BLOOMS OF Raphidiopsis raciborskii (WOLOSZYNSKA) AGUILERA & AL. 2018 IN SEMIARID RESERVOIRS: AN APPROACH INTEGRATED BAYESIAN.

França Nery, Janiele; Moura Nery, Gleydson Kleyton; Brasileiro Ferreira, Weruska; da Costa Batista, Fabiane Rabelo

Objective: This study aims to determine which environmental factors are the most important in explaining the variation in the abundance of Raphidiopsis raciborskii in semi-arid reservoirs using Bayesian networks. Method/design/approach: To compile the database, a search for articles was carried out on the Scielo and Scopus platforms. Only articles that reported the occurrence of R. raciborskii were considered. After reading, 3 works were selected, which presented values of water transparency (m), water temperature (ºC), pH, electrical conductivity (μS/cm), dissolved oxygen (mg/L), ammonium concentrations (μg/L), SRP (reactive soluble phosphorus) (μg/L), total phosphorus (μg/L), cyanobacterial density (cel/mL) and Raphidiopsis raciborskii (cel/L). Using these data, a Bayesian network was built to analyze the sensitivity of bloom development to different environmental factors, considering three risk categories (low, medium and high) based on cell abundance. Results and conclusion: Water transparency and reservoir volume were the most important variables for predicting blooms in semiarid systems. Regardless of the storage capacity of the reservoirs and complexity of interactions between the variables, the proposed Bayesian network proved to be a useful tool for: exploring the interactions between volume oscillation in reservoirs with nutrients and temperature simultaneously. thus constituting a potential tool for management and management of water resources in the semi-arid region. Research implications: The Bayseian network constitutes a potential tool for the management of water resources in the semi-arid region. Originality/Value: The originality of this research resides in the proposed use of Bayesian networks as a predictability tool for the management of water resources in the treatment of cyanobacterial blooms, especially for the cosmopolitan and highly resistant species R. raciborskii.

CYANOBACTERIA; WATER management; CYANOBACTERIAL blooms; META-analysis; BAYESIAN analysis; ARID regions; PROBABILITY theory; BACTERIOPLANKTON; WATER temperature; CYANOBACTERIAL toxins

Environmental & Social Management Journal / Revista de Gestão Social e Ambiental, 2024, Vol 18, Issue 2, p1

1981-982X

Academic Journal

10.24857/rgsa.v18n2-044