Going through the motions: incorporating movement analyses into disease research

Elucidating nematode diversity and prevalence in moose across a wide latitudinal gradient using DNA metabarcoding

Parasitic nematodes are ubiquitous and can negatively impact their host by reducing fecundity or increasing mortality, yet the driver of variation in the parasite community across a wildlife host's geographic distribution remains elusive for most species. Based on an extensive collection of fecal samples (n = 264) from GPS marked moose (Alces alces), we used DNA metabarcoding to characterize the individual (sex, age class) and seasonal parasitic nematode community in relation to habitat use and migration behavior in five populations distributed across a wide latitudinal gradient (59.6°N to 70.5°N) in Norway. We detected 21 distinct nematode taxa with the six most common being Ostertagia spp., Nematodirella spp., Trichostongylus spp., T. axei, Elaphostrongylus alces, and an unclassified Strongylida. There was higher prevalence of livestock parasites in areas with larger sheep populations indicating a higher risk of spillover events. The individual level nematode richness was mostly consistent across study areas, while the number and type of nematode taxa detected at each study area varied considerably but did not follow a latitudinal gradient. While migration distance affected nematode beta-diversity across all sites, it had a positive effect on richness at only two of the five study areas suggesting population specific effects. Unexpectedly, nematode richness was higher in winter than summer when very few nematodes were detected. Here we provide the first extensive description of the parasitic nematode community of moose across a wide latitudinal range. Overall, the population-specific impact of migration on parasitism across the distribution range and variation in sympatry with other ruminants suggest local characteristics affect host-parasite relationships.

Movement-driven modelling reveals new patterns in disease transmission networks

Interspecific interactions are highly relevant in the potential transmission of shared pathogens in multi-host systems. In recent decades, several technologies have been developed to study pathogen transmission, such as proximity loggers, GPS tracking devices and/or camera traps. Despite the diversity of methods aimed at detecting contacts, the analysis of transmission risk is often reduced to contact rates and the probability of transmission given the contact. However, the latter process is continuous over time and unique for each contact, and is influenced by the characteristics of the contact and the pathogen's relationship with both the host and the environment. Our objective was to assess whether a more comprehensive approach, using a movement-based model which assigns a unique transmission risk to each contact by decomposing transmission into contact formation, contact duration and host characteristics, could reveal disease transmission dynamics that are not detected with more traditional approaches. The model was built from GPS-collar data from two management systems in Spain where animal tuberculosis (TB) circulates: a national park with extensively reared endemic cattle, and an area with extensive free-range pigs and cattle farms. In addition, we evaluated the effect of the GPS device fix rate on the performance of the model. Different transmission dynamics were identified between both management systems. Considering the specific conditions under which each contact occurs (i.e. whether the contact is direct or indirect, its duration, the hosts characteristics, the environmental conditions, etc.) resulted in the identification of different transmission dynamics compared to using only contact rates. We found that fix intervals greater than 30 min in the GPS tracking data resulted in missed interactions, and intervals greater than 2 h may be insufficient for epidemiological purposes. Our study shows that neglecting the conditions under which each contact occurs may result in a misidentification of the real role of each species in disease transmission. This study describes a clear and repeatable framework to study pathogen transmission from GPS data and provides further insights to understand how TB is maintained in multi-host systems in Mediterranean environments.

Estimating encounter-habitat relationships with scale-integrated resource selection functions

Encounters between animals occur when animals are close in space and time. Encounters are important in many ecological processes including sociality, predation and disease transmission. Despite this, there is little theory regarding the spatial distribution of encounters and no formal framework to relate environmental characteristics to encounters. The probability of encounter could be estimated with resource selection functions (RSFs) by comparing locations where encounters occurred to available locations where they may have occurred, but this estimate is complicated by the hierarchical nature of habitat selection. We developed a method to relate resources to the relative probability of encounter based on a scale-integrated habitat selection framework. This framework integrates habitat selection at multiple scales to obtain an appropriate estimate of availability for encounters. Using this approach, we related encounter probabilities to landscape resources. The RSFs describe habitat associations at four scales, home ranges within the study area, areas of overlap within home ranges, locations within areas of overlap, and encounters compared to other locations, which can be combined into a single scale-integrated RSF. We apply this method to intraspecific encounter data from two species: white-tailed deer (Odocoileus virginianus) and elk (Cervus elaphus) and interspecific encounter data from a two-species system of caribou (Rangifer tarandus) and coyote (Canis latrans). Our method produced scale-integrated RSFs that represented the relative probability of encounter. The predicted spatial distribution of encounters obtained based on this scale-integrated approach produced distributions that more accurately predicted novel encounters than a naïve approach or any individual scale alone. Our results highlight the importance of accounting for the conditional nature of habitat selection in estimating the habitat associations of animal encounters as opposed to 'naïve' comparisons of encounter locations with general availability. This method has direct relevance for testing hypotheses about the relationship between habitat and social or predator-prey behaviour and generating spatial predictions of encounters. Such spatial predictions may be vital for understanding the distribution of encounters driving disease transmission, predation rates and other population and community-level processes.

A scoping review on bovine tuberculosis highlights the need for novel data streams and analytical approaches to curb zoonotic diseases

Zoonotic diseases represent a significant societal challenge in terms of their health and economic impacts. One Health approaches to managing zoonotic diseases are becoming more prevalent, but require novel thinking, tools and cross-disciplinary collaboration. Bovine tuberculosis (bTB) is one example of a costly One Health challenge with a complex epidemiology involving humans, domestic animals, wildlife and environmental factors, which require sophisticated collaborative approaches. We undertook a scoping review of multi-host bTB epidemiology to identify trends in species publication focus, methodologies, and One Health approaches. We aimed to identify knowledge gaps where novel research could provide insights to inform control policy, for bTB and other zoonoses. The review included 532 articles. We found different levels of research attention across episystems, with a significant proportion of the literature focusing on the badger-cattle-TB episystem, with far less attention given to tropical multi-host episystems. We found a limited number of studies focusing on management solutions and their efficacy, with very few studies looking at modelling exit strategies. Only a small number of studies looked at the effect of human disturbances on the spread of bTB involving wildlife hosts. Most of the studies we reviewed focused on the effect of badger vaccination and culling on bTB dynamics with few looking at how roads, human perturbations and habitat change may affect wildlife movement and disease spread. Finally, we observed a lack of studies considering the effect of weather variables on bTB spread, which is particularly relevant when studying zoonoses under climate change scenarios. Significant technological and methodological advances have been applied to bTB episystems, providing explicit insights into its spread and maintenance across populations. We identified a prominent bias towards certain species and locations. Generating more high-quality empirical data on wildlife host distribution and abundance, high-resolution individual behaviours and greater use of mathematical models and simulations are key areas for future research. Integrating data sources across disciplines, and a "virtuous cycle" of well-designed empirical data collection linked with mathematical and simulation modelling could provide additional gains for policy-makers and managers, enabling optimised bTB management with broader insights for other zoonoses.

