Landscape structure affects temporal dynamics in the bumble bee virome: Landscape heterogeneity supports colony resilience

Virus spillovers from managed honey bees, Apis mellifera, are thought to contribute to the decline of wild pollinators, including bumble bees. However, data on the impact of such viruses on wild pollinators remain scarce, and the influence of landscape structure on virus dynamics is poorly understood. In this study, we deployed bumble bee colonies in an agricultural landscape and studied changes in the bumble bee virome during field placement under varying habitat composition and configuration using a multiscale analytical framework. We estimated prevalence of viruses and viral loads (i.e. number of viral genomic equivalent copies) in bumble bees before and after placing them in the field using next generation sequencing and quantitative PCR. The results show that viral loads and number of different viruses present increased during placement in the field and that the virus composition of the colonies shifted from an initial dominance of honey bee associated viruses to a higher number (in both viral loads and number of viruses present) of bumble bee associated viruses. Especially DWV-B, typical for honey bees, drastically decreased after the time in the field. Viral loads prior to placing colonies in the field showed no effect on colony development, suggesting low impacts of these viruses in field settings. Notably, we further demonstrate that increased habitat diversity results in a lower number of different viruses present in Bombus colonies, while colonies in areas with well-connected farmland patches decreased in their total viral load after field placement. Our results emphasize the importance of landscape heterogeneity and connectivity for wild pollinator health and that these influences predominate at fine spatial scales.

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.

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.

Population connectivity in voles (Microtus sp.) as a gauge for tall grass prairie restoration in midwestern North America

Ecological restoration can promote biodiversity conservation in anthropogenically fragmented habitats, but effectiveness of these management efforts need to be statistically validated to determine ’success.’ One such approach is to gauge the extent of recolonization as a measure of landscape permeability and, in turn, population connectivity. In this context, we estimated dispersal and population connectivity in prairie vole (Microtus ochrogaster; N = 231) and meadow vole (M. pennsylvanicus; N = 83) within five tall-grass prairie restoration sites embedded within the agricultural matrix of midwestern North America. We predicted that vole dispersal would be constrained by the extent of agricultural land surrounding restored habitat patches, spatially isolating vole populations and resulting in significant genetic structure. We first employed genetic assignment tests based on 15 microsatellite DNA loci to validate field-derived species-designations, then tested reclassified samples with multivariate and Bayesian clustering to assay for spatial and temporal genetic structure. Population connectivity was further evaluated by calculating pairwise F_ST, then potential demographic effects explored by computing migration rates, effective population size (N_e), and average relatedness (r). Genetic species assignments reclassified 25% of initial field identifications (N = 11 M. ochrogaster; N = 67 M. pennsylvanicus). In M. ochrogaster population connectivity was high across the study area, reflected in little to no spatial or temporal genetic structure. In M. pennsylvanicus genetic structure was detected, but relatedness estimates identified it as kin-clustering instead, underscoring social behavior among populations rather than spatial isolation as the cause. Estimates of N_e and r were stable across years, reflecting high dispersal and demographic resilience. Combined, these metrics suggest the agricultural matrix is highly permeable for voles and does not impede dispersal. High connectivity observed confirms that the restored landscape is productive and permeable for specific management targets such as voles and also demonstrates population genetic assays as a tool to statistically evaluate effectiveness of conservation initiatives.

Modeling scale up of anthropogenic impacts from individual pollinator behavior to pollination systems

Understanding how anthropogenic disturbances affect plant-pollinator systems has important implications for the conservation of biodiversity and ecosystem functioning. Previous laboratory studies show that pesticides and pathogens, which have been implicated in the rapid global decline of pollinators over recent years, can impair behavioral processes needed for pollinators to adaptively exploit floral resources and effectively transfer pollen among plants. However, the potential for these sublethal stressor effects on pollinator-plant interactions at the individual level to scale up into changes to the dynamics of wild plant and pollinator populations at the system level remains unclear. We developed an empirically parameterized agent-based model of a bumblebee pollination system called SimBee to test for effects of stressor-induced decreases in the memory capacity and information processing speed of individual foragers on bee abundance (scenario 1), plant diversity (scenario 2), and bee-plant system stability (scenario 3) over 20 virtual seasons. Modeling of a simple pollination network of a bumblebee and four co-flowering bee-pollinated plant species indicated that bee decline and plant species extinction events could occur when only 25% of the forager population showed cognitive impairment. Higher percentages of impairment caused 50% bee loss in just five virtual seasons and system-wide extinction events in less than 20 virtual seasons under some conditions. Plant species extinctions occurred regardless of bee population size, indicating that stressor-induced changes to pollinator behavior alone could drive species loss from plant communities. These findings indicate that sublethal stressor effects on pollinator behavioral mechanisms, although seemingly insignificant at the level of individuals, have the cumulative potential in principle to degrade plant-pollinator species interactions at the system level. Our work highlights the importance of an agent-based modeling approach for the identification and mitigation of anthropogenic impacts on plant-pollinator systems.

