Predicting cyanobacterial biovolume from water temperature and conductivity using a Bayesian compound Poisson-Gamma model.

Eutrophication and climate change scenarios engender the need to develop good predictive models for harmful cyanobacterial blooms (CyanoHABs). Nevertheless, modeling cyanobacterial biomass is a challenging task due to strongly skewed distributions that include many absences as well as extreme values (dense blooms). Most modeling approaches alter the natural distribution of the data by splitting them into zeros (absences) and positive values, assuming that different processes underlie these two components.

A multilevel trait-based approach to the ecological performance of Microcystis aeruginosa complex from headwaters to the ocean.

The Microcystis aeruginosa complex (MAC) clusters cosmopolitan and conspicuous harmful bloom-forming cyanobacteria able to produce cyanotoxins. It is hypothesized that low temperatures and brackish salinities are the main barriers to MAC proliferation. Here, patterns at multiple levels of organization irrespective of taxonomic identity (i.