Chronic wasting disease alters the movement behavior and habitat use of mule deer during clinical stages of infection

Integrating host movement and pathogen data is a central issue in wildlife disease ecology that will allow for a better understanding of disease transmission. We examined how adult female mule deer (Odocoileus hemionus) responded behaviorally to infection with chronic wasting disease (CWD). We compared movement and habitat use of CWD-infected deer (n = 18) to those that succumbed to starvation (and were CWD-negative by ELISA and IHC; n = 8) and others in which CWD was not detected (n = 111, including animals that survived the duration of the study) using GPS collar data from two distinct populations collared in central Wyoming, USA during 2018-2022. CWD and predation were the leading causes of mortality during our study (32/91 deaths attributed to CWD and 27/91 deaths attributed to predation). Deer infected with CWD moved slower and used lower elevation areas closer to rivers in the months preceding death compared with uninfected deer that did not succumb to starvation. Although CWD-infected deer and those that died of starvation moved at similar speeds during the final months of life, CWD-infected deer used areas closer to streams with less herbaceous biomass than starved deer. These behavioral differences may allow for the development of predictive models of disease status from movement data, which will be useful to supplement field and laboratory diagnostics or when mortalities cannot be quickly retrieved to assess cause-specific mortality. Furthermore, identifying individuals who are sick before predation events could help to assess the extent to which disease mortality is compensatory with predation. Finally, infected animals began to slow down around 4 months prior to death from CWD. Our approach for detecting the timing of infection-induced shifts in movement behavior may be useful in application to other disease systems to better understand the response of wildlife to infectious disease.

Caterpillar movement mediates spatially local interactions and determines the relationship between population density and contact

BACKGROUND

While interactions in nature are inherently local, ecological models often assume homogeneity across space, allowing for generalization across systems and greater mathematical tractability. Density-dependent disease models are a prominent example of models that assume homogeneous interactions, leading to the prediction that disease transmission will scale linearly with population density. In this study, we examined how the scale of larval butterfly movement interacts with the resource landscape to influence the relationship between larval contact and population density in the Baltimore checkerspot (Euphydryas phaeton). Our study was inspired by the recent discovery of a viral pathogen that is transmitted horizontally among Baltimore checkerspot larvae.

METHODS

We used multi-year larvae location data across six Baltimore checkerspot populations in the eastern U.S. to test whether larval nests are spatially clustered. We then integrated these spatial data with larval movement data in different resource contexts to investigate whether heterogeneity in spatially local interactions alters the assumed linear relationship between larval nest density and contact. We used Correlated Random Walk (CRW) models and field observations of larval movement behavior to construct Probability Distribution Functions (PDFs) of larval dispersal, and calculated the overlap in these PDFs to estimate conspecific contact within each population.

RESULTS

We found that all populations exhibited significant spatial clustering in their habitat use. Subsequent larval movement rates were influenced by encounters with host plants and larval age, and under many movement scenarios, the scale of predicted larval movement was not sufficient to allow for the "homogeneous mixing" assumed in density dependent disease models. Therefore, relationships between population density and larval contact were typically non-linear. We also found that observed use of available habitat patches led to significantly greater contact than would occur if habitat use were spatially random.

CONCLUSIONS

These findings strongly suggest that incorporating larval movement and spatial variation in larval interactions is critical to modeling disease outcomes in E. phaeton. Epidemiological models that assume a linear relationship between population density and larval contact have the potential to underestimate transmission rates, especially in small populations that are already vulnerable to extinction.

Nonrandom foraging and resource distributions affect the relationships between host density, contact rates and parasite transmission

Nonrandom foraging can cause animals to aggregate in resource dense areas, increasing host density, contact rates and pathogen transmission, but when should nonrandom foraging and resource distributions also have density-independent effects? Here, we used a factorial experiment with constant resource and host densities to quantify host contact rates across seven resource distributions. We also used an agent-based model to compare pathogen transmission when host movement was based on random foraging, optimal foraging or something between those states. Nonrandom foraging strongly depressed contact rates and transmission relative to the classic random movement assumptions used in most epidemiological models. Given nonrandom foraging in the agent-based model and experiment, contact rates and transmission increased with resource aggregation and average distance to resource patches due to increased host movement in search of resources. Overall, we describe three density-independent mechanisms by which host behaviour and resource distributions alter contact rate functions and pathogen transmission.

Assessing the Efficiency of Local Rabies Vaccination Strategies for Raccoons (Procyon lotor) in an Urban Setting

Raccoon rabies virus (RRV) has been managed using multiple vaccination strategies, including oral rabies vaccination and trap-vaccinate-release (TVR). Identifying a rabies vaccination strategy for an area is a nontrivial task. Vaccination strategies differ in the amount of effort and monetary costs required to achieve a particular level of vaccine seroprevalence (efficiency). Simulating host movement relative to different vaccination strategies in silico can provide a useful tool for exploring the efficiency of different vaccination strategies. We refined a previously developed individual-based model of raccoon movement to evaluate vaccination strategies for urban Hamilton, Ontario, Canada. We combined different oral rabies vaccination baiting (hand baiting, helicopter, and bait stations) with TVR strategies and used GPS data to parameterize and simulate raccoon movement in Hamilton. We developed a total of 560 vaccination strategies, in consultation with the Ontario Ministry of Natural Resources and Forestry, for RRV control in Hamilton. We documented the monetary costs of each vaccination strategy and estimated the population seroprevalence. Intervention costs and seroprevalence estimates were used to calculate the efficiency of each strategy to meet targets set for the purpose of RRV control. Estimated seroprevalence across different strategies varied widely, ranging from less than 5% to more than 70%. Increasing bait densities (distributed using by hand or helicopter) led to negligible increase in seroprevalence. Helicopter baiting was the most efficient and TVR was the least efficient, but helicopter-based strategies led to lower levels of seroprevalence (6-12%) than did TVR-based strategies (17-70%). Our simulations indicated that a mixed strategy including at least some TVR may be the most efficient strategy for a local urban RRV control program when seroprevalence levels >30% may be required. Our simulations provide information regarding the efficiency of different vaccination strategies for raccoon populations, to guide local RRV control in urban settings.

Resource selection by New York City deer reveals the effective interface between wildlife, zoonotic hazards and humans

Although the role of host movement in shaping infectious disease dynamics is widely acknowledged, methodological separation between animal movement and disease ecology has prevented researchers from leveraging empirical insights from movement data to advance landscape scale understanding of infectious disease risk. To address this knowledge gap, we examine how movement behaviour and resource utilization by white-tailed deer (Odocoileus virginianus) determines blacklegged tick (Ixodes scapularis) distribution, which depend on deer for dispersal in a highly fragmented New York City borough. Multi-scale hierarchical resource selection analysis and movement modelling provide insight into how deer's movements contribute to the risk landscape for human exposure to the Lyme disease vector-I. scapularis. We find deer select highly vegetated and accessible residential properties which support blacklegged tick survival. We conclude the distribution of tick-borne disease risk results from the individual resource selection by deer across spatial scales in response to habitat fragmentation and anthropogenic disturbances.

