Russian Journal of Earth Sciences Russian Journal of Earth Sciences Russian Journal of Earth Sciences 1681-1208 48760 10.2205/2021ES000782 ОРИГИНАЛЬНЫЕ СТАТЬИ ORIGINAL ARTICLES ОРИГИНАЛЬНЫЕ СТАТЬИ New Approach for Analyzing Marine Ecosystem Structure Using Bayesian Networks New Approach for Analyzing Marine Ecosystem Structure Using Bayesian Networks Krivoguz D. Krivoguz D. Chernyi S. Chernyi S. Manukov A. Manukov A. Russian Federal Research Institute of Fisheries and Oceanography Россия Russian Federal Research Institute of Fisheries and Oceanography Russian Federation Kerch State Maritime Technological University Россия Kerch State Maritime Technological University Russian Federation Kerch State Maritime Technological University Россия Kerch State Maritime Technological University Russian Federation 04 02 2022 04 02 2022 21 6 1 8 08 06 2021 27 10 2021 https://rjes.ru/en/nauka/article/48760/view

Aquatic ecosystems of the Black Sea are complex multiparametric systems with a hierarchical structure. Thus, the main goal of our research was to investigate possibilities of using Bayesian networks to study the structure fo the natural systems in the Black Sea. We used CMEMS Black Sea environmental dataset, which consists of 7 different variables, that, in our opinion, can describe structural relations in the Black Sea ecosystem – sea surface temperature and salinity, concentrations of nitrates and phosphates, amount of chlorophyll-a and net primary production and also dissolved oxygen concentration. We think, that these variables can generally define interactions in water environment of the Black Sea, organisms, that live there and human activity. As a modelling result, we receive a structure of environmental variables interactions. At the top of this structure is a dissolved oxygen, as a final result of the ecosystem functioning. Further, we think it’s more appropriate to use Dynamic Bayesian networks for investigation of spatio-temporal changes to distinguish main drivers of changes and provide more balanced management of natural territories.

Aquatic ecosystems of the Black Sea are complex multiparametric systems with a hierarchical structure. Thus, the main goal of our research was to investigate possibilities of using Bayesian networks to study the structure fo the natural systems in the Black Sea. We used CMEMS Black Sea environmental dataset, which consists of 7 different variables, that, in our opinion, can describe structural relations in the Black Sea ecosystem – sea surface temperature and salinity, concentrations of nitrates and phosphates, amount of chlorophyll-a and net primary production and also dissolved oxygen concentration. We think, that these variables can generally define interactions in water environment of the Black Sea, organisms, that live there and human activity. As a modelling result, we receive a structure of environmental variables interactions. At the top of this structure is a dissolved oxygen, as a final result of the ecosystem functioning. Further, we think it’s more appropriate to use Dynamic Bayesian networks for investigation of spatio-temporal changes to distinguish main drivers of changes and provide more balanced management of natural territories.

 machine learning Black Sea Bayesian networks ecosystem analysis machine learning Black Sea Bayesian networks ecosystem analysis In this paper we suggest using of Bayesian networks for investigation and analysis of natural systems structure. We used CMEMS Black Sea environmental dataset, which consists of 7 different variables, that, in our opinion, can describe structural relations in the Black Sea ecosystem — sea surface temperature and salinity, concentrations of nitrates and phosphates, amount of chlorophyll-a and net primary production and also dissolved oxygen concentration. We think, that these variables can generally define interactions in water environment of the Black Sea, organisms, that live there and human activity.‌‌‌ As a modelling result, we receive a structure of environmental variables interactions. At the top of this structure is a dissolved oxygen, as a final result of the ecosystem functioning. Further, we think it’s more appropriate to use Dynamic Bayesian networks for investigation of spatiotemporal changes to distinguish main drivers of changes and provide more balanced management of natural territories. In this paper we suggest using of Bayesian networks for investigation and analysis of natural systems structure. We used CMEMS Black Sea environmental dataset, which consists of 7 different variables, that, in our opinion, can describe structural relations in the Black Sea ecosystem — sea surface temperature and salinity, concentrations of nitrates and phosphates, amount of chlorophyll-a and net primary production and also dissolved oxygen concentration. We think, that these variables can generally define interactions in water environment of the Black Sea, organisms, that live there and human activity.‌‌‌ As a modelling result, we receive a structure of environmental variables interactions. At the top of this structure is a dissolved oxygen, as a final result of the ecosystem functioning. Further, we think it’s more appropriate to use Dynamic Bayesian networks for investigation of spatiotemporal changes to distinguish main drivers of changes and provide more balanced management of natural territories.

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