Emerging prion disease drives host selection in a wildlife population

Infectious diseases are increasingly recognized as an important force driving population dynamics, conservation biology, and natural selection in wildlife populations. Infectious agents have been implicated in the decline of small or endangered populations and may act to constrain population size, distribution, growth rates, or migration patterns. Further, diseases may provide selective pressures that shape the genetic diversity of populations or species. Thus, understanding disease dynamics and selective pressures from pathogens is crucial to understanding population processes, managing wildlife diseases, and conserving biological diversity. There is ample evidence that variation in the prion protein gene (PRNP) impacts host susceptibility to prion diseases. Still, little is known about how genetic differences might influence natural selection within wildlife populations. Here we link genetic variation with differential susceptibility of white-tailed deer to chronic wasting disease (CWD), with implications for fitness and disease-driven genetic selection. We developed a single nucleotide polymorphism (SNP) assay to efficiently genotype deer at the locus of interest (in the 96th codon of the PRNP gene). Then, using a Bayesian modeling approach, we found that the more susceptible genotype had over four times greater risk of CWD infection; and, once infected, deer with the resistant genotype survived 49% longer (8.25 more months). We used these epidemiological parameters in a multi-stage population matrix model to evaluate relative fitness based on genotype-specific population growth rates. The differences in disease infection and mortality rates allowed genetically resistant deer to achieve higher population growth and obtain a long-term fitness advantage, which translated into a selection coefficient of over 1% favoring the CWD-resistant genotype. This selective pressure suggests that the resistant allele could become dominant in the population within an evolutionarily short time frame. Our work provides a rare example of a quantifiable disease-driven selection process in a wildlife population, demonstrating the potential for infectious diseases to alter host populations. This will have direct bearing on the epidemiology, dynamics, and future trends in CWD transmission and spread. Understanding genotype-specific epidemiology will improve predictive models and inform management strategies for CWD-affected cervid populations.     

Conservation implications of physiological carry‐over effects in bats recovering from white‐nose syndrome

Although it is well documented that infectious diseases can pose threats to biodiversity, the potential long‐term consequences of pathogen exposure on individual fitness and its effects on population viability have rarely been studied. We tested the hypothesis that pathogen exposure causes physiological carry‐over effects with a pathogen that is uniquely suited to this question because the infection period is specific and time limited. The fungus Pseudogymnoascus destructans causes white‐nose syndrome (WNS) in hibernating bats, which either die due to the infection while hibernating or recover following emergence from hibernation. The fungus infects all exposed individuals in an overwintering site simultaneously, and bats that survive infection during hibernation clear the pathogen within a few weeks following emergence. We quantified chronic stress during the active season, when bats are not infected, by measuring cortisol in bat claws. Free‐ranging Myotis lucifugus who survived previous exposure to P. destructans had significantly higher levels of claw cortisol than naïve individuals. Thus, cryptic physiological carry‐over effects of pathogen exposure may persist in asymptomatic, recovered individuals. If these effects result in reduced survival or reproductive success, they could also affect population viability and even act as a third stream in the extinction vortex. For example, significant increases in chronic stress, such as those indicated here, are correlated with reduced reproductive success in a number of species. Future research should directly explore the link between pathogen exposure and the viability of apparently recovered populations to improve understanding of the true impacts of infectious diseases on threatened populations.     

Differences in sensitivity to the fungal pathogen Batrachochytrium dendrobatidis among amphibian populations

Contributing to the worldwide biodiversity crisis are emerging infectious diseases, which can lead to extirpations and extinctions of hosts. For example, the infectious fungal pathogen Batrachochytrium dendrobatidis (Bd) is associated with worldwide amphibian population declines and extinctions. Sensitivity to Bd varies with species, season, and life stage. However, there is little information on whether sensitivity to Bd differs among populations, which is essential for understanding Bd‐infection dynamics and for formulating conservation strategies. We experimentally investigated intraspecific differences in host sensitivity to Bd across 10 populations of wood frogs (Lithobates sylvaticus) raised from eggs to metamorphosis. We exposed the post‐metamorphic wood frogs to Bd and monitored survival for 30 days under controlled laboratory conditions. Populations differed in overall survival and mortality rate. Infection load also differed among populations but was not correlated with population differences in risk of mortality. Such population‐level variation in sensitivity to Bd may result in reservoir populations that may be a source for the transmission of Bd to other sensitive populations or species. Alternatively, remnant populations that are less sensitive to Bd could serve as sources for recolonization after epidemic events.     

