skip to main content


Title: Trade‐offs with telemetry‐derived contact networks for infectious disease studies in wildlife
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.

 
more » « less
Award ID(s):
1654609
NSF-PAR ID:
10454204
Author(s) / Creator(s):
 ;  ;  ;
Publisher / Repository:
Wiley-Blackwell
Date Published:
Journal Name:
Methods in Ecology and Evolution
Volume:
12
Issue:
1
ISSN:
2041-210X
Page Range / eLocation ID:
p. 76-87
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. Abstract

    The spatial organization of a population can influence the spread of information, behaviour and pathogens. Group territory size and territory overlap and components of spatial organization, provide key information as these metrics may be indicators of habitat quality, resource dispersion, contact rates and environmental risk (e.g. indirectly transmitted pathogens). Furthermore, sociality and behaviour can also shape space use, and subsequently, how space use and habitat quality together impact demography.

    Our study aims to identify factors shaping the spatial organization of wildlife populations and assess the impact of epizootics on space use. We further aim to explore the mechanisms by which disease perturbations could cause changes in spatial organization.

    Here we assessed the seasonal spatial organization of Serengeti lions and Yellowstone wolves at the group level. We use network analysis to describe spatial organization and connectivity of social groups. We then examine the factors predicting mean territory size and mean territory overlap for each population using generalized additive models.

    We demonstrate that lions and wolves were similar in that group‐level factors, such as number of groups and shaped spatial organization more than population‐level factors, such as population density. Factors shaping territory size were slightly different than factors shaping territory overlap; for example, wolf pack size was an important predictor of territory overlap, but not territory size. Lion spatial networks were more highly connected, while wolf spatial networks varied seasonally. We found that resource dispersion may be more important for driving territory size and overlap for wolves than for lions. Additionally, canine distemper epizootics may have altered lion spatial organization, highlighting the importance of including infectious disease epizootics in studies of behavioural and movement ecology.

    We provide insight about when we might expect to observe the impacts of resource dispersion, disease perturbations, and other ecological factors on spatial organization. Our work highlights the importance of monitoring and managing social carnivore populations at the group level. Future research should elucidate the complex relationships between demographics, social and spatial structure, abiotic and biotic conditions and pathogen infections.

     
    more » « less
  2. 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.

     
    more » « less
  3. Abstract

    Habitat conversion to farmland has increased human‐wildlife interactions, which often lead to conflict, injury or death for people and animals. Understanding the behavioural and landscape drivers of human‐wildlife conflict is critical for managing wildlife populations. Staging behaviour prior to crop incursions has been described across multiple taxa and offers potential utility in managing conflict, but few quantitative assessments of staging have been undertaken. Animal movement data can provide valuable, fine‐scale information on such behaviour with opportunities for application to real‐time management for conflict prediction.

    We developed an approach to assess the efficacy of six widely used metrics of animal movement to identify staging behaviour prior to agricultural incursions. We applied this approach to GPS data from 55 African elephants in the Serengeti‐Mara ecosystem and found tortuosity and HMM‐derived behavioural states to be the most effective for identifying staging events. We then assessed temporal patterns of defined staging at daily and seasonal scales and explored environmental and anthropogenic drivers of staging from spatial generalized logistic mixed models. Finally, we tested the viability of combining movement and simple spatial metrics to predict crop incursions based on GPS data.

    Our approach identified staging behaviour that appeared to be driven largely by human activity and diurnal availability of protective cover from forest, riverine vegetation, and topography. Staging also varied substantially by season. Tortuosity and behavioural state metrics identified different staging strategies with distinct spatial distributions and anthropogenic drivers, and appeared to be linked to the juxtaposition between protected and cultivated lands. Tortuosity‐based staging combined with distance‐to‐agriculture produced promising results for pre‐event prediction of crop incursion.

    Synthesis and applications. Our study found staging by elephants prior to crop use could be identified from GPS tracking data, indicating that a better understanding of movement behaviour can inform targeted and proactive human‐wildlife conflict management and inform spatial planning efforts. Our approach is extendable to other conflict‐prone species to assess pre‐conflict behaviours and space use and demonstrates some of the challenges and advantages of using animal behaviour to assess temporal and spatial heterogeneity in human‐wildlife conflict.

     
    more » « less
  4. Abstract

    World‐wide, infectious diseases represent a major source of mortality in humans and livestock. For wildlife populations, disease‐induced mortality is likely even greater, but remains notoriously difficult to estimate—especially for endemic infections. Approaches for quantifying wildlife mortality due to endemic infections have historically been limited by an inability to directly observe wildlife mortality in nature.

