Summary 1.Emerging wildlife diseases pose a significant threat to natural and human systems. Because of real or perceived risks of delayed actions, disease management strategies such as culling are often implemented before thorough scientific knowledge of disease dynamics is available. Adaptive management is a valuable approach in addressing the uncertainty and complexity associated with wildlife disease problems and can be facilitated by using a formal model. 2.We developed a multi-state computer simulation model using age, sex, infection-stage, and seasonality as a tool for scientific learning and managing chronic wasting disease (CWD) in white-tailed deer Odocoileus virginianus . Our matrix model used disease transmission parameters based on data collected through disease management activities. We used this model to evaluate management issues on density-(DD) and frequency-dependent (FD) transmission, time since disease introduction, and deer culling on the demographics, epizootiology, and management of CWD. 3. Both DD and FD models fit the Wisconsin data for a harvested white-tailed deer population, but FD was slightly better. Time since disease introduction was estimated as 36 (95% CI, 24-50) and 188 (41->200) years for DD and FD transmission, respectively. Deer harvest using intermediate to high non-selective rates can be used to reduce uncertainty between DD and FD transmission and improve our prediction of long-term epidemic patterns and host population impacts. A higher harvest rate allows earlier detection of these differences, but substantially reduces deer abundance. 4. Results showed that CWD has spread slowly within Wisconsin deer populations, and therefore, epidemics and disease management are expected to last for decades. Non-hunted deer populations can develop and sustain a high level of infection, generating a substantial risk of disease spread. In contrast, CWD prevalence remains lower in hunted deer populations, but at a higher prevalence the disease competes with recreational hunting to reduce deer abundance. 5. Synthesis and applications. Uncertainty about density-or frequency-dependent transmission hinders predictions about the long-term impacts of chronic wasting disease on cervid populations and the development of appropriate management strategies. An adaptive management strategy using computer modelling coupled with experimental management and monitoring can be used to test model predictions, identify the likely mode of disease transmission, and evaluate the risks of alternative management responses.

A bacterial pathogen of wild songbirds evolved higher virulence following its emergence in two separate regions of the host range.

One of the leading theories for the evolutionary stability of sex in eukaryotes relies on parasite-mediated selection against locally common host genotypes (the Red Queen hypothesis). As such, parasites would be expected to be better at infecting sympatric host populations than allopatric host populations. Here we examined all published and unpublished infection experiments on a snail-trematode system (Potamopyrgus antipodarum and Microphallus sp., respectively). A meta-analysis demonstrated significant local adaptation by the parasite, and a variance components analysis showed that the variance due to the host-parasite interaction far exceeded the variance due to the main effects of host source and parasite source. The meta-analysis also indicated that asexual host populations were more resistant to allopatric sources of parasites than were (mostly) sexual host populations, but we found no significant differences among parasite populations in the strength of local adaptation. This result suggests that triploid asexual snails are more resistant to remote sources of parasites, but the parasite has, through coevolution, overcome the difference. Finally, we found that the degree of local adaptation did not depend on the genetic distance among host populations. Taken together, the results demonstrate that the parasites are adapted, on average, to infecting their local host populations and suggest that they may be a factor in selecting against common host genotypes in natural populations.

Underlying dynamic event processes unfolding in continuous time give rise to spatiotemporal patterns that are sometimes observable at only a few discrete times. Such event processes may be modulated simultaneously over several spatial (e.g., latitude and longitude) and temporal (e.g., age, calendar time, and cohort) dimensions. The ecological challenge is to understand the dynamic latent processes that were integrated over several dimensions (space and time) to produce the observed pattern: a so-called inverse problem. An example of such a problem is characterizing epidemiological rate processes from spatially referenced age-specific prevalence data for a wildlife disease such as chronic wasting disease (CWD). With age-specific prevalence data, the exact infection times are not observed, which complicates the direct estimation of rates. However, the relationship between the observed data and the unobserved rate variables can be described with likelihood equations. Typically, for problems with multiple timescales, the likelihoods are integral equations without closed forms. The complexity of the likelihoods often makes traditional maximum-likelihood approaches untenable. Here, using seven years of hunter-harvest prevalence data from the CWD epidemic in white-tailed deer (Odocoileus virginianus) in Wisconsin, USA, we develop and explore a Bayesian approach that allows for a detailed examination of factors modulating the infection rates over space, age, and time, and their interactions. Our approach relies on the Bayesian ability to borrow strength from neighbors in both space and time. Synthesizing a number of areas of event time analysis (current-status data, age/period/cohort models, Bayesian spatial shared frailty models), our general framework has very broad ecological applicability beyond disease prevalence data to a number of important ecological event time analyses, including general survival studies with multiple time dimensions for which existing methodology is limited. We observed strong associations of infection rates with age, gender, and location. The infection rate appears to be increasing with time. We could not detect growth hotspots, or location by time interactions, which suggests that spatial variation in infection rates is determined primarily by when the disease arrives locally, rather than how fast it grows. We emphasize assumptions and the potential consequences of their violations.

