To answer key fundamental questions (Section 4 3), three-dimensio

To answer key fundamental questions (Section 4.3), three-dimensional habitat characteristics at particularly fine spatial (<10 m) and temporal scales (seconds) are required to define physical conditions Anti-cancer Compound Library mouse at the precise time of seabird dives or preys presence. Ideally this requires in situ measurements during surveys as oceanographic models or predictions based upon existing datasets cannot account for stochastic variations occurring

at these scales. In this respect, hydroacoustics methods have major advantages over GPS–TDR combinations in that oceanographic instruments deployed from either vessels or moorings can record physical conditions within these micro-habitats to the accuracy required to answer these questions. However, comparing pelagic prey characteristics and diving behaviours among different micro-habitats would still yield useful information. Therefore, oceanographical models and predictions based upon existing datasets could help to define the micro-habitat where preys behaviour or seabird dives were recorded. With limited time to plan and licence installations, it is essential that the populations most vulnerable to Rapamycin collisions with tidal stream turbines are identified. Although it seems likely that Auks, Cormorants and Divers face the highest risks [8], variations among populations and over time seem likely. This variance

can be attributed to various factors ranging from prey preferences to device design. However, the mechanistic links between physical conditions, prey availability and foraging opportunities Grape seed extract could help

to explain much of this variance. Therefore, predicting a populations’ spatial overlap requires a fundamental understanding of these processes. Ultimately, particular conditions at the habitat and micro-habitat scale need to be associated with certain species or species assemblages. Particular conditions in the micro-habitats occupied by tidal stream turbines also need to be associated with certain diving behaviours or prey characteristics. Only with this knowledge can spatial overlap and collisions risks be estimated with a reasonable degree of accuracy. However, the level of confidence in these predictions will grow with increasing sample size. This not only includes collecting datasets over several seasons and years from the same locations, but also collecting and comparing datasets from many different locations. Therefore, data sharing among parties should be encouraged, and a strategic governance approach to collating the wide range of distributional, physical and prey datasets currently being collected could facilitate this. This research was funded by a NERC Case PhD studentship supported by Openhydro Ltd. “
“The deep-sea—defined here as ocean beyond the shelf break and depths greater than 200 m—is increasingly recognized as a fertile area for offshore industrialization.

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