Gyro-scope: An individual-based computer model to forecast gyrodactylid infections on fish hosts

• Published in: International Journal for Parasitology
• Main Authors: Wright, H., Cable, J., Potter, R., van Oosterhout, C.
• Format: Article in Journal/Newspaper
• Language: English
• Published: Elsevier 2007
• Subjects: Parasitology; Infectious Diseases; Salmo salar
• Online Access: https://hull-repository.worktribe.com/output/471536; https://doi.org/10.1016/j.ijpara.2007.09.016

Individual-based computer models (IBM) feature prominently in current theoretical ecology but have only been applied in a small number of parasitological studies. Here we designed an IBM to simulate the infection dynamics of gyrodactylid parasites and immune defence of naïve hosts (i.e. fish previously not exposed to these parasites). We compared the results of the model with empirical data from guppies (Poecilia reticulata) infected with Gyrodactylus parasites. The laboratory experiments on guppies showed that larger fish acquired a heavier parasite load at the peak of the infection. The survival probability declined with increased body size and no fish survived a parasite load of 80 or more worms in this experiment (i.e. lethal load). The model was a good predictor of the Gyrodactylus infection dynamics of guppies and the model output was congruent with previously published data on Gyrodactylus salaris infections of salmon (Salmo salar). Computer simulations indicated that the infections persisted longer on larger hosts and that the parasite load increased exponentially with the body size of the host. Simulations furthermore predicted that the parasite load of fish with a standard length in excess of 17 mm (i.e. the size of adult guppies) reached a lethal load. This suggests that in the conditions of the experiment, the immune defence of naïve guppies can offer moderate protection against gyrodactylid infections to juveniles, but not to naïve adult guppies. The model is a useful tool to forecast the development of gyrodactylid infections on single hosts and make predictions about optimal life history strategies of parasites.