PHYTOPLANKTON in the SEA of OKHOTSK along WESTERN KAMCHATKA: WARM vs COLD YEARS

Pak, S.Ya.; Abakumov, A.I.

doi:10.1016/j.ecolmodel.2020.109244

Cited by 4 publications

(4 citation statements)

References 30 publications

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“…Это согласуется с известными полевыми наблюдениями [38][39][40]. В целом, модели демонстрируют способность более точно описывать процессы биопродукции по сравнению с моделями, разработанными нами ранее [41,42]. При наличии данных дистанционных наблюдений и результатов лабораторных экспериментов представленные модели могут быть использованы для оценки биопродуктивности водных экосистем.…”

Section: Discussionunclassified

Two Approaches to Modeling Phytoplankton Biomass Dynamics Based on the Droop Model

Abakumov¹,

Pak²

2022

Math.Biol.Bioinf.

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This work continues the study of the Droop model based on the concept of cell quota. Description of the photosynthetic processes in phytoplankton includes in the model structure. The concept of chlorophyll quota is used. It is the proportion of photosynthetic substances in plant cells. In addition to the chlorophyll quota, the photosynthetic activity of phytoplankton is determined by external conditions, primarily by the level of photosynthetically active radiation (PAR). The model is based on separating the dependence of phytoplankton reproduction on external conditions according to the stages of photosynthesis. The light stage is largely determined by the PAR, and the dark stage is limited by the nutrient resource under the controlling influence of the temperature of the aquatic environment. In order to develop the model, the storage of energy in the light stage of photosynthesis is described in detail. Energy is stored in the form of energy-intensive substances in macroergic molecules (macroergs). The most common cell macroerg is adenosine triphosphate (ATP). The proportion of ATP in phytoplankton varies depending on the light regime and on the energy amount stored in the dark stage. The model includes the Droop kinetics and equations for the dynamics of the chlorophyll quota and the ATP pool. The conditions for the existence and stability of equilibrium solutions are compared for the same values of parameters common to both models. The greatest influence on the dynamic modes of the minimum value of the cell quota has been established. The proportion of biomass associated with the light period of photosynthesis is also significant. For the first model that is the biomass produced during daylight hours. And in terms of the second model, it is the biomass formed due to the energy of ATP stored in the light phase. The influence of the structure of dynamic models on the daily and annual dynamics of phytoplankton was revealed. Scenarios of behavior of models under various lighting conditions, including constant and periodically changing lighting, have been studied.

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Section: Discussionunclassified

Two Approaches to Modeling Phytoplankton Biomass Dynamics Based on the Droop Model

Abakumov¹,

Pak²

2022

Math.Biol.Bioinf.

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“…where S, H, and K are three invariants of the Jacobian matrix (13). The conditions for codimension one bifurcations of system (6) are found based on coefficients of its characteristic polynomial (14) and have the form [48]:…”

Section: Stability Regions Of the Fixed Points Of Model (6): Evolutio...mentioning

confidence: 99%

“…From the point of view of evolutionary processes, the main interest is in the stability and mechanisms of its loss by nontrivial fixed points ( 11) and (12), which correspond to the coexistence of phytoplankton and zooplankton; in the first solution, the phytoplankton population is monomorphic, and in the second solution it is polymorphic. The Jacobian (13) at point (11) takes the form:…”

Section: Boundaries Of the Stability Regions Of The Fixed Points Of S...mentioning

confidence: 99%

“…Direct estimates of phytoplankton biomass and tens of thousands of species of herbivorous zooplankton that make up the plankton community are traditionally complex and expensive [10]. Recently, biomass estimates of phytoplankton based on satellite observations have become increasingly popular [11][12][13][14]. The identification and interpretation of the part of the spectrum emitted by plankton based on satellite monitoring data remains a non-simple challenge [15][16][17][18].…”

Section: Introductionmentioning

confidence: 99%

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Mathematical Modeling of the Evolutionary Dynamics of a Planktonic Community Using a Discrete-Time Model

Neverova,

Zhdanova

2023

Mathematics

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This study proposes a discrete-time eco-genetic model of a planktonic community that includes zooplankton and two competing phytoplankton haplotypes with and without a toxicity trait. The Holling type II response function describes predator consumption. We use the Ricker model to consider density limitation and regulation. The model is analytically and numerically studied. The loss of stability of fixed points occurs via the Neimark–Sacker scenario and a cascade of period-doubling bifurcations. The model reveals bistability and multistability. Therefore, the initial conditions can determine which of the coexisting dynamic modes will be attracted. If the competition of haplotypes is weaker than their self-regulation, then the variation in the current densities of community components can shift the observed dynamics, while the evolution direction remains unchanged. The ratio of haplotype fitnesses and predator pressure generally determines the asymptotic genetic composition of phytoplankton. If competition of haplotypes is higher than their self-regulation, then the bistability of monomorphic fixed points occurs when the displacement of one haplotype by another depends on initial conditions. The presence of predators can maintain the genetic polymorphism of the prey. This system shows dynamic modes similar to experimental dynamics: oscillation with delay, long-period antiphase fluctuations, and cryptic cycles emerging due to rapid evolution.

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Winter microalgal communities of the southern Sea of Okhotsk: A comparison of sea ice, coastal, and basinal seawater

Yan

Nishioka

Toyota

et al. 2022

Progress in Oceanography

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PHYTOPLANKTON in the SEA of OKHOTSK along WESTERN KAMCHATKA: WARM vs COLD YEARS

Cited by 4 publications

References 30 publications

Two Approaches to Modeling Phytoplankton Biomass Dynamics Based on the Droop Model

Two Approaches to Modeling Phytoplankton Biomass Dynamics Based on the Droop Model

Mathematical Modeling of the Evolutionary Dynamics of a Planktonic Community Using a Discrete-Time Model

Winter microalgal communities of the southern Sea of Okhotsk: A comparison of sea ice, coastal, and basinal seawater

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