Automatic classification of field-collected dinoflagellates by artificial neural network

Automatic taxonomic categorisation of 23 species of dinoflagellates was demonstrated using field-collected specimens. These dinoflagellates have been responsible for the majority of toxic and noxious phytoplankton blooms which have occurred in the coastal waters of the European Union in recent years and make severe impact on the aquaculture industry. The performance by human 'expert' ecologists/taxonomists in identifying these species was compared to that achieved by 2 artificial neural network classifiers (multilayer perceptron and radial basis function networks) and 2 other statistical techniques, k-Nearest Neighbour and Quadratic Discriminant Analysis. The neural network classifiers outperform the classical statistical techniques. Over extended trials, the human experts averaged 85% while the radial basis network achieved a best performance of 83%, the multilayer perceptron 66%, k-Nearest Neighbour 60%, and the Quadratic Discriminant Analysis 56%.

Satellite discrimination of Karenia mikimotoi and Phaeocystis harmful algal blooms in European coastal waters: Merged classification of ocean colour data

The detection of dense harmful algal blooms (HABs) by satellite remote sensing is usually based on analysis of chlorophyll-a as a proxy. However, this approach does not provide information about the potential harm of bloom, nor can it identify the dominant species. The developed HAB risk classification method employs a fully automatic data-driven approach to identify key characteristics of water leaving radiances and derived quantities, and to classify pixels into “harmful”, “non-harmful” and “no bloom” categories using Linear Discriminant Analysis (LDA). Discrimination accuracy is increased through the use of spectral ratios of water leaving radiances, absorption and backscattering. To reduce the false alarm rate the data that cannot be reliably classified are automatically labelled as “unknown”. This method can be trained on different HAB species or extended to new sensors and then applied to generate independent HAB risk maps; these can be fused with other sensors to fill gaps or improve spatial or temporal resolution. The HAB discrimination technique has obtained accurate results on MODIS and MERIS data, correctly identifying 89% of Phaeocystis globosa HABs in the southern North Sea and 88% of Karenia mikimotoi blooms in the Western English Channel. A linear transformation of the ocean colour discriminants is used to estimate harmful cell counts, demonstrating greater accuracy than if based on chlorophyll-a; this will facilitate its integration into a HAB early warning system operating in the southern North Sea.

What are the local impacts of energy systems on marine ecosystem services: a systematic map protocol

Background: Increasing concentrations of atmospheric greenhouse gases (GHG) and its impact on the climate has resulted in many international governments committing to reduce their GHG emissions. The UK, for example, has committed to reducing its carbon emissions by 80% by 2050. Suggested ways of reaching such a target are to increase dependency on offshore wind, offshore gas and nuclear. It is not clear, however, how the construction, operation and decommissioning of these energy systems will impact marine ecosystem services, i.e. the services obtained by people from the natural environment such as food provisioning, climate regulation and cultural inspiration. Research on ecosystem service impacts associated with offshore energy technologies is still in its infancy. The objective of this review is to bolster the evidence base by firstly, recording and describing the impacts of energy technologies at the marine ecosystems and human level in a consistent and transparent way; secondly, to translate these ecosystem and human impacts into ecosystem service impacts by using a framework to ensure consistency and comparability. The output of this process will be an objective synthesis of ecosystem service impacts comprehensive enough to cover different types of energy under the same analysis and to assist in informing how the provision of ecosystem services will change under different energy provisioning scenarios. Methods: Relevant studies will be sourced using publication databases and selected using a set of selection criteria including the identification of: (i) relevant subject populations such as marine and coastal species, marine habitat types and the general public; (ii) relevant exposure types including offshore wind farms, offshore oil and gas platforms and offshore structures connected with nuclear; (iii) relevant outcomes including changes in species structure and diversity; changes in benthic, demersal and pelagic habitats; and changes in cultural services. The impacts will be synthesised and described using a systematic map. To translate these findings into ecosystem service impacts, the Common International Classification of Ecosystem Services (CICES) and Millennium Ecosystem Assessment (MEA) frameworks are used and a detailed description of the steps taken provided to ensure transparency and replicability.