The triple helix of Plantago lanceolata: genetics and the environment interact to determine population dynamics

The theory of evolution via natural selection predicts that the genetic composition of wild populations changes over time in response to the environment. Different genotypes should exhibit different demographic patterns, but genetic variation in demography is often impossible to separate from environmental variation. Here, we asked if genetic variation is important in determining demographic patterns. We answer this question using a long-term field experiment combined with general linear modeling of deterministic population growth rates (lambda), deterministic life table response experiment (LTRE) analysis, and stochastic simulation of demography by paternal lineage in a short-lived perennial plant, Plantago lanceolata, in which we replicated genotypes across four cohorts using a standard breeding design. General linear modeling showed that growth rate varied significantly with year, spatial block, and sire. In LTRE analysis of all cohorts, the strongest influences on growth rate were from year x spatial block, and cohort x year x spatial block interactions. In analysis of genetics vs. temporal environmental variation, the strongest impacts on growth rate were from year and year x sire. Finally, stochastic simulation suggested different genetic composition among cohorts after 100 years, and different population growth rates when genetic differences were accounted for than when they were not. We argue that genetic variation, genotype x environment interactions, natural selection, and cohort effects should be better integrated into population ecological studies, as these processes should result in deviations from projected deterministic and stochastic population parameters.     

Geographical variation in metacercarial infection levels in marine invertebrate hosts: parasite species character versus local factors

Despite the important roles played by parasites in local population dynamics and community structure of marine ecosystems, there is a lack of information on the geographical variation in infection levels displayed by particular host–parasite species combinations. This study examines geographical variation in infection levels by the metacercarial stages of trematode parasites in crustacean and bivalve second intermediate hosts. Analyses were based on a dataset compiled from the literature, consisting of 164 local samples representing 49 host–parasite species pairs for crustaceans, and 338 entries representing 36 host–parasite species pairs for bivalves. The analyses indicate that for all measures of infection levels [prevalence (percentage of individuals infected), intensity (mean no. of metacercariae per infected individual), abundance (mean no. of metacercariae across all individuals in a sample)], there was statistically significant repeatability of infection values within host–parasite species pairs. However, it is only for values of intensity and abundance of infection in crustacean hosts that the repeatability was strong; this suggests that infection levels are specific properties of crustacean–trematode species pairs, showing significant consistency across localities despite spatial variation in abiotic and biotic conditions. Although the magnitude of variation in infection levels within parasite species pairs (measured as coefficients of variation) was independent of scale in crustacean hosts, infection levels in bivalves increased in variability at large (>100 km) spatial scales. These results suggest that there is a considerable geographical consistency in parasite load, especially in crustacean hosts, which should lead to consistent ecological and ecosystem effects of marine trematodes. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

Nonparametric spatial regression of survival probability: visualization of population sinks in Eurasian woodcock

Both evolutionary ecologists and wildlife managers make inference based on how fitness and demography vary in space. Spatial variation in survival can be difficult to assess in the wild because (1) multisite study designs are not well suited to populations that are continuously distributed across a large area and (2) available statistical models accounting for detectability less than 1.0 do not easily cope with geographical coordinates. Here we use penalized splines within a Bayesian state-space modeling framework to estimate and visualize survival probability in two dimensions. The approach is flexible in that no parametric form for the relationship between survival and coordinates need be specified a priori. To illustrate our method, we study a game species, the Eurasian Woodcock Scolopax rusticola, based on band recovery data (5000 individuals) collected over a > 50 000-km2 area in west-central France with contrasted habitats and hunting pressures. We find that spatial variation in survival probability matches an index of hunting pressure and creates a mosaic of population sources and sinks. Such analyses could provide guidance concerning the spatial management of hunting intensity or could be used to identify pathways of spatial variation in fitness, for example, to study adaptation to changing landscape and climate.     