    Here we address a question that can rarely be answered for endemic pathogens of wildlife: what are the population‐ and landscape‐level effects of infection on host mortality? We combined laboratory experiments, extensive field data and novel mathematical models to indirectly estimate the magnitude of mortality induced by an endemic, virulent trematode parasite (Ribeiroia ondatrae) on hundreds of amphibian populations spanning four native species.

    We developed a flexible statistical model that uses patterns of aggregation in parasite abundance to infer host mortality. Our model improves on previous approaches for inferring host mortality from parasite abundance data by (i) relaxing restrictive assumptions on the timing of host mortality and sampling, (ii) placing all mortality inference within a Bayesian framework to better quantify uncertainty and (iii) accommodating data from laboratory experiments and field sampling to allow for estimates and comparisons of mortality within and among host populations.

    Applying our approach to 301 amphibian populations, we found that trematode infection was associated with an average of between 13% and 40% population‐level mortality. For three of the four amphibian species, our models predicted that some populations experienced >90% mortality due to infection, leading to mortality of thousands of amphibian larvae within a pond. At the landscape scale, the total number of amphibians predicted to succumb to infection was driven by a few high mortality sites, with fewer than 20% of sites contributing to greater than 80% of amphibian mortality on the landscape.

    The mortality estimates in this study provide a rare glimpse into the magnitude of effects that endemic parasites can have on wildlife populations and our theoretical framework for indirectly inferring parasite‐induced mortality can be applied to other host–parasite systems to help reveal the hidden death toll of pathogens on wildlife hosts.

     
    more » « less
  5. Abstract

    The prevalence and intensity of parasites in wild hosts varies across space and is a key determinant of infection risk in humans, domestic animals and threatened wildlife. Because the immune system serves as the primary barrier to infection, replication and transmission following exposure, we here consider the environmental drivers of immunity. Spatial variation in parasite pressure, abiotic and biotic conditions, and anthropogenic factors can all shape immunity across spatial scales. Identifying the most important spatial drivers of immunity could help pre‐empt infectious disease risks, especially in the context of how large‐scale factors such as urbanization affect defence by changing environmental conditions.

    We provide a synthesis of how to apply macroecological approaches to the study of ecoimmunology (i.e. macroimmunology). We first review spatial factors that could generate spatial variation in defence, highlighting the need for large‐scale studies that can differentiate competing environmental predictors of immunity and detailing contexts where this approach might be favoured over small‐scale experimental studies. We next conduct a systematic review of the literature to assess the frequency of spatial studies and to classify them according to taxa, immune measures, spatial replication and extent, and statistical methods.

    We review 210 ecoimmunology studies sampling multiple host populations. We show that whereas spatial approaches are relatively common, spatial replication is generally low and unlikely to provide sufficient environmental variation or power to differentiate competing spatial hypotheses. We also highlight statistical biases in macroimmunology, in that few studies characterize and account for spatial dependence statistically, potentially affecting inferences for the relationships between environmental conditions and immune defence.

    We use these findings to describe tools from geostatistics and spatial modelling that can improve inference about the associations between environmental and immunological variation. In particular, we emphasize exploratory tools that can guide spatial sampling and highlight the need for greater use of mixed‐effects models that account for spatial variability while also allowing researchers to account for both individual‐ and habitat‐level covariates.

    We finally discuss future research priorities for macroimmunology, including focusing on latitudinal gradients, range expansions and urbanization as being especially amenable to large‐scale spatial approaches. Methodologically, we highlight critical opportunities posed by assessing spatial variation in host tolerance, using metagenomics to quantify spatial variation in parasite pressure, coupling large‐scale field studies with small‐scale field experiments and longitudinal approaches, and applying statistical tools from macroecology and meta‐analysis to identify generalizable spatial patterns. Such work will facilitate scaling ecoimmunology from individual‐ to habitat‐level insights about the drivers of immune defence and help predict where environmental change may most alter infectious disease risk.

     
    more » « less