• Energy Research

We show how theoretical developments in macroecology, life-history theory and food-web ecology can be combined to formulate a simple model for predicting the potential biomass, production, size and trophic structure of consumer communities. The strength of our approach is that it uses remote sensing data to predict properties of consumer communities in environments that are challenging and expensive to sample directly. An application of the model to the marine environment on a global scale, using primary production and temperature estimates from satellite remote sensing as inputs, suggests that the global biomass of marine animals more than 10 −5 g wet weight is 2.62×10 9 t (=8.16 g m −2 ocean) and production is 1.00×10 10 t yr −1 (31.15 g m −2 yr −1 ). Based on the life-history theory, we propose and apply an approximation for distinguishing the relative contributions of different animal groups. Fish biomass and production, for example, are estimated as 8.99×10 8 t (2.80 g m −2 ) and 7.91×10 8 t yr −1 (2.46 g m −2 yr −1 ), respectively, and 50% of fish biomass is shown to occur in 17% of the total ocean area (8.22 g m −2 ). The analyses show that emerging ecological theory can be synthesized to set baselines for assessing human and climate impacts on global scales.

AbstractPhytoplankton primary production is at the base of the marine food web; changes in primary production have direct or indirect effects on higher trophic levels, from zooplankton organisms to marine mammals and seabirds. Here, we present a new time‐series on gross primary production in the North Sea, from 1988 to 2013, estimated using in situ measurements of chlorophyll and underwater light. This shows that recent decades have seen a significant decline in primary production in the North Sea. Moreover, primary production differs in magnitude between six hydrodynamic regions within the North Sea. Sea surface warming and reduced riverine nutrient inputs are found to be likely contributors to the declining levels of primary production. In turn, significant correlations are found between observed changes in primary production and the dynamics of higher trophic levels including (small) copepods and a standardized index of fish recruitment, averaged over seven stocks of high commercial significance in the North Sea. Given positive (bottom‐up) associations between primary production, zooplankton abundance and fish stock recruitment, this study provides strong evidence that if the decline in primary production continues, knock‐on effects upon the productivity of fisheries are to be expected unless these fisheries are managed effectively and cautiously.

An experiment was performed to test an alternative dredging strategy for the Westerschelde estuary. Clean sand dredged from the navigation channel was disposed seawards of an eroding intertidal flat in order to modify morphology and hydrodynamics, improving the multi-channel system with ecologically productive shallow water habitat. Five years of intensive monitoring revealed that part of the disposed sediment moved slowly towards the flat, increasing the very shallow subtidal and intertidal area, as planned. The sand in the impact zone became gradually finer after disposal, possibly due to reduced current velocities. Nevertheless, no changes in macrobenthic biomass, density, species richness and composition were detected in the subtidal zone, also demonstrating rapid macrobenthic recovery. In the intertidal zone, no ecological effects could be revealed superimposed on trends associated with long-term sediment fining. Thus, despite morphological success and absence of detected negative ecological impacts of the experiment, new beneficial habitat was not created.

Available decision-support tools rarely account for the welfare of technicians in maintenance scheduling for offshore wind farms. This creates uncertainties, especially since current operational limits might make a wind farm accessible but the vibrations from transits might be unacceptable to technicians. We explore technician exposure to vibration in transit based on the levels of discomfort and the likelihood of seasickness occurring on crew transfer vessels (CTVs). Vessel motion monitoring systems deployed on CTVs operating in the North Sea and sea-state data are used in a machine learning (ML) process to model the welfare of technicians based on operational limits applied to modelled proxy variables including composite weighted RMS acceleration (aWRMS) and motion sickness incidence (MSI). The model results revealed poor to moderate performance in predicting the proxies based on selected model evaluation criteria, raising the possibility of more data and relevant variables being needed to improve model performance. Therefore, this research presents a framework for an ML approach towards accounting for the wellbeing of technicians in sailing decisions once the highlighted limitations can be addressed.