Increasing the quality, comparability and accessibility of phytoplankton species composition time-series data

Phytoplankton diversity and its variation over an extended time scale can provide answers to a wide range of questions relevant to societal needs. These include human health, the safe and sustained use of marine resources and the ecological status of the marine environment, including long-term changes under the impact of multiple stressors. The analysis of phytoplankton data collected at the same place over time, as well as the comparison among different sampling sites, provide key information for assessing environmental change, and evaluating new actions that must be made to reduce human induced pressures on the environment. To achieve these aims, phytoplankton data may be used several decades later by users that have not participated in their production, including automatic data retrieval and analysis. The methods used in phytoplankton species analysis vary widely among research and monitoring groups, while quality control procedures have not been implemented in most cases. Here we highlight some of the main differences in the sampling and analytical procedures applied to phytoplankton analysis and identify critical steps that are required to improve the quality and inter-comparability of data obtained at different sites and/or times. Harmonization of methods may not be a realistic goal, considering the wide range of purposes of phytoplankton time-series data collection. However, we propose that more consistent and detailed metadata and complementary information be recorded and made available along with phytoplankton time series, including description of the procedures and elements allowing for a quality control of the data. To keep up with the progress in taxonomic research, there is a need for continued training of taxonomists, and for supporting and complementing existing web resources, in order to allow a constant upgrade of knowledge in phytoplankton classification and identification. Efforts towards the improvement of metadata recording, data annotation and quality control procedures will ensure the internal consistency of phytoplankton time series and facilitate their comparability and accessibility, thus strongly increasing the value of the precious information they provide. Ultimately, the sharing of quality controlled data will allow one to recoup the high cost of obtaining the data through the multiple use of the time series data in various projects over many decades

Cell Volumes of Marine Phytoplankton from Globally Distributed Coastal Data Sets

Globally there are numerous long-term time series measuring phytoplanton abundance. With appropriate conversion factors, numerical species abundance can be expressed as biovolume and then converted to phytoplankton carbon. To-date there has been no attempt to analyze globally distributed phytoplankton data sets to determine the most appropriate species-specific mean cell volume. We have determined phytoplankton cell volumes for 214 of the most common species found in globally distributed coastal time series. The cell volume, carbon/cell and cell density of large diatoms is 20,000, 20,000 and 0.1 times respectively, compared to small diatoms. The cell volume, carbon/cell and cell density of large dinoflagellates is 1500, 1000 and 0.7 times respectively, compared to small dinoflagellates. The range in diatom biovolumes is > 10 times greater than across dinoflagellates (i.e. >20,000 vs. 1500 times) and within any diatom species, the range in biovolume is up to 10-fold. Variation in diatom cell volumes are the single largest source of uncertainty in community phytoplankton carbon estimates and greatly exceeds the uncertainty associated with the different volume to carbon estimates. Small diatoms have 10 times more carbon density than large diatoms and small dinoflagellates have 1.5 times more carbon density than large cells. However, carbon density varies relatively little compared to biovolume. We recommend that monthly biovolumes should be determined on field samples, at least for the most important species in each study area, since these measurements will incorporate the effects of variations in light, temperature, nutrients and life cycles. Since biovolumes of diatoms are particularly variable, the use of size classes will help to capture the percentage of large and small cells for each species at certain times of the year. This summary of global datasets of phytoplankton biovolumes is useful in order to evaluate where locally determined biovolumes lie within the global spectrum of spatial and temporal variations and may be used as a species cell volume reference where no locally determined volume estimates are available. There is a need to adopt standard protocols for estimating biovolumes and documenting the accompanying metadata which would improve inter-comparability among time series data sets