Artificial intelligence to explain the variables that favor the cyanobacteria steady-state in tropical ecosystems: A Bayeasian network approach

• Published in: Anais da Academia Brasileira de Ciências Vol. 95; no. suppl 2; p. e20220056
• Main Authors: Oliveira, Fábio Henrique P C DE, Shinohara, Neide K S, Cunha Filho, Moacyr
• Format: Journal Article
• Language: English
• Published: Brazil Academia Brasileira de Ciências 01-01-2023
• Subjects: abiotic variables; Artificial Intelligence; Biomass; climate; Cyanobacteria; Ecosystem; Eutrophication; Lakes; MULTIDISCIPLINARY SCIENCES; phytoplankton; predictive models; phytoplankton; abiotic variables; climate; predictive models; eutrophication
• ISSN: 0001-3765; 1678-2690; 1678-2690
• DOI: 10.1590/0001-3765202320220056

The steady-state is a situation of little variability of species dominance and total biomass over time. Maintenance of cyanobacteria are often observed in tropical and eutrophic ecosystems and can cause imbalances in aquatic ecosystem. Bayeasian networks allow the construction of simpls models that summarizes a large amount of variables and can predict the probability of occurrence of a given event. Studies considering Bayeasian networks built from environmental data to predict the occurrence of steady-state in aquatic ecosystems are scarce. This study aims to propose a Bayeasian network model to assess the occurrence, composition and duration of cyanobacteria steady-state in a tropical and eutrophic ecosystem. It was hypothesized long lasting steady-state events, composed by filamentous cyanobacteria species and directly influenced by eutrophication and drought. Our model showed steady-state lasting between 3 and 17 weeks with the monodominance or co-dominance of filamentous species, mainly Raphidiopsis raciborskii and Planktothrix agardhii. These evens occurred frequently under drought and high turbidity, however higher nutrients concentrations did not increase the probability steady-state occurrence or longer duration. The proposed model appears as a tool to assess the effects of future warming on steady-state occurrence and it can be a useful to more traditional process-based models for reservoirs.