We propose a Bayesian hierarchical Jolly-Seber model that can accommodate a semiparametric functional relationship between external covariates and capture probabilities, individual heterogeneity in departure due to an internal time-varying covariate and the dependence of arrival time on external covariates. Modelwise, we consider a stochastic process to characterize the evolution of the partially observable internal covariate that is linked to departure probabilities. Computationally, we develop a well-tailored Markov chain Monte Carlo algorithm that is free of tuning through data augmentation. Inferentially, our model allows us to make inference about stopover duration and population sizes, the impacts of various covariates on departure and arrival time and to identify flexible yet data-driven functional relationships between external covariates and capture probabilities. We demonstrate the effectiveness of our model through a motivating dataset collected for studying the migration of mallards (Arias platyrhynchos) in Sweden.

Many pathogens rely on the mobility of their hosts for dispersal. In order to understand and predict how a disease can rapidly sweep across entire continents, illuminating the contributions of host movements to disease spread is pivotal. While elegant proposals have been made to elucidate the spread of human infectious diseases, the direct observation of long-distance dispersal events of animal pathogens is challenging. Pathogens like avian influenza A viruses, causing only short disease in their animal hosts, have proven exceptionally hard to study. Here, we integrate comprehensive data on population and disease dynamics for low-pathogenic avian influenza viruses in one of their main hosts, the mallard, with a novel movement model trained from empirical, high-resolution tracks ofmallardmigrations. This allowed us to simulate individualmallard migrations from a key stopover site in the Baltic Sea for the entire population and link these movements to infection simulations. Using this novel approach, we were able to estimate the dispersal of low-pathogenic avian influenza viruses by migrating mallards throughout several autumn migratory seasons and predicted areas that are at risk of importing these viruses. We found that mallards are competent vectors and on average dispersed viruses over distances of 160 km in just 3 h. Surprisingly, our simulations suggest that such dispersal events are rare even throughout the entire autumn migratory season. Our approach directly combines simulated population-level movements with local infection dynamics and offers a potential converging point for movement and disease ecology.

Disease prevalence in wildlife is governed by epidemiological parameters (infection and recovery rates) and response to infection, both of which vary within and among individual hosts. Studies quantifying these individual-scale parameters and documenting their source of variation in wild hosts are fundamental for predicting disease dynamics. Such studies do not exist for the influenza A virus (IAV), despite its strong impact on the global economy and public health. Using capture-recaptures of 3500 individual mallards Anas platyrhynchos during seven migration seasons at a stopover site in southern Sweden, we provide the first empirical description of the individual-based mechanisms of IAV dynamics in a wild reservoir host. For most years, prevalence and risk of IAV infection peaked at a single time during the autumn migration season, but the timing, shape and intensity of the infection curve showed strong annual heterogeneity. In contrast, the seasonal pattern of recovery rate only varied in intensity across years. Adults and juveniles displayed similar seasonal patterns of infection and recovery each year. However, compared to adults, juveniles experienced twice the risk of becoming infected, whereas recovery rates were similar across age categories. Finally, we did not find evidence that infection influenced the timing of emigration.Synthesis and applications. Our study provides robust empirical estimates of epidemiological parameters for predicting influenza A virus (IAV) dynamics. However, the strong annual variation in infection curves makes forecasting difficult. Prevalence data can provide reliable surveillance indicators as long as they catch the variation in infection risk. However, individual-based monitoring of infection is required to verify this assumption in areas where surveillance occurs. In this context, monitoring of captive sentinel birds kept in close contact with wild birds is useful. The fact that infection does not impact the timing of migration underpins the potential for mallards to spread viruses rapidly over large geographical scales. Hence, we strongly encourage IAV surveillance with a multistate capture-recapture approach along the entire migratory flyway of mallards.

