Human activities have fundamental impacts on the distribution of species through altered land use, but also directly by dispersal of propagules. Rare long-distance dispersal events have a disproportionate importance for the spread of species including invasions. While it is widely accepted that humans may act as vectors of long-distance dispersal, there are few studies that quantify this process. We studied in detail a mechanism of human-mediated dispersal (HMD). For two plant species we measured, over a wide range of distances, how many seeds are carried by humans on shoes. While over half of the seeds fell off within 5 m, seeds were regularly still attached to shoes after 5 km. Semi-mechanistic models were fitted, and these suggested that long-distance dispersal on shoes is facilitated by decreasing seed detachment probability with distance. Mechanistic modelling showed that the primary vector, wind, was less important as an agent of long-distance dispersal, dispersing seeds less than 250 m. Full dispersal kernels were derived by combining the models for primary dispersal by wind and secondary dispersal by humans. These suggest that walking humans can disperse seeds to very long distances, up to at least 10 km, and provide some of the first quantified dispersal kernels for HMD.
Wild waterfowl populations form a natural reservoir of Avian Influenza (AI) virus, and fears exist that these birds may contribute to an AI pandemic by spreading the virus along their migratory flyways. Observational studies suggest that individuals infected with AI virus may delay departure from migratory staging sites. Here, we explore the epidemiological dynamics of avian influenza virus in a migrating mallard (Anas platyrhynchos) population with a specific view to understanding the role of infection-induced migration delays on the spread of virus strains of differing transmissibility. We develop a host-pathogen model that combines the transmission dynamics of influenza with the migration, reproduction and mortality of the host bird species. Our modeling predicts that delayed migration of individuals influences both the timing and size of outbreaks of AI virus. We find that (1) delayed migration leads to a lower total number of cases of infection each year than in the absence of migration delay, (2) when the transmission rate of a strain is high, the outbreak starts at the staging sites at which birds arrive in the early part of the fall migration, (3) when the transmission rate is low, infection predominantly occurs later in the season, which is further delayed when there is a migration delay. As such, the rise of more virulent AI strains in waterfowl could lead to a higher prevalence of infection later in the year, which could change the exposure risk for farmed poultry. A sensitivity analysis shows the importance of generation time and loss of immunity for the effect of migration delays. Thus, we demonstrate, in contrast to many current transmission risk models solely using empirical information on bird movements to assess the potential for transmission, that a consideration of infection-induced delays is critical to understanding the dynamics of AI infection along the entire flyway.
Bitumen tanks were tested to investigate the capability of industrial heating loads to provide frequency response to an electric power system. A decentralized control algorithm was developed enabling the tanks to alter their power consumption in proportion to the variations of grid frequency. The control maintains the normal operation of tanks and causes little impact on their primary function of storing hot bitumen. Field investigations were undertaken on 76 tanks with power ratings from 17 to 75 kW. A model of a population of controlled tanks was developed. The behavior of the tanks was compared between the simulations and the field tests. The model of controlled tanks was then integrated with a simplified Great Britain power system model. It was shown that the controlled tanks were able to contribute to the grid frequency control in a manner similar to and faster than that provided by frequency-sensitive generation.Index Terms-Demand response, dynamic control, electric power system, frequency response, industrial bitumen tanks, smart grid.
Studies of external seed transport on animals usually assume that the probability of detachment is constant, so that seed retention should show a simple exponential relationship with time. Th is assumption has not been tested explicitly, and may lead to inaccurate representation of long distance seed dispersal by animals. We test the assumption by comparing the fi t to empirical data of simple, two-parameter functions. Fifty-two data sets were obtained from fi ve published studies, describing seed retention of 32 plant species on sheep, cattle, deer, goats and mice. Model selection suggested a simple exponential function was adequate for data sets in which seed retention was followed for short periods ( Ͻ 48 h). Th e data gathered over longer periods (49 -219 days) were best described by the power exponential function, a form of the stretched exponential which allows a changing dropping rate. In these cases the power exponential showed that seed dropping rate decreased with time, suggesting that seeds vary in attachment, with some seeds becoming deeply buried or wound up in the animal ' s coat. Comparison of fi tted parameters across all the data sets also confi rmed that seeds with adhesive structures have lower dropping rates than those without. We conclude that the seed dropping rate often changes with time during external transport on animals and that the power exponential is an eff ective function to describe this change. We advise that, to analyse seed dropping rates adequately, retention should be measured over reasonable time periods -until most seeds are dropped -and both the simple and power exponential functions should be fi tted to the resulting data. To increase its utility, we provide functions describing the seed dropping rate and the dispersal kernel resulting from the power exponential relationship.
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