Understanding the exposure risk of aerosolized Coccidioides in a Valley fever endemic metropolis.

Coccidioides is the fungal causative agent of Valley fever, a primarily pulmonary disease caused by inhalation of fungal arthroconidia, or spores. Although Coccidioides has been an established pathogen for 120 years and is responsible for hundreds of thousands of infections per year, little is known about when and where infectious Coccidioides arthroconidia are present within the ambient air in endemic regions. Long-term air sampling programs provide a means to investigate these characteristics across space and time. Here we present data from > 18 months of collections from 11 air sampling sites across the Phoenix, Arizona, metropolitan area. Overall, prevalence was highly variable across space and time with no obvious spatial or temporal correlations. Several high prevalence periods were identified at select sites, with no obvious spatial or temporal associations. Comparing these data with weather and environmental factor data, wind gusts and temperature were positively associated with Coccidioides detection, while soil moisture was negatively associated with Coccidioides detection. These results provide critical insights into the frequency and distribution of airborne arthroconidia and the associated risk of inhalation and potential disease that is present across space and time in a highly endemic locale.

SPARSEMODr: Rapidly simulate spatially explicit and stochastic models of COVID-19 and other infectious diseases.

Building realistically complex models of infectious disease transmission that are relevant for informing public health is conceptually challenging and requires knowledge of coding architecture that can implement key modeling conventions. For example, many of the models built to understand COVID-19 dynamics have included stochasticity, transmission dynamics that change throughout the epidemic due to changes in host behavior or public health interventions, and spatial structures that account for important spatio-temporal heterogeneities. Here we introduce an R package, SPARSEMODr, that allows users to simulate disease models that are stochastic and spatially explicit, including a model for COVID-19 that was useful in the early phases of the epidemic. SPARSEMOD stands for SPAtial Resolution-SEnsitive Models of Outbreak Dynamics, and our goal is to demonstrate particular conventions for rapidly simulating the dynamics of more complex, spatial models of infectious disease. In this report, we outline the features and workflows of our software package that allow for user-customized simulations. We believe the example models provided in our package will be useful in educational settings, as the coding conventions are adaptable, and will help new modelers to better understand important assumptions that were built into sophisticated COVID-19 models.

Quantifying mechanisms of coexistence in disease ecology.

Pathogen coexistence depends on ecological processes operating at both within and between-host scales, making it difficult to quantify which processes may promote or prevent coexistence. Here, we propose that adapting modern coexistence theory-traditionally applied in plant communities-to pathogen systems provides an exciting approach for examining mechanisms of coexistence operating across different spatial scales. We first overview modern coexistence theory and its mechanistic decomposition; we subsequently adapt the framework to quantify how spatial variation in pathogen density, host resources and immunity, and their interaction may promote pathogen coexistence. We apply this derivation to an example two pathogen, multiscale model comparing two scenarios with generalist and strain-specific immunity: one with demographic equivalency among pathogens and one with demographic trade-offs among pathogens. We then show how host-pathogen feedbacks generate spatial heterogeneity that promote pathogen coexistence and decompose those mechanisms to quantify how each spatial heterogeneity contributes to that coexistence. Specifically, coexistence of demographically equivalent pathogens occurs due to spatial variation in host resources, immune responses, and pathogen aggregation. With a competition-colonization trade-off, the superior colonizer requires spatial heterogeneity to coexist, whereas the superior competitor does not. Finally, we suggest ways forward for linking theory and empirical tests of coexistence in disease systems.

PM10 and Other Climatic Variables Are Important Predictors of Seasonal Variability of Coccidioidomycosis in Arizona.