Movement can mediate temporal mismatches between resource availability and biological events in host-pathogen interactions

Global change is shifting the timing of biological events, leading to temporal mismatches between biological events and resource availability. These temporal mismatches can threaten species' populations. Importantly, temporal mismatches not only exert strong pressures on the population dynamics of the focal species, but can also lead to substantial changes in pairwise species interactions such as host-pathogen systems. We adapted an established individual-based model of host-pathogen dynamics. The model describes a viral agent in a social host, while accounting for the host's explicit movement decisions. We aimed to investigate how temporal mismatches between seasonal resource availability and host life-history events affect host-pathogen coexistence, that is, disease persistence. Seasonal resource fluctuations only increased coexistence probability when in synchrony with the hosts' biological events. However, a temporal mismatch reduced host-pathogen coexistence, but only marginally. In tandem with an increasing temporal mismatch, our model showed a shift in the spatial distribution of infected hosts. It shifted from an even distribution under synchronous conditions toward the formation of disease hotspots, when host life history and resource availability mismatched completely. The spatial restriction of infected hosts to small hotspots in the landscape initially suggested a lower coexistence probability due to the critical loss of susceptible host individuals within those hotspots. However, the surrounding landscape facilitated demographic rescue through habitat-dependent movement. Our work demonstrates that the negative effects of temporal mismatches between host resource availability and host life history on host-pathogen coexistence can be reduced through the formation of temporary disease hotspots and host movement decisions, with implications for disease management under disturbances and global change.

Life expectancy and mortality in 363 cities of Latin America

The concept of a so-called urban advantage in health ignores the possibility of heterogeneity in health outcomes across cities. Using a harmonized dataset from the SALURBAL project, we describe variability and predictors of life expectancy and proportionate mortality in 363 cities across nine Latin American countries. Life expectancy differed substantially across cities within the same country. Cause-specific mortality also varied across cities, with some causes of death (unintentional and violent injuries and deaths) showing large variation within countries, whereas other causes of death (communicable, maternal, neonatal and nutritional, cancer, cardiovascular disease and other noncommunicable diseases) varied substantially between countries. In multivariable mixed models, higher levels of education, water access and sanitation and less overcrowding were associated with longer life expectancy, a relatively lower proportion of communicable, maternal, neonatal and nutritional deaths and a higher proportion of deaths from cancer, cardiovascular disease and other noncommunicable diseases. These results highlight considerable heterogeneity in life expectancy and causes of death across cities of Latin America, revealing modifiable factors that could be amenable to urban policies aimed toward improving urban health in Latin America and more generally in other urban environments.

City-level analysis of data from the SALURBAL project shows vast heterogeneity in life expectancy across cities within the same country, in addition to substantive differences in causes of death among nine Latin American countries, revealing modifiable factors that could be leveraged by municipal-level policies aimed toward improving health in urban environments.

Host relatedness and landscape connectivity shape pathogen spread in the puma, a large secretive carnivore

Urban expansion can fundamentally alter wildlife movement and gene flow, but how urbanization alters pathogen spread is poorly understood. Here, we combine high resolution host and viral genomic data with landscape variables to examine the context of viral spread in puma (Puma concolor) from two contrasting regions: one bounded by the wildland urban interface (WUI) and one unbounded with minimal anthropogenic development (UB). We found landscape variables and host gene flow explained significant amounts of variation of feline immunodeficiency virus (FIV) spread in the WUI, but not in the unbounded region. The most important predictors of viral spread also differed; host spatial proximity, host relatedness, and mountain ranges played a role in FIV spread in the WUI, whereas roads might have facilitated viral spread in the unbounded region. Our research demonstrates how anthropogenic landscapes can alter pathogen spread, providing a more nuanced understanding of host-pathogen relationships to inform disease ecology in free-ranging species.

Variation in host home range size decreases rabies vaccination effectiveness by increasing the spatial spread of rabies virus

