The habitat template of phytoplankton morphology-based functional groups

The identification of the main factors driving phytoplankton community structure is essential to understand and adequately manage freshwater ecosystems. We hypothesize that differences in morphological traits reflect phytoplankton functional properties that will be selected under particular environmental conditions, namely their habitat template. We apply a morphology-based functional groups (MBFG) approach to classify phytoplankton organisms and define each group template. We use machine learning techniques to classify a large number of phytoplankton communities and environmental variables from different climate zones and continents. Random forest analysis explained well the distribution of most groups' biovolume and the selected variables reflected ecological preferences according to morphology. By means of a classification tree it was also possible to identify thresholds of the environmental variables promoting groups dominance in different lakes. For example group III (filaments with aerotopes and high surface/volume including potentially toxic species) was dominant when light attenuation coefficient was > 3.9 m(-1) and total nitrogen was > 2,800 mu g l(-1). We demonstrate that morphology captures ecological preferences of phytoplankton groups and provides empirical values to describe their habitat template.