Raccoon spatial ecology in the rural southeastern United States

The movement ecology of raccoons varies widely across habitats with important implications for the management of zoonotic diseases such as rabies. However, the spatial ecology of raccoons remains poorly understood in many regions of the United States, particularly in the southeast. To better understand the spatial ecology of raccoons in the southeastern US, we investigated the role of sex, season, and habitat on monthly raccoon home range and core area sizes in three common rural habitats (bottomland hardwood, upland pine, and riparian forest) in South Carolina, USA. From 2018-2022, we obtained 264 monthly home ranges from 46 raccoons. Mean monthly 95% utilization distribution (UD) sizes ranged from 1.05 ± 0.48 km2 (breeding bottomland females) to 5.69 ± 3.37 km2 (fall riparian males) and mean monthly 60% UD sizes ranged from 0.25 ± 0.15 km2 (breeding bottomland females) to 1.59 ± 1.02 km2 (summer riparian males). Males maintained home range and core areas ~2-5 times larger than females in upland pine and riparian habitat throughout the year, whereas those of bottomland males were only larger than females during the breeding season. Home ranges and core areas of females did not vary across habitats, whereas male raccoons had home ranges and core areas ~2-3 times larger in upland pine and riparian compared to bottomland hardwood throughout much of the year. The home ranges of males in upland pine and riparian are among the largest recorded for raccoons in the United States. Such large and variable home ranges likely contribute to elevated risk of zoonotic disease spread by males in these habitats. These results can be used to inform disease mitigation strategies in the southeastern United States.

Multi-scale movement syndromes for comparative analyses of animal movement patterns

BACKGROUND

Animal movement is a behavioral trait shaped by the need to find food and suitable habitat, avoid predators, and reproduce. Using high-resolution tracking data, it is possible to describe movement in greater detail than ever before, which has led to many discoveries about the behavioral strategies of particular species. Recently, enough data been become available to enable a comparative approach, which has the potential to uncover general causes and consequences of variation in movement patterns, but which must be scale specific.

METHODS

Here we introduce a new multi-scale movement syndrome (MSMS) framework for describing and comparing animal movements and use it to explore the behavior of four sympatric mammals. MSMS incorporates four hierarchical scales of animal movement: (1) fine-scale movement steps which accumulate into (2) daily paths which then, over weeks or months, form a (3) life-history phase. Finally, (4) the lifetime track of an individual consists of multiple life-history phases connected by dispersal or migration events. We suggest a series of metrics to describe patterns of movement at each of these scales and use the first three scales of this framework to compare the movement of 46 animals from four frugivorous mammal species.

RESULTS

While subtle differences exist between the four species in their step-level movements, they cluster into three distinct movement syndromes in both path- and life-history phase level analyses. Differences in feeding ecology were a better predictor of movement patterns than a species' locomotory or sensory adaptations.

CONCLUSIONS

Given the role these species play as seed dispersers, these movement syndromes could have important ecosystem implications by affecting the pattern of seed deposition. This multiscale approach provides a hierarchical framework for comparing animal movement for addressing ecological and evolutionary questions. It parallels scales of analyses for resource selection functions, offering the potential to connect movement process with emergent patterns of space use.

Disease ecology: When a GPS logger tells you more than a blood sample

Long-term movement tracking revealed sublethal effects of avian influenza infection in vultures, adding an important element to our understanding of the subtleties of the feedback loop between host movement and pathogen transmission.

Population genomics of diarrheagenic Escherichia coli uncovers high connectivity between urban and rural communities in Ecuador

Human movement may be an important driver of transmission dynamics for enteric pathogens but has largely been underappreciated except for international 'travelers' diarrhea or cholera. Phylodynamic methods, which combine genomic and epidemiological data, are used to examine rates and dynamics of disease matching underlying evolutionary history and biogeographic distributions, but these methods often are not applied to enteric bacterial pathogens. We used phylodynamics to explore the phylogeographic and evolutionary patterns of diarrheagenic E. coli in northern Ecuador to investigate the role of human travel in the geographic distribution of strains across the country. Using whole genome sequences of diarrheagenic E. coli isolates, we built a core genome phylogeny, reconstructed discrete ancestral states across urban and rural sites, and estimated migration rates between E. coli populations. We found minimal structuring based on site locations, urban vs. rural locality, pathotype, or clinical status. Ancestral states of phylogenomic nodes and tips were inferred to have 51% urban ancestry and 49% rural ancestry. Lack of structuring by location or pathotype E. coli isolates imply highly connected communities and extensive sharing of genomic characteristics across isolates. Using an approximate structured coalescent model, we estimated rates of migration among circulating isolates were 6.7 times larger for urban towards rural populations compared to rural towards urban populations. This suggests increased inferred migration rates of diarrheagenic E. coli from urban populations towards rural populations. Our results indicate that investments in water and sanitation prevention in urban areas could limit the spread of enteric bacterial pathogens among rural populations.

Accelerometer-based detection of African swine fever infection in wild boar

Infectious wildlife diseases that circulate at the interface with domestic animals pose significant threats worldwide and require early detection and warning. Although animal tracking technologies are used to discern behavioural changes, they are rarely used to monitor wildlife diseases. Common disease-induced behavioural changes include reduced activity and lethargy ('sickness behaviour'). Here, we investigated whether accelerometer sensors could detect the onset of African swine fever (ASF), a viral infection that induces high mortality in suids for which no vaccine is currently available. Taking advantage of an experiment designed to test an oral ASF vaccine, we equipped 12 wild boars with an accelerometer tag and quantified how ASF affects their activity pattern and behavioural fingerprint, using overall dynamic body acceleration. Wild boars showed a daily reduction in activity of 10-20% from the healthy to the viremia phase. Using change point statistics and comparing healthy individuals living in semi-free and free-ranging conditions, we show how the onset of disease-induced sickness can be detected and how such early detection could work in natural settings. Timely detection of infection in animals is crucial for disease surveillance and control, and accelerometer technology on sentinel animals provides a viable complementary tool to existing disease management approaches.

Variation in herbivore space use: comparing two savanna ecosystems with different anthrax outbreak patterns in southern Africa

BACKGROUND

The distribution of resources can affect animal range sizes, which in turn may alter infectious disease dynamics in heterogenous environments. The risk of pathogen exposure or the spatial extent of outbreaks may vary with host range size. This study examined the range sizes of herbivorous anthrax host species in two ecosystems and relationships between spatial movement behavior and patterns of disease outbreaks for a multi-host environmentally transmitted pathogen.

METHODS

We examined range sizes for seven host species and the spatial extent of anthrax outbreaks in Etosha National Park, Namibia and Kruger National Park, South Africa, where the main host species and outbreak sizes differ. We evaluated host range sizes using the local convex hull method at different temporal scales, within-individual temporal range overlap, and relationships between ranging behavior and species contributions to anthrax cases in each park. We estimated the spatial extent of annual anthrax mortalities and evaluated whether the extent was correlated with case numbers of a given host species.

