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%.

Influences of the Oceans on Human Health and Well-Being

Human health and well-being are tied to the vitality of the global ocean and coastal systems on which so many live and rely. We engage with these extraordinary environments to enhance both our health and our well-being. But, we need to recognize that introducing contaminants and otherwise altering these ocean systems can harm human health and well-being in significant and substantial ways. These are complex, challenging, and critically important themes. How the human relationship to the oceans evolves in coming decades may be one of the most important connections in understanding our personal and social well-being. Yet, our understanding of this relationship is far too limited. This remarkable volume brings experts from diverse disciplines and builds a workable understanding of breadth and depth of the processes – both social and environmental – that will help us to limit future costs and enhance the benefits of sustainable marine systems. In particular, the authors have developed a shared view that the global coastal environment is under threat through intensified natural resource utilization, as well as changes to global climate and other environmental systems. All these changes contribute individually, but more importantly cumulatively, to higher risks for public health and to the global burden of disease. This pioneering book will be of value to advanced undergraduate and postgraduate students taking courses in public health, environmental, economic, and policy fields. Additionally, the treatment of these complex systems is of essential value to the policy community responsible for these questions and to the broader audience for whom these issues are more directly connected to their own health and well-being.

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.

Horizon scanning for invasive alien species with the potential to threaten biodiversity in Great Britain

Invasive alien species (IAS) are considered one of the greatest threats to biodiversity, particularly through their interactions with other drivers of change. Horizon scanning, the systematic examination of future potential threats and opportunities, leading to prioritization of IAS threats is seen as an essential component of IAS management. Our aim was to consider IAS that were likely to impact on native biodiversity but were not yet established in the wild in Great Britain. To achieve this, we developed an approach which coupled consensus methods (which have previously been used for collaboratively identifying priorities in other contexts) with rapid risk assessment. The process involved two distinct phases: 1. Preliminary consultation with experts within five groups (plants, terrestrial invertebrates, freshwater invertebrates, vertebrates and marine species) to derive ranked lists of potential IAS. 2. Consensus-building across expert groups to compile and rank the entire list of potential IAS. Five hundred and ninety-one species not native to Great Britain were considered. Ninety-three of these species were agreed to constitute at least a medium risk (based on score and consensus) with respect to them arriving, establishing and posing a threat to native biodiversity. The quagga mussel, Dreissena rostriformis bugensis, received maximum scores for risk of arrival, establishment and impact; following discussions the unanimous consensus was to rank it in the top position. A further 29 species were considered to constitute a high risk and were grouped according to their ranked risk. The remaining 63 species were considered as medium risk, and included in an unranked long list. The information collated through this novel extension of the consensus method for horizon scanning provides evidence for underpinning and prioritizing management both for the species and, perhaps more importantly, their pathways of arrival. Although our study focused on Great Britain, we suggest that the methods adopted are applicable globally.

The contribution of volunteer recorders to our understanding of biological invasions

The process of invasion and the desire to predict the invasiveness (and associated impacts) of new arrivals has been a focus of attention for ecologists over centuries. The volunteer recording community has made unique and inspiring contributions to our understanding of invasion biology within Britain. Indeed information on non-native species (NNS) compiled within the GB Non-Native Species Information Portal (GB-NNSIP) would not have been possible without the involvement of volunteer experts from across Britain. Here we review examples of ways in which biological records have informed invasion biology. We specifically examine NNS information available within the GB-NNSIP to describe patterns in the arrival and establishment of NNS providing an overview of habitat associations of NNS in terrestrial, marine and freshwater environments. Monitoring and surveillance of the subset of NNS that are considered to be adversely affecting biodiversity, society or the economy, termed invasive non-native species (INNS), is critical for early warning and rapid response. Volunteers are major contributors to monitoring and surveillance of INNS and not only provide records from across Britain but also underpin the system of verification necessary to confirm the identification of sightings. Here we describe the so-called ‘alert system’ which links volunteer experts with the wider recording community to provide early warning of INNS occurrence. We highlight the need to increase understanding of community and ecosystem-level effects of invasions and particularly understanding of ecological resilience. Detailed field observations, through biological recording, will provide the spatial, temporal and taxonomic breadth required for such research. The role of the volunteer recording community in contributing to the understanding of invasion biology has been invaluable and it is clear that their expertise and commitment will continue to be so. © 2015 The Linnean Society of London, Biological Journal of the Linnean Society, 2015,

Report from an International Nutrient Workshop focusing on Phosphate Analysis

A practical analytical workshop at NIOZ (Royal Netherlands Institute for Sea Research), The Netherlands, was held on 12-15 November 2012. The aim of the workshop was to gain information from the global nutrient analytical community about general problems which arise when measuring nutrients, and then to attempt to investigate these problems in the laboratory, with a small select representative group of International nutrient analysts conducting the lab work. 18 experts were participated and worked simultaneously on four different PO4 gas segmented CFA systems. This report documents the finding of the workshop and describes recommendations based on group consensus which can hopefully assist the larger community of labs worldwide participating in the Inter-Laboratory Comparison RMNS 2012 studies organized by MRI in Japan.

Spatio-temporal Bayesian network models with latent variables for revealing trophic dynamics and functional networks in fisheries ecology

Ecosystems consist of complex dynamic interactions among species and the environment, the understanding of which has implications for predicting the environmental response to changes in climate and biodiversity. However, with the recent adoption of more explorative tools, like Bayesian networks, in predictive ecology, few assumptions can be made about the data and complex, spatially varying interactions can be recovered from collected field data. In this study, we compare Bayesian network modelling approaches accounting for latent effects to reveal species dynamics for 7 geographically and temporally varied areas within the North Sea. We also apply structure learning techniques to identify functional relationships such as prey–predator between trophic groups of species that vary across space and time. We examine if the use of a general hidden variable can reflect overall changes in the trophic dynamics of each spatial system and whether the inclusion of a specific hidden variable can model unmeasured group of species. The general hidden variable appears to capture changes in the variance of different groups of species biomass. Models that include both general and specific hidden variables resulted in identifying similarity with the underlying food web dynamics and modelling spatial unmeasured effect. We predict the biomass of the trophic groups and find that predictive accuracy varies with the models' features and across the different spatial areas thus proposing a model that allows for spatial autocorrelation and two hidden variables. Our proposed model was able to produce novel insights on this ecosystem's dynamics and ecological interactions mainly because we account for the heterogeneous nature of the driving factors within each area and their changes over time. Our findings demonstrate that accounting for additional sources of variation, by combining structure learning from data and experts' knowledge in the model architecture, has the potential for gaining deeper insights into the structure and stability of ecosystems. Finally, we were able to discover meaningful functional networks that were spatially and temporally differentiated with the particular mechanisms varying from trophic associations through interactions with climate and commercial fisheries.