The last decade has seen a surge in research on avian influenza A viruses (IAVs), in part fuelled by the emergence, spread and potential zoonotic importance of highly pathogenic virus subtypes. The mallard (Anas platyrhynchos) is the most numerous and widespread dabbling duck in the world, and one of the most important natural hosts for studying IAV transmission dynamics. In order to predict the likelihood of IAV transmission between individual ducks and to other hosts, as well as between geographical regions, it is important to understand how IAV infection affects the host. In this study, we analysed the movements of 40 mallards equipped with GPS transmitters and three-dimensional accelerometers, of which 20 were naturally infected with low pathogenic avian influenza virus (LPAIV), at a major stopover site in the Northwest European flyway. Movements differed substantially between day and night, as well as between mallards returning to the capture site and those feeding in natural habitats. However, movement patterns did not differ between LPAIV infected and uninfected birds. Hence, LPAIV infection probably does not affect mallard movements during stopover, with high possibility of virus spread along the migration route as a consequence.

Wild waterfowl are important reservoir hosts for influenza A virus (IAV) and a potential source of spillover infections in other hosts, including poultry and swine. The emergence of highly pathogenic avian influenza (HPAI) viruses, such as H5N1 and H5N8, and subsequent spread along migratory flyways prompted the initiation of several programs in Europe, North America, and Africa to monitor circulation of HPAI and low-pathogenicity precursor viruses (low-pathogenicity avian influenza [LPAI] viruses). Given the costs of maintaining such programs, it is essential to establish best practice for field methodologies to provide robust data for epidemiological interpretation. Here, we use long-term surveillance data from a single site to evaluate the influence of a number of parameters on virus detection and isolation of LPAI viruses. A total of 26,586 samples (oropharyngeal, fecal, and cloacal) collected from wild mallards were screened by real-time PCR, and positive samples were subjected to isolation in embryonated chicken eggs. The LPAI virus detection rate was influenced by the sample type: cloacal/fecal samples showed a consistently higher detection rate and lower cycle threshold (Ct) value than oropharyngeal samples. Molecular detection was more sensitive than isolation, and virus isolation success was proportional to the number of RNA copies in the sample. Interestingly, for a given Ct value, the isolation success was lower in samples from adult birds than in those from juveniles. Comparing the results of specific real-time reverse transcriptase (RRT)-PCRs and of isolation, it was clear that coinfections were common in the investigated birds. The effects of sample type and detection methods warrant some caution in interpretation of the surveillance data.

Multiple infections, or simultaneous infection of a host with multiple parasites, are the rule rather than the exception. Interactions between co-occurring pathogens in a population may be mutualistic, competitive or facilitative. For some pathogen combinations, these interrelated effects will have epidemiological consequences; however this is as yet poorly incorporated into practical disease ecology. For example, screening of Mallards for influenza A viruses (IAV) have repeatedly revealed high prevalence and large subtype diversity in the Northern Hemisphere. Other studies have identified avian paramyxovirus type 1 (APMV-1) and coronaviruses (CoVs) in Mallards, but without making inferences on the larger viral assemblage. In this study we followed 144 wild Mallards across an autumn season in a natural stopover site and constructed infection histories of IAV, APMV-1 and CoV. There was a high prevalence of IAV, comprising of 27 subtype combinations, while APMV-1 had a comparatively low prevalence (with a peak of 2%) and limited strain variation, similar to previous findings. Avian CoVs were common, with prevalence up to 12%, and sequence analysis identified different putative genetic lineages. An investigation of the dynamics of co-infections revealed a synergistic effect between CoV and IAV, whereby Coy prevalence was higher given that the birds were co-infected with IAV. There were no interactive effects between IAV and APMV-1. Disease dynamics are the result of an interplay between parasites, host immune responses, and resources; and is imperative that we begin to include all factors to better understand infectious disease risk. (C) 2014 Elsevier B.V. All rights reserved.