Coccidioidomycosis (Valley fever) is a disease caused by the fungal pathogens Coccidioides immitis and Coccidioides posadasii that are endemic to the southwestern United States and parts of Mexico and South America. Throughout the range where the pathogens are endemic, there are seasonal patterns of infection rates that are associated with certain climatic variables. Previous studies that looked at annual and monthly relationships of coccidioidomycosis and climate suggest that infection numbers are linked with precipitation and temperature fluctuations; however, these analytic methods may miss important nonlinear, nonmonotonic seasonal relationships between the response (Valley fever cases) and explanatory variables (climate) influencing disease outbreaks. To improve our current knowledge and to retest relationships, we used case data from three counties of high endemicity in southern Arizona paired with climate data to construct a generalized additive statistical model that explores which meteorological parameters are most useful in predicting Valley fever incidence throughout the year. We then use our model to forecast the pattern of Valley fever cases by month. Our model shows that maximum monthly temperature, average PM10, and total precipitation 1 month prior to reported cases (lagged model) were all significant in predicting Valley fever cases. Our model fits Valley fever case data in the region of endemicity of southern Arizona and captures the seasonal relationships that predict when the public is at higher risk of being infected. This study builds on and retests relationships described by previous studies regarding climate variables that are important for predicting risk of infection and understanding this fungal pathogen. IMPORTANCE The inhalation of environmental infectious propagules from the fungal pathogens Coccidioides immitis and Coccidioides posadasii by susceptible mammals can result in coccidioidomycosis (Valley fever). Arizona is known to be a region where the pathogen is hyperendemic, and reported cases are increasing throughout the western United States. Coccidioides spp. are naturally occurring fungi in arid soils. Little is known about ecological factors that influence the growth of these fungi, and a higher environmental burden may result in increases in human exposure and therefore case rates. By examining case and climate data from Arizona and using generalized additive statistical models, we were able to examine the relationship between disease outbreaks and climatic variables and predict seasonal time points of increased infection risk.

Can Eco-Evo Theory Explain Population Cycles in the Field?

AbstractEfforts to explain animal population cycles often invoke consumer-resource theory, which has shown that consumer-resource interactions alone can drive population cycles. Eco-evo theory instead argues that population cycles are partly driven by fluctuating selection for resistance in the resource, but support for eco-evo theory has come almost entirely from laboratory microcosms. Here we ask, Can eco-evo theory explain population cycles in the field? We compared the ability of eco-evo models and classical "eco-only" models to explain data on cycles in the insect Lymantria dispar, in which outbreaks of the insect are terminated by a fatal baculovirus. We carried out a statistical comparison of the ability of eco-only and eco-evo models to explain combined data from L. dispar outbreak cycles and baculovirus epizootics (epidemics in animals). Both models require high host variation in resistance to explain the epizootic data, but high host variation in the eco-evo model leads to consistently accurate predictions of outbreak cycles, whereas in the presence of high host variation the eco-only model can explain outbreak cycles only by invoking high levels of stochasticity, which leads to highly variable and often inaccurate predictions of outbreak cycles. Our work provides statistically robust evidence that eco-evo models can explain population cycles in the field.

A sliding window approach to optimize the time-varying parameters of a spatially-explicit and stochastic model of COVID-19.

The implementation of non-pharmaceutical public health interventions can have simultaneous impacts on pathogen transmission rates as well as host mobility rates. For instance, with SARS-CoV-2, masking can influence host-to-host transmission, while stay-at-home orders can influence mobility. Importantly, variations in transmission rates and mobility patterns can influence pathogen-induced hospitalization rates. This poses a significant challenge for the use of mathematical models of disease dynamics in forecasting the spread of a pathogen; to create accurate forecasts in spatial models of disease spread, we must simultaneously account for time-varying rates of transmission and host movement. In this study, we develop a statistical model-fitting algorithm to estimate dynamic rates of SARS-CoV-2 transmission and host movement from geo-referenced hospitalization data. Using simulated data sets, we then test whether our method can accurately estimate these time-varying rates simultaneously, and how this accuracy is influenced by the spatial population structure. Our model-fitting method relies on a highly parallelized process of grid search and a sliding window technique that allows us to estimate time-varying transmission rates with high accuracy and precision, as well as movement rates with somewhat lower precision. Estimated parameters also had lower precision in more rural data sets, due to lower hospitalization rates (i.e., these areas are less data-rich). This model-fitting routine could easily be generalized to any stochastic, spatially-explicit modeling framework, offering a flexible and efficient method to estimate time-varying parameters from geo-referenced data sets.

Leptospira in river and soil in a highly endemic area of Ecuador.