RESULTS

Range size differences among species were not linearly related to anthrax case numbers. In Kruger the main host species had small range sizes and high range overlap, which may heighten exposure when outbreaks occur within their ranges. However, different patterns were observed in Etosha, where the main host species had large range sizes and relatively little overlap. The spatial extent of anthrax mortalities was similar between parks but less variable in Etosha than Kruger. In Kruger outbreaks varied from small local clusters to large areas and the spatial extent correlated with case numbers and species affected. Secondary host species contributed relatively few cases to outbreaks; however, for these species with large range sizes, case numbers positively correlated with outbreak extent.

CONCLUSIONS

Our results provide new information on the spatiotemporal structuring of ranging movements of anthrax host species in two ecosystems. The results linking anthrax dynamics to host space use are correlative, yet suggest that, though partial and proximate, host range size and overlap may be contributing factors in outbreak characteristics for environmentally transmitted pathogens.

Variations in small-scale movements of, Rousettus aegyptiacus, a Marburg virus reservoir across a seasonal gradient

BACKGROUND

Bats are increasingly being recognized as important hosts for viruses, some of which are zoonotic and carry the potential for spillover within human and livestock populations. Biosurveillance studies focused on assessing the risk of pathogen transmission, however, have largely focused on the virological component and have not always considered the ecological implications of different species as viral hosts. The movements of known viral hosts are an important component for disease risk assessments as they can potentially identify regions of higher risk of contact and spillover. As such, this study aimed to synthesize data from both virological and ecological fields to provide a more holistic assessment of the risk of pathogen transmission from bats to people.

RESULTS

Using radiotelemetry, we tracked the small-scale movements of Rousettus aegyptiacus, a species of bat known to host Marburg virus and other viruses with zoonotic potential, in a rural settlement in Limpopo Province, South Africa. The tracked bats exhibited seasonal variations in their movement patterns including variable usage of residential areas which could translate to contact between bats and humans and may facilitate spillover. We identified a trend for increased usage of residential areas during the winter months with July specifically experiencing the highest levels of bat activity within residential areas. July has previously been identified as a key period for increased spillover risk for viruses associated with R. aegyptiacus from this colony and paired with the increased activity levels, illustrates the risk for spillover to human populations.

CONCLUSION

This study emphasizes the importance of incorporating ecological data such as movement patterns with virological data to provide a better understanding of the risk of pathogen spillover and transmission.

Foraging and mating behaviors of Hypsignathus monstrosus at the bat-human interface in a central African rainforest

Studying wildlife space use in human-modified environments contributes to characterize wildlife-human interactions to assess potential risks of zoonotic-pathogens transmission, and to pinpoint conservation issues. In central African rainforests with human dwelling and activities, we conducted a telemetry study on a group of males of Hypsignathus monstrosus, a lek-mating fruit bat identified as a potential maintenance host for Ebola virus. During a lekking season in 2020, we investigated the foraging-habitat selection and the individual nighttime space use during both mating and foraging activities close to villages and their surrounding agricultural landscape. At night, marked individuals strongly selected agricultural lands and more generally areas near watercourses to forage, where they spent more time compared to forest ones. Furthermore, the probability and duration of the presence of bats in the lek during nighttime decreased with the distance to their roost site but remained relatively high within a 10 km radius. Individuals adjusted foraging behaviors according to mating activity by reducing both the overall time spent in foraging areas and the number of forest areas used to forage when they spent more time in the lek. Finally, the probability of a bat revisiting a foraging area in the following 48 hours increased with the previous time spent in that foraging area. These behaviors occurring close to or in human-modified habitats can trigger direct and indirect bat-human contacts, which could thus facilitate pathogen transmission such as Ebola virus.

Deriving spatially explicit direct and indirect interaction networks from animal movement data

Quantifying spatiotemporally explicit interactions within animal populations facilitates the understanding of social structure and its relationship with ecological processes. Data from animal tracking technologies (Global Positioning Systems [“GPS”]) can circumvent longstanding challenges in the estimation of spatiotemporally explicit interactions, but the discrete nature and coarse temporal resolution of data mean that ephemeral interactions that occur between consecutive GPS locations go undetected. Here, we developed a method to quantify individual and spatial patterns of interaction using continuous‐time movement models (CTMMs) fit to GPS tracking data. We first applied CTMMs to infer the full movement trajectories at an arbitrarily fine temporal scale before estimating interactions, thus allowing inference of interactions occurring between observed GPS locations. Our framework then infers indirect interactions—individuals occurring at the same location, but at different times—while allowing the identification of indirect interactions to vary with ecological context based on CTMM outputs. We assessed the performance of our new method using simulations and illustrated its implementation by deriving disease‐relevant interaction networks for two behaviorally differentiated species, wild pigs (Sus scrofa) that can host African Swine Fever and mule deer (Odocoileus hemionus) that can host chronic wasting disease. Simulations showed that interactions derived from observed GPS data can be substantially underestimated when temporal resolution of movement data exceeds 30‐min intervals. Empirical application suggested that underestimation occurred in both interaction rates and their spatial distributions. CTMM‐Interaction method, which can introduce uncertainties, recovered majority of true interactions. Our method leverages advances in movement ecology to quantify fine‐scale spatiotemporal interactions between individuals from lower temporal resolution GPS data. It can be leveraged to infer dynamic social networks, transmission potential in disease systems, consumer–resource interactions, information sharing, and beyond. The method also sets the stage for future predictive models linking observed spatiotemporal interaction patterns to environmental drivers.

Cross-municipality migration and spread of tuberculosis in South Africa

Human migration facilitates the spread of infectious disease. However, little is known about the contribution of migration to the spread of tuberculosis in South Africa. We analyzed longitudinal data on all tuberculosis test results recorded by South Africa's National Health Laboratory Service (NHLS), January 2011-July 2017, alongside municipality-level migration flows estimated from the 2016 South African Community Survey. We first assessed migration patterns in people with laboratory-diagnosed tuberculosis and analyzed demographic predictors. We then quantified the impact of cross-municipality migration on tuberculosis incidence in municipality-level regression models. The NHLS database included 921,888 patients with multiple clinic visits with TB tests. Of these, 147,513 (16%) had tests in different municipalities. The median (IQR) distance travelled was 304 (163 to 536) km. Migration was most common at ages 20-39 years and rates were similar for men and women. In municipality-level regression models, each 1% increase in migration-adjusted tuberculosis prevalence was associated with a 0.47% (95% CI: 0.03% to 0.90%) increase in the incidence of drug-susceptible tuberculosis two years later, even after controlling for baseline prevalence. Similar results were found for rifampicin-resistant tuberculosis. Accounting for migration improved our ability to predict future incidence of tuberculosis.

