RESUMEN
The environmental similarity scores between source and recipient locations are essential in ballast water risk assessment (BWRA) models used to estimate the potential for non-indigenous species (NIS) introduction, survival, and establishment, and to guide management strategies aiming to minimize biodiversity loss and economic impacts. Previous BWRA models incorporate annual-scale environmental data, which may overlook seasonal variability. In this study, temporal variation in sea surface temperature and salinity data were examined at global ports, and the influence of this variation on environmental distance calculations (and corresponding risk of NIS) was examined for ballast water discharges in Canada by comparing outputs from monthly and annual scale assessments in a BWRA model. Except for some outliers in the Pacific region, the environmental distances based on monthly scale data generally become smaller in all regions, demonstrating that the model using annual decadal average environmental data to inform environmental matching can underestimate risk of NIS survival and establishment in comparison to monthly data. The results of this study suggest future evaluations incorporating the date of ballast water uptake and discharge can provide a more sensitive assessment of risk reflecting seasonal variability compared to an annual average risk model.
RESUMEN
The classification of ships based on their trajectory descriptors is a common practice that is helpful in various contexts, such as maritime security and traffic management. For the most part, the descriptors are either geometric, which capture the shape of a ship's trajectory, or kinematic, which capture the motion properties of a ship's movement. Understanding the implications of the type of descriptor that is used in classification is important for feature engineering and model interpretation. However, this matter has not yet been deeply studied. This article contributes to feature engineering within this field by introducing proper similarity measures between the descriptors and defining sound benchmark classifiers, based on which we compared the predictive performance of geometric and kinematic descriptors. The performance profiles of geometric and kinematic descriptors, along with several standard tools in interpretable machine learning, helped us to provide an account of how different ships differ in movement. Our results indicated that the predictive performance of geometric and kinematic descriptors varied greatly, depending on the classification problem at hand. We also showed that the movement of certain ship classes solely differed geometrically while some other classes differed kinematically and that this difference could be formulated in simple terms. On the other hand, the movement characteristics of some other ship classes could not be delineated along these lines and were more complicated to express. Finally, this study verified the conjecture that the geometric-kinematic taxonomy could be further developed as a tool for more accessible feature selection.