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Abstract 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.
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Constructing social networks from automated telemetry data: A worked example using within‐ and across‐group associations in cooperatively breeding birds
Abstract Advances in data‐logging technologies have provided a way to monitor the movement of individual animals at unprecedented spatial and temporal scales. When used in conjunction with social network analyses, these data can provide deep insight into the structure and dynamics of animal social systems. Emergence of these new technologies demands concomitant progress in workflows to translate data streams from automated systems to social networks, based on biologically relevant metrics.Here we outline key considerations for constructing social networks from automated telemetry data. We highlight the need for paying particular attention to the spatial arrangement of receiver stations with respect to the ecology of study system and developing appropriate criteria for quantifying associations.We provide a case study for constructing social networks from automated telemetry data collected over 1 month during a study of acorn woodpeckersMelanerpes formicivorus, a cooperatively breeding bird. The data consisted of detections of known birds near receiver stations placed within core areas of group territories. We use this system to demonstrate how to build social networks to investigate biological questions about patterns of associations between group members and territory visitors across the landscape.
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- Award ID(s):
- 1750606
- PAR ID:
- 10446469
- Publisher / Repository:
- Wiley-Blackwell
- Date Published:
- Journal Name:
- Methods in Ecology and Evolution
- Volume:
- 13
- Issue:
- 1
- ISSN:
- 2041-210X
- Page Range / eLocation ID:
- p. 133-143
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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