Use of Continuous Plankton Recorder information in support of marine management: applications in fisheries, environmental protection, and in the study of ecosystem response to environmental change

The Continuous Plankton Recorder (CPR) survey was conceived from the outset as a programme of applied research designed to assist the fishing industry. Its survival and continuing vigour after 70 years is a testament to its utility, which has been achieved in spite of great changes in our understanding of the marine environment and in our concerns over how to manage it. The CPR has been superseded in several respects by other technologies, such as acoustics and remote sensing, but it continues to provide unrivalled seasonal and geographic information about a wide range of zooplankton and phytoplankton taxa. The value of this coverage increases with time and provides the basis for placing recent observations into the context of long-term, large-scale variability and thus suggesting what the causes are likely to be. Information from the CPR is used extensively in judging environmental impacts and producing quality status reports (QSR); it has shown the distributions of fish stocks, which had not previously been exploited; it has pointed to the extent of ungrazed phytoplankton production in the North Atlantic, which was a vital element in establishing the importance of carbon sequestration by phytoplankton. The CPR continues to be the principal source of large-scale, long-term information about the plankton ecosystem of the North Atlantic. It has recently provided extensive information about the biodiversity of the plankton and about the distribution of introduced species. It serves as a valuable example for the design of future monitoring of the marine environment and it has been essential to the design and implementation of most North Atlantic plankton research.

Analysis of classification accuracy for pre-filtered multichannel remote sensing data

Noise is one of the main factors degrading the quality of original multichannel remote sensing data and its presence influences classification efficiency, object detection, etc. Thus, pre-filtering is often used to remove noise and improve the solving of final tasks of multichannel remote sensing. Recent studies indicate that a classical model of additive noise is not adequate enough for images formed by modern multichannel sensors operating in visible and infrared bands. However, this fact is often ignored by researchers designing noise removal methods and algorithms. Because of this, we focus on the classification of multichannel remote sensing images in the case of signal-dependent noise present in component images. Three approaches to filtering of multichannel images for the considered noise model are analysed, all based on discrete cosine transform in blocks. The study is carried out not only in terms of conventional efficiency metrics used in filtering (MSE) but also in terms of multichannel data classification accuracy (probability of correct classification, confusion matrix). The proposed classification system combines the pre-processing stage where a DCT-based filter processes the blocks of the multichannel remote sensing image and the classification stage. Two modern classifiers are employed, radial basis function neural network and support vector machines. Simulations are carried out for three-channel image of Landsat TM sensor. Different cases of learning are considered: using noise-free samples of the test multichannel image, the noisy multichannel image and the pre-filtered one. It is shown that the use of the pre-filtered image for training produces better classification in comparison to the case of learning for the noisy image. It is demonstrated that the best results for both groups of quantitative criteria are provided if a proposed 3D discrete cosine transform filter equipped by variance stabilizing transform is applied. The classification results obtained for data pre-filtered in different ways are in agreement for both considered classifiers. Comparison of classifier performance is carried out as well. The radial basis neural network classifier is less sensitive to noise in original images, but after pre-filtering the performance of both classifiers is approximately the same.

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.

GB Non-native Species Information Portal: documenting the arrival of non-native species in Britain

Information on non-native species (NNS) is often scattered among a multitude of sources, such as regional and national databases, peer-reviewed and grey literature, unpublished research projects, institutional datasets and with taxonomic experts. Here we report on the development of a database designed for the collation of information in Britain. The project involved working with volunteer experts to populate a database of NNS (hereafter called “the species register”). Each species occupies a row within the database with information on aspects of the species’ biology such as environment (marine, freshwater, terrestrial etc.), functional type (predator, parasite etc.), habitats occupied in the invaded range (using EUNIS classification), invasion pathways, establishment status in Britain and impacts. The information is delivered through the Great Britain Non-Native Species Information Portal hosted by the Non-Native Species Secretariat. By the end of 2011 there were 1958 established NNS in Britain. There has been a dramatic increase over time in the rate of NNS arriving in Britain and those becoming established. The majority of established NNS are higher plants (1,376 species). Insects are the next most numerous group (344 species) followed by non-insect invertebrates (158 species), vertebrates (50 species), algae (24 species) and lower plants (6 species). Inventories of NNS are seen as an essential tool in the management of biological invasions. The use of such lists is diverse and far-reaching. However, the increasing number of new arrivals highlights both the dynamic nature of invasions and the importance of updating NNS inventories.