Strict coupling between the development ofPlanktothrix rubescensand microcystin content in two nearby lakes south of the Alps (lakes Garda and Ledro)

Abstract

-Cyanobacteria and their principal cyanotoxins were studied in the largest Italian lake (Lake Garda, 65 m a.s.l.) and in a near smaller reservoir (Lake Ledro, 652 m a.s.l.). The two lakes share a fraction of water due to a pipe pumping water from and into the lakes and the same dominant cyanobacterial species (Planktothrix rubescens). Despite the differences in the concentrations of cyanotoxins (mostly microcystins, MCs) and biovolumes of P. rubescens (BV Pr ) (over one order of magnitude), the Bayesian analyses linking these two variables showed striking similarities, suggesting the existence of similar toxic genotypes colonizing the two water bodies and a constitutive MC production. It was stressed that a greater sensitivity and reliability in the management strategies aimed at minimizing the risks due to cyanobacteria should also contemplate the use of specific lake-tailored models linking MCs and BV Pr .

Red Algae Bloom Detection in Occhito Lake Combining MERIS and MODIS Data

“…The bloom development followed a typical trend already described in other Italian lakes (eg [30,31]) with a marked winter-spring peak followed by a decrease of the P. rubescens population in summer. P. rubescens cell density was around 40 10 6 cell l -1 in February and reached a maximum of about 160 10 6 cell l -1 in march [24].…”

Vertical and horizontal distribution of the microcystin producer Planktothrix rubescens (Cyanobacteria) in a small perialpine reservoir

“…9) gives an estimate of the increase of MC per unit increase of biovolume (fg µm -3 ), therefore providing an estimate of the cell quota, CQ, the quantity of toxins per unit biovolume. Though based on a more extended dataset of Lake Ledro, including also data collected between 5 m and 15 m, the slope relating MC and the biovolumes of P. rubescens obtained in this work (0.53) was coincident with the slopes computed by Salmaso et al, (2013Salmaso et al, ( , 2014a using both ordinary least square linear regressions (0.51) and Bayesian analyses (0.51). These CQ were the same as those estimated from the samples collected in Lake Garda, suggesting the presence of similar chemotype populations in the two lakes.…”

Functional classifications and their application in phytoplankton ecology

“…At the finer taxonomic levels, and considering the rapid progress in both molecular genetics and ecological metabolomics, future directions should also take into account ‘cryptic adaptive traits’. Examples include the ability to produce a variety of toxins or to withstand hydrostatic pressure gradients through the synthesis of gas vesicles of different strengths in different strains of cyanobacteria (D'Alelio et al ., ; Kurmayer et al ., ; Salmaso et al ., ); the ability to exploit various light intensities and nitrogen compounds in different genotypes of Prochlorococcus (Moore et al ., ); the increasing presence of mycosporine‐like amino acids (MAAs) and enhanced absorption of ultraviolet radiation of phytoplankton in high‐altitude lakes (Ficek, Dera & Woźniak, ). As a cautionary note, the existence of ecotypes with different physiological adaptations (see also Rohrlack et al ., ; Zapomělová et al ., ; Üveges et al ., ; D'Alelio, Salmaso & Gandolfi, ) should be taken into account in the evaluation of the limits implicit in the use and interpretation of functional approaches based on easily measurable traits.…”