Advancing the Spatiotemporal Dimension of Wildlife-Pollution Interactions

Abstract

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.

Figure 1

(A) Spatial layers that will influence the exposure risk and outcomes for wildlife. (B) Broad spatiotemporal wildlife–pollutant interactions and possible effects on the individual movement of fish from a hypothetical population. (C) Simple framework outlining potential pathways and dynamic feedback mechanisms between spatiotemporal variation in pollutants and animals that are described in this paper (the shading of the arrows helps represent the spatial layers that are influencing one another). The “seasonal” symbol in the top right-hand side of (C) represents the importance of changing environmental variables in determining spatiotemporal wildlife–pollutant interactions (e.g., via effects on habitat characteristics [ice versus free-flowing river], contaminant discharge rates [seasonal changes in agricultural practices or rainfall patterns], individual space use [seasonal differences in foraging areas or dispersal]).

Figure 2

Recently developed and established methodological and technological approaches that can facilitate the study of the spatiotemporal dynamics of wildlife–pollution interactions: wildlife–pollutant positioning;^,− pollutant positioning;⁻ pollutant modeling;⁻ modeling; wildlife modeling;^, wildlife positioning.⁻ Approaches that can combine all of these different techniques (e.g., gray center of the Venn diagram)—such as agent-based models that incorporate empirical data from the spatiotemporal distribution of both wildlife and pollutants—may be particularly promising in predicting the outcomes of spatiotemporal dynamic wildlife–pollutant interactions.