Gilbertson Marie L J, White Lauren A, Craft Meggan E
Veterinary Population Medicine Department, University of Minnesota, St. Paul, MN, USA.
National Socio-Environmental Synthesis Center, Annapolis, MD, USA.
Methods Ecol Evol. 2021 Jan;12(1):76-87. doi: 10.1111/2041-210x.13355. Epub 2020 Jan 23.
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 (1) the consequences of telemetry sampling on our ability to estimate network structure, (2) whether contact networks can be approximated using purely spatial contact definitions, and (3) 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 three 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.
野生动物传染病的网络分析可以揭示对病原体传播至关重要的特征或个体，并有助于为疾病管理策略提供信息。然而，动物之间接触情况的估计非常难以获得。研究人员通常使用遥测技术来识别动物之间的关联；但此类数据可能具有不同的采样间隔，并且通常只能捕获种群中的一小部分。本研究的目的是通过确定以下内容来概述传染病网络研究中遥测采样的最佳实践：（1）遥测采样对我们估计网络结构能力的影响；（2）是否可以使用纯粹的空间接触定义来近似接触网络；（3）野生动物的空间配置如何影响遥测采样要求。我们使用类似家域的运动模型模拟野生动物种群的个体运动轨迹，创建完整的位置数据集和相应的“完整”网络。为了模拟遥测数据，我们通过以一系列采样间隔（从每分钟到每三天）对种群（个体的10% - 100%）进行二次采样来创建“样本”网络。我们改变了样本网络的接触定义，使用时空重叠或空间重叠，并改变了种群的空间配置（随机、晶格或聚集）。为了比较完整网络和样本网络，我们计算了七个对疾病传播很重要的网络指标，并评估了完整网络和样本网络指标之间的平均排名相关系数和百分比误差。遥测采样严重降低了我们计算全局节点级网络指标的能力，但对局部和网络级指标的影响较小。即便如此，在关联不频繁的种群中，高强度的遥测采样可能仍然是必要的。用空间重叠来定义接触通常会导致网络连接过度，但在某些情况下，可以弥补原本粗糙的遥测数据。通过将运动和疾病生态学与计算方法相结合，我们描述了对于在个体运动生态研究之外使用野生动物遥测数据很重要的权衡，并发现谨慎使用遥测数据有可能为网络模型提供信息。因此，通过明智地应用遥测数据，我们可以在利用其更好地理解和管理野生动物传染病方面取得重大进展。
