CAREER: Linking behavior, genetics and the environment to understand ecological and evolutionary processes

Understanding how populations differ - and how new species arise - is a central challenge in biology. While genetic and environmental influences on evolution are well studied, a key missing link is how animal behavior connects these processes. Behavior shapes how organisms move, interact, and respond to their environment, yet it is rarely studied in ways that link individual actions to large-scale patterns such as population connectivity and diversification. This project addresses that gap by examining how differences in behavior among individuals and populations contribute to variation in movement, how environmental conditions help maintain biological diversity within populations, and how interactions among individuals influence behavioral traits and their evolution. Because behavioral variation is widespread across organisms, the findings will be broadly relevant to processes such as species invasions, disease spread, and responses to environmental change. This project also contributes to workforce development by providing hands-on research opportunities for undergraduate and graduate students, creating new inquiry-based learning experiences in university courses, and developing adaptable teaching materials for K–12 educators. Outreach efforts will engage broader audiences through community partnerships and accessible science communication and provide critical information to managers and citizens world-wide about a species that is invasive in many parts of the world. This project investigates how behavioral variation contributes to ecological and evolutionary processes by integrating behavioral measurements, environmental data, and population genomic analyses across natural populations. The central hypothesis is that environmental variation shapes behavioral traits and movement tendencies, that these behaviors influence patterns of genetic connectivity, and that both environmental and social contexts contribute to the maintenance and evolution of phenotypic variation. The research is organized around three objectives. First, it will quantify variation and co-variation in multiple behavioral traits within and among populations and test how these traits predict movement tendencies and genetic connectivity. Second, it will evaluate how environmental factors contribute to the maintenance of discrete phenotypic variation in natural populations, providing insight into mechanisms that preserve genetic diversity under selection. Third, it will examine how social interactions among individuals influence the expression of behavioral traits and their evolutionary dynamics, including effects on trait correlations and population-level patterns. To address these objectives, the project will combine standardized behavioral assays, controlled measurements of movement, and field-based characterization of environmental conditions across multiple populations. Environmental data will include both traditional field measurements and emerging approaches such as environmental DNA (eDNA) to characterize ecological communities and variation in biotic interactions. Population genomic data will be generated to estimate genetic structure, gene flow, and demographic history. By integrating behavioral ecology, environmental variation, and genomics within a unified framework, this project will provide generalizable insight into how behavior contributes to the generation, maintenance, and evolution of biological diversity. 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: 2544355 | Program: 01003031DB NSF RESEARCH & RELATED ACTIVIT,01002627DB NSF RESEARCH & RELATED ACTIVIT | Principal Investigator: Zachary Culumber | Institution: University of Alabama in Huntsville, HUNTSVILLE, AL | Award Amount: $777,120 View on NSF Award Search: https://www.nsf.gov/awardsearch/show-award/?AWD_ID=2544355 View on Research.gov: https://www.research.gov/awardapi-service/v1/awards/2544355.html