Contact network modeling of infectious disease in wildlife can reveal traits or individuals critical to pathogen transmission and help inform disease management strategies. However, estimates of contact between animals are notoriously difficult to acquire. Researchers commonly use telemetry technologies to identify animal interactions; such data, however, may have different sampling intervals and often captures a small subset of the population. The objective of this study was to understand the consequences of telemetry sampling on our ability to detect contacts and estimates of network structure. We simulated individual movement trajectories for wildlife populations using a home range-like movement model, creating full location datasets and corresponding “complete” networks. To mimic telemetry data, we created “sample” networks by subsampling the population (10-100% of individuals) and sampling interval (every minute to every three days). We varied the definition of contact for sample networks, using either spatio-temporal or home range overlap. To compare complete and sample networks, we calculated six network metrics known to be important for disease transmission and assessed mean ranked correlation coefficients between complete and sample network metrics.
Results/Conclusions
Telemetry sampling severely reduced our ability to calculate the network metrics of betweenness and transitivity, with less impact on other metrics. In populations with infrequent interactions, however, high intensity sampling may still be necessary. Defining contact in terms of spatial overlap generally resulted in overly connected networks, but in some instances, could optimize otherwise coarse telemetry data. By synthesizing movement ecology, computational, and disease ecology approaches, we characterized trade-offs important for determining the utility of wildlife telemetry data beyond ecological studies of individual movement, and found careful use of telemetry data has the potential to inform network models. Thus, with informed application of telemetry data, we can make significant advances in leveraging its use for better understanding and management of wildlife infectious disease.