ABSTRACT
Accurate seabed substrate maps are essential for marine management, as substrate is an important component of the habitat type and used as a proxy for the prevailing benthic community. The provision of substrate maps, however, is hampered by the excessive costs of at-sea observations and, consequently, the uncertainty associated with spatial models used to interpolate these observations to full-coverage maps. Here, we tested whether high-resolution distributions of bottom trawling activity, readily collected under EU law, could improve the accuracy of substrate interpolations. Fishing distributions contain indirect information of the substrate type, as targeted species often show habitat preferences and gear types are designed for particular substrates. For two study areas in the Danish North Sea, we demonstrate that including spatial distributions of bottom trawl fisheries in substrate interpolation models results in more accurate substrate predictions. This potentially opens a novel source of previously unused information for improved seabed substrate interpolation.
Subject(s)
Ecosystem , Fisheries , North SeaABSTRACT
For grain sizes finer than coarse sand, the first flow-transverse bedforms to develop are current ripples. Although numerous studies have analysed different aspects of bedform morphodynamics, to date no comprehensive physical explanation for the formation of ripples has been given. We offer such an explanation based on a virtual boundary layer concept, and present a model predicting ripple height on the basis of grain size, current velocity and water depth. The model contradicts the conventional view of current ripples as bedforms not scaling with flow depth. Furthermore, it confirms the dependence of ripple dimensions on grain size, and their relative insensitivity to flow strength.
ABSTRACT
Dissipation time is a key parameter when studying and modeling the environmental fate of pesticides. This study was conducted to characterize the variability of pesticide disappearance in soil and to identify possible controlling parameters related to intrinsic soil properties and microbiology. Multivariate data analysis was used to study spatial variability in three horizons from 24 sandy soil profiles. The time for 50% disappearance (DT(50)) was characterized for two herbicides, metribuzin (MBZ) and MCPA, and methyltriazine amine (MTA; transformation product of metsulfuron-methyl, tribenuron-methyl, thifensulfuron-methyl, and chlorsulfuron). Normal and log-normal distributions were compared for DT(50) and soil properties and descriptive statistics were calculated. Conformity with log-transformed distributions was observed and assuming normality of the DT(50) data would cause 5 to 35% overestimation. Mean DT(50) were: MCPA 9.5, MBZ 168, and MTA 127. Significant effect of soil depth on DT(50) was shown for MCPA and MBZ, with low values in deeper horizons. Simple linear correlation for combinations of MCPA, MTA, pH, and total organic carbon (TOC) was observed. Using partial least squares regression (PLS) 71 to 85% of the total DT(50) variance was explained. A specific predictor variable could not be identified as the controlling components differed within horizons and compounds. For MCPA the overall important predictor variables were microbiology and TOC, whereas for MTA and MBZ it was inorganic variables (Al, Fe, cation exchange capacity, base saturation percent, and pH) and microbiology. The study indicates that PLS generated input data can improve pesticide fate modeling and reduce the uncertainty in dissipation estimation.
