Co-occurrence of Cylindrospermopsis raciborskii (Woloszynska) Seenaya & Subba Raju and Microcystis panniformis Komárek et al. in Mundaú reservoir, a semiarid Brazilian ecosystem

Abstract

The influence of temperature and nutrients on the co-occurrence of Cylindrospermopsis raciborskii and Microcystis panniformis in Mundaú reservoir was investigated. Samples were collected bimonthly from September 2008 to March 2009 with a Van Dorn bottle at two depths (surface and bottom) (n = 16). Water temperature was greater than 22.50 °C; pH values ranged from 6.09 to 8.42; and nitrogen-phosphorus ratios were low (0.11-1.46). The low N:P ratios indicated high phosphorus input, and an eutrophic to hypereutrophic condition in the reservoir. A significant positive correlation of spatial and temporal distribution of C. raciborskii with M. panniformis was observed. Canonical correlation analysis (CCA) results revealed significant association of the biomass of most cyanobacterial species with temperature and nutrients concentration. However, these factors did not explain the co-occurrence of C. raciborskii and M. panniformis. On the other hand, morphological and physiological adaptations such as the possession of aerotopes and production of mucilage, and co-operation between the two species permitted niche overlap, and consequently the co-occurrence of C. raciborskii and M. panniformis in Mundaú reservoir.

Isolated and combined effects of a submerged macrophyte and a cladoceran on the interactions between Microcystis aeruginosa (Cyanobacteria) and Raphidocelis subcapitata (Chlorophyta)

“…However, in experimental studies, Guo and Xie (2006) found that populations of M. micrura pre-exposed to toxic strains M. aeruginosa may become more resistant to cyanobacteria metabolites compared to other large cladocerans, enabling the predation of cyanobacteria. Herein, the cladoceran M. micrura proved to be resistant to toxins from the Microcystis strain, which may have favored its predation on cyanobacteria, considering that the cladoceran was isolated from a reservoir with a history of Microcystis blooms (Moura et al, 2015;. Similarly, Santos et al (2021) showed that cladocerans isolated from lakes with cyanobacterial blooms are less affected by cyanotoxins through the diet or the absorption of dissolved toxins.…”

“…This reservoir presents intense cyanobacteria blooms formed mainly by Microcystis spp. and Raphidiopsis raciborskii (Woloszynska) Aguilera, Berrendero Gómez, Kastovsky, Echenique & Salerno (Moura et al, 2015;. This cladoceran was collected by filtering 100 l of water with a 68 µm mesh plankton net and identified under an optical microscope using specialized bibliography.…”

Cyanobacterial blooms in freshwaters bodies in a semiarid region, northeastern Brazil: A review

“…These species are often responsible for the formation of blooms in tropical ecosystems (Mowe et al, 2015). They share important adaptive traits, such as the presence of aerotopes and the ability to produce toxins Johansen, 2015a, 2015b), that favour the Accepted Article www.jlimnol.it dominance of this group in reservoirs (Soares et al, 2013;Moura et al, 2015). Previously, the emergence of these species in freshwater ecosystems, under various hydrologic, physical, and chemical conditions, has been found to be related to a set of adaptive strategies developed to reflect the changes caused by global warming, such as wide phenotypic plasticity and a greater ecological tolerance (Kling, 2009), resulting in the successful proliferation of these organisms (Paerl and Huisman, 2009).…”

Data Analytics Determines Co-occurrence of Odorants in Raw Water and Evaluates Drinking Water Treatment Removal Strategies

“…23 Correlation analysis, determining the degree that chemicals or species in the environment are related to each other, could supply complementary information about the co-occurrence and potential sources of chemicals or species. 24,25 Classification and regression trees (CARTs) are supervised machine learning techniques that develop a decision tree through recursive partitioning in order to predict a dependent variable. Classification trees use categorical dependent variables, while regression trees use continuous numeric dependent variables such as chemical concentration.…”

“…However, selecting appropriate data analytical tools is a key challenge experienced by many environmental scientists to effectively identify correlations and/or patterns within the data . Correlation analysis, determining the degree that chemicals or species in the environment are related to each other, could supply complementary information about the co-occurrence and potential sources of chemicals or species. , …”