Movement predictability of individual barn owls facilitates estimation of home range size and survival

BACKGROUND

There is growing attention to individuality in movement, its causes and consequences. Similarly to other well-established personality traits (e.g., boldness or sociability), conspecifics also differ repeatedly in their spatial behaviors, forming behavioral types ("spatial-BTs"). These spatial-BTs are typically described as the difference in the mean-level among individuals, and the intra-individual variation (IIV, i.e., predictability) is only rarely considered. Furthermore, the factors determining predictability or its ecological consequences for broader space-use patterns are largely unknown, in part because predictability was mostly tested in captivity (e.g., with repeated boldness assays). Here we test if (i) individuals differ in their movement and specifically in their predictability. We then investigate (ii) the consequences of this variation for home-range size and survival estimates, and (iii) the factors that affect individual predictability.

METHODS

We tracked 92 barn owls (Tyto alba) with an ATLAS system and monitored their survival. From these high-resolution (every few seconds) and extensive trajectories (115.2 ± 112.1 nights; X̅ ± SD) we calculated movement and space-use indices (e.g., max-displacement and home-range size, respectively). We then used double-hierarchical and generalized linear mix-models to assess spatial-BTs, individual predictability in nightly max-displacement, and its consistency across time. Finally, we explored if predictability levels were associated with home-range size and survival, as well as the seasonal, geographical, and demographic factors affecting it (e.g., age, sex, and owls' density).

RESULTS

Our dataset (with 74 individuals after filtering) revealed clear patterns of individualism in owls' movement. Individuals differed consistently both in their mean movement (e.g., max-displacement) and their IIV around it (i.e., predictability). More predictable individuals had smaller home-ranges and lower survival rates, on top and beyond the expected effects of their spatial-BT (max-displacement), sex, age and ecological environments. Juveniles were less predictable than adults, but the sexes did not differ in their predictability.

CONCLUSION

These results demonstrate that individual predictability may act as an overlooked axis of spatial-BT with potential implications for relevant ecological processes at the population level and individual fitness. Considering how individuals differ in their IIV of movement beyond the mean-effect can facilitate understanding the intraspecific diversity, predicting their responses to changing ecological conditions and their population management.

Effect of legal regulation of supplemental feeding on space use of red deer in an area with chronic wasting disease

Supplemental feeding of cervids during winter is a widespread management practice, but feeding may increase the risk of disease transmission. Therefore, legal regulations to limit supplemental feeding are often implemented when dealing with severe infectious diseases, such as chronic wasting disease (CWD) in cervids. However, it is currently unclear whether these regulations result in decreased spatial clustering and aggregation as intended. Supplemental feeding is expected to restrict the movement of cervids. Therefore, a ban on feeding may also result in wider space use and a risk of geographic spread of disease. The space use of 63 GPS-marked red deer (Cervus elaphus) was investigated before (n = 34) and after (n = 29) the implementation of a legal regulation aimed at limiting the supplemental feeding of cervids during winter in a CWD-affected region of Nordfjella, Norway. Snow depth was the main determinant of the space use for red deer. A moderate reduction in the number of GPS positions in spatial clusters was evident during periods of deep snow once the ban was in place. Sizes of core areas (Kernel 50%), home ranges (Kernel 95%), and dispersion (MCP 100%, number of 1 km2 pixels visited per deer) declined from January to March and with increasing snow depth. Dispersion (number of 1 km2 pixels visited per deer) did not depend on snow depth after the ban, and red deer used larger areas when snow depth was high after the ban compared to before. The ban on supplementary feeding had no effect on size of core areas or home ranges. Several potential factors can explain the overall weak effect of the ban on space use, including the use of agricultural fields by red deer, other anthropogenic feeding, and landscape topography. This study highlights that snow depth is the main factor determining space use during winter, and it remains to be determined whether the moderate reduction in spatial clustering during deep snow after the ban was sufficient to lower the risk of disease transmission.

Capturing complex interactions in disease ecology with simplicial sets

Network approaches have revolutionized the study of ecological interactions. Social, movement and ecological networks have all been integral to studying infectious disease ecology. However, conventional (dyadic) network approaches are limited in their ability to capture higher-order interactions. We present simplicial sets as a tool that addresses this limitation. First, we explain what simplicial sets are. Second, we explain why their use would be beneficial in different subject areas. Third, we detail where these areas are: social, transmission, movement/spatial and ecological networks and when using them would help most in each context. To demonstrate their application, we develop a novel approach to identify how pathogens persist within a host population. Fourth, we provide an overview of how to use simplicial sets, highlighting specific metrics, generative models and software. Finally, we synthesize key research questions simplicial sets will help us answer and draw attention to methodological developments that will facilitate this.

Simulation applications to support teaching and research in epidemiological dynamics

BACKGROUND

An understanding of epidemiological dynamics, once confined to mathematical epidemiologists and applied mathematicians, can be disseminated to a non-mathematical community of health care professionals and applied biologists through simple-to-use simulation applications. We used Numerus Model Builder RAMP ^Ⓡ (Runtime Alterable Model Platform) technology, to construct deterministic and stochastic versions of compartmental SIR (Susceptible, Infectious, Recovered with immunity) models as simple-to-use, freely available, epidemic simulation application programs.

RESULTS

We take the reader through simulations used to demonstrate the following concepts: 1) disease prevalence curves of unmitigated outbreaks have a single peak and result in epidemics that 'burn' through the population to become extinguished when the proportion of the susceptible population drops below a critical level; 2) if immunity in recovered individuals wanes sufficiently fast then the disease persists indefinitely as an endemic state, with possible dampening oscillations following the initial outbreak phase; 3) the steepness and initial peak of the prevalence curve are influenced by the basic reproductive value R₀, which must exceed 1 for an epidemic to occur; 4) the probability that a single infectious individual in a closed population (i.e. no migration) gives rise to an epidemic increases with the value of R₀>1; 5) behavior that adaptively decreases the contact rate among individuals with increasing prevalence has major effects on the prevalence curve including dramatic flattening of the prevalence curve along with the generation of multiple prevalence peaks; 6) the impacts of treatment are complicated to model because they effect multiple processes including transmission, recovery and mortality; 7) the impacts of vaccination policies, constrained by a fixed number of vaccination regimens and by the rate and timing of delivery, are crucially important to maximizing the ability of vaccination programs to reduce mortality.

CONCLUSION

Our presentation makes transparent the key assumptions underlying SIR epidemic models. Our RAMP simulators are meant to augment rather than replace classroom material when teaching epidemiological dynamics. They are sufficiently versatile to be used by students to address a range of research questions for term papers and even dissertations.