Background: Movements of animals have important consequences, at both the individual and population levels. Due to its important implications in the evolutionary dynamics of populations, dispersal is one of the most studied types of movement. In contrast, non-permanent extra home-range movements are often paid less attention. However, these movements may occur in response to important biological processes such as mating or predation avoidance. In addition, these forays are often preludes to permanent dispersal, because they may help individuals gain cues about their surroundings prior to settlement in a new place. In the European hare, exploration forays occur predominantly in juveniles, the time at which most hares disperse. In France, the timing of dispersal also overlaps with the hare hunting period. However, the determinants of such behaviour have not yet been studied. Herein, we investigate whether these non-permanent explorations are dispersal attempts/preludes or, in contrast, whether they are triggered by other factors such as disturbances related to hunting. Results: Contrary to natal dispersal, we did not find strong male-bias in the propensity to engage in explorations. Exploration forays occurred less in juveniles than in adults and later in the season than natal dispersal. This was the case both for philopatric movements and for movements occurring after dispersal and settlement. These movements were also more likely to occur during the hare hunting period and the mating season. Conclusions: We suggest that explorations in hares are triggered by factors other than dispersal and that hares may respond to hunting disturbances. Overall, we emphasize the need to account for human-related predation risk as a factor driving space-use in harvested species.

Data on long-term circulation of pathogens in wildlife populations are seldom collected, and hence understanding of spatial-temporal variation in prevalence and genotypes is limited. Here, we analysed a long-term surveillance series on influenza A virus (IAV) in mallards collected at an important migratory stopover site from 2002 to 2010, and characterized seasonal dynamics in virus prevalence and subtype diversity. Prevalence dynamics were influenced by year, but retained a common pattern for all years whereby prevalence was low in spring and summer, but increased in early autumn with a first peak in August, and a second more pronounced peak during October-November. A total of 74 haemagglutinin (HA)/neuraminidase (NA) combinations were isolated, including all NA and most HA (H1-H12) subtypes. The most common subtype combinations were H4N6, H1N1, H2N3, H5N2, H6N2 and H11N9, and showed a clear linkage between specific HA and NA subtypes. Furthermore, there was a temporal structuring of subtypes within seasons based on HA phylogenetic relatedness. Dissimilar HA subtypes tended to have different temporal occurrence within seasons, where the subtypes that dominated in early autumn were rare in late autumn, and vice versa. This suggests that build-up of herd immunity affected IAV dynamics in this system.

The mallard (Anas platyrhynchos) is a focal species in game management, epidemiology and ornithology, but comparably little research has focused on the ecology of the migration seasons. We studied habitat use, time-budgets, home-range sizes, habitat selection, and movements based on spatial data collected with GPS devices attached to wild mallards trapped at an autumn stopover site in the Northwest European flyway. Sixteen individuals (13 males, 3 females) were followed for 15-38 days in October to December 2010. Forty-nine percent (SD = 8.4%) of the ducks' total time, and 85% of the day-time (SD = 28.3%), was spent at sheltered reefs and bays on the coast. Two ducks used ponds, rather than coast, as day-roosts instead. Mallards spent most of the night (76% of total time, SD = 15.8%) on wetlands, mainly on alvar steppe, or in various flooded areas (e.g. coastal meadows). Crop fields with maize were also selectively utilized. Movements between roosting and foraging areas mainly took place at dawn and dusk, and the home-ranges observed in our study are among the largest ever documented for mallards (mean = 6,859 ha; SD = 5,872 ha). This study provides insights into relatively unknown aspects of mallard ecology. The fact that autumn-staging migratory mallards have a well-developed diel activity pattern tightly linked to the use of specific habitats has implications for wetland management, hunting and conservation, as well as for the epidemiology of diseases shared between wildlife and domestic animals.

Influenza A viruses (IAV) can dramatically alter both genotype and phenotype at a rapid rate as a product of co-infection and reassortment Avian IAV exhibit high levels of phylogenetic incongruence, suggesting high levels of reassortment in the virus reservoir. Using a natural-experimental system, we reconstructed relationships amongst 92 viruses across 15 subtypes from 10 Mallards in an autumn season. Phylogenetic analyses estimated that 56% of the isolated viruses were reassorted. Network analysis demonstrated different patterns of reassortment and limited exchange of segments between primary and secondary infections. No clear patterns of linkage between segments were found, and patterns within a season were likely the consequence of continued introduction ofnew constellations, high viral load and diversity in the wild bird reservoir, and co-infections. This is the first IAV study to implement multiple tools available for elucidating factors governing reassortment patterns in naturally infected Mallards.