Capturing heterogeneity in contact patterns in animal populations is essential for understanding the spread of infectious diseases. In contrast to other regions of the world in which livestock movement networks are integral to pathogen prevention and control policies, contact networks are understudied in pastoral regions of Africa due to the challenge of measuring contact among mobile herds of cattle whose movements are driven by access to resources. Furthermore, the extent to which seasonal changes in the distribution of water and resources impacts the structure of contact networks in cattle is uncertain. Contact networks may be more conducive to pathogen spread in the dry season due to congregation at limited water sources. Alternatively, less abundant forage may result in decreased pathogen transmission due to competitive avoidance among herds, as measured by reduced contact rates. Here, we use GPS technology to concurrently track 49 free-roaming cattle herds within a semi-arid region of Kenya, and use these data to characterize seasonal contact networks and model the spread of a highly infectious pathogen. This work provides the first empirical data on the local contact network structure of mobile herds based on quantifiable contact events. The contact network demonstrated high levels of interconnectivity. An increase in contacts near to water resources in the dry season resulted in networks with both higher contact rates and higher potential for pathogen spread than in the wet season. Simulated disease outbreaks were also larger in the dry season. Results support the hypothesis that limited water resources enhance connectivity and transmission within contact networks, as opposed to reducing connectivity as a result of competitive avoidance. These results cast light on the impact of seasonal heterogeneity in resource availability on predicting pathogen transmission dynamics, which has implications for other free-ranging wild and domestic populations.

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.

Pathogen transmission depends on host density, mobility and contact. These components emerge from host and pathogen movements that themselves arise through interactions with the surrounding environment. The environment, the emergent host and pathogen movements, and the subsequent patterns of density, mobility and contact form an ‘epidemiological landscape’ connecting the environment to specific locations where transmissions occur. Conventionally, the epidemiological landscape has been described in terms of the geographical coordinates where hosts or pathogens are located. We advocate for an alternative approach that relates those locations to attributes of the local environment. Environmental descriptions can strengthen epidemiological forecasts by allowing for predictions even when local geographical data are not available. Environmental predictions are more accessible than ever thanks to new tools from movement ecology, and we introduce a ‘movement‐pathogen pace of life’ heuristic to help identify aspects of movement that have the most influence on spatial epidemiology. By linking pathogen transmission directly to the environment, the epidemiological landscape offers an efficient path for using environmental information to inform models describing when and where transmission will occur.

Abstract. Understanding pathogen spread in wildlife has important implications for conservation and management efforts. This is particularly the case for taxa that are susceptible to disease spillover events resulting in outbreaks and rapid population declines, such as carnivores. However, assessment of the spatial structure of pathogen exposure (pathogen spatial autocorrelation) is relatively rare for these kinds of taxa. Structure in pathogen exposure may reflect a number of important features, including host traits, pathogen traits, and detection methods utilized. The relatively wide-ranging nature of many carnivores may lead to rapid pathogen spread and obfuscate any spatial autocorrelation being detectable, but this has not yet been explicitly evaluated. Here, we tested for evidence of spatial structuring of pathogen exposure and coexposures for puma (Puma concolor) and bobcat (Lynx rufus), both mobile and wide-ranging felid species. The study included 440 puma and 639 bobcat from six study regions (one in Florida, two in Colorado, and three in California), as well as each animal's capture location and exposure status for up to eight pathogens. This allowed a thorough examination of spatial patterns of pathogen exposure across different pathogen transmission types, different habitats, and different host ecology. We tested for spatial autocorrelation for each pathogen in each host species at each site, as well as both host species combined. In addition, we tested for coexposure between all pathogens in the study, and for those pathogens that were correlated, we tested for spatial clusters of coexposure. We detected spatial autocorrelation in exposure status for approximately 2% and 10% of examined cases for puma and bobcats, respectively, and spatial clustering in approximately 17% of cases where pathogen coexposures were detected. These results suggest that wide-ranging species, such as puma and bobcat, may rapidly disseminate pathogens across their populations, precluding substantive detection of autocorrelation in pathogen exposure by traditional serological and infection detection methods. Thus, targeted pathogen surveillance or control might focus on individual host characteristics, and advances in understanding pathogen spread in these secretive felids may necessitate examinations of spatial structure in both pathogen and host genetics.