BACKGROUND: Leptospira are shed into the environment via urine of infected animals. Rivers are thought to be an important risk factor for transmission to humans, though much is unknown about the types of environment or characteristics that favor survival. To address this, we screened for Leptospira DNA in two rivers in rural Ecuador where Leptospirosis is endemic. RESULTS: We collected 112 longitudinal samples and recorded pH, temperature, river depth, precipitation, and dissolved oxygen. We also performed a series of three experiments designed to provide insight into Leptospira presence in the soil. In the first soil experiment, we characterized prevalence and co-occurrence of Leptospira with other bacterial taxa in the soil at dispersed sites along the rivers (n = 64). In the second soil experiment, we collected 24 river samples and 48 soil samples at three points along eight transects to compare the likelihood of finding Leptospira in the river and on the shore at different distances from the river. In a third experiment, we tested whether Leptospira presence is associated with soil moisture by collecting 25 soil samples from two different sites. In our river experiment, we found pathogenic Leptospira in only 4 (3.7%) of samples. In contrast, pathogenic Leptospira species were found in 22% of shore soil at dispersed sites, 16.7% of soil samples (compared to 4.2% of river samples) in the transects, and 40% of soil samples to test for associations with soil moisture. CONCLUSIONS: Our data are limited to two sites in a highly endemic area, but the scarcity of Leptospira DNA in the river is not consistent with the widespread contention of the importance of river water for leptospirosis transmission. While Leptospira may be shed directly into the river, onto the shores, or washed into the river from more remote sites, massive dilution and limited persistence in rivers may reduce the environmental load and therefore, the epidemiological significance of such sources. It is also possible that transmission may occur more frequently on shores where people are liable to be barefoot. Molecular studies that further explore the role of rivers and water bodies in the epidemiology of leptospirosis are needed.

Predicting the current and future distribution of the western black-legged tick, Ixodes pacificus, across the Western US using citizen science collections.

In the twenty-first century, ticks and tick-borne diseases have expanded their ranges and impact across the US. With this spread, it has become vital to monitor vector and disease distributions, as these shifts have public health implications. Typically, tick-borne disease surveillance (e.g., Lyme disease) is passive and relies on case reports, while disease risk is calculated using active surveillance, where researchers collect ticks from the environment. Case reports provide the basis for estimating the number of cases; however, they provide minimal information on vector population or pathogen dynamics. Active surveillance monitors ticks and sylvatic pathogens at local scales, but it is resource-intensive. As a result, data are often sparse and aggregated across time and space to increase statistical power to model or identify range changes. Engaging public participation in surveillance efforts allows spatially and temporally diverse samples to be collected with minimal effort. These citizen-driven tick collections have the potential to provide a powerful tool for tracking vector and pathogen changes. We used MaxEnt species distribution models to predict the current and future distribution of Ixodes pacificus across the Western US through the use of a nationwide citizen science tick collection program. Here, we present niche models produced through citizen science tick collections over two years. Despite obvious limitations with citizen science collections, the models are consistent with previously-predicted species ranges in California that utilized more than thirty years of traditional surveillance data. Additionally, citizen science allows for an expanded understanding of I. pacificus distribution in Oregon and Washington. With the potential for rapid environmental changes instigated by a burgeoning human population and rapid climate change, the development of tools, concepts, and methodologies that provide rapid, current, and accurate assessment of important ecological qualities will be invaluable for monitoring and predicting disease across time and space.

Use of a mechanistic growth model in evaluating post-restoration habitat quality for juvenile salmonids.

Individual growth data are useful in assessing relative habitat quality, but this approach is less common when evaluating the efficacy of habitat restoration. Furthermore, available models describing growth are infrequently combined with computational approaches capable of handling large data sets. We apply a mechanistic model to evaluate whether selection of restored habitat can affect individual growth. We used mark-recapture to collect size and growth data on sub-yearling Chinook salmon and steelhead in restored and unrestored habitat in five sampling years (2009, 2010, 2012, 2013, 2016). Modeling strategies differed for the two species: For Chinook, we compared growth patterns of individuals recaptured in restored habitat over 15-60 d with those not recaptured regardless of initial habitat at marking. For steelhead, we had enough recaptured fish in each habitat type to use the model to directly compare habitats. The model generated spatially explicit growth parameters describing size of fish over the growing season in restored vs. unrestored habitat. Model parameters showed benefits of restoration for both species, but that varied by year and time of season, consistent with known patterns of habitat partitioning among them. The model was also supported by direct measurement of growth rates in steelhead and by known patterns of spatio-temporal partitioning of habitat between these two species. Model parameters described not only the rate of growth, but the timing of size increases, and is spatially explicit, accounting for habitat differences, making it widely applicable across taxa. The model usually supported data on density differences among habitat types in Chinook, but only in a couple of cases in steelhead. Modeling growth can thus prevent overconfidence in distributional data, which are commonly used as the metric of restoration success.

