Spatial abundance patterns and recruitment of a virus-affected commercial mollusc fishery

 Infectious pathogens figure prominently among those factors threatening marine wildlife. Mass mortality events caused by pathogens can fundamentally alter the structure of wild fish stocks and depress recruitment rates and yield. In the most severe instances, this can precipitate stock collapses resulting in dramatic economic losses to once valuable commercial fisheries. An outbreak of a herpes-like virus among commercially fished abalone populations in the south-west fishery of Victoria, Australia, during 2006-2007, has been associated with high mortality rates among all cohorts. Long-term records from fishery-independent surveys of blacklip abalone Haliotis rubra (Leach) enabled abundance from pre- and post-viral periods to be analysed to estimate stock density and biomass. The spatial distribution of abundance in relation to physical habitat variables derived from high-resolution bathymetric LiDAR data was investigated. Significant differences were observed in both measures between pre- and post-viral periods. Although there was some limited evidence of gradual stock improvement in recent years, disease-affected reefs have remained below productivity rates prior to the disease outbreak suggesting a reduction in larval availability or settlement success. This was corroborated by trends in sublegal sized blacklip abalone abundance that has yet to show substantial recovery post-disease. Abundance data were modelled as a function of habitat variables using a generalised additive model (GAM) and indicated that high abundance was associated with complex reef structures of coastal waters (<15 m). This study highlights the importance of long-term surveys to understand abalone recovery following mass mortality and the links between stock abundance and seafloor variability.

Discovery of stop regions for understanding repeat travel behaviors of moving objects

GPS trajectory dataset with high sampling-rates is usually in large volume that challenges the processing efficiency. Most of the data points on trajectories are useless. This paper summarizes trajectories using stop points. We define a new concept of stay stability (i.e., time dividing distance or reciprocal of speed) between any two GPS points to detect stop points on individual trajectories. We propose a novel Mining Repeat Travel Behaviors Using Stop Regions (MRTBUSR) method. In MRTBUSR, a stop region is a popular region containing a certain number of close stop points that can be grouped into a cluster. We then retrieve common sequences of stop regions to denote repeat route patterns and further analyze the stop durations on a stop region to find repeat travel behaviors. The experiments on 20 labeled trajectories selected from GeoLife demonstrated the semantic effect, accuracy and near linear efficiency of our proposed method.