Genetic Structure of Fragmented Populations of the Endangered Daisy Rutidosis leptorrhynchoides

Abstract: The endangered grassland daisy Rutidosis leptorrhynchoides has been subject to severe habitat destruction and fragmentation over the past century. Using allozyme markers, we examined the genetic diversity and structure of 16 fragmented populations. The species had high genetic variation compared to other plant species, and both polymorphism and allelic richness showed strong positive relationships with log reproductive population size, reflecting a loss of rare alleles (frequency of q < 0.1) in smaller populations. Fixation coefficients were positively related to size, due either to a lack of rare homozygotes in small populations or to Wahlund effects (owing to spatial genetic structure) in large ones. Neither gene diversity nor heterozygosity was related to population size, and other population parameters such as density, spatial contagion, and isolation had no apparent effect on genetic variation. Genetic divergence among populations was low , despite a large north-to-south break in the species' current distribution. To preserve maximum genetic variation, conservation strategies should aim to maintain the five populations larger than 5000 reproductive plants, all of which occur in the north of the range, as well as the largest southern population of 626 plants at Truganina. Only one of these is currently under formal protection. High heterozygosity in smaller populations suggests that they are unlikely to be suffering from inbreeding depression and so are also valuable for conservation. Erosion of allelic richness at self-incompatibility loci, however, may limit the reproductive capacity of populations numbering less than 20 flowering plants.     

Why are metapopulations so rare?

Roughly 40 years after its introduction, the metapopulation concept is central to population ecology. The notion that local populations and their dynamics may be coupled by dispersal is without any doubt of great importance for our understanding of population-level processes. A metapopulation describes a set of subpopulations linked by (rare) dispersal events in a dynamic equilibrium of extinctions and recolonizations. In the large body of literature that has accumulated, the term "metapopulation" is often used in a very broad sense; most of the time it simply implies spatial heterogeneity. A number of reviews have recently addressed this problem and have pointed out that, despite the large and still growing popularity of the metapopulation concept, there are only very few empirical examples that conform with the strict classical metapopulation (CM) definition. In order to understand this discrepancy between theory and observation, we use an individual-based modeling approach that allows us to pinpoint the environmental conditions and the life-history attributes required for the emergence of a CM structure. We find that CM dynamics are restricted to a specific parameter range at the border between spatially structured but completely occupied and globally extinct populations. Considering general life-history attributes, our simulations suggest that CMs are more likely to occur in arthropod species than in (large) vertebrates. Since the specific type of spatial population structure determines conservation concepts, our findings have important implications for conservation biology. Our model suggests that most spatially structured populations are panmictic, patchy, or of mainland-island type, which makes efforts spent on increasing connectivity (e.g., corridors) questionable. If one does observe a true CM structure, this means that the focal metapopulation is on the brink of extinction and that drastic conservation measures are needed.     

Population Viability Analysis for an Endangered Plant

Abstract: Demographic modeling is used to understand the population viability of Furbish's lousewort, Pedicularis furbishiae , a perennial plant species endemic to the St. John River Valley in northern Maine. Environment-specific summaries of demographic parameters (survivorship, growth, and fecundity) over four years, organized into stage-based projection matrices, provide predictions of future population dynamics given a deterministic extension of past conditions. Stochastic modeling, using (I) empirically observed variation in demographic parameters, and (2) estimated rates of natural catastrophes, leads to predictions of extinction probability.
P. furbishiae viability has varied widely over the study period Viable populations with finite rates of increase > 1 are found where cover is low, woody plants do not dominate, and disturbance does not occur. Rates of increase vary over time, suggesting that stochastic analyses would be realistic. Stochastic measures of population viability incorporating environmental variation suggest that early successional environments, especially wetter sites, can support viable populations in the absence of disturbance. However; observed rates of natural catastrophe dominate viability estimates of individual populations. Metapopulation dynamics feature extinction rates that are greater than recolonization rates, and may be affected by land use in the watershed Species management needs to consider a large-scale view of the riverine corridor.     

Physical structure of lakes constrains epidemics in Daphnia populations

Parasites are integral parts of most ecosystems, yet attention has only recently focused on how community structure and abiotic factors impact host-parasite interactions. In lakes, both factors are influenced by habitat morphology. To investigate the role of habitat structure in mediating parasitism in the plankton, we quantified timing and prevalence of a common microparasite (Metschnikowia bicuspidata) in its host, Daphnia dentifera, in 18 lakes that vary in basin size and shape. Over three years, we found substantial spatial and temporal variation in the severity of epidemics. Although infection rates reached as high as 50% in some lakes, they did not occur in most lakes in most years. Host density, often considered to be a key determinant of disease spread, did not explain a significant amount of variation in the occurrence of epidemics. Furthermore, host resistance does not fully explain this parasite's distribution, since we easily infected hosts in the laboratory. Rather, basin shape predicted epidemics well; epidemics occurred only in lakes with steep-sided basins. In these lakes, the magnitude of epidemics varied with year. We suggest that biological (predation) and physical (turbulence) effects of basin shape interact with annual weather patterns to determine the regional distribution of this parasite.     

