We compared and contrasted 11 European case studies to identify challenges and opportunities toward the operationalization of marine and coastal ecosystem service (MCES) assessments in Europe. This work is the output of a panel convened by the Marine Working Group of the Ecosystem Services Partnership in September 2016. The MCES assessments were used to (1) address multiple policy objectives simultaneously, (2) interpret EU-wide policies to smaller scales and (3) inform local decision-making. Most of the studies did inform decision makers, but only in a few cases, the outputs were applied or informed decision-making. Significant limitations among the 11 assessments were the absence of shared understanding of the ES concept, data and knowledge gaps, difficulties in accounting for marine social–ecological systems complexity and partial stakeholder involvement. The findings of the expert panel call for continuous involvement of MCES ‘end users’, integrated knowledge on marine social–ecological systems, defining thresholds to MCES use and raising awareness to the general public. Such improvements at the intersection of science, policy and practice are essential starting points toward building a stronger science foundation supporting management of European marine ecosystems.

During the past decades, the composition and distribution of marine species have changed due to multiple anthropogenic pressures. Monitoring these changes in a cost-effective manner is of high relevance to assess the environmental status and evaluate the effectiveness of management measures. In particular, recent studies point to a rise of jellyfish populations on a global scale, negatively affecting diverse marine sectors like commercial fishing or the tourism industry. Past monitoring efforts using underwater video observations tended to be time-consuming and costly due to human-based data processing. In this paper, we present Jellytoring, a system to automatically detect and quantify different species of jellyfish based on a deep object detection neural network, allowing us to automatically record jellyfish presence during long periods of time. Jellytoring demonstrates outstanding performance on the jellyfish detection task, reaching an F1 score of 95.2%; and also on the jellyfish quantification task, as it correctly quantifies the number and class of jellyfish on a real-time processed video sequence up to a 93.8% of its duration. The results of this study are encouraging and provide the means towards a efficient way to monitor jellyfish, which can be used for the development of a jellyfish early-warning system, providing highly valuable information for marine biologists and contributing to the reduction of jellyfish impacts on humans.