An agent-based model of red colobus resources and disease dynamics implicates key resource sites as hot spots of disease transmission

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.

Social Network Predicts Exposure to Respiratory Infection in a Wild Chimpanzee Group

Respiratory pathogens are expected to spread through social contacts, but outbreaks often occur quickly and unpredictably, making it challenging to simultaneously record social contact and disease incidence data, especially in wildlife. Thus, the role of social contacts in the spread of infectious disease is often treated as an assumption in disease simulation studies, and few studies have empirically demonstrated how pathogens spread through social networks. In July-August 2015, an outbreak of respiratory disease was observed in a wild chimpanzee community in Kibale National Park, Uganda, during an ongoing behavioral study of male chimpanzees, offering a rare opportunity to evaluate how social behavior affects individual exposure to socially transmissible diseases. From May to August 2015, we identified adult and adolescent male chimpanzees displaying coughs and rhinorrhea and recorded 5-m proximity data on males (N = 40). Using the network k-test, we found significant relationships between male network connectivity and the distribution of cases within the network, supporting the importance of short-distance contacts for the spread of the respiratory outbreak. Additionally, chimpanzees central to the network were more likely to display clinical signs than those with fewer connections. Although our analyses were limited to male chimpanzees, these findings underscore the value of social connectivity data in predicting disease outcomes and elucidate a potential evolutionary cost of being social.

An introduction to agent-based models as an accessible surrogate to field-based research and teaching

There are many barriers to fieldwork including cost, time, and physical ability. Unfortunately, these barriers disproportionately affect minority communities and create a disparity in access to fieldwork in the natural sciences. Travel restrictions, concerns about our carbon footprint, and the global lockdown have extended this barrier to fieldwork across the community and led to increased anxiety about gaps in productivity, especially among graduate students and early-career researchers. In this paper, we discuss agent-based modeling as an open-source, accessible, and inclusive resource to substitute for lost fieldwork during COVID-19 and for future scenarios of travel restrictions such as climate change and economic downturn. We describe the benefits of Agent-Based models as a teaching and training resource for students across education levels. We discuss how and why educators and research scientists can implement them with examples from the literature on how agent-based models can be applied broadly across life science research. We aim to amplify awareness and adoption of this technique to broaden the diversity and size of the agent-based modeling community in ecology and evolutionary research. Finally, we discuss the challenges facing agent-based modeling and discuss how quantitative ecology can work in tandem with traditional field ecology to improve both methods.

Behavioural ecology and infectious disease: implications for conservation of biodiversity

Behaviour underpins interactions among conspecifics and between species, with consequences for the transmission of disease-causing parasites. Because many parasites lead to declines in population size and increased risk of extinction for threatened species, understanding the link between host behaviour and disease transmission is particularly important for conservation management. Here, we consider the intersection of behaviour, ecology and parasite transmission, broadly encompassing micro- and macroparasites. We focus on behaviours that have direct impacts on transmission, as well as the behaviours that result from infection. Given the important role of parasites in host survival and reproduction, the effects of behaviour on parasitism can scale up to population-level processes, thus affecting species conservation. Understanding how conservation and infectious disease control strategies actually affect transmission potential can therefore often only be understood through a behavioural lens. We highlight how behavioural perspectives of disease ecology apply to conservation by reviewing the different ways that behavioural ecology influences parasite transmission and conservation goals. This article is part of the theme issue 'Linking behaviour to dynamics of populations and communities: application of novel approaches in behavioural ecology to conservation'.

