Despite increasing recognition of the potential of aquatic biota to act as ‘geomorphic agents’, key knowledge gaps exist in relation to biotic drivers of fine sediment dynamics at microscales and particularly the role of invasive species. This study explores the impacts of invasive signal crayfish on suspended sediment dynamics at the patch scale through laboratory and field study. Three hypotheses are presented and tested: (1) that signal crayfish generate pulses of fine sediment mobilisation through burrowing and movement that are detectable in the flow field; (2) that such pulses may be more frequent during nocturnal periods when signal crayfish are known to be most active; and (3) that cumulatively the pulses would be sufficient to drive an overall increase in turbidity. Laboratory mesocosm experiments were used to explore crayfish impacts on suspended sediment concentrations for two treatments: clay banks and clay bed substrate. For the field study, high frequency near‐bed and mid‐flow turbidity time series from a lowland river with known high densities of signal crayfish were examined. Laboratory data demonstrate the direct influence of signal crayfish on mobilisation of pulses of fine sediment through burrowing into banks and fine bed material, with evidence of enhanced activity levels around the mid‐point of the nocturnal period. Similar patterns of pulsed fine sediment mobilisation identified under field conditions follow a clear nocturnal trend and appear capable of driving an increase in ambient turbidity levels. The findings indicate that signal crayfish have the potential to influence suspended sediment yields, with implications for morphological change, physical habitat quality and the transfer of nutrients and contaminants. This is particularly important given the spread of signal crayfish across Europe and their presence in extremely high densities in many catchments. Further process‐based studies are required to develop a full understanding of impacts across a range of river styles. Copyright © 2013 John Wiley & Sons, Ltd.

We report on the development of a computer vision system that analyses video from CCTV systems installed on fishing trawlers for the purpose of monitoring and quantifying discarded fish catch. Our system is designed to operate in spite of the challenging computer vision problem posed by conditions on-board fishing trawlers. We describe the approaches developed for isolating and segmenting individual fish and for species classification. We present an analysis of the variability of manual species identification performed by expert human observers and contrast the performance of our species classifier against this benchmark. We also quantify the effect of the domain gap on the performance of modern deep neural network-based computer vision systems.

Vulnerable marine ecosystems (VMEs) are particularly susceptible to bottom-fishing activity as they are easily disturbed and slow to recover. A data-driven approach was developed to provide management options for the protection of VMEs under the European Union “deep-sea access regulations.” A total of two options within two scenarios were developed. The first scenario defined VME closure areas without consideration of fishing activity. Option 1 proposed closures for the protection of VME habitats and likely habitat, while Option 2 also included areas where four types of VME geophysical elements were present. The second scenario additionally considered fishing. This scenario used VME biomass—fishing intensity relationships to identify a threshold where effort of mobile bottom-contact gears was low and unlikely to have caused significant adverse impacts. Achieving a high level of VME protection requires the creation of many closures (> 100), made up of many small (∼50 km2) and fewer larger closures (> 1000 km2). The greatest protection of VMEs will affect approximately 9% of the mobile fleet fishing effort, while closure scenarios that avoid highly fished areas reduce this to around 4–6%. The framework allows managers to choose the level of risk-aversion they wish to apply in protecting VMEs by comparing alternative strategies.

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