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Abstract Spatial aggregation of environmental or trophically transmitted parasites has the potential to influence host–parasite interactions. The distribution of parasites on hosts is one result of those interactions, and the role of spatial aggregation is unclear. We use a spatially explicit agent‐based model to determine how spatial aggregation of parasites influences the distribution of parasite burdens across a range of parasite densities and host recovery rates. Our model simulates the random movement of hosts across landscapes with varying spatial configurations of areas occupied by environmental parasites, allowing hosts to acquire parasites they encounter and subsequently lose them. When parasites are more spatially aggregated in the environment, the aggregation of parasite burdens on hosts is higher (i.e., more hosts with few parasites, fewer hosts with many parasites), but the effect is less pronounced at high parasite density and fast host recovery rates. In addition, the correlation between individual hosts' final parasite burdens and their cumulative parasite burdens (including lost parasites) is greater at higher levels of spatial parasite aggregation. Our work suggests that fine‐scale spatial patterns of parasites can play a strong role in shaping how hosts are parasitized, particularly when parasite density is low‐to‐moderate and recovery rates are slow. 

ABSTRACT Efficient learning about disease dynamics in free‐ranging wildlife systems can benefit from active surveillance that is standardized across different ecological contexts. For example, active surveillance that targets specific individuals and populations with standardized sampling across ecological contexts (landscape‐scale targeted surveillance) is important for developing a mechanistic understanding of disease emergence, which is the foundation for improving risk assessment of zoonotic or wildlife‐livestock disease outbreaks and predicting hotspots of disease emergence. However, landscape‐scale targeted surveillance systems are rare and challenging to implement. Increasing experience and infrastructure for landscape‐scale targeted surveillance will improve readiness for rapid deployment of this type of surveillance in response to new disease emergence events. Here, we describe our experience developing and rapidly deploying a landscape‐scale targeted surveillance system for severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) in two free‐ranging deer species across their ranges in the United States. Our surveillance system was designed to collect data across individual, population, and landscape scales for future analyses aimed at understanding mechanisms and risk factors of SARS‐CoV‐2 transmission, evolution, and persistence. Our approach leveraged partnerships between state and federal public service sectors and academic researchers in a landscape‐scale targeted surveillance research network. Methods describe our approach to developing the surveillance network and sampling design. Results report challenges with implementing our intended sampling design, specifically how the design was adapted as different challenges arose and summarize the sampling design that has been implemented thus far. In the discussion, we describe strategies that were important for the successful deployment of landscape‐scale targeted surveillance, development and operation of the research network, construction of similar networks in the future, and analytical approaches for the data based on the sampling design. 

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. 

Abstract Most studies on the evolution of migration focus on food, mates and/or climate as factors influencing these movements, whereas negative species interactions such as predators, parasites and pathogens are often ignored. Although infection and its associated costs clearly have the potential to influence migration, thoroughly studying these interactions is challenging without a solid theoretical framework from which to develop testable predictions in natural systems.Here, we aim to understand when parasites favour the evolution of migration.We develop a general model which enables us to explore a broad range of biological conditions and to capture population and infection dynamics over both ecological and evolutionary time‐scales.We show that when migration evolves depends on whether the costs of migration and infection are paid in reduced fecundity or survival. Also important are the parasite transmission mode and spatiotemporal dynamics of infection and recovery (if it occurs). Finally, we find that partial migration (where only a fraction of the population migrates) can evolve but only when parasite transmission is density‐dependent.Our results highlight the critical, if overlooked, role of parasites in shaping long‐distance movement patterns, and suggest that infection should be considered alongside more traditional drivers of migration in both empirical and theoretical studies. 

Abstract Determining parameters that govern pathogen transmission (such as the force of infection, FOI), and pathogen impacts on morbidity and mortality, is exceptionally challenging for wildlife. Vital parameters can vary, for example across host populations, between sexes and within an individual's lifetime.Feline immunodeficiency virus (FIV) is a lentivirus affecting domestic and wild cat species, forming species‐specific viral–host associations. FIV infection is common in populations of puma (Puma concolor), yet uncertainty remains over transmission parameters and the significance of FIV infection for puma mortality. In this study, the age‐specific FOI of FIV in pumas was estimated from prevalence data, and the evidence for disease‐associated mortality was assessed.We fitted candidate models to FIV prevalence data and adopted a maximum likelihood method to estimate parameter values in each model. The models with the best fit were determined to infer the most likely FOI curves. We applied this strategy for female and male pumas from California, Colorado, and Florida.When splitting the data by sex and area, our FOI modeling revealed no evidence of disease‐associated mortality in any population. Both sex and location were found to influence the FOI, which was generally higher for male pumas than for females. For female pumas at all sites, and male pumas from California and Colorado, the FOI did not vary with puma age, implying FIV transmission can happen throughout life; this result supports the idea that transmission can occur from mothers to cubs and also throughout adult life. For Florida males, the FOI was a decreasing function of puma age, indicating an increased risk of infection in the early years, and a decreased risk at older ages.This research provides critical insight into pathogen transmission and impact in a secretive and solitary carnivore. Our findings shed light on the debate on whether FIV causes mortality in wild felids like puma, and our approach may be adopted for other diseases and species. The methodology we present can be used for identifying likely transmission routes of a pathogen and also estimating any disease‐associated mortality, both of which can be difficult to establish for wildlife diseases in particular.