Challenges and Opportunities Developing Mathematical Models of Shared Pathogens of Domestic and Wild Animals

Diseases that affect both wild and domestic animals can be particularly difficult to prevent, predict, mitigate, and control. Such multi-host diseases can have devastating economic impacts on domestic animal producers and can present significant challenges to wildlife populations, particularly for populations of conservation concern. Few mathematical models exist that capture the complexities of these multi-host pathogens, yet the development of such models would allow us to estimate and compare the potential effectiveness of management actions for mitigating or suppressing disease in wildlife and/or livestock host populations. We conducted a workshop in March 2014 to identify the challenges associated with developing models of pathogen transmission across the wildlife-livestock interface. The development of mathematical models of pathogen transmission at this interface is hampered by the difficulties associated with describing the host-pathogen systems, including: (1) the identity of wildlife hosts, their distributions, and movement patterns; (2) the pathogen transmission pathways between wildlife and domestic animals; (3) the effects of the disease and concomitant mitigation efforts on wild and domestic animal populations; and (4) barriers to communication between sectors. To promote the development of mathematical models of transmission at this interface, we recommend further integration of modern quantitative techniques and improvement of communication among wildlife biologists, mathematical modelers, veterinary medicine professionals, producers, and other stakeholders concerned with the consequences of pathogen transmission at this important, yet poorly understood, interface.

Investigating behavioral drivers of seasonal Shiga-Toxigenic Escherichia Coli (STEC) patterns in grazing cattle using an agent-based model

The causes of seasonal variability in pathogen transmission are not well understood, and have not been comprehensively investigated. In an example for enteric pathogens, incidence of Escherichia coli O157 (STEC) colonization in cattle is consistently higher during warmer months compared to cooler months in various cattle production systems. However, actual mechanisms for this seasonality remain elusive. In addition, the influence of host (cattle) behavior on this pattern has not been thoroughly considered. To that end, we constructed a spatially explicit agent-based model that accounted for the effect of temperature fluctuations on cattle behavior (direct contact among cattle and indirect between cattle and environment), as well as its effect on pathogen survival in the environment. We then simulated the model in a factorial approach to evaluate the hypothesis that temperature fluctuations can lead to seasonal STEC transmission dynamics by influencing cattle aggregation, grazing, and drinking behaviors. Simulation results showed that higher temperatures increased the frequency at which cattle aggregated under shade in pasture, resulting in increased direct contact and transmission of STEC between individual cattle, and hence higher incidence over model simulations in the warm season. In contrast, increased drinking behavior during warm season was not an important transmission pathway. Although sensitivity analyses suggested that the relative importance of direct vs. indirect (environmental) pathways depend to upon model parameterization, model simulations indicated that factors influencing cattle aggregation, such as temperature, were likely strong drivers of transmission dynamics of enteric pathogens.

Disease outbreak thresholds emerge from interactions between movement behavior, landscape structure, and epidemiology

Disease models have provided conflicting evidence as to whether spatial heterogeneity promotes or impedes pathogen persistence. Moreover, there has been limited theoretical investigation into how animal movement behavior interacts with the spatial organization of resources (e.g., clustered, random, uniform) across a landscape to affect infectious disease dynamics. Importantly, spatial heterogeneity of resources can sometimes lead to nonlinear or counterintuitive outcomes depending on the host and pathogen system. There is a clear need to develop a general theoretical framework that could be used to create testable predictions for specific host-pathogen systems. Here, we develop an individual-based model integrated with movement ecology approaches to investigate how host movement behaviors interact with landscape heterogeneity (in the form of various levels of resource abundance and clustering) to affect pathogen dynamics. For most of the parameter space, our results support the counterintuitive idea that fragmentation promotes pathogen persistence, but this finding was largely dependent on perceptual range of the host, conspecific density, and recovery rate. For simulations with high conspecific density, slower recovery rates, and larger perceptual ranges, more complex disease dynamics emerged, and the most fragmented landscapes were not necessarily the most conducive to outbreaks or pathogen persistence. These results point to the importance of interactions between landscape structure, individual movement behavior, and pathogen transmission for predicting and understanding disease dynamics.

