training through a new course-based undergraduate research experience on signal processing in avian ecology along with immersive workshops on bioacoustics. These courses are designed as an integral component of the research project, where students will directly contribute to both data collection and analysis. This research addresses a fundamental question in ecology: how do species? Functional traits determine responses to environmental variability and affect adaptation and survival? Avian migration, a large-scale, time-sensitive process, offers a suitable system for exploring this question. Investigators will examine how migration timing arises from species-specific physiological thresholds and morphological adaptations, and assess the ecological mechanisms underlying these patterns among species with different natural history traits. To achieve this, investigators will implement an artificial intelligence (AI) workflow using open-source programming languages to identify species-specific nocturnal flight calls from migratory birds. This approach leverages deep neural network models to detect birds from nocturnal audio recordings and collect high-resolution migration data. This information will be integrated with life history traits derived from mark-recapture data to explain migratory temporal dynamics across avian species. Finally, investigators will evaluate how urban noise and artificial light at night influence migration behavior through its effects on acoustic communication and the use of social cues. Findings from this work will advance understanding of the mechanistic drivers of migration phenology across species and environmental conditions. This project also advances NSF?S priorities in artificial intelligence. 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.- subawards are not planned for this award.