Trade-offs with telemetry-derived contact networks for infectious disease studies in wildlife

Marie L.J. Gilbertson, Lauren A. White, Meggan E. Craft

Research output: Contribution to journalArticlepeer-review


Network analysis 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 associations, but such data may have different sampling intervals and often captures a small subset of the population. The objectives of this study were to outline best practices for telemetry sampling in network studies of infectious disease by determining (a) the consequences of telemetry sampling on our ability to estimate network structure, (b) whether contact networks can be approximated using purely spatial contact definitions and (c) how wildlife spatial configurations may influence telemetry sampling requirements. 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) with a range of sampling intervals (every minute to every 3 days). We varied the definition of contact for sample networks, using either spatiotemporal or spatial overlap, and varied the spatial configuration of populations (random, lattice or clustered). To compare complete and sample networks, we calculated seven network metrics important for disease transmission and assessed mean ranked correlation coefficients and percent error between complete and sample network metrics. Telemetry sampling severely reduced our ability to calculate global node-level network metrics, but had less impact on local and network-level metrics. Even so, in populations with infrequent associations, high intensity telemetry sampling may still be necessary. Defining contact in terms of spatial overlap generally resulted in overly connected networks, but in some instances, could compensate for otherwise coarse telemetry data. By synthesizing movement and disease ecology with computational approaches, we characterized trade-offs important for using wildlife telemetry data beyond ecological studies of individual movement, and found that 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 a better understanding and management of wildlife infectious disease.

Original languageEnglish (US)
Pages (from-to)76-87
Number of pages12
JournalMethods in Ecology and Evolution
Issue number1
StatePublished - Jan 2021

Bibliographical note

Funding Information:
Thanks to K. VanderWaal for meaningful feedback. M.L.J.G. was supported by the Office of the Director, National Institutes of Health under award number NIH T32OD010993. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. L.A.W. was supported by the National Socio-Environmental Synthesis Center (SESYNC) under funding received from the National Science Foundation DBI-1639145. M.E.C. was funded by the National Science Foundation (DEB-1413925 and 1654609) and CVM Research Office UMN Ag Experiment Station General Ag Research Funds. The authors acknowledge the Minnesota Supercomputing Institute at the University of Minnesota for providing resources that contributed to the research results reported within this paper.


  • contact rate
  • disease ecology
  • disease modelling
  • network structure
  • remote contact detection
  • social network analysis
  • spatial overlap
  • telemetry sampling

Fingerprint Dive into the research topics of 'Trade-offs with telemetry-derived contact networks for infectious disease studies in wildlife'. Together they form a unique fingerprint.

Cite this