A framework for integrating inferred movement behavior into disease risk models

Movement behavior is an important contributor to habitat selection and its incorporation in disease risk models has been somewhat neglected. The habitat preferences of host individuals affect their probability of exposure to pathogens. If preference behavior can be incorporated in ecological niche models (ENMs) when data on pathogen distributions are available, then variation in such behavior may dramatically impact exposure risk. Here we use data from the anthrax endemic system of Etosha National Park, Namibia, to demonstrate how integrating inferred movement behavior alters the construction of disease risk maps. We used a Maximum Entropy (MaxEnt) model that associated soil, bioclimatic, and vegetation variables with the best available pathogen presence data collected at anthrax carcass sites to map areas of most likely Bacillus anthracis (the causative bacterium of anthrax) persistence. We then used a hidden Markov model (HMM) to distinguish foraging and non-foraging behavioral states along the movement tracks of nine zebra (Equus quagga) during the 2009 and 2010 anthrax seasons. The resulting tracks, decomposed on the basis of the inferred behavioral state, formed the basis of step-selection functions (SSFs) that used the MaxEnt output as a potential predictor variable. Our analyses revealed different risks of exposure during different zebra behavioral states, which were obscured when the full movement tracks were analyzed without consideration of the underlying behavioral states of individuals. Pathogen (or vector) distribution models may be misleading with regard to the actual risk faced by host animal populations when specific behavioral states are not explicitly accounted for in selection analyses. To more accurately evaluate exposure risk, especially in the case of environmentally transmitted pathogens, selection functions could be built for each identified behavioral state and then used to assess the comparative exposure risk across relevant states. The scale of data collection and analysis, however, introduces complexities and limitations for consideration when interpreting results.

Parasite-induced shifts in host movement may explain the transient coexistence of high- and low-pathogenic disease strains

Many parasites induce decreased host movement, known as lethargy, which can impact disease spread and the evolution of virulence. Mathematical models have investigated virulence evolution when parasites cause host death, but disease-induced decreased host movement has received relatively less attention. Here, we consider a model where, due to the within-host parasite replication rate, an infected host can become lethargic and shift from a moving to a resting state, where it can die. We find that when the lethargy and disease-induced mortality costs to the parasites are not high, then evolutionary bistability can arise, and either moderate or high virulence can evolve depending on the initial virulence and the magnitude of mutation. These results suggest, firstly, the coexistence of strains with different virulence, which may explain the transient coexistence of low- and high-pathogenic strains of avian influenza viruses, and secondly, that medical interventions to treat the symptoms of lethargy or prevent disease-induced host deaths can result in a large jump in virulence and the rapid evolution of high virulence. In complement to existing results that show bistability when hosts are heterogeneous at the population level, we show that evolutionary bistability may arise due to transmission heterogeneity at the individual host level.

Eyes on the herd: Quantifying ungulate density from satellite, unmanned aerial systems, and GPScollar data

Novel approaches to quantifying density and distributions could help biologists adaptively manage wildlife populations, particularly if methods are accurate, consistent, cost-effective, rapid, and sensitive to change. Such approaches may also improve research on interactions between density and processes of interest, such as disease transmission across multiple populations. We assess how satellite imagery, unmanned aerial system (UAS) imagery, and Global Positioning System (GPS) collar data vary in characterizing elk density, distribution, and count patterns across times with and without supplemental feeding at the National Elk Refuge (NER) in the US state of Wyoming. We also present the first comparison of satellite imagery data with traditional counts for ungulates in a temperate system. We further evaluate seven different aggregation metrics to identify the most consistent and sensitive metrics for comparing density and distribution across time and populations. All three data sources detected higher densities and aggregation locations of elk during supplemental feeding than non-feeding at the NER. Kernel density estimates (KDEs), KDE polygon areas, and the first quantile of interelk distances detected differences with the highest sensitivity and were most highly correlated across data sources. Both UAS and satellite imagery provide snapshots of density and distribution patterns of most animals in the area at lower cost than GPS collars. While satellite-based counts were lower than traditional counts, aggregation metrics matched those from UAS and GPS data sources when animals appeared in high contrast to the landscape, including brown elk against new snow in open areas. UAS counts of elk were similar to traditional ground-based counts on feed grounds and are the best data source for assessing changes in small spatial extents. Satellite, UAS, or GPS data can provide appropriate data for assessing density and changes in density from adaptive management actions. For the NER, where high elk densities are beneath controlled airspace, GPS collar data will be most useful for evaluating how management actions, including changes in the dates of supplemental feeding, influence elk density and aggregation across large spatial extents. Using consistent and sensitive measures of density may improve research on the drivers and effects of density within and across a wide range of species.

Climate change increases cross-species viral transmission risk

At least 10,000 virus species have the ability to infect humans but, at present, the vast majority are circulating silently in wild mammals^1,2. However, changes in climate and land use will lead to opportunities for viral sharing among previously geographically isolated species of wildlife^3,4. In some cases, this will facilitate zoonotic spillover-a mechanistic link between global environmental change and disease emergence. Here we simulate potential hotspots of future viral sharing, using a phylogeographical model of the mammal-virus network, and projections of geographical range shifts for 3,139 mammal species under climate-change and land-use scenarios for the year 2070. We predict that species will aggregate in new combinations at high elevations, in biodiversity hotspots, and in areas of high human population density in Asia and Africa, causing the cross-species transmission of their associated viruses an estimated 4,000 times. Owing to their unique dispersal ability, bats account for the majority of novel viral sharing and are likely to share viruses along evolutionary pathways that will facilitate future emergence in humans. Notably, we find that this ecological transition may already be underway, and holding warming under 2 °C within the twenty-first century will not reduce future viral sharing. Our findings highlight an urgent need to pair viral surveillance and discovery efforts with biodiversity surveys tracking the range shifts of species, especially in tropical regions that contain the most zoonoses and are experiencing rapid warming.

Asymmetric host movement reshapes local disease dynamics in metapopulations

Understanding how the movement of individuals affects disease dynamics is critical to accurately predicting and responding to the spread of disease in an increasingly interconnected world. In particular, it is not yet known how movement between patches affects local disease dynamics (e.g., whether pathogen prevalence remains steady or oscillates through time). Considering a set of small, archetypal metapopulations, we find three surprisingly simple patterns emerge in local disease dynamics following the introduction of movement between patches: (1) movement between identical patches with cyclical pathogen prevalence dampens oscillations in the destination while increasing synchrony between patches; (2) when patches differ from one another in the absence of movement, adding movement allows dynamics to propagate between patches, alternatively stabilizing or destabilizing dynamics in the destination based on the dynamics at the origin; and (3) it is easier for movement to induce cyclical dynamics than to induce a steady-state. Considering these archetypal networks (and the patterns they exemplify) as building blocks of larger, more realistically complex metapopulations provides an avenue for novel insights into the role of host movement on disease dynamics. Moreover, this work demonstrates a framework for future predictive modelling of disease spread in real populations.

A model for leveraging animal movement to understand spatio-temporal disease dynamics

The ongoing explosion of fine-resolution movement data in animal systems provides a unique opportunity to empirically quantify spatial, temporal and individual variation in transmission risk and improve our ability to forecast disease outbreaks. However, we lack a generalizable model that can leverage movement data to quantify transmission risk and how it affects pathogen invasion and persistence on heterogeneous landscapes. We developed a flexible model 'Movement-driven modelling of spatio-temporal infection risk' (MoveSTIR) that leverages diverse data on animal movement to derive metrics of direct and indirect contact by decomposing transmission into constituent processes of contact formation and duration and pathogen deposition and acquisition. We use MoveSTIR to demonstrate that ignoring fine-scale animal movements on actual landscapes can mis-characterize transmission risk and epidemiological dynamics. MoveSTIR unifies previous work on epidemiological contact networks and can address applied and theoretical questions at the nexus of movement and disease ecology.

