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. Our objectives were (1) to develop a probabilistic model relating cyanobacterial biovolume to environmental variables in the Río de la Plata Estuary (35°S, 56°W, n = 205 observations) considering all biovolume values (zeros and positive biomass) as part of the same process; and (2) to use the model to predict cyanobacterial biovolume under different risk level scenarios using water temperature and conductivity as explanatory variables. We developed a compound Poisson-Gamma (CPG) regression model, an approach that has not previously been used for modeling phytoplankton biovolume, within a Bayesian hierarchical framework. Posterior predictive checks showed that the fitted model had a good overall fit to the observed cyanobacterial biovolume and to more specific features of the data, such as the proportion of samples crossing three threshold risk levels (0.2, 1 and 2 mm³ L-1) at different water temperatures and conductivities. The CPG model highlights the strong control of cyanobacterial biovolume by nonlinear and interactive effects of water temperature and conductivity. The highest probability of crossing the three biovolume levels occurred at 22.2 °C and at the lowest observed conductivity (∼0.1 mS cm-1). Cross-validation of the fitted model using out-of-sample observations (n = 72) showed the model's potential to be used in situ, as it enabled prediction of cyanobacterial biomass based on two readily measured variables (temperature and conductivity), making it an interesting tool for early alert systems and management strategies. Furthermore, this novel application demonstrates the potential of the Bayesian CPG approach for predicting cyanobacterial dynamics in response to environmental change.

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.e. a trait-based approach) were analyzed. MAC responses from the intracellular (e.g. respiratory activity) to the ecosystem level (e.g. blooms) were evaluated in wide environmental gradients. Experimental results on buoyancy and respiratory activity in response to increased salinity (0-35) and a literature review of maximum growth rates under different temperatures and salinities were combined with field sampling from headwaters (800km upstream) to the marine end of the Rio de la Plata estuary (Uruguay-South America). Salinity and temperature were the major variables affecting MAC responses. Experimentally, freshwater MAC cells remained active for 24h in brackish waters (salinity=15) while colonies increased their flotation velocity. At the population level, maximum growth rate decreased with salinity and presented a unimodal exponential response with temperature, showing an optimum at 27.5°C and a rapid decrease thereafter. At the community and ecosystem levels, MAC occurred from fresh to marine waters (salinity 30) with a sustained relative increase of large mucilaginous colonies biovolume with respect to individual cells. Similarly, total biomass and, specific and morphological richness decreased with salinity while blooms were only detected in freshwater both at high (33°C) and low (11°C) temperatures. In brackish waters, large mucilaginous colonies presented advantages under osmotic restrictive conditions. These traits values have also been associated with higher toxicity potential. This suggest salinity or low temperatures would not represent effective barriers for the survival and transport of potentially toxic MAC under likely near future scenarios of increasing human impacts (i.e. eutrophication, dam construction and climate change).