Integrated field, laboratory, and theoretical study of PKD spread in a Swiss prealpine river

Significance

Predicting how temperature, climate change, and emerging infectious diseases interact to drive local extinction risk for natural populations requires complex integrated approaches involving field data [fish and environmental DNA (eDNA) sampling and hydrological and geomorphological surveys], laboratory studies (eDNA analyses and disease prevalence assessment), and metacommunity modeling. Together, these tools reproduce all of the relevant biological and ecohydrological features of proliferative kidney disease, a major emerging disease impacting native salmonid stocks. We thus provide a predictive framework, applicable to other aquatic pathogens, that may function as a baseline for environmental management decisions aimed at preserving declining and iconic salmonid species.

Abstract

Proliferative kidney disease (PKD) is a major threat to wild and farmed salmonid populations because of its lethal effect at high water temperatures. Its causative agent, the myxozoan Tetracapsuloides bryosalmonae, has a complex lifecycle exploiting freshwater bryozoans as primary hosts and salmonids as secondary hosts. We carried out an integrated study of PKD in a prealpine Swiss river (the Wigger). During a 3-year period, data on fish abundance, disease prevalence, concentration of primary hosts’ DNA in environmental samples [environmental DNA (eDNA)], hydrological variables, and water temperatures gathered at various locations within the catchment were integrated into a newly developed metacommunity model, which includes ecological and epidemiological dynamics of fish and bryozoans, connectivity effects, and hydrothermal drivers. Infection dynamics were captured well by the epidemiological model, especially with regard to the spatial prevalence patterns. PKD prevalence in the sampled sites for both young-of-the-year (YOY) and adult brown trout attained 100% at the end of summer, while seasonal population decay was higher in YOY than in adults. We introduce a method based on decay distance of eDNA signal predicting local species’ density, accounting for variation in environmental drivers (such as morphology and geology). The model provides a whole-network overview of the disease prevalence. In this study, we show how spatial and environmental characteristics of river networks can be used to study epidemiology and disease dynamics of waterborne diseases.