Animal movement influences the spatial spread of directly transmitted wildlife disease through host-host contact structure. Wildlife disease hosts vary in home range-associated foraging and social behaviours, which may increase the spread and intensity of disease outbreaks. The consequences of variation in host home range movement and space use on wildlife disease dynamics are poorly understood, but could help to predict disease spread and determine more effective disease management strategies. We developed a spatially explicit individual-based model to examine the effect of spatiotemporal variation in host home range size on the spatial spread rate, persistence and incidence of rabies virus (RABV) in raccoons (Procyon lotor). We tested the hypothesis that variation in home range size increases RABV spread and decreases vaccination effectiveness in host populations following pathogen invasion into a vaccination zone. We simulated raccoon demography and RABV dynamics across a range of magnitudes and variances in weekly home range size for raccoons. We examined how variable home range size influenced the relative effectiveness of three components of oral rabies vaccination (ORV) programmes targeting raccoons-timing and frequency of bait delivery, width of the ORV zone and proportion of hosts immunized. Variability in weekly home range size increased RABV spread rates by 1.2-fold to 5.2-fold compared to simulations that assumed a fixed home range size. More variable host home range sizes decreased relative vaccination effectiveness by 71% compared to less variable host home range sizes under conventional vaccination conditions. We found that vaccination timing was more influential for vaccination effectiveness than vaccination frequency or vaccination zone width. Our results suggest that variation in wildlife home range movement behaviour increases the spatial spread and incidence of RABV. Our vaccination results underscore the importance of prioritizing individual-level space use and movement data collection to understand wildlife disease dynamics and plan their effective control and elimination.

Movement rule selection through eco-genetic modeling: Application to diurnal vertical movement

Agent-based, spatially-explicit models that incorporate movement rules are used across ecological disciplines for a variety of applications. However, appropriate movement rules may be difficult to implement due to the complexity of an individual's response to both proximate and ultimate cues, as well as the difficulty in directly assessing how organisms choose to move across their environment. Environmental cues may be complex and dynamic, and therefore, movement responses may require tradeoffs between preferred levels of different environmental variables (e.g., temperature, light level, and prey availability). Here, we present an approach to determine appropriate movement rules by setting them as heritable traits in an eco-genetic modeling framework and allowing movement rules to evolve during the model rather than setting them a priori. We modeled yellow perch, Perca flavescens, movement in a simulated environment and allowed perch to move in response to high-resolution vertical gradients in temperature, dissolved oxygen, light, predators, and prey. Evolving movement rules ultimately increased fish growth and survival over generations in our model, indicating that evolving movement rules led to improved individual performance. We found that emergent movement rules were consistent across trials, with evolved movement rules incorporating different weights of these environmental factors and the most rapid selection on temperature preference. This case study presents a flexible method using eco-genetic modeling to determine appropriate movement rules that can be applied to diverse scenarios in spatially-explicit ecological modeling.

Feline immunodeficiency virus in puma: Estimation of force of infection reveals insights into transmission

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

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.

Infections on the move: how transient phases of host movement influence disease spread

Animal movement impacts the spread of human and wildlife diseases, and there is significant interest in understanding the role of migrations, biological invasions and other wildlife movements in spatial infection dynamics. However, the influence of processes acting on infections during transient phases of host movement is poorly understood. We propose a conceptual framework that explicitly considers infection dynamics during transient phases of host movement to better predict infection spread through spatial host networks. Accounting for host transient movement captures key processes that occur while hosts move between locations, which together determine the rate at which hosts spread infections through networks. We review theoretical and empirical studies of host movement and infection spread, highlighting the multiple factors that impact the infection status of hosts. We then outline characteristics of hosts, parasites and the environment that influence these dynamics. Recent technological advances provide disease ecologists unprecedented ability to track the fine-scale movement of organisms. These, in conjunction with experimental testing of the factors driving infection dynamics during host movement, can inform models of infection spread based on constituent biological processes.

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.

The effects of demographic, social, and environmental characteristics on pathogen prevalence in wild felids across a gradient of urbanization

Transmission of pathogens among animals is influenced by demographic, social, and environmental factors. Anthropogenic alteration of landscapes can impact patterns of disease dynamics in wildlife populations, increasing the potential for spillover and spread of emerging infectious diseases in wildlife, human, and domestic animal populations. We evaluated the effects of multiple ecological mechanisms on patterns of pathogen exposure in animal populations. Specifically, we evaluated how ecological factors affected the prevalence of Toxoplasma gondii (Toxoplasma), Bartonella spp. (Bartonella), feline immunodeficiency virus (FIV), and feline calicivirus (FCV) in bobcat and puma populations across wildland-urban interface (WUI), low-density exurban development, and wildland habitat on the Western Slope (WS) and Front Range (FR) of Colorado during 2009-2011. Samples were collected from 37 bobcats and 29 pumas on the WS and FR. As predicted, age appeared to be positively related to the exposure to pathogens that are both environmentally transmitted (Toxoplasma) and directly transmitted between animals (FIV). In addition, WS bobcats appeared more likely to be exposed to Toxoplasma with increasing intraspecific space-use overlap. However, counter to our predictions, exposure to directly-transmitted pathogens (FCV and FIV) was more likely with decreasing space-use overlap (FCV: WS bobcats) and potential intraspecific contacts (FIV: FR pumas). Environmental factors, including urbanization and landscape covariates, were generally unsupported in our models. This study is an approximation of how pathogens can be evaluated in relation to demographic, social, and environmental factors to understand pathogen exposure in wild animal populations.