An Empirical Test of the Role of Small-Scale Transmission in Large-Scale Disease Dynamics.

A key assumption of epidemiological models is that population-scale disease spread is driven by close contact between hosts and pathogens. At larger scales, however, mechanisms such as spatial structure in host and pathogen populations and environmental heterogeneity could alter disease spread. The assumption that small-scale transmission mechanisms are sufficient to explain large-scale infection rates, however, is rarely tested. Here, we provide a rigorous test using an insect-baculovirus system. We fit a mathematical model to data from forest-wide epizootics while constraining the model parameters with data from branch-scale experiments, a difference in spatial scale of four orders of magnitude. This experimentally constrained model fits the epizootic data well, supporting the role of small-scale transmission, but variability is high. We then compare this model's performance to an unconstrained model that ignores the experimental data, which serves as a proxy for models with additional mechanisms. The unconstrained model has a superior fit, revealing a higher transmission rate across forests compared with branch-scale estimates. Our study suggests that small-scale transmission is insufficient to explain baculovirus epizootics. Further research is needed to identify the mechanisms that contribute to disease spread across large spatial scales, and synthesizing models and multiscale data are key to understanding these dynamics.

Metacommunity theory for transmission of heritable symbionts within insect communities.

Microbial organisms are ubiquitous in nature and often form communities closely associated with their host, referred to as the microbiome. The microbiome has strong influence on species interactions, but microbiome studies rarely take interactions between hosts into account, and network interaction studies rarely consider microbiomes. Here, we propose to use metacommunity theory as a framework to unify research on microbiomes and host communities by considering host insects and their microbes as discretely defined "communities of communities" linked by dispersal (transmission) through biotic interactions. We provide an overview of the effects of heritable symbiotic bacteria on their insect hosts and how those effects subsequently influence host interactions, thereby altering the host community. We suggest multiple scenarios for integrating the microbiome into metacommunity ecology and demonstrate ways in which to employ and parameterize models of symbiont transmission to quantitatively assess metacommunity processes in host-associated microbial systems. Successfully incorporating microbiota into community-level studies is a crucial step for understanding the importance of the microbiome to host species and their interactions.

Fungal infection alters the selection, dispersal and drift processes structuring the amphibian skin microbiome.

Symbiotic microbial communities are important for host health, but the processes shaping these communities are poorly understood. Understanding how community assembly processes jointly affect microbial community composition is limited because inflexible community models rely on rejecting dispersal and drift before considering selection. We developed a flexible community assembly model based on neutral theory to ask: How do dispersal, drift and selection concurrently affect the microbiome across environmental gradients? We applied this approach to examine how a fungal pathogen affected the assembly processes structuring the amphibian skin microbiome. We found that the rejection of neutrality for the amphibian microbiome across a fungal gradient was not strictly due to selection processes, but was also a result of species-specific changes in dispersal and drift. Our modelling framework brings the qualitative recognition that niche and neutral processes jointly structure microbiomes into quantitative focus, allowing for improved predictions of microbial community turnover across environmental gradients.

Do New World pitvipers "scale-down" at high elevations? Macroecological patterns of scale characters and body size.

Bergmann's rule describes the macroecological pattern of increasing body size in response to higher latitudes and elevations. This pattern is extensively documented in endothermic vertebrates, within and among species; however, studies involving ectotherms are less common and suggest no consistent pattern for amphibians and reptiles. Moreover, adaptive traits, such as epidermal features like scales, have not been widely examined in conjunction with Bergmann's rule, even though these traits affect physiological processes, such as thermoregulation, which are hypothesized as underlying mechanisms for the pattern. Here, we investigate how scale characters correlate with elevation among 122 New World pitviper species, representing 15 genera. We found a contra-Bergmann's pattern, where body size is smaller at higher elevations. This pattern was mainly driven by the presence of small-bodied clades at high elevations and large-bodied clades at low elevations, emphasizing the importance of taxonomic scope in studying macroecological patterns. Within a subset of speciose clades, we found that only Crotalus demonstrated a significant negative relationship between body size and elevation, perhaps because of its wide elevational range. In addition, we found a positive correlation between scale counts and body size but no independent effect of elevation on scale numbers. Our study increases our knowledge of Bergmann's rule in reptiles by specifically examining characters of squamation and suggests a need to reexamine macroecological patterns for this group.

