Schistosomiasis is a severe neglected tropical disease caused by trematodes and transmitted by freshwater snails. Snails are known to be highly tolerant to agricultural pesticides. However, little attention has been paid to the ecological consequences of pesticide pollution in areas endemic for schistosomiasis, where people live in close contact with non-sanitized freshwaters. In complementary laboratory and field studies on Kenyan inland areas along Lake Victoria, we show that pesticide pollution is a major driver in increasing the occurrence of host snails and thus the risk of schistosomiasis transmission. In the laboratory, snails showed higher insecticide tolerance to commonly found pesticides than associated invertebrates, in particular to the neonicotinoid Imidacloprid and the organophosphate Diazinon. In the field, we demonstrated at 48 sites that snails were present exclusively in habitats characterized by pesticide pollution and eutrophication. Our analysis revealed that insensitive snails dominated over their less tolerant competitors. The study shows for the first time that in the field, pesticide concentrations considered "safe" in environmental risk assessment have indirect effects on human health. Thus we conclude there is a need for rethinking the environmental risk of low pesticide concentrations and of integrating agricultural mitigation measures in the control of schistosomiasis.

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Ecological stability refers to a range of concepts used to quantify how species and environments change over time and in response to disturbances. Most empirically tractable ecological stability metrics assume that systems have simple dynamics and static equilibria. However, ecological systems are typically complex and often lack static equilibria (e.g., predator–prey oscillations, transient dynamics, chaos). Failing to account for these factors can lead to biased estimates of stability, in particular, by conflating effects of observation error, process noise, and underlying deterministic dynamics. To distinguish among these processes, we combine three existing approaches: state space models; delay embedding methods; and particle filtering. Jointly, these provide something akin to a deterministically “detrended” version of the coefficient of variation, separately tracking variability due to deterministic dynamics versus stochastic perturbations. Moreover, these variability estimates can be used to forecast dynamics, classify underlying sources of stochastic dynamics, and estimate the “exit time” before a state change takes place (e.g., local extinction events). Importantly, the time‐delay embedding methods that we employ make very few assumptions about the functions governing deterministic dynamics, which facilitates applications in systems with limited data and a priori biological knowledge. To demonstrate how complex dynamics without static equilibria can bias ecological stability estimates, we analyze simulated time series of abundance dynamics in a system with time‐varying carrying capacity and empirically observed abundance dynamics of the green algae Chlamydomonas terricola grown in a diverse microcosm mixture under variable temperature conditions. We show that stability estimates based on raw observations greatly overestimate temporal variability and fail to accurately forecast time to extinction. In contrast, joint application of state space modeling, delay embedding, and particle filters were able to: (1) correctly quantify the contributions of deterministic versus stochastic variability; (2) successfully estimate “true” abundance dynamics; and (3) correctly forecast time to extinction. Our results therefore demonstrate the importance of accounting for effects of complex, nonstatic dynamics in studies of ecological stability and provide an empirically tractable and flexible toolkit for conducting these measurements.

Improved understanding of complex dynamics has revealed insights across many facets of ecology, and has enabled improved forecasts and management of future ecosystem states. However, an enduring challenge in forecasting complex dynamics remains the differentiation between complexity and stochasticity, that is, to determine whether declines in predictability are caused by stochasticity, nonlinearity, or chaos. Here, we show how to quantify the relative contributions of these factors to prediction error using Georgii Gause's iconic predator–prey microcosm experiments, which, critically, include experimental replicates that differ from one another only in initial abundances. We show that these differences in initial abundances interact with stochasticity, nonlinearity, and chaos in unique ways, allowing us to identify the impacts of these factors on prediction error. Our results suggest that jointly analyzing replicate time series across multiple, distinct starting points may be necessary for understanding and predicting the wide range of potential dynamic types in complex ecological systems.