Urban landscapes can change virus gene flow and evolution in a fragmentation-sensitive carnivore

Urban expansion has widespread impacts on wildlife species globally, including the transmission and emergence of infectious diseases. However, there is almost no information about how urban landscapes shape transmission dynamics in wildlife. Using an innovative phylodynamic approach combining host and pathogen molecular data with landscape characteristics and host traits, we untangle the complex factors that drive transmission networks of feline immunodeficiency virus (FIV) in bobcats (Lynx rufus). We found that the urban landscape played a significant role in shaping FIV transmission. Even though bobcats were often trapped within the urban matrix, FIV transmission events were more likely to occur in areas with more natural habitat elements. Urban fragmentation also resulted in lower rates of pathogen evolution, possibly owing to a narrower range of host genotypes in the fragmented area. Combined, our findings show that urban landscapes can have impacts on a pathogen and its evolution in a carnivore living in one of the most fragmented and urban systems in North America. The analytical approach used here can be broadly applied to other host-pathogen systems, including humans.

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.

Symptomatic Raccoon Dogs and Sarcoptic Mange Along an Urban Gradient

We quantitatively evaluated the effects of landscape factors on the distribution of symptomatic raccoon dogs with sarcoptic mange along an urban gradient. We used 246 camera traps (182 traps from April 2005 to December 2006; 64 traps from September 2009 to October 2010) to record the occurrence of asymptomatic and symptomatic raccoon dogs at 21 survey sites along an urban-rural gradient in the Tama Hills area of Tokyo. Each occurrence was explained in terms of the surrounding forest, agricultural, and grassland areas and additional factors (i.e., seasonal variations and survey methods) at various spatial scales using a generalized additive mixed model (GAMM). In our analysis, a 1000-m radius was identified as the important spatial scale for asymptomatic and symptomatic raccoon dog occurrence. The peak of the predicted occurrence probability of asymptomatic raccoon dogs appeared in the intermediate forest landscape as opposed to non-forest and forest landscapes. However, a high occurrence probability of symptomatic raccoon dogs was detected in non-forest and intermediate forest landscapes (i.e., urban and suburban) as opposed to a forest landscape, presumably because of animals occurring at much higher densities in more urbanized areas. Therefore, our results suggest that human-modified landscapes play an important role in the high occurrence of sarcoptic mange in raccoon dogs.

A Systematic Literature Review of Agents Applied in Healthcare

Intelligent agents and healthcare have been intimately linked in the last years. The intrinsic complexity and diversity of care can be tackled with the flexibility, dynamics and reliability of multi-agent systems. The purpose of this review is to show the feasibility of applying intelligent agents in the healthcare domain and use the findings to provide a discussion of current trends and devise future research directions. A review of the most recent literature (2009-2014) of applications of agents in healthcare is discussed, and two classifications considering the main goal of the health systems as well as the main actors involved have been investigated. This review shows that the number of published works exhibits a growing interest of researchers in this field in a wide range of applications.

Panmictic and Clonal Evolution on a Single Patchy Resource Produces Polymorphic Foraging Guilds

We develop a stochastic, agent-based model to study how genetic traits and experiential changes in the state of agents and available resources influence individuals’ foraging and movement behaviors. These behaviors are manifest as decisions on when to stay and exploit a current resource patch or move to a particular neighboring patch, based on information of the resource qualities of the patches and the anticipated level of intraspecific competition within patches. We use a genetic algorithm approach and an individual’s biomass as a fitness surrogate to explore the foraging strategy diversity of evolving guilds under clonal versus hermaphroditic sexual reproduction. We first present the resource exploitation processes, movement on cellular arrays, and genetic algorithm components of the model. We then discuss their implementation on the Nova software platform. This platform seamlessly combines the dynamical systems modeling of consumer-resource interactions with agent-based modeling of individuals moving over a landscapes, using an architecture that lays transparent the following four hierarchical simulation levels: 1.) within-patch consumer-resource dynamics, 2.) within-generation movement and competition mitigation processes, 3.) across-generation evolutionary processes, and 4.) multiple runs to generate the statistics needed for comparative analyses. The focus of our analysis is on the question of how the biomass production efficiency and the diversity of guilds of foraging strategy types, exploiting resources over a patchy landscape, evolve under clonal versus random hermaphroditic sexual reproduction. Our results indicate greater biomass production efficiency under clonal reproduction only at higher population densities, and demonstrate that polymorphisms evolve and are maintained under random mating systems. The latter result questions the notion that some type of associative mating structure is needed to maintain genetic polymorphisms among individuals exploiting a common patchy resource on an otherwise spatially homogeneous landscape.