BackgroundResearch on ecosystem services has grown exponentially during the last decade. Most of the studies have focused on assessing and mapping terrestrial ecosystem services highlighting a knowledge gap on marine and coastal ecosystem services (MCES) and an urgent need to assess them.Methodology/Principal FindingsWe reviewed and summarized existing scientific literature related to MCES with the aim of extracting and classifying indicators used to assess and map them. We found 145 papers that specifically assessed marine and coastal ecosystem services from which we extracted 476 indicators. Food provision, in particular fisheries, was the most extensively analyzed MCES while water purification and coastal protection were the most frequently studied regulating and maintenance services. Also recreation and tourism under the cultural services was relatively well assessed. We highlight knowledge gaps regarding the availability of indicators that measure the capacity, flow or benefit derived from each ecosystem service. The majority of the case studies was found in mangroves and coastal wetlands and was mainly concentrated in Europe and North America. Our systematic review highlighted the need of an improved ecosystem service classification for marine and coastal systems, which is herein proposed with definitions and links to previous classifications.Conclusions/SignificanceThis review summarizes the state of available information related to ecosystem services associated with marine and coastal ecosystems. The cataloging of MCES indicators and the integrated classification of MCES provided in this paper establish a background that can facilitate the planning and integration of future assessments. The final goal is to establish a consistent structure and populate it with information able to support the implementation of biodiversity conservation policies.

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Acoustic surveys were conducted from 2002 to 2006 in the East China Sea off the Japanese coast in order to develop a quantitative classification typology of a pelagic fish community and other co-occurring fishes based on acoustic descriptors. Acoustic data were postprocessed to detect and extract fish aggregations from echograms. Based on the expert visual examination of the echograms, detected schools were divided into three broad fish groups according to their schooling characteristics and ethological properties. Each fish school was described by a set of associated descriptors in order to objectively allocate each echo trace to its fish group. Two methods of supervised classification were employed, the discriminant function analysis (DFA) and the artificial neural network technique (ANN). We evaluated and compared the performance of both methods, which showed encouraging and about equally highly correct classification rates (ANN 87.6%; DFA 85.1%). In both techniques, positional and then morphological parameters were most important in discriminating among fish schools. Fish catch composition from midwater trawling validated the fish group classification through one representative example of each grouping. Both methods provided the essential information required for assessing fish stocks. Similar techniques of fish classification might be applicable to marine ecosystems with high pelagic fish diversity.

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