Local spatial modeling of white-tailed deer distribution

Complex spatial heterogeneity of ecological systems is difficult to capture and interpret using global models alone. For this reason, recent attention has been paid to local spatial modeling techniques. We used one local modeling approach, geographically weighted regression (GWR), to investigate the effects of local spatial heterogeneity on multivariate relationships of white-tailed deer distribution using land cover patch metrics and climate factors. The results of these analyses quantify differences in the contributions of model parameters to estimates of deer density over space. A GWR model with local kernel bandwidth was compared to a GWR model with global kernel bandwidth and an ordinary least-squares regression (OLS) model with the same parameters to evaluate their relative abilities in modeling deer distributions. The results indicated that the GWR models predicted deer density better than the traditional ordinary least-squares model and also provided useful information regarding local environmental processes affecting deer distribution. GWR model comparisons showed that the local kernel bandwidth GWR model was more realistic than the global kernel bandwidth GWR model, as the latter exaggerated local spatial variation. The parameter estimates and model statistics (e.g., model R²) of the GWR models were mapped using geographic information systems (GIS) to illustrate local spatial variation in the regression relationship and to identify causes of large-scale model misspecifications and low estimation efficiencies.     

Consequences of ignoring dispersal variation in network models for landscape connectivity

Habitat loss and fragmentation can negatively influence population persistence and biodiversity, but the effects can be mitigated if species successfully disperse between isolated habitat patches. Network models are the primary tool for quantifying landscape connectivity, yet in practice, an overly simplistic view of species dispersal is applied. These models often ignore individual variation in dispersal ability under the assumption that all individuals move the same fixed distance with equal probability. We developed a modeling approach to address this problem. We incorporated dispersal kernels into network models to determine how individual variation in dispersal alters understanding of landscape-level connectivity and implemented our approach on a fragmented grassland landscape in Minnesota. Ignoring dispersal variation consistently overestimated a population's robustness to local extinctions and underestimated its robustness to local habitat loss. Furthermore, a simplified view of dispersal underestimated the amount of habitat substructure for small populations but overestimated habitat substructure for large populations. Our results demonstrate that considering biologically realistic dispersal alters understanding of landscape connectivity in ecological theory and conservation practice.     

Semiparametric space–time survival modeling of chronic wasting disease in deer

In this paper, we propose a semiparametric survival model to investigate the pattern of spatial and temporal variation in disease prevalence of chronic wasting disease (CWD) in wild deer in Wisconsin over the years 2002 and 2006. The semiparametric survival model we suggested allows to build a more flexible model than the parametric model with fewer parametric assumptions by modeling the baseline hazard using a Gamma process prior. Based on the proposed model, we investigate the geographical distribution of CWD, and assess the effect of sex on disease prevalence. We use a Bayesian hierarchical framework where latent parameters capture temporal and spatial trends in disease incidence, incorporating sex and spatially correlated random effects. We also propose bivariate baseline hazard which change over age and time simultaneously to adopt different effects of age and time on the baseline hazard. Inference is carried out by using MCMC simulation techniques in a fully Bayesian framework. Our results suggest that disease has been spreaded mainly in the disease eradication zone and male deer show a significantly higher infection probability than female deer.     

Population Viability Estimates: Theory and Practice for a Wild Gorilla Population

The logic of demographic modeling, the apparent simplicity of its quantifiably substantiated answers, and the ready availability of software correlate with increasing use of demographic modeling as the means of applying biology to the conservation of potentially endangered populations. I investigated that use by considering a small population (about 300 individuals) of a large, forest-dwelling mammal of the tropics, the Virunga gorilla ( Gorilla gorilla ) of Zaire, Uganda, and Rwanda. Because censuses of forest populations are so inaccurate and data on variance of some parameters takes so long to collect, models might not be broadly applicable. Therefore, simple demographic indices of potential extinction should replace sophisticated models. The current best index could be problematic, however, because it is based on detecting adult mortality, perhaps the most difficult demographic parameter to measure. Models of the Virunga gorilla population that incorporate aspects of demographic heterogeneity valuably indicate genetic and demographic persistence for several hundred years. Deterministic change in habitat is a greater threat than stochastic demographic variation, and yet our ecological ignorance is such that we could not begin to model the consequences of removal of even the main food plant. We must add to our ability to model outcomes of demographic perturbation a far greater understanding of the processes by which the perturbations occur. Demography allows us to model demographic response to demographic change, but we usually need ecology to tell us how the threat produced the demographic change in the first place. In a time of change, accurate prediction requires ecological understanding of process as well as demographic understanding of outcome.     

