Incorporating environmental data in abundance-based algorithms for deriving phytoplankton size classes in the Atlantic Ocean

Environmental conditions are important drivers in regulating the distribution pattern of phytoplankton composition in the world's oceans. We constructed models that predict pico-, nano- and micro-phytoplankton size classes and assessed the impact of separately including sea surface temperature (SST) and estimates of light level in the surface mixed-layer on model skill. The empirical models were trained using size classes estimated by chemotaxonomic analysis of in situ high performance liquid chromatography (HPLC) pigments and environmental data originating from the Atlantic Ocean. As the accuracy of transforming pigment data into quantitative size classes is crucial when constructing phytoplankton size composition (PSC) models, we also quantified the resulting differences of our and several existing PSC models when using class sizes derived from HPLC pigments by two common chemotaxonomic methods, CHEMTAX and Diagnostic Pigments (DP). Addition of the environmental variables to abundance-based models using our approach improved the skill of correctly predicting PSC, reducing the root mean square difference (RMSD) by 10 to 20% in the best cases. Addition of SST yielded the highest percentage decreases, on average, for all three size classes, with greatest improvement in microplankton and nanoplankton fractions. These models performed equal to or better than several existing abundance-based models. The improvements in model predictions, however, could be obscured by the choice of pigment method used to generate the initial PSC data set. Insufficient data is available to assess whether CHEMTAX or DP is the more appropriate chemotaxonomic method to employ when estimating PSC. Further collection and analysis of additional water samples for phytoplankton taxa and size by microscopic methods - including traditional microscopic cell counts and automated methods - and HPLC pigment data are required to answer this question.