Going through the motions: incorporating movement analyses into disease research

Though epidemiology dates back to the 1700s, most mathematical representations of epidemics still use transmission rates averaged at the population scale, especially for wildlife diseases. In simplifying the contact process, we ignore the heterogeneities in host movements that complicate the real world, and overlook their impact on spatiotemporal patterns of disease burden. Movement ecology offers a set of tools that help unpack the transmission process, letting researchers more accurately model how animals within a population interact and spread pathogens. Analytical techniques from this growing field can also help expose the reverse process: how infection impacts movement behaviours, and therefore other ecological processes like feeding, reproduction, and dispersal. Here, we synthesise the contributions of movement ecology in disease research, with a particular focus on studies that have successfully used movement-based methods to quantify individual heterogeneity in exposure and transmission risk. Throughout, we highlight the rapid growth of both disease and movement ecology and comment on promising but unexplored avenues for research at their overlap. Ultimately, we suggest, including movement empowers ecologists to pose new questions, expanding our understanding of host-pathogen dynamics and improving our predictive capacity for wildlife and even human diseases.

A Framework for Modeling Emerging Diseases to Inform Management

The rapid emergence and reemergence of zoonotic diseases requires the ability to rapidly evaluate and implement optimal management decisions. Actions to control or mitigate the effects of emerging pathogens are commonly delayed because of uncertainty in the estimates and the predicted outcomes of the control tactics. The development of models that describe the best-known information regarding the disease system at the early stages of disease emergence is an essential step for optimal decision-making. Models can predict the potential effects of the pathogen, provide guidance for assessing the likelihood of success of different proposed management actions, quantify the uncertainty surrounding the choice of the optimal decision, and highlight critical areas for immediate research. We demonstrate how to develop models that can be used as a part of a decision-making framework to determine the likelihood of success of different management actions given current knowledge.

Dynamic, spatial models of parasite transmission in wildlife: Their structure, applications and remaining challenges

Individual differences in contact rate can arise from host, group and landscape heterogeneity and can result in different patterns of spatial spread for diseases in wildlife populations with concomitant implications for disease control in wildlife of conservation concern, livestock and humans. While dynamic disease models can provide a better understanding of the drivers of spatial spread, the effects of landscape heterogeneity have only been modelled in a few well-studied wildlife systems such as rabies and bovine tuberculosis. Such spatial models tend to be either purely theoretical with intrinsic limiting assumptions or individual-based models that are often highly species- and system-specific, limiting the breadth of their utility. Our goal was to review studies that have utilized dynamic, spatial models to answer questions about pathogen transmission in wildlife and identify key gaps in the literature. We begin by providing an overview of the main types of dynamic, spatial models (e.g., metapopulation, network, lattice, cellular automata, individual-based and continuous-space) and their relation to each other. We investigate different types of ecological questions that these models have been used to explore: pathogen invasion dynamics and range expansion, spatial heterogeneity and pathogen persistence, the implications of management and intervention strategies and the role of evolution in host-pathogen dynamics. We reviewed 168 studies that consider pathogen transmission in free-ranging wildlife and classify them by the model type employed, the focal host-pathogen system, and their overall research themes and motivation. We observed a significant focus on mammalian hosts, a few well-studied or purely theoretical pathogen systems, and a lack of studies occurring at the wildlife-public health or wildlife-livestock interfaces. Finally, we discuss challenges and future directions in the context of unprecedented human-mediated environmental change. Spatial models may provide new insights into understanding, for example, how global warming and habitat disturbance contribute to disease maintenance and emergence. Moving forward, better integration of dynamic, spatial disease models with approaches from movement ecology, landscape genetics/genomics and ecoimmunology may provide new avenues for investigation and aid in the control of zoonotic and emerging infectious diseases.

Validation of a Method for the Assessment of Urinary Neopterin Levels to Monitor Health Status in Non-human-primate Species

