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Computer Research and Modeling, 2021, Volume 13, Issue 6, Pages 1177–1190
DOI: https://doi.org/10.20537/2076-7633-2021-13-6-1177-1190
(Mi crm943)
 

This article is cited in 3 scientific papers (total in 3 papers)

ANALYSIS AND MODELING OF COMPLEX LIVING SYSTEMS

Models of phytoplankton distribution over chlorophyll in various habitat conditions. Estimation of aquatic ecosystem bioproductivity

A. I. Abakumov, Yu. G. Izrailsky

Institute of Automation and Control Processes, 5, Radio st., Vladivostok, 690041, Russia
References:
Abstract: A model of the phytoplankton abundance dynamics depending on changes in the content of chlorophyll in phytoplankton under the influence of changing environmental conditions is proposed. The model takes into account the dependence of biomass growth on environmental conditions, as well as on photosynthetic chlorophyll activity. The light and dark stages of photosynthesis have been identified. The processes of chlorophyll consumption during photosynthesis in the light and the growth of chlorophyll mass together with phytoplankton biomass are described. The model takes into account environmental conditions such as mineral nutrients, illumination and water temperature. The model is spatially distributed, the spatial variable corresponds to mass fraction of chlorophyll in phytoplankton. Thereby possible spreads of the chlorophyll contents in phytoplankton are taken into consideration. The model calculates the density distribution of phytoplankton by the proportion of chlorophyll in it. In addition, the rate of production of new phytoplankton biomass is calculated. In parallel, point analogs of the distributed model are considered. The diurnal and seasonal (during the year) dynamics of phytoplankton distribution by chlorophyll fraction are demonstrated. The characteristics of the rate of primary production in daily or seasonally changing environmental conditions are indicated. Model characteristics of the dynamics of phytoplankton biomass growth show that in the light this growth is about twice as large as in the dark. It shows, that illumination significantly affects the rate of production. Seasonal dynamics demonstrates an accelerated growth of biomass in spring and autumn. The spring maximum is associated with warming under the conditions of biogenic substances accumulated in winter, and the autumn, slightly smaller maximum, with the accumulation of nutrients during the summer decline in phytoplankton biomass. And the biomass in summer decreases, again due to a deficiency of nutrients. Thus, in the presence of light, mineral nutrition plays the main role in phytoplankton dynamics.
In general, the model demonstrates the dynamics of phytoplankton biomass, qualitatively similar to classical concepts, under daily and seasonal changes in the environment. The model seems to be suitable for assessing the bioproductivity of aquatic ecosystems. It can be supplemented with equations and terms of equations for a more detailed description of complex processes of photosynthesis. The introduction of variables in the physical habitat space and the conjunction of the model with satellite information on the surface of the reservoir leads to model estimates of the bioproductivity of vast marine areas. Introduction of physical space variables habitat and the interface of the model with satellite information about the surface of the basin leads to model estimates of the bioproductivity of vast marine areas.
Keywords: mathematical model, differential equations, phytoplankton, chlorophyll, illumination, temperature.
Received: 05.07.2021
Revised: 22.10.2021
Accepted: 26.10.2021
Document Type: Article
UDC: 519.8
Language: Russian
Citation: A. I. Abakumov, Yu. G. Izrailsky, “Models of phytoplankton distribution over chlorophyll in various habitat conditions. Estimation of aquatic ecosystem bioproductivity”, Computer Research and Modeling, 13:6 (2021), 1177–1190
Citation in format AMSBIB
\Bibitem{AbaIzr21}
\by A.~I.~Abakumov, Yu.~G.~Izrailsky
\paper Models of phytoplankton distribution over chlorophyll in various habitat conditions. Estimation of aquatic ecosystem bioproductivity
\jour Computer Research and Modeling
\yr 2021
\vol 13
\issue 6
\pages 1177--1190
\mathnet{http://mi.mathnet.ru/crm943}
\crossref{https://doi.org/10.20537/2076-7633-2021-13-6-1177-1190}
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  • https://www.mathnet.ru/eng/crm/v13/i6/p1177
  • This publication is cited in the following 3 articles:
    Citing articles in Google Scholar: Russian citations, English citations
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    Computer Research and Modeling
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