Drifting blooms of the endemic filamentous brown alga Hincksia sordida at Noosa on the subtropical east Australian coast

Since 2002, the usually uncommon endemic filamentous brown alga Hincksia sordida (Harvey) Silva (Ectocarpales, Phaeophyta) has formed nuisance blooms annually during spring/early summer at Main Beach, Noosa on the subtropical east Australian coast. The Hincksia bloom coincides with the normally intensive recreational use of the popular bathing beach by the local population and tourists. The alga forms dense accumulations in the surf zone at Main Beach, giving the seawater a distinct brown coloration and deterring swimmers from entering the water. Decomposing algae stranded by receding tides emit a nauseating sulphurous stench which hangs over the beach. The stranded algal biomass is removed from the beach by bulldozers. During blooms, the usually crowded Main Beach is deserted, bathers preferring to use the many unaffected beaches on the Sunshine Coast to the south of Main Beach. The bloom worsens with north-easterly winds and is cleared from Noosa by south easterly winds, observations which have prompted the untenable proposal by local authorities that the bloom is forming offshore of Fraser Island in the South Pacific Ocean. The Noosa River estuarine system/Laguna Bay is the more probable source of the bloom and the nutrient inputs into this system must be substantial to generate the high bloom biomass. Current mitigation procedures of removing the blooming alga off the beach with bulldozers treat the symptom, not the cause and are proving ineffective. Environmental management must be based on science and the Noosa bloom would benefit greatly from the accurate ecological data on which to base management options. (c) 2006 Elsevier Ltd. All rights reserved.

How does your model grow? An empirical investigation of the effects of ontological clarity and application domain size on query performance

Semantic data models provide a map of the components of an information system. The characteristics of these models affect their usefulness for various tasks (e.g., information retrieval). The quality of information retrieval has obvious important consequences, both economic and otherwise. Traditionally, data base designers have produced parsimonious logical data models. In spite of their increased size, ontologically clearer conceptual models have been shown to facilitate better performance for both problem solving and information retrieval tasks in experimental settings. The experiments producing evidence of enhanced performance for ontologically clearer models have, however, used application domains of modest size. Data models in organizational settings are likely to be substantially larger than those used in these experiments. This research used an experiment to investigate whether the benefits of improved information retrieval performance associated with ontologically clearer models are robust as the size of the application domains increase. The experiment used an application domain of approximately twice the size as tested in prior experiments. The results indicate that, relative to the users of the parsimonious implementation, end users of the ontologically clearer implementation made significantly more semantic errors, took significantly more time to compose their queries, and were significantly less confident in the accuracy of their queries.