Determining individual health status is of great importance for a better understanding of life history trade-offs between growth, reproduction, and maintenance. However, existing immunological methods are invasive and therefore not suitable for investigating health status in wild populations. Thus, there is an urgent need for non-invasive methods to assess the immune status of animals. Neopterin is involved in the cell-mediated pathway of the immune response (Th1–type), secreted during the activation of monocytes and macrophages. We investigated if urinary neopterin could serve as a biomarker of health status in bonobos and chimpanzees. First, we performed a chemical validation of a commercial neopterin enzyme immune assay (EIA) for bonobo and chimpanzee urine. We then examined if urinary neopterin levels in bonobos increase during the acute period of respiratory infections. We found that neopterin levels can be reliably measured in urine of the two species with a commercial EIA. Stability experiments revealed considerable changes in urinary neopterin levels in relation to multiple freeze–thaw cycles and extended exposure to room temperature. Exposure to sunlight led to a degradation of urinary neopterin, whereas sample storage up to 2 years did not affect urinary neopterin levels. There was no detectable diurnal variation in neopterin levels, and levels remained very stable across several days in healthy individuals. While urinary neopterin levels were independent of sex, non-adult individuals had higher urinary neopterin levels than adults. Most importantly, there was a significant increase in urinary neopterin levels during a period of respiratory infection. Our results demonstrate that regular urine sample collection would allow for the monitoring of individual health status and disease progression with minimal disturbance of the subjects. In combination with behavioral, life history, and endocrinological parameters, the method can be used to investigate questions related to immunocompetence handicaps or life history trade-offs.

A cross-validation-based approach for delimiting reliable home range estimates

BACKGROUND

With decreasing costs of GPS telemetry devices, data repositories of animal movement paths are increasing almost exponentially in size. A series of complex statistical tools have been developed in conjunction with this increase in data. Each of these methods offers certain improvements over previously proposed methods, but each has certain assumptions or shortcomings that make its general application difficult. In the case of the recently developed Time Local Convex Hull (T-LoCoH) method, the subjectivity in parameter selection serves as one of the primary impediments to its more widespread use. While there are certain advantages to the flexibility it offers for question-driven research, the lack of an objective approach for parameter selection may prevent some users from exploring the benefits of the method.

METHODS

Here we present a cross-validation-based approach for selecting parameter values to optimize the T-LoCoH algorithm. We demonstrate the utility of the approach using a case study from the Etosha National Park anthrax system.

RESULTS

Utilizing the proposed algorithm, rather than the guidelines in the T-LoCoH documentation, results in significantly different values for derived site fidelity metrics.

CONCLUSIONS

Due to its basis in principles of cross-validation, the application of this method offers a more objective approach than the relatively subjective guidelines set forth in the T-LoCoH documentation and enables a more accurate basis for the comparison of home ranges among individuals and species, as well as among studies.

CystiSim - An Agent-Based Model for Taenia solium Transmission and Control

Taenia solium taeniosis/cysticercosis was declared eradicable by the International Task Force for Disease Eradication in 1993, but remains a neglected zoonosis. To assist in the attempt to regionally eliminate this parasite, we developed cystiSim, an agent-based model for T. solium transmission and control. The model was developed in R and available as an R package (http://cran.r-project.org/package=cystiSim). cystiSim was adapted to an observed setting using field data from Tanzania, but adaptable to other settings if necessary. The model description adheres to the Overview, Design concepts, and Details (ODD) protocol and consists of two entities—pigs and humans. Pigs acquire cysticercosis through the environment or by direct contact with a tapeworm carrier's faeces. Humans acquire taeniosis from slaughtered pigs proportional to their infection intensity. The model allows for evaluation of three interventions measures or combinations hereof: treatment of humans, treatment of pigs, and pig vaccination, and allows for customary coverage and efficacy settings. cystiSim is the first agent-based transmission model for T. solium and suggests that control using a strategy consisting of an intervention only targeting the porcine host is possible, but that coverage and efficacy must be high if elimination is the ultimate goal. Good coverage of the intervention is important, but can be compensated for by including an additional intervention targeting the human host. cystiSim shows that the scenarios combining interventions in both hosts, mass drug administration to humans, and vaccination and treatment of pigs, have a high probability of success if coverage of 75% can be maintained over at least a four year period. In comparison with an existing mathematical model for T. solium transmission, cystiSim also includes parasite maturation, host immunity, and environmental contamination. Adding these biological parameters to the model resulted in new insights in the potential effect of intervention measures.

