Abstract. Human introduction of nonindigenous species constitutes a serious threat to many ecosystems, particularly lakes. Recent attempts to predict invasions have focused on the supply of propagules of nonindigenous species to recipient ecosystems from source populations. Here we develop a spatially explicit ''gravity'' model to test this concept for Bythotrephes longimanus, a crustacean waterflea from Eurasia that is rapidly invading lakes in Ontario, Canada. The gravity model predicted spread of Bythotrephes based upon seven identified risk factors (e.g., use of contaminated fishing or boat anchor line) that may allow dispersal of either live individuals or their resting eggs from invaded to noninvaded lakes, as well as based on the spatial arrangement of invaded and noninvaded lakes in Ontario. Discriminant analysis of lake gravity scores successfully identified invasion status for 74% of 170 inland lakes. A retrospective analysis of 31 invaded lakes revealed that the order in which lakes were invaded was directly related to the magnitude of vector inflows from invaded sources. Analysis of the dominant vector inflow to each invaded lake revealed a ''stepping stone'' pattern in which at least five lakes were sequentially invaded from the source population in Lake Huron. One invaded lake (Muskoka) apparently served as an invasion ''hub,'' resulting in up to 18 additional direct and 17 indirect invasions. Species spread occurred via a combination of dominant, local diffusion (median distance 12.5 km) and rare, long-distance (Ͼ100 km) dispersal. Eleven of 131 lakes that were not invaded in 2000 were reported invaded in 2001. Gravity scores of these lakes were significantly higher than those of other noninvaded systems, indicating that susceptibility to invasion can be related to the magnitude of vector inflows. A GIS model based on gravity scores indicated that distribution of Bythotrephes is expected to expand to eastern and northwestern Ontario, although most new invasions are expected to occur in the central region of the province. Our results indicate that quantitative analysis of human dispersal vectors provides a robust starting point with which to assess vulnerability of discrete ecosystems to invasion. Management efforts focused on reducing the number and magnitude of human-mediated dispersal vectors may reduce the rate of invasion of new ecosystems.

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In the context of hazard monitoring, using sensor web technology to monitor and detect hazardous conditions in near-real-time can result in large amounts of spatial data that can be used to drive analysis at an instrumented site. These data can be used for decision making and problem solving, however as with any analysis problem the success of analyzing hazard potential is governed by many factors such as: the quality of the sensor data used as input; the meaning that can be derived from those data; the reliability of the model used to describe the problem; the strength of the analysis methods; and the ability to effectively communicate the end results of the analysis. For decision makers to make use of sensor web data these issues must be dealt with to some degree. The work described in this paper addresses all of these areas by showing how raw sensor data can be automatically transformed into a representation which matches a predefined model of the problem context. This model can be understood by analysis software that leverages rule-based logic and inference techniques to reason with, and draw conclusions about, spatial data. These tools are integrated with a well known Geographic Information System (GIS) and existing geospatial and sensor web infrastructure standards, providing expert users with the tools needed to thoroughly explore a problem site and investigate hazards in any domain.

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