Multi-class secondary metabolites in cyanobacterial blooms from a tropical water body: Distribution patterns and real-time prediction.

Cyanobacterial blooms that produce toxins occur in freshwaters worldwide and yet, the occurrence and distribution patterns of many cyanobacterial secondary metabolites particularly in tropical regions are still not fully understood. Moreover, predictive models for these metabolites by using easily accessible water quality indicators are rarely discussed. In this study, we investigated the co-occurrence and spatiotemporal trends of 18 well-known and less-studied cyanobacterial metabolites (including [D-Asp3] microcystin-LR (DM-LR), [D-Asp3] microcystin-RR (DM-RR), microcystin-HilR (MC-HilR), microcystin-HtyR (MC-HtyR), microcystin-LA (MC-LA), microcystin-LF (MC-LF), microcystin-LR (MC-LR), microcystin-LW (MC-LW), microcystin-LY (MC-LY), microcystin-RR (MC-RR) and microcystin-WR (MC-WR), Anatoxin-a (ATX-a), homoanatoxin-a (HATX-a), cylindrospermospin (CYN), nodularin (NOD), anabaenopeptin A (AptA) and anabaenopeptin B (AptB)) in a tropical freshwater lake often plagued with blooms. Random forest (RF) models were developed to predict MCs and CYN and assess the relative importance of 22 potential predictors that determined their concentrations. The results showed that 11 MCs, CYN, ATX-a, HATX-a, AptA and AptB were found at least once in the studied water body, with MC-RR and CYN being the most frequently occurring, intracellularly and extracellularly. AptA and AptB were detected for the first time in tropical freshwaters at low concentrations. The metabolite profiles were highly variable at both temporal and spatial scales, in line with spatially different phytoplankton assemblages. Notably, MCs decreased with the increase of CYN, possibly revealing interspecific competition of cyanobacteria. The rapid RF prediction models for MCs and CYN were successfully developed using 4 identified drivers (i.e., chlorophyll-a, total carbon, rainfall and ammonium for MCs prediction; and chloride, total carbon, rainfall and nitrate for CYN prediction). The established models can help to better understand the potential relationships between cyanotoxins and environmental variables as well as provide useful information for making policy decisions.

Application of a Mechanistic Model for the Prediction of Microcystin Production by Microcystis in Lab Cultures and Tropical Lake.

Microcystin is an algal toxin that is commonly found in eutrophic freshwaters throughout the world. Many studies have been conducted to elucidate the factors affecting its production, but few studies have attempted mechanistic models of its production to aid water managers in predicting its occurrence. Here, a mechanistic model was developed based on microcystin production by Microcystis spp. under laboratory culture and ambient field conditions. The model was built on STELLA, a dynamic modelling software, and is based on constitutive cell quota that varies with nitrogen, phosphorus, and temperature. In addition to these factors, varying the decay rate of microcystin according to its proportion in the intracellular and extracellular phase was important for the model's performance. With all these effects, the model predicted most of the observations with a model efficiency that was >0.72 and >0.45 for the lab and field conditions respectively. However, some large discrepancies were observed. These may have arisen from the non-constitutive microcystin production that appear to have a precondition of nitrogen abundance. Another reason for the large root mean square error is that cell quota is affected by factors differently between strains.