Meiofauna Techniques

Meiofaunal organisms are mobile multicellular animals that are smaller than macrofauna and larger than microfauna. The size boundaries of meiofauna are generally based on the standardised mesh apertures of sieves with 500 μm (or 1000 μm) as upper and 63 μm (or 42 μm) as lower limits. Meiofauna are ubiquitous, inhabiting most marine substrata, often in high densities. Meiofauna are highly diverse, and several phyla are only known to occur as meiofauna. Owing to their small size and high densities, specialised techniques are required to collect, preserve and examine meiofauna. These are described, along with approaches to determine biomass of these small animals. Their small size also makes them useful candidates for manipulative experiments, and culturing of individual species and approaches to experiments on whole communities are briefly discussed.

An intercomparison of bio-optical techniques for detecting dominant phytoplankton size class from satellite remote sensing

Satellite remote sensing of ocean colour is the only method currently available for synoptically measuring wide-area properties of ocean ecosystems, such as phytoplankton chlorophyll biomass. Recently, a variety of bio-optical and ecological methods have been established that use satellite data to identify and differentiate between either phytoplankton functional types (PFTs) or phytoplankton size classes (PSCs). In this study, several of these techniques were evaluated against in situ observations to determine their ability to detect dominant phytoplankton size classes (micro-, nano- and picoplankton). The techniques are applied to a 10-year ocean-colour data series from the SeaWiFS satellite sensor and compared with in situ data (6504 samples) from a variety of locations in the global ocean. Results show that spectral-response, ecological and abundance-based approaches can all perform with similar accuracy. Detection of microplankton and picoplankton were generally better than detection of nanoplankton. Abundance-based approaches were shown to provide better spatial retrieval of PSCs. Individual model performance varied according to PSC, input satellite data sources and in situ validation data types. Uncertainty in the comparison procedure and data sources was considered. Improved availability of in situ observations would aid ongoing research in this field.

Channel Catchments Cluster. Recent developments in tools and techniques for water quality management in the France (Channel) England region

The Channel Catchments Cluster (3C) aims to capitalise on outputs from some of the recent projects funded through the INTERREG IVa France (Channel) England programme. The river catchment basins draining into the Channel region drain an area of 137,000km2 and support a human population of over 19M. Throughout history, these catchments, rivers and estuaries have been centres of habitation, developed through commerce and industry, providing transport links to hinterland areas. These catchments also provide drinking water and food through provision of agriculture, fisheries and aquaculture. In addition, many parts of the region are also economically important now for the tourism and leisure industries. Consequently, there is a need to manage the balance of these many and varied human activities within the catchments, rivers, estuaries and marine areas to ensure that they are maintained or restored to good environmental condition . This document highlights some of the recent work carried out by projects within the INTERREG IVa programme that provide tools and techniques to assist in the achievement of these goals.

Evaluating machine-learning techniques for recruitment forecasting of seven North East Atlantic fish species

The effect of different factors (spawning biomass, environmental conditions) on recruitment is a subject of great importance in the management of fisheries, recovery plans and scenario exploration. In this study, recently proposed supervised classification techniques, tested by the machine-learning community, are applied to forecast the recruitment of seven fish species of North East Atlantic (anchovy, sardine, mackerel, horse mackerel, hake, blue whiting and albacore), using spawning, environmental and climatic data. In addition, the use of the probabilistic flexible naive Bayes classifier (FNBC) is proposed as modelling approach in order to reduce uncertainty for fisheries management purposes. Those improvements aim is to improve probability estimations of each possible outcome (low, medium and high recruitment) based in kernel density estimation, which is crucial for informed management decision making with high uncertainty. Finally, a comparison between goodness-of-fit and generalization power is provided, in order to assess the reliability of the final forecasting models. It is found that in most cases the proposed methodology provides useful information for management whereas the case of horse mackerel is an example of the limitations of the approach. The proposed improvements allow for a better probabilistic estimation of the different scenarios, i.e. to reduce the uncertainty in the provided forecasts.