How do genetic relatedness and spatial proximity shape African swine fever infections in wild boar?

The importance of social and spatial structuring of wildlife populations for disease spread, though widely recognized, is still poorly understood in many host-pathogen systems. In particular, system specific kin relationships among hosts can create contact heterogeneities and differential disease transmission rates. Here, we investigate how distance-dependent infection risk is influenced by genetic relatedness in a novel host-pathogen system: wild boar (Sus scrofa) and African swine fever (ASF). We hypothesized that infection risk would correlate positively with proximity and relatedness to ASF-infected individuals but expected those relationships to weaken with distance between individuals due to decay in contact rates and genetic similarity. We genotyped 323 wild boar samples (243 ASF-negative and 80 ASF-positive) collected in north-eastern Poland in 2014-2016 and modeled the effects of geographic distance, genetic relatedness, and ASF virus transmission mode (direct or carcass-based) on the probability of ASF infection. Infection risk was positively associated with spatial proximity and genetic relatedness to infected individuals with generally stronger effect of distance. In the high-contact zone (0-2 km), infection risk was shaped by the presence of infected individuals rather than by relatedness to them. In the medium-contact zone (2-5 km), infection risk decreased but was still associated with relatedness and paired infections were more frequent among relatives. At farther distances, infection risk further declined with relatedness and proximity to positive individuals, and was 60% lower among unrelated individuals in the no-contact zone (33% in10-20 km) compared with among relatives in the high-contact zone (93% in 0-2 km). Transmission mode influenced the relationship between proximity or relatedness and infection risk. Our results indicate that the presence of nearby infected individuals is most important for shaping ASF infection rates through carcass-based transmission, while relatedness plays an important role in shaping transmission rates between live animals. This article is protected by copyright. All rights reserved.

Water sources aggregate parasites with increasing effects in more arid conditions

Shifts in landscape heterogeneity and climate can influence animal movement in ways that profoundly alter disease transmission. Water sources that are foci of animal activity have great potential to promote disease transmission, but it is unknown how this varies across a range of hosts and climatic contexts. For fecal-oral parasites, water resources can aggregate many different hosts in small areas, concentrate infectious material, and function as disease hotspots. This may be exacerbated where water is scarce and for species requiring frequent water access. Working in an East African savanna, we show via experimental and observational methods that water sources increase the density of wild and domestic herbivore feces and thus, the concentration of fecal-oral parasites in the environment, by up to two orders of magnitude. We show that this effect is amplified in drier areas and drier periods, creating dynamic and heterogeneous disease landscapes across space and time. We also show that herbivore grazing behaviors that expose them to fecal-oral parasites often increase at water sources relative to background sites, increasing potential parasite transmission at these hotspots. Critically, this effect varies by herbivore species, with strongest effects for two animals of concern for conservation and development: elephants and cattle.

Analyses of Contact Networks of Community Dogs on a University Campus in Nakhon Pathom, Thailand

Free-roaming dogs have been identified as an important reservoir of rabies in many countries including Thailand. There is a need for novel insights to improve current rabies control strategies in these countries. Network analysis is commonly used to study the interactions between individuals or organizations and has been applied in preventive veterinary medicine. However, contact networks of domestic free-roaming dogs are mostly unexplored. The objective of this study was to explore the contact network of free-roaming dogs residing on a university campus. Three one-mode networks were created using co-appearances of dogs as edges. A two-mode network was created by associating the dog with the pre-defined area it was seen in. The average number of contacts a dog had was 6.74. The normalized degree for the weekend network was significantly higher compared to the weekday network. All one-mode networks displayed small-world network characteristics. Most dogs were observed in only one area. The average number of dogs which shared an area was 8.67. In this study, we demonstrated the potential of observational methods to create networks of contacts. The network information acquired can be further used in network modeling and designing targeted disease control programs.

Personality, spatiotemporal ecological variation and resident/explorer movement syndromes in the sleepy lizard

Individual variation in movement is profoundly important for fitness and offers key insights into the spatial and temporal dynamics of populations and communities. Nonetheless, individual variation in fine-scale movement behaviours is rarely examined even though animal tracking devices offer the long-term, high-resolution, repeatable data in natural conditions that are ideal for studying this variation. Furthermore, of the few studies that consider individual variation in movement, even fewer also consider the internal traits and environmental factors that drive movement behaviour which are necessary for contextualising individual differences in movement patterns. In this study, we GPS tracked a free-ranging population of sleepy lizards Tiliqua rugosa, each Austral spring over 5 years to examine consistent among-individual variation in movement patterns, as well as how these differences were mediated by key internal and ecological factors. We found that individuals consistently differed in a suite of weekly movement traits, and that these traits strongly covaried among-individuals, forming movement syndromes. Lizards fell on a primary movement continuum, from 'residents' that spent extended periods of time residing within smaller core areas of their home range, to 'explorers' that moved greater distances and explored vaster areas of the environment. Importantly, we also found that these consistent differences in lizard movement were related to two ecologically important animal personality traits (boldness and aggression), their sex, key features of the environment (including food availability, and a key water resource), habitat type and seasonal variation (cool/moist vs. hot/drier) in environmental conditions. Broadly, these movement specialisations likely reflect variation in life-history tactics including foraging and mating tactics that ultimately underlie key differences in space use. Such information can be used to connect phenotypic population structure to key ecological and evolutionary processes, for example social networks and disease-transmission pathways, further highlighting the value of examining individual variation in movement behaviour.

Mechanistic movement models reveal ecological drivers of tick-borne pathogen spread

Identifying ecological drivers of tick-borne pathogen spread has great value for tick-borne disease management. However, theoretical investigations into the consequences of host movement behaviour on pathogen spread dynamics in heterogeneous landscapes remain limited because spatially explicit epidemiological models that incorporate more realistic mechanisms governing host movement are rare. We built a mechanistic movement model to investigate how the interplay between multiple ecological drivers affects the risk of tick-borne pathogen spread across heterogeneous landscapes. We used the model to generate simulations of tick dispersal by migratory birds and terrestrial hosts across theoretical landscapes varying in resource aggregation, and we performed a sensitivity analysis to explore the impacts of different parameters on the infected tick spread rate, tick infection prevalence and infected tick density. Our findings highlight the importance of host movement and tick population dynamics in explaining the infected tick spread rate into new regions. Tick infection prevalence and infected tick density were driven by predictors related to the infection process and tick population dynamics, respectively. Our results suggest that control strategies aiming to reduce tick burden on tick reproduction hosts and encounter rate between immature ticks and pathogen amplification hosts will be most effective at reducing tick-borne disease risk.

