Testing the robustness of transmission network models to predict ectoparasite loads. One lizard, two ticks and four years

• Transmission networks predict the paths of parasite infections around a population.
• We developed transmission networks for ticks on lizards based on refuge sharing.
• The networks predicted infestation loads of one tick species but not another.
• Results suggest different dynamics of transmission for ecologically similar species.

We investigated transmission pathways for two tick species, Bothriocroton hydrosauri and Amblyomma limbatum, among their sleepy lizard (Tiliqua rugosa) hosts in a natural population in South Australia. Our aim was to determine whether a transmission network model continued to predict parasite load patterns effectively under varying ecological conditions. Using GPS loggers we identified the refuge sites used by each lizard on each day. We estimated infectious time windows for ticks that detached from a lizard in a refuge. Time windows were from the time when a detached tick molted and become infective, until the time it died from desiccation while waiting for a new host. Previous research has shown that A. limbatum molts earlier and survives longer than B. hydrosauri. We developed two transmission network models based on these differences in infective time windows for the two tick species. Directed edges were generated in the network if one lizard used a refuge that had previously been used by another lizard within the infectious time window. We used those models to generate values of network node in-strength for each lizard, a measure of how strongly connected an individual is to other lizards in the transmission network, and a prediction of infection risk for each host. The consistent correlations over time between B. hydrosauri infection intensity and network derived infection risk suggest that network models can be robust to environmental variation among years. However, the contrasting lack of consistent correlation in A. limbatum suggests that the utility of the same network models may depend on the specific biology of a parasite species.

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