Using demography and movement behavior to predict range expansion of the southern sea otter

In addition to forecasting population growth, basic demographic data combined with movement data provide a means for predicting rates of range expansion. Quantitative models of range expansion have rarely been applied to large vertebrates, although such tools could be useful for restoration and management of many threatened but recovering populations. Using the southern sea otter (Enhydra lutris nereis) as a case study, we utilized integro-difference equations in combination with a stage-structured projection matrix that incorporated spatial variation in dispersal and demography to make forecasts of population recovery and range recolonization. In addition to these basic predictions, we emphasize how to make these modeling predictions useful in a management context through the inclusion of parameter uncertainty and sensitivity analysis. Our models resulted in hind-cast (1989-2003) predictions of net population growth and range expansion that closely matched observed patterns. We next made projections of future range expansion and population growth, incorporating uncertainty in all model parameters, and explored the sensitivity of model predictions to variation in spatially explicit survival and dispersal rates. The predicted rate of southward range expansion (median = 5.2 km/yr) was sensitive to both dispersal and survival rates; elasticity analysis indicated that changes in adult survival would have the greatest potential effect on the rate of range expansion, while perturbation analysis showed that variation in subadult dispersal contributed most to variance in model predictions. Variation in survival and dispersal of females at the south end of the range contributed most of the variance in predicted southward range expansion. Our approach provides guidance for the acquisition of further data and a means of forecasting the consequence of specific management actions. Similar methods could aid in the management of other recovering populations.     

Multimethod,multistate Bayesian hierarchical modeling approach for use in regional monitoring of wolves

In many cases, the first step in large‐carnivore management is to obtain objective, reliable, and cost‐effective estimates of population parameters through procedures that are reproducible over time. However, monitoring predators over large areas is difficult, and the data have a high level of uncertainty. We devised a practical multimethod and multistate modeling approach based on Bayesian hierarchical‐site‐occupancy models that combined multiple survey methods to estimate different population states for use in monitoring large predators at a regional scale. We used wolves (Canis lupus) as our model species and generated reliable estimates of the number of sites with wolf reproduction (presence of pups). We used 2 wolf data sets from Spain (Western Galicia in 2013 and Asturias in 2004) to test the approach. Based on howling surveys, the naïve estimation (i.e., estimate based only on observations) of the number of sites with reproduction was 9 and 25 sites in Western Galicia and Asturias, respectively. Our model showed 33.4 (SD 9.6) and 34.4 (3.9) sites with wolf reproduction, respectively. The number of occupied sites with wolf reproduction was 0.67 (SD 0.19) and 0.76 (0.11), respectively. This approach can be used to design more cost‐effective monitoring programs (i.e., to define the sampling effort needed per site). Our approach should inspire well‐coordinated surveys across multiple administrative borders and populations and lead to improved decision making for management of large carnivores on a landscape level. The use of this Bayesian framework provides a simple way to visualize the degree of uncertainty around population‐parameter estimates and thus provides managers and stakeholders an intuitive approach to interpreting monitoring results. Our approach can be widely applied to large spatial scales in wildlife monitoring where detection probabilities differ between population states and where several methods are being used to estimate different population parameters.     

Localized deer absence leads to tick amplification

Deer support high tick intensities, perpetuating tick populations, but they do not support tick-borne pathogen transmission, so are dilution hosts. We test the hypothesis that absence of deer (loss of a dilution host) will result in either an increase or a reduction in tick density, and that the outcome is scale dependent. We use a complementary methodological approach starting with meta-analysis, followed up by a field experiment. Meta-analysis indicated that larger deer exclosures reduce questing (host-seeking) tick density, but as the exclosure becomes smaller (<2.5 ha) the questing tick density is increased (amplified). To determine the consequences for tick-borne pathogen transmission we carried out a field experiment, comparing the intensity of ticks that fed on hosts competent for tickborne pathogen transmission (rodents) in two small (<1 ha) deer exclosures and their replicated controls. Intensity of larval ticks on rodents was not significantly different between treatments, but nymph intensity, the tick stage responsible for tick-borne encephalitis (TBE) transmission, was higher in deer exclosures. TBE seropositive rodents were found in a deer exclosure but not in the controls. We propose that localized absence of deer (loss of a dilution host) increases tick feeding on rodents, leading to the potential for tick-borne disease hotspots.     