Taenia solium is the leading cause of preventable epilepsy and the highest ranking foodborne parasite in terms of disease burden worldwide. Currently there are no large scale control programmes implemented against T. solium, but efficacious intervention tools are there, making control programmes the next step forward. Because of the zoonotic properties of the parasite, existing in both humans and pigs, a combination of intervention tools is likely to be needed. cystiSim is an agent-based disease model that provides insight into which intervention tools, and the frequency of administration of these tools, are needed to yield an effect on disease prevalence. cystiSim is a valuable tool in designing future control programmes and will assist in the elimination of T. solium as a public health problem.

Social structure and Escherichia coli sharing in a group-living wild primate, Verreaux's sifaka

Background

Epidemiological models often use information on host social contacts to predict the potential impact of infectious diseases on host populations and the efficiency of control measures. It can be difficult, however, to determine whether social contacts are actually meaningful predictors of transmission. We investigated the role of host social structure in the transmission of Escherichia coli in a wild population of primates, Verreaux’s sifakas (Propithecus verreauxi). Using multilocus sequence typing (MLST), we compared genetic similarities between E. coli isolates from different individuals and groups to infer transmission pathways.

Results

Correlation of social and transmission networks revealed that membership to the same group significantly predicted sharing of E. coli MLST sequence types (ST). Intergroup encounter rate and a measure of space-use sharing provided equally potent explanations for type sharing between social groups when closely related STs were taken into account, whereas animal age, sex and dispersal history had no influence. No antibiotic resistance was found, suggesting low rates of E. coli spillover from humans into this arboreal species.

Conclusions

We show that patterns of E. coli transmission reflect the social structure of this group-living lemur species. We discuss our results in the light of the species’ ecology and propose scent-marking, a type of social contact not considered in previous epidemiological studies, as a likely route of transmission between groups. However, further studies are needed to explicitly test this hypothesis and to further elucidate the relative roles of direct contact and environmental transmission in pathogen transfer.

Electronic supplementary material

The online version of this article (doi:10.1186/s12898-016-0059-y) contains supplementary material, which is available to authorized users.

Fecal microbiomes of non-human primates in Western Uganda reveal species-specific communities largely resistant to habitat perturbation

Primate gastrointestinal microbial communities are becoming increasingly appreciated for their relevance to comparative medicine and conservation, but the factors that structure primate "microbiomes" remain controversial. This study examined a community of primates in Kibale National Park, Uganda, to assess the relative importance of host species and location in structuring gastrointestinal microbiomes. Fecal samples were collected from primates in intact forest and from primates in highly disturbed forest fragments. People and livestock living nearby were also included, as was a geographically distant population of related red colobus in Kenya. A culture-free microbial community fingerprinting technique was used to analyze fecal microbiomes from 124 individual red colobus (Procolobus rufomitratus), 100 individual black-and-white colobus (Colobus guereza), 111 individual red-tailed guenons (Cercopithecus ascanius), 578 human volunteers, and 364 domestic animals, including cattle (Bos indicus and B. indicus × B. taurus crosses), goats (Caprus hircus), sheep (Ovis aries), and pigs (Sus scrofa). Microbiomes sorted strongly by host species, and forest fragmentation did not alter this pattern. Microbiomes of Kenyan red colobus sorted distinctly from microbiomes of Ugandan red colobus, but microbiomes from these two red colobus populations clustered more closely with each other than with any other species. Microbiomes from red colobus and black-and-white colobus were more differentiated than would be predicted by the phylogenetic relatedness of these two species, perhaps reflecting heretofore underappreciated differences in digestive physiology between the species. Within Kibale, social group membership influenced intra-specific variation among microbiomes. However, intra-specific variation was higher among primates in forest fragments than among primates in intact forest, perhaps reflecting the physical separation of fragments. These results suggest that, in this system, species-specific processes such as gastrointestinal physiology strongly structure microbial communities, and that primate microbiomes are relatively resistant to perturbation, even across large geographic distances or in the face of habitat disturbance.

