Investigating the effect of pesticides on Daphnia population dynamics by inferring structure and parameters of a stochastic model

• Published in: Ecological modelling Vol. 472; p. 110076
• Main Authors: Palamara, Gian Marco, Dennis, Stuart R., Haenggi, Corinne, Schuwirth, Nele, Reichert, Peter
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.10.2022
• Subjects: age structure; Age-structured model; Bayesian inference; Bayesian theory; binomial distribution; chronic toxicity; Daphnia galeata; Demographic stochasticity; diazinon; diuron; Ecotoxicology; environmental assessment; life history; Model selection; mortality; Nested stochastic population models; population dynamics; reproduction; stochastic processes; Demographic stochasticity; Nested stochastic population models; Age-structured model; Ecotoxicology; Bayesian inference; Model selection
• ISSN: 0304-3800; 1872-7026
• DOI: 10.1016/j.ecolmodel.2022.110076

Identifying sublethal pesticide effects on aquatic organisms is a challenge for environmental risk assessment. Long-term population experiments can help assessing chronic toxicity. However, population experiments are subject to stochasticity (demographic, environmental, and genetic). Therefore, identifying sublethal chronic effects from “noisy” data can be difficult. Model-based analysis can support this process. We use stochastic, age-structured population models applied to data from long-term population experiments with Daphnia galeata in 1L aquaria with and without chronic pesticide treatments (diazinon and diuron) at sublethal concentrations. Posterior analysis following Bayesian inference of model parameters and states helped choosing an adequate description of life-history characteristics under the specific experimental conditions (a zero-inflated negative binomial distribution for reproduction and mortality without density dependence). For the insecticide treatments, the inferred marginal posterior parameter distributions indicated the need for a mortality rate that increases with time, indicating cumulative chronic toxic effects of diazinon on Daphnia populations. With this study, we demonstrate how stochastic models can be used to infer mechanisms from population data to help identifying sublethal pesticide effects. [Display omitted] •We use structured stochastic population models describing Daphnia population dynamics.•We used a nested multi-model approach to infer processes from population time series.•Iterative adaptation of the model formulation is based on observed model deficits.•Daphnia fertility is best described by a zero-inflated negative binomial distribution.•Chronic exposure to 186 ng/L diazinon leads to an increase of mortality rate over time.