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.