Emergent group level navigation: an agent-based evaluation of movement patterns in a folivorous primate

The foraging activity of many organisms reveal strategic movement patterns, showing efficient use of spatially distributed resources. The underlying mechanisms behind these movement patterns, such as the use of spatial memory, are topics of considerable debate. To augment existing evidence of spatial memory use in primates, we generated movement patterns from simulated primate agents with simple sensory and behavioral capabilities. We developed agents representing various hypotheses of memory use, and compared the movement patterns of simulated groups to those of an observed group of red colobus monkeys (Procolobus rufomitratus), testing for: the effects of memory type (Euclidian or landmark based), amount of memory retention, and the effects of social rules in making foraging choices at the scale of the group (independent or leader led). Our results indicate that red colobus movement patterns fit best with simulated groups that have landmark based memory and a follow the leader foraging strategy. Comparisons between simulated agents revealed that social rules had the greatest impact on a group's step length, whereas the type of memory had the highest impact on a group's path tortuosity and cohesion. Using simulation studies as experimental trials to test theories of spatial memory use allows the development of insight into the behavioral mechanisms behind animal movement, developing case-specific results, as well as general results informing how changes to perception and behavior influence movement patterns.

A test of agent-based models as a tool for predicting patterns of pathogen transmission in complex landscapes

Background

Landscape complexity can mitigate or facilitate host dispersal, influencing patterns of pathogen transmission. Spatial transmission of pathogens through landscapes, therefore, presents an important but not fully elucidated aspect of transmission dynamics. Using an agent-based model (LiNK) that incorporates GIS data, we examined the effects of landscape information on the spatial patterns of host movement and pathogen transmission in a system of long-tailed macaques and their gut parasites. We first examined the role of the landscape to identify any individual or additive effects on host movement. We then compared modeled dispersal distance to patterns of actual macaque gene flow to both confirm our model’s predictions and to understand the role of individual land uses on dispersal. Finally, we compared the rate and the spread of two gastrointestinal parasites, Entamoeba histolytica and E. dispar, to understand how landscape complexity influences spatial patterns of pathogen transmission.

Results

LiNK captured emergent properties of the landscape, finding that interaction effects between landscape layers could mitigate the rate of infection in a non-additive way. We also found that the inclusion of landscape information facilitated an accurate prediction of macaque dispersal patterns across a complex landscape, as confirmed by Mantel tests comparing genetic and simulated dispersed distances. Finally, we demonstrated that landscape heterogeneity proved a significant barrier for a highly virulent pathogen, limiting the dispersal ability of hosts and thus its own transmission into distant populations.

Conclusions

Landscape complexity plays a significant role in determining the path of host dispersal and patterns of pathogen transmission. Incorporating landscape heterogeneity and host behavior into disease management decisions can be important in targeting response efforts, identifying cryptic transmission opportunities, and reducing or understanding potential for unintended ecological and evolutionary consequences. The inclusion of these data into models of pathogen transmission patterns improves our understanding of these dynamics, ultimately proving beneficial for sound public health policy.

Wildlife disease prevalence in human-modified landscapes

Human-induced landscape change associated with habitat loss and fragmentation places wildlife populations at risk. One issue in these landscapes is a change in the prevalence of disease which may result in increased mortality and reduced fecundity. Our understanding of the influence of habitat loss and fragmentation on the prevalence of wildlife diseases is still in its infancy. What is evident is that changes in disease prevalence as a result of human-induced landscape modification are highly variable. The importance of infectious diseases for the conservation of wildlife will increase as the amount and quality of suitable habitat decreases due to human land-use pressures. We review the experimental and observational literature of the influence of human-induced landscape change on wildlife disease prevalence, and discuss disease transmission types and host responses as mechanisms that are likely to determine the extent of change in disease prevalence. It is likely that transmission dynamics will be the key process in determining a pathogen's impact on a host population, while the host response may ultimately determine the extent of disease prevalence. Finally, we conceptualize mechanisms and identify future research directions to increase our understanding of the relationship between human-modified landscapes and wildlife disease prevalence. This review highlights that there are rarely consistent relationships between wildlife diseases and human-modified landscapes. In addition, variation is evident between transmission types and landscape types, with the greatest positive influence on disease prevalence being in urban landscapes and directly transmitted disease systems. While we have a limited understanding of the potential influence of habitat loss and fragmentation on wildlife disease, there are a number of important areas to address in future research, particularly to account for the variability in increased and decreased disease prevalence. Previous studies have been based on a one-dimensional comparison between unmodified and modified sites. What is lacking are spatially and temporally explicit quantitative approaches which are required to enable an understanding of the range of key causal mechanisms and the reasons for variability. This is particularly important for replicated studies across different host-pathogen systems. Furthermore, there are few studies that have attempted to separate the independent effects of habitat loss and fragmentation on wildlife disease, which are the major determinants of wildlife population dynamics in human-modified landscapes. There is an urgent need to understand better the potential causal links between the processes of human-induced landscape change and the associated influences of habitat fragmentation, matrix hostility and loss of connectivity on an animal's physiological stress, immune response and disease susceptibility. This review identified no study that had assessed the influence of human-induced landscape change on the prevalence of a wildlife sexually transmitted disease. A better understanding of the various mechanisms linking human-induced landscape change and the prevalence of wildlife disease will lead to more successful conservation management outcomes.

