The Reynolds Functional Groups (RFG) classification scheme is an informative and widely used method in ecological studies of freshwater phytoplankton. It clusters species with similar traits, as well as common environmental sensitivities and tolerances. However, researchers face the difficulty to classify species into RFG because it relies in expert opinion, taxonomical knowledge and environmental information, which are not always accessible. Thus, a step forward is to build general statistical models to classify species into RFG. Under the hypothesis that an organism's response to environmental conditions determines their functional traits, here represented by the RFG, we predict that morphology and classification into broad taxonomic groups will explain RFG independently from environmental information and expert knowledge. To evaluate the predictive ability of morphological traits (e.g. volume) and taxonomic affiliation (e.g. chroococcal Cyanobacteria) as discriminant variables of RFG, we compiled 1,300 species (264 waterbodies) and applied Random Forest (RF) and Classification and Regression Trees (CART). We divided the data to train the models and test their performance. RF successfully classified species into the 28 RFG (only c. 10% test error) with an average individual RFG success rate of 84.6 (range = 33%–100%). This is a relatively high percentage of success from an ecological point of view. It suggests that the selected variables are able to reconstruct the RFG and represent well environmental preferences, without including information about local environmental conditions as classifiers. Our results reinforce the functional basis of the RFG and support both morphological traits and taxonomic classification as good proxies of phytoplankton responses to environmental conditions. A dichotomous key based on the CART was constructed, and an R code to classify species into the RFG is freely available. This work may help users to classify species into the RFG, including those that were not previously listed in the Reynolds classification system.

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The harmful bloom-forming cyanobacterium Cylindrospermopsis raciborskii grows in freshwaters over a wide range of light conditions. This species has increased its global distribution recently. The influence of ultraviolet radiation (UVR) on the fitness and toxin production of C. raciborskii has not previously been explored. We performed short-term experiments with three C. raciborskii strains (MVCC19, LB2897, and CYP011 K), and we compared their responses with other bloom-forming species (Microcystis sp.1 and Plankthotrix agardhii) to determine the impact of UV-B radiation on pigments, biomass, and morphological traits. In addition, we analyzed the effect of UV-B on the saxitoxin content and sxtU gene expression in the strain MVCC19. C. raciborskii strains were stressed differentially by UV-B exposure as evidenced by changes in growth, morphology, and heterocytes number. A significant increase in saxitoxin concentration and sxtU gene expression under UV-B suggests that toxin production in C. raciborskii can be a response to UV-B stress. In comparison, Microcystis sp.1 was more tolerant, while P. agardhii was severely impacted by UV-B, indicating also different sensitivities among cyanobacteria to UVR. Our results underscore the influence of UVR on C. raciborskii and the differences between strains which showed phenotypic plasticity, which potentially could affect its distribution in freshwaters.

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