Abstract

Cyanobacteria dominance threats ecological integrity and diminishes ecological service of lakes throughout the world. Variety kinds of chemical and physical environmental factors have been used to model cyanobacteria condition in lakes except biological environmental factors. Essential resources competition plays a central role in shaping algae community in lakes. Cyanobacteria and diatoms are assumed to have conserved species’ functional traits and use resources and habitats in similar ways. Therefore, diatom metric, inferred-total phosphorus based on diatoms (DI-TP), could be a valuable predictor for cyanobacteria biomass in lakes. With data sets from the 2007 National Lakes Assessment (NLA) of the US Environmental Protection Agency, we compared the performance of DI-TP with TP and other predictors on predicting cyanobacteria biomass (CBB) by boost regression tree analysis in around 1,000 lakes. In light of effects of lake types and diverse cyanobacteria functional groups on model performance, we did a priori classification on lakes based on lakes types (deep/shallow, natural/man-made lakes) and cyanobacteria functional groups (bloom-forming, potential toxigenic, heterocyst-producing and potential N2-fixing cyanobacteria). Our results showed: (1) DI-TP was informative for modeling CBB in different lake types or various cyanobacteria functional groups, but its importance was not as significant as traditional TP; (2) Performance of DI-TP on modeling CBB was better in deep man-made lakes than in shallow natural lakes; (3) DI-TP performed better on modeling the biomass of potential N2-fixing cyanobacteria than other functional groups of cyanobacteria. The lower importance of DI-TP could be caused by different sampling locations of diatom and cyanobacteria from a same site. The uneven distribution of number of shallow lakes along TP gradient could contribute to a better performance of DI-TP on predicting CBB in deep lakes than in shallow lakes. In man-made lakes, a shorter water residence helped diatoms coexist with cyanobacteria and contributed to a better performance of DI-TP on predicting CBB. We conclude that DI-TP performed better on modeling CBB in deep man-made lakes and potential N2-fixing cyanobacteria biomass. Further studies are needed to thoroughly assess the valuableness of DI-TP on predicting CBB in lakes.

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