Primate disease ecology in comparative and theoretical perspective

Infectious disease plays a major role in the lives of wild primates, and the past decade has witnessed significant strides in our understanding of primate disease ecology. In this review, I briefly describe some key findings from phylogenetic comparative approaches, focusing on analyses of parasite richness that use the Global Mammal Parasite Database. While these studies have provided new answers to fundamental questions, new questions have arisen, including questions about the underlying epidemiological mechanisms that produce the broader phylogenetic patterns. I discuss two examples in which theoretical models have given us new traction on these comparative questions. First, drawing on findings of a positive association between range use intensity and the richness of helminth parasites, we developed a spatially explicit agent-based model to investigate the underlying drivers of this pattern. From this model, we are gaining deeper understanding of how range use intensity results in greater exposure to parasites, thus producing higher prevalence in the simulated populations-and, plausibly, higher parasite richness in comparative analyses. Second, I show how a model of disease spread on social networks provides solid theoretical foundations for understanding the effects of sociality and group size on parasitism across primate species. This study further revealed that larger social groups are more subdivided, which should slow the spread of infectious diseases. This effect could offset the increased disease risk expected in larger social groups, which has yet to receive strong empirical support in our comparative analyses. In addition to these examples, I discuss the need for more meta-analyses of individual-level phenomena documented in the field, and for greater linkage between theoretical modeling and field research.

Socialization strategies and disease transmission in captive colonies of nonhuman primates

In captive research environments for nonhuman primates (NHP), social housing strategies are often in conflict with protocols designed to minimize disease transmission. This is particularly true in breeding colonies, and is especially relevant when attempting to eliminate specific pathogens from a population of primates. Numerous strategies have been used to establish such specific pathogen free (SPF) breeding colonies (primarily of macaques), ranging from nursery rearing of neonates to single housing of socially reared yearlings to the rearing of infants in large social groups. All these strategies attempt to balance the effects of the chosen socialization strategy on parameters related to disease transmission, including the ultimate elimination of the target pathogens. Such strategies may affect the overall disease states of NHP breeding colonies through selective breeding processes. This can occur either by creating subpopulations of animals that do not have target diseases (SPF colonies), but may have other issues; or by creating situations in which the "best" animals are sold and the breeding colony is stocked with animals that may be more disease susceptible than those that were sold. The disease states of NHP research colonies also may be affected by selective utilization programs, in which animals removed from the breeding colony for health/behavior reasons, are preferentially chosen for use in scientific investigations. Such utilization criteria raise the question of whether ideal subjects are being chosen for use in research. Finally, captive primate colonies, where both socialization and disease states are intensely managed, may provide opportunities for those testing predictions from models of the interactions of socialization and disease transmission in the evolution of wild populations of NHP. This would be especially true for some extreme conditions of these disease ecology models, given the exceedingly high social densities and levels of pathogen control that exist in many captive nonhuman primate colonies.