Temporal, spatial, and between-host comparisons of patterns of parasitism in lake zooplankton

In nature, multiple parasite species infect multiple host species and are influenced by processes operating across different spatial and temporal scales. Data sets incorporating these complexities offer exciting opportunities to examine factors that shape epidemics. We present a method using generalized linear mixed models in a multilevel modeling framework to analyze patterns of variances and correlations in binomially distributed prevalence data. We then apply it to a multi-lake, multiyear data set involving two Daphnia host species and nine microparasite species. We found that the largest source of variation in parasite prevalence was the species identities of host-parasite pairs, indicating strong host-parasite specificity. Within host-parasite combinations, spatial variation (among lakes) exceeded interannual variation. This suggests that factors promoting differences among lakes (e.g., habitat characteristics and species interactions) better explain variation in peak infection prevalence in our data set than factors driving differences among years (e.g., climate). Prevalences of parasites in D. dentifera were more positively correlated than those for D. pulicaria, suggesting that similar factors influenced epidemic size among parasites in D. dentifera. Overall, this study demonstrates a method for parsing patterns of variation and covariation in infection prevalence data, providing greater insight into the relative importance of different underlying drivers of parasitism.     

Prevalence of the Crayfish Plague Pathogen Aphanomyces astaci in Invasive American Crayfishes in the Czech Republic

Abstract:  In Central Europe invasive North American crayfishes are carriers of the oomycete Aphanomyces astaci, which causes crayfish plague. This lethal disease currently represents one of the major threats to native European crayfishes. We used molecular methods—species-specific amplification and sequencing of the pathogen DNA—to investigate the prevalence of individuals latently infected with A. astaci in 28 populations of two invasive American crayfish species (6 of the signal crayfish [ Pacifastacus leniusculus ] and 22 of the spiny-cheek crayfish [ Orconectes limosus ]) in the Czech Republic. The pathogen occurred in 17 investigated populations. We recorded a high variation in positive reactions, ranging from 0% to 100%, in populations of O. limosus . In P. leniusculus, however, only one individual out of 124 tested positive for the pathogen. There was a clear relationship between the water body type and pathogen prevalence in O. limosus . Infection ratios in isolated standing waters were usually low, whereas in running waters, pathogen prevalence often exceeded 50%. Other evaluated characteristics of potential plague pathogen carriers (size, sex, and the presence of melanized spots in the cuticle) seemed to be unrelated to infection. Our data suggest that in contrast to other European countries, O. limosus seems to be the primary reservoir of crayfish plague in the Czech Republic. Although all populations of alien American crayfishes may be potential sources of infections and should be managed as such, knowledge on the prevalence of the plague pathogen at various localities may allow managers to focus conservation efforts on the most directly endangered populations of native crayfishes.     

The influence of host diversity and composition on epidemiological patterns at multiple spatial scales

Spatial patterns of pathogen prevalence are determined by ecological processes acting across multiple spatial scales. Host-pathogen interactions are influenced by community composition, landscape structure, and environmental factors. Explaining prevalence patterns requires an understanding of how local determinants of infection, such as community composition, are mediated by landscape characteristics and regional-scale environmental drivers. Here we investigate the role of local community interactions and the effects of landscape structure on the dynamics of barley and cereal yellow dwarf viruses (B/CYDV) in the open meadows of the Cascade Mountains of Oregon. B/CYDV is an aphid-transmitted, generalist pathogen of over 100 wild and cultivated grass species. We used variance components analysis and model selection techniques to partition the sources of variation in B/CYDV prevalence and to determine which abiotic and biotic factors influence host-pathogen interactions in a Cascades meadowsystem. B/CYDV prevalence in Cascades meadows varied by host species identity, with a significantly higher proportion of infected Festuca idahoensis individuals than Elymus glaucus or Bromus carinatus. Although there was significant variation in prevalence among host species and among meadows in the same meadow complex, there was no evidence of any significant variation in prevalence among different meadow complexes at a larger spatial scale. Variation in prevalence among meadows was primarily associated with the local community context (host identity, the relative abundance of different host species, and host species richness) and the physical landscape attributes of the meadow. These results highlight the importance of local host community composition, mediated by landscape characteristics such as meadow aspect, as a determinant of the spatial pattern of infection of a multi-host pathogen.