A database of marine phytoplankton abundance, biomass and species composition in Australian waters.

There have been many individual phytoplankton datasets collected across Australia since the mid 1900s, but most are unavailable to the research community. We have searched archives, contacted researchers, and scanned the primary and grey literature to collate 3,621,847 records of marine phytoplankton species from Australian waters from 1844 to the present. Many of these are small datasets collected for local questions, but combined they provide over 170 years of data on phytoplankton communities in Australian waters. Units and taxonomy have been standardised, obviously erroneous data removed, and all metadata included. We have lodged this dataset with the Australian Ocean Data Network (http://portal.aodn.org.au/) allowing public access. The Australian Phytoplankton Database will be invaluable for global change studies, as it allows analysis of ecological indicators of climate change and eutrophication (e.g., changes in distribution; diatom:dinoflagellate ratios). In addition, the standardised conversion of abundance records to biomass provides modellers with quantifiable data to initialise and validate ecosystem models of lower marine trophic levels.

Roles of land use resolution and unit-area load rates in assessment of diffuse nutrient emissions.

In contrast to its counterparts in Europe and North America, the Australian National Pollutant Inventory (NPI) includes estimates of aggregated emissions of nutrients (total nitrogen and total phosphorus) from catchments and facilities. Sparse or inadequate data limit the extent to which nutrient exports may be estimated from direct observations. The paucity of data for calibration and simulation limits the use of sophisticated models in most Australian catchments. Therefore, a simple unit-area load model-Catchment Management Support System (CMSS)-was selected to estimate aggregated catchment emissions for the NPI. Estimates from models like CMSS are sensitive to spatial and categorical resolution of land uses identified within the catchment and to nutrient generation rates selected for each land use category. Using three Hawkesbury-Nepean subcatchments, we show that while high spatial resolution of land use mapping is useful, only four or five major land use categories with carefully selected generation rates were required to estimate potential nutrient exports sufficiently well and to determine subcatchments contributing most. Nutrient emission estimates proved to be highly dependent on selection of generation rates so a bootstrap technique was adopted to reduce subjectivity and to improve estimates of confidence limits. This led to a specification of new generation rates for Natural, Unimproved pasture, Rural and Urban land uses and to establishment of uncertainty limits.