We explore pathogen virulence evolution during the spatial expansion of an infectious disease epidemic, in the presence of a novel host movement trade-off, using a simple spatially explicit mathematical model. This work is motivated by empirical observations of the Mycoplasma gallisepticum invasion into North American House Finch (Haemorhous mexicanus) populations; however, our results likely have important applications to other emerging infectious diseases in mobile hosts. We assume that infection reduces host movement and survival, and that across pathogen strains the severity of these reductions increases with pathogen infectiousness. Assuming these trade-offs between pathogen virulence (host mortality), pathogen transmission, and host movement, we find that pathogen virulence levels near the epidemic front (that maximize wave speed) are lower than the virulence level with a short-term growth rate advantage or that ultimately prevails (i.e., are evolutionarily stable) near the epicenter and where infection becomes endemic (i.e., that maximizes the pathogen basic reproductive ratio). We predict that, under these trade-offs, less virulent pathogen strains will dominate the periphery of an epidemic, and that more virulent strains will increase in frequency after invasion where disease is endemic. These results have important implications for observing and interpreting spatio-temporal epidemic data, and may help explain transient virulence dynamics of emerging infectious diseases.

Emerging infectious diseases threaten wildlife populations and human health. Understanding the spatial distributions of these new diseases is important for disease management and policy makers; however, the data are complicated by heterogeneities across host classes, sampling variance, sampling biases, and the space-time epidemic process. Ignoring these issues can lead to false conclusions or obscure important patterns in the data, such as spatial variation in disease prevalence. Here, we applied hierarchical Bayesian disease mapping methods to account for risk factors and to estimate spatial and temporal patterns of infection by chronic wasting disease (CWD) in white-tailed deer (Odocoileus virginianus) of Wisconsin, U.S.A. We found significant heterogeneities for infection due to age, sex, and spatial location. Infection probability increased with age for all young deer, increased with age faster for young males, and then declined for some older animals, as expected from disease-associated mortality and age-related changes in infection risk. We found that disease prevalence was clustered in a central location, as expected under a simple spatial epidemic process where disease prevalence should increase with time and expand spatially. However, we could not detect any consistent temporal or spatiotemporal trends in CWD prevalence. Estimates of the temporal trend indicated that prevalence may have decreased or increased with nearly equal posterior probability, and the model without temporal or spatiotemporal effects was nearly equivalent to models with these effects based on deviance information criteria. For maximum interpretability of the role of location as a disease risk factor, we used the technique of direct standardization for prevalence mapping, which we develop and describe. These mapping results allow disease management actions to be employed with reference to the estimated spatial distribution of the disease and to those host classes most at risk. Future wildlife epidemiology studies should employ hierarchical Bayesian methods to smooth estimated quantities across space and time, account for heterogeneities, and then report disease rates based on an appropriate standardization.

Immune memory evolved to protect hosts from reinfection, but incomplete responses that allow future reinfection may inadvertently select for more-harmful pathogens. We present empirical and modeling evidence that incomplete immunity promotes the evolution of higher virulence in a natural host-pathogen system. We performed sequential infections of house finches with strains of various levels of virulence. Virulent bacterial strains generated stronger host protection against reinfection than less virulent strains and thus excluded less virulent strains from infecting previously exposed hosts. In a two-strain model, the resulting fitness advantage selected for an almost twofold increase in pathogen virulence. Thus, the same immune systems that protect hosts from infection can concomitantly drive the evolution of more-harmful pathogens in nature.

Many pathogens and parasites are transmitted through hosts that differ in species, sex, genotype, or immune status. In addition, virulence (here defined as disease-induced mortality) and transmission can vary during the infectious period within hosts of different state. Most models of virulence evolution assume that transmission and virulence are constant over the infectious period and that the host population is homogenous. Here, we examine a multispecies susceptible-infected-recovered (SIR) model where transmission occurs within and between species, and transmission and virulence varied during the infectious period. This allows us to understand virulence evolution in a broader range of situations that characterize many emerging diseases. Because emerging pathogens are by definition new to their host populations, they should be expected to rapidly adapt after emergence. We illustrate these evolutionary effects using the framework of adaptive dynamics to examine how virulence evolves after emergence in response to the relative strength of selection on pathogen fitness and mutational variance for virulence. We illustrate the role of evolution by simulating adaptive walks to an evolutionarily stable virulence. We found that the magnitude of between-species transmission and the relative timing of transmission and mortality across species were of primary importance for determining the evolutionarily stable virulence. K E Y W O R D S :Adaptive dynamics, basic reproductive ratio, dilution effect, ESS, pairwise invasion plot, pathogens.How sick should a pathogen make its host? This question has been of considerable interest to theoretical biologists for many decades (Levin and