An epidemiological model for proliferative kidney disease in salmonid populations

Abstract

Background Proliferative kidney disease (PKD) affects salmonid populations in European and North-American rivers. It is caused by the endoparasitic myxozoan Tetracapsuloides bryosalmonae, which exploits freshwater bryozoans and salmonids as hosts. Incidence and severity of PKD in brown trout populations have recently increased rapidly, causing a decline in fish catches and local extinctions in many river systems. PKD incidence and fish mortality are known to be enhanced by warmer water temperatures. Therefore, environmental change is feared to increase the severity of PKD outbreaks and extend the disease range to higher latitude and altitude regions. We present the first mathematical model regarding the epidemiology of PKD, including the complex life-cycle of its causative agent across multiple hosts. Methods A dynamical model of PKD epidemiology in riverine host populations is developed. The model accounts for local demographic and epidemiological dynamics of bryozoans and fish, explicitly incorporates the role of temperature, and couples intra-seasonal and inter-seasonal dynamics. The former are described in a continuous-time domain, the latter in a discrete-time domain. Stability and sensitivity analyses are performed to investigate the key processes controlling parasite invasion and persistence. Results Stability analysis shows that, for realistic parameter ranges, a disease-free system is highly invasible, which implies that the introduction of the parasite in a susceptible community is very likely to trigger a disease outbreak. Sensitivity analysis shows that, when the disease is endemic, the impact of PKD outbreaks is mostly controlled by the rates of disease development in the fish population. Conclusions The developed mathematical model helps further our understanding of the modes of transmission of PKD in wild salmonid populations, and provides the basis for the design of interventions or mitigation strategies. It can also be used to project changes in disease severity and prevalence because of temperature regime shifts, and to guide field and laboratory experiments