Maria Paola Satta

Water-mass influences on summer Antarctic phytoplankton biomass and community structure

The association between the variability of phytoplankton biomass and community structure and the distribution of water masses around the Antarctic Peninsula were examined during austral summer 1993. Phytoplankton biomass showed high variability, and was dominated by an autotrophic flagellate (Cryptomonas sp.) that represented, on average, 91% of total phytoplankton biomass. The lowest phytoplankton biomasses were associated with the strongly mixed, saline, cold waters characteristic of the Weddell Sea water mass, and with the waters influenced by ice melt from the Bellingshausen Sea. The highest biomasses were found in the confluence of these water masses, where a front develops. Community composition also differed among water masses, with eukariotic picoplankton and diatoms having their highest relative contribution to community biomass in stations with Bellingshausen Sea and Weddell Sea water masses, whereas the abundance of Cryptomonas sp. was highest at the confluence of these waters. These results indicate that mesoscale processes, that determine water mass distribution, are of paramount importance in controlling the time and space variability of Antarctic phytoplankton.

CHAPTER 14 – Background knowledge and tools for prediction of ecological impacts

The species pool in a particular locality is determined by its biogeographic context. The evolution of the species pool is a dynamic and ongoing process. Biodiversity patterns on a broad-scale are the functions of adaptation, extinction, and speciation. The species pool may also change following the introduction of alien species, often through human activities. Broad-scale biodiversity patterns are influenced by major physical factors, such as climate, currents, upwelling, tidal elevation, wave climate, salinity, coastal topography, and seabed composition, which can all vary with geographical location. The environmental impact of any amount of cases can be predicted by numerical modeling. The result for each case is a set of BioMar class values for physical parameters being designated for each cell. A procedure is then applied that selects the biotopes that can occur within the predicted set of parameter class values for each cell. Biotopes recorded in the field during baseline data survey can be compared with those predicted by the model. This enables calibration of the model to the present situation and allows evaluation of the type and magnitude of changes for each computed case in a straightforward fashion. These initial trials with the model are encouraging, and the model is still being refined to develop a tool to more accurately predict change in the identity and extent of biotopes because of the addition of breakwaters.