Untangling the dynamics of persistence and colonization in microbial communities.

A central goal of community ecology is to infer biotic interactions from observed distributions of co-occurring species. Evidence for biotic interactions, however, can be obscured by shared environmental requirements, posing a challenge for statistical inference. Here, we introduce a dynamic statistical model, based on probit regression, that quantifies the effects of spatial and temporal covariance in longitudinal co-occurrence data. We separate the fixed pairwise effects of species occurrences on persistence and colonization rates, a potential signal of direct interactions, from latent pairwise correlations in occurrence, a potential signal of shared environmental responses. We first validate our modeling framework with several simulation studies. Then, we apply the approach to a pressing epidemiological question by examining how human papillomavirus (HPV) types coexist. Our results suggest that while HPV types respond similarly to common host traits, direct interactions are sparse and weak, so that HPV type diversity depends largely on shared environmental drivers. Our modeling approach is widely applicable to microbial communities and provides valuable insights that should lead to more directed hypothesis testing and mechanistic modeling.

Evaluating the Within-Host Dynamics of Ranavirus Infection with Mechanistic Disease Models and Experimental Data.

Mechanistic models are critical for our understanding of both within-host dynamics (i.e., pathogen replication and immune system processes) and among-host dynamics (i.e., transmission). Within-host models, however, are not often fit to experimental data, which can serve as a robust method of hypothesis testing and hypothesis generation. In this study, we use mechanistic models and empirical, time-series data of viral titer to better understand the replication of ranaviruses within their amphibian hosts and the immune dynamics that limit viral replication. Specifically, we fit a suite of potential models to our data, where each model represents a hypothesis about the interactions between viral replication and immune defense. Through formal model comparison, we find a parsimonious model that captures key features of our time-series data: The viral titer rises and falls through time, likely due to an immune system response, and that the initial viral dosage affects both the peak viral titer and the timing of the peak. Importantly, our model makes several predictions, including the existence of long-term viral infections, which can be validated in future studies.

Co-exposure to multiple ranavirus types enhances viral infectivity and replication in a larval amphibian system.

Multiple pathogens commonly co-occur in animal populations, yet few studies demonstrate how co-exposure of individual hosts scales up to affect transmission. Although viruses in the genus Ranavirus are globally widespread, and multiple virus species or strains likely co-occur in nature, no studies have examined how co-exposure affects infection dynamics in larval amphibians. We exposed individual northern red-legged frog Rana aurora larvae to 2 species of ranavirus, namely Ambystoma tigrinum virus (ATV), frog virus 3 (FV3), or an FV3-like strain isolated from a frog-culturing facility in Georgia, USA (RCV-Z2). We compared single-virus to pairwise co-exposures while experimentally accounting for dosage. Co-exposure to ATV and FV3-like strains resulted in almost twice as many infected individuals compared to single-virus exposures, suggesting an effect of co-exposure on viral infectivity. The viral load in infected individuals exposed to ATV and FV3 was also higher than the single-dose FV3 treatment, suggesting an effect of co-exposure on viral replication. In a follow-up experiment, we examined how the co-occurrence of ATV and FV3 affected epizootics in mesocosm populations of larval western chorus frogs Pseudacris triseriata. Although ATV did not generally establish within host populations (<4% prevalence), when ATV and FV3 were both present, this co-exposure resulted in a larger epizootic of FV3. Our results emphasize the importance of multi-pathogen interactions in epizootic dynamics and have management implications for natural and commercial amphibian populations.

Parasite metacommunities: Evaluating the roles of host community composition and environmental gradients in structuring symbiont communities within amphibians.