Transmission of infectious diseases en route to habitat hotspots

BACKGROUND

The spread of infectious diseases in wildlife populations is influenced by patterns of between-host contacts. Habitat "hotspots"--places attracting a large numbers of individuals or social groups--can significantly alter contact patterns and, hence, disease propagation. Research on the importance of habitat hotspots in wildlife epidemiology has primarily focused on how inter-individual contacts occurring at the hotspot itself increase disease transmission. However, in territorial animals, epidemiologically important contacts may primarily occur as animals cross through territories of conspecifics en route to habitat hotspots. So far, the phenomenon has received little attention. Here, we investigate the importance of these contacts in the case where infectious individuals keep visiting the hotspots and in the case where these individuals are not able to travel to the hotspot any more.

METHODOLOGY AND PRINCIPAL FINDINGS

We developed a simulation epidemiological model to investigate both cases in a scenario when transmission at the hotspot does not occur. We find that (i) hotspots still exacerbate epidemics, (ii) when infectious individuals do not travel to the hotspot, the most vulnerable individuals are those residing at intermediate distances from the hotspot rather than nearby, and (iii) the epidemiological vulnerability of a population is the highest when the number of hotspots is intermediate.

CONCLUSIONS AND SIGNIFICANCE

By altering animal movements in their vicinity, habitat hotspots can thus strongly increase the spread of infectious diseases, even when disease transmission does not occur at the hotspot itself. Interestingly, when animals only visit the nearest hotspot, creating additional artificial hotspots, rather than reducing their number, may be an efficient disease control measure.

Protozoan parasites in group-living primates: testing the biological island hypothesis

A series of articles by W.J. Freeland published in the 1970s proposed that social organization and behavioral processes were heavily influenced by parasitic infections, which led to a number of intriguing hypotheses concerning how natural selection might act on social factors because of the benefits of avoiding parasite infections. For example, Freeland [1979] showed that all individuals within a given group harbored identical gastrointestinal protozoan faunas, which led him to postulate that social groups were akin to "biological islands" and suggest how this isolation could select specific types of ranging and dispersal patterns. Here, we reexamine the biological island hypothesis by quantifying the protozoan faunas of the same primate species examined by Freeland in the same location; our results do not support this hypothesis. In contrast, we quantified two general changes in protozoan parasite community of primates in the study area of Kibale National Park, Uganda, over the nearly 35 years between sample collections: (1) the colobines found free of parasites in the early 1970s are now infected with numerous intestinal protozoan parasites and (2) groups are no longer biological islands in terms of their protozoan parasites. Whatever the ultimate explanation for these changes, our findings have implications for studies proposing selective forces shaping primate behavior and social organization.

The spread of fecally transmitted parasites in socially-structured populations

Mammals are infected by a wide array of gastrointestinal parasites, including parasites that also infect humans and domesticated animals. Many of these parasites are acquired through contact with infectious stages present in soil, feces or vegetation, suggesting that ranging behavior will have a major impact on their spread. We developed an individual-based spatial simulation model to investigate how range use intensity, home range overlap, and defecation rate impact the spread of fecally transmitted parasites in a population composed of social groups (i.e., a socially structured population). We also investigated the effects of epidemiological parameters involving host and parasite mortality rates, transmissibility, disease-related mortality, and group size. The model was spatially explicit and involved the spillover of a gastrointestinal parasite from a reservoir population along the edge of a simulated reserve, which was designed to mimic the introduction pathogens into protected areas. Animals ranged randomly within a "core" area, with biased movement toward the range center when outside the core. We systematically varied model parameters using a Latin hypercube sampling design. Analyses of simulation output revealed a strong positive association between range use intensity and the prevalence of infection. Moreover, the effects of range use intensity were similar in magnitude to effects of group size, mortality rates, and the per-contact probability of transmission. Defecation rate covaried positively with gastrointestinal parasite prevalence. Greater home range overlap had no positive effects on prevalence, with a smaller core resulting in less range overlap yet more intensive use of the home range and higher prevalence. Collectively, our results reveal that parasites with fecal-oral transmission spread effectively in socially structured populations. Future application should focus on parameterizing the model with empirically derived ranging behavior for different species or populations and data on transmission characteristics of different infectious organisms.