Advancing the Spatiotemporal Dimension of Wildlife–Pollution Interactions

Chemical pollution is one of the fastest-growing agents of global change. Numerous pollutants are known to disrupt animal behavior, alter ecological interactions, and shift evolutionary trajectories. Crucially, both chemical pollutants and individual organisms are nonrandomly distributed throughout the environment. Despite this fact, the current evidence for chemical-induced impacts on wildlife largely stems from tests that restrict organism movement and force homogeneous exposures. While such approaches have provided pivotal ecotoxicological insights, they overlook the dynamic spatiotemporal interactions that shape wildlife–pollution relationships in nature. Indeed, the seemingly simple notion that pollutants and animals move nonrandomly in the environment creates a complex of dynamic interactions, many of which have never been theoretically modeled or experimentally tested. Here, we conceptualize dynamic interactions between spatiotemporal variation in pollutants and organisms and highlight their ecological and evolutionary implications. We propose a three-pronged approach─integrating in silico modeling, laboratory experiments that allow movement, and field-based tracking of free-ranging animals─to bridge the gap between controlled ecotoxicological studies and real-world wildlife exposures. Advances in telemetry, remote sensing, and computational models provide the necessary tools to quantify these interactions, paving the way for a new era of ecotoxicology that accounts for spatiotemporal complexity.