Ecologists increasingly report the structures of metacommunities for free-living species, yet far less is known about the composition of symbiont communities through space and time. Understanding the drivers of symbiont community patterns has implications ranging from emerging infectious disease to managing host microbiomes. Using symbiont communities from amphibian hosts sampled from wetlands of California, USA, we quantified the effects of spatial structure, habitat filtering and host community components on symbiont occupancy and overall metacommunity structure. We built upon a statistical method to describe metacommunity structure that accounts for imperfect detection in survey data-detection error-corrected elements of metacommunity structure-by adding an analysis to identify covariates of community turnover. We applied our model to a metacommunity of eight parasite taxa observed in 3,571 Pacific chorus frogs (Pseudacris regilla) surveyed from 174 wetlands over 5 years. Symbiont metacommunity structure varied across years, showing nested structure in 3 years and random structure in 2 years. Species turnover was most consistently influenced by spatial and host community components. Occupancy generally increased in more southeastern wetlands, and snail (intermediate host) community composition had strong effects on most symbiont taxa. We have used sophisticated but accessible statistical methods to reveal that spatial components-which influence colonization-and host community composition-which mediates transmission-both drive symbiont community composition in this system. These methods allow us to associate broad patterns of community turnover to local, species-level effects, ultimately improving our understanding of spatial community dynamics.

Using multispecies occupancy models to improve the characterization and understanding of metacommunity structure.

Two of the most prominent frameworks to develop in ecology over the past decade are metacommunity ecology, which seeks to characterize multispecies distributions across space, and occupancy modeling, which corrects for imperfect detection in an effort to better understand species occurrence patterns. Although their goals are complementary, metacommunity theory and statistical occupancy modeling methods have developed independently. For instance, the elements of metacommunity structure (EMS) framework uses species occurrence data to classify metacommunity structure and link it to underlying environmental gradients. While the efficacy of this approach relies on the quality of the data, few studies have considered how imperfect detection, which is widespread in ecological surveys and the major focus of occupancy modeling, affects the outcome. We introduce a framework that integrates multispecies occupancy models with the current EMS framework, detection error-corrected EMS (DECEMS). This method offers two distinct advantages. First, DECEMS reduces bias in characterizing metacommunity structure by using repeated surveys and occupancy models to disentangle species-specific occupancy and detection probabilities, ultimately bringing metacommunity structure classification into a more probabilistic framework. Second, occupancy modeling allows estimation of species-specific responses to environmental covariates, which will increase our ability to link species-level effects to metacommunity-wide patterns. After reviewing the EMS framework, we introduce a simple multispecies occupancy model and show how DECEMS can work in practice, highlighting that detection error often causes EMS to assign incorrect structures. To emphasize the broader applicability of this approach, we further illustrate that DECEMS can reduce the rate of structure misclassification by more than 20% in some cases, even proving useful when detection error rates are quite low (-10%). Integrating occupancy models and the EMS framework will lead to more accurate descriptions of metacommunity structure and to a greater understanding of the mechanisms by which different structures arise.

The scaling of host density with richness affects the direction, shape, and detectability of diversity-disease relationships.

Pathogen transmission responds differently to host richness and abundance, two unique components of host diversity. However, the heated debate around whether biodiversity generally increases or decreases disease has not considered the relationships between host richness and abundance that may exist in natural systems. Here we use a multi-species model to study how the scaling of total host community abundance with species richness mediates diversity-disease relationships. For pathogens with density-dependent transmission, non-monotonic trends emerge between pathogen transmission and host richness when host community abundance saturates with richness. Further, host species identity drives high variability in pathogen transmission in depauperate communities, but this effect diminishes as host richness accumulates. Using simulation we show that high variability in low richness communities and the non-monotonic relationship observed with host community saturation may reduce the detectability of trends in empirical data. Our study emphasizes that understanding the patterns and predictability of host community composition and pathogen transmission mode will be crucial for predicting where and when specific diversity-disease relationships should occur in natural systems.

Does life history mediate changing disease risk when communities disassemble?

Biodiversity loss sometimes increases disease risk or parasite transmission in humans, wildlife and plants. Some have suggested that this pattern can emerge when host species that persist throughout community disassembly show high host competence - the ability to acquire and transmit infections. Here, we briefly assess the current empirical evidence for covariance between host competence and extirpation risk, and evaluate the consequences for disease dynamics in host communities undergoing disassembly. We find evidence for such covariance, but the mechanisms for and variability around this relationship have received limited consideration. This deficit could lead to spurious assumptions about how and why disease dynamics respond to community disassembly. Using a stochastic simulation model, we demonstrate that weak covariance between competence and extirpation risk may account for inconsistent effects of host diversity on disease risk that have been observed empirically. This model highlights the predictive utility of understanding the degree to which host competence relates to extirpation risk, and the need for a better understanding of the mechanisms underlying such relationships.