Spatial and temporal dynamics of space use by free-ranging domestic dogs Canis familiaris in rural Africa

Variation in the spatial ecology of animals influences the transmission of infections and so understanding host behavior can improve the control of diseases. Despite the global distribution of free-ranging domestic dogs Canis familiaris and their role as reservoirs for zoonotic diseases, little is known about the dynamics of their space use. We deployed GPS loggers on owned but free-ranging dogs from six villages in rural Chad, and tracked the movements of 174 individuals in the dry season and 151 in the wet season. We calculated 95% and core home ranges using auto-correlated kernel density estimates (AKDE₉₅ and AKDE_core ), determined the degree to which their movements were predictable, and identified correlates of movement patterns. The median AKDE₉₅ range in the dry season was 0.54 km² and in the wet season was 0.31 km² , while the median AKDE_core range in the dry season was 0.08 km² and in the wet season was 0.04 km² . Seasonal variation was, in part, related to owner activities; dogs from hunting households had ranges that were five times larger in the dry season. At least 70% of individuals were more predictably "at home" (<50 m from the household) throughout the day in the dry season, 80% of dogs demonstrated periodicity in activity levels (speed), and just over half the dogs exhibited periodicity in location (repeated space use). In the wet season, dogs mostly exhibited 24-h cycles in activity and location, with peaks at midday. In the dry season, dogs exhibited both 12- and 24-h cycles, with either a single peak at midday, or one peak between 06:00 and 12:00 and a second between 18:00 and 22:00. Strategies to control canine-mediated zoonoses can be improved by tailoring operations to the local spatial ecology of free-ranging dogs. Interventions using a door-to-door strategy in rural Chad would best conduct operations during the dry season, when access to dogs around their household more reliably exceeds 70% throughout the day. Given the importance of use in hunting for explaining variation in dog space-use, targeting approaches to disease control at the household level on the basis of owner activities offers potential to improve access to dogs.

Disease or drought: environmental fluctuations release zebra from a potential pathogen-triggered ecological trap

When a transmission hotspot for an environmentally persistent pathogen establishes in otherwise high-quality habitat, the disease may exert a strong impact on a host population. However, fluctuating environmental conditions lead to heterogeneity in habitat quality and animal habitat preference, which may interrupt the overlap between selected and risky habitats. We evaluated spatio-temporal patterns in anthrax mortalities in a plains zebra (Equus quagga) population in Etosha National Park, Namibia, incorporating remote-sensing and host telemetry data. A higher proportion of anthrax mortalities of herbivores was detected in open habitats than in other habitat types. Resource selection functions showed that the zebra population shifted habitat selection in response to changes in rainfall and vegetation productivity. Average to high rainfall years supported larger anthrax outbreaks, with animals congregating in preferred open habitats, while a severe drought forced animals into otherwise less preferred habitats, leading to few anthrax mortalities. Thus, the timing of anthrax outbreaks was congruent with preference for open plains habitats and a corresponding increase in pathogen exposure. Given shifts in habitat preference, the overlap in high-quality habitat and high-risk habitat is intermittent, reducing the adverse consequences for the population.

The Relationship Between GPS Sampling Interval and Estimated Daily Travel Distances in Chacma Baboons (Papio ursinus)

Modern studies of animal movement use the Global Positioning System (GPS) to estimate animals’ distance traveled. The temporal resolution of GPS fixes recorded should match those of the behavior of interest; otherwise estimates are likely to be inappropriate. Here, we investigate how different GPS sampling intervals affect estimated daily travel distances for wild chacma baboons (Papio ursinus). By subsampling GPS data collected at one fix per second for 143 daily travel distances (12 baboons over 11–12 days), we found that less frequent GPS fixes result in smaller estimated travel distances. Moving from a GPS frequency of one fix every second to one fix every 30 s resulted in a 33% reduction in estimated daily travel distance, while using hourly GPS fixes resulted in a 66% reduction. We then use the relationship we find between estimated travel distance and GPS sampling interval to recalculate published baboon daily travel distances and find that accounting for the predicted effect of sampling interval does not affect conclusions of previous comparative analyses. However, if short-interval or continuous GPS data—which are becoming more common in studies of primate movement ecology—are compared with historical (longer interval) GPS data in future work, controlling for sampling interval is necessary.

Transient disease dynamics across ecological scales

Analyses of transient dynamics are critical to understanding infectious disease transmission and persistence. Identifying and predicting transients across scales, from within-host to community-level patterns, plays an important role in combating ongoing epidemics and mitigating the risk of future outbreaks. Moreover, greater emphases on non-asymptotic processes will enable timely evaluations of wildlife and human diseases and lead to improved surveillance efforts, preventive responses, and intervention strategies. Here, we explore the contributions of transient analyses in recent models spanning the fields of epidemiology, movement ecology, and parasitology. In addition to their roles in predicting epidemic patterns and endemic outbreaks, we explore transients in the contexts of pathogen transmission, resistance, and avoidance at various scales of the ecological hierarchy. Examples illustrate how (i) transient movement dynamics at the individual host level can modify opportunities for transmission events over time; (ii) within-host energetic processes often lead to transient dynamics in immunity, pathogen load, and transmission potential; (iii) transient connectivity between discrete populations in response to environmental factors and outbreak dynamics can affect disease spread across spatial networks; and (iv) increasing species richness in a community can provide transient protection to individuals against infection. Ultimately, we suggest that transient analyses offer deeper insights and raise new, interdisciplinary questions for disease research, consequently broadening the applications of dynamical models for outbreak preparedness and management.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s12080-021-00514-w.

Elk migration influences the risk of disease spillover in the Greater Yellowstone Ecosystem

Wildlife migrations provide important ecosystem services, but they are declining. Within the Greater Yellowstone Ecosystem (GYE), some elk Cervus canadensis herds are losing migratory tendencies, which may increase spatiotemporal overlap between elk and livestock (domestic bison Bison bison and cattle Bos taurus), potentially exacerbating pathogen transmission risk.

We combined disease, movement, demographic and environmental data from eight elk herds in the GYE to examine the differential risk of brucellosis transmission (through aborted foetuses) from migrant and resident elk to livestock.

For both migrants and residents, we found that transmission risk from elk to livestock occurred almost exclusively on private ranchlands as opposed to state or federal grazing allotments. Weather variability affected the estimated distribution of spillover risk from migrant elk to livestock, with a 7%–12% increase in migrant abortions on private ranchlands during years with heavier snowfall. In contrast, weather variability did not affect spillover risk from resident elk.

Migrant elk were responsible for the majority (68%) of disease spillover risk to livestock because they occurred in greater numbers than resident elk. On a per‐capita basis, however, our analyses suggested that resident elk disproportionately contributed to spillover risk. In five of seven herds, we estimated that the per‐capita spillover risk was greater from residents than from migrants. Averaged across herds, an individual resident elk was 23% more likely than an individual migrant elk to abort on private ranchlands.

Our results demonstrate links between migration behaviour, spillover risk and environmental variability, and highlight the utility of integrating models of pathogen transmission and host movement to generate new insights about the role of migration in disease spillover risk. Furthermore, they add to the accumulating body of evidence across taxa that suggests that migrants and residents should be considered separately during investigations of wildlife disease ecology. Finally, our findings have applied implications for elk and brucellosis in the GYE. They suggest that managers should prioritize actions that maintain spatial separation of elk and livestock on private ranchlands during years when snowpack persists into the risk period.