Research article

Investigation of a nutrient-plankton model with stochastic fluctuation and impulsive control


  • Received: 06 May 2023 Revised: 03 July 2023 Accepted: 04 July 2023 Published: 26 July 2023
  • In this paper, we investigate a stochastic nutrient-plankton model with impulsive control of the nutrient concentration and zooplankton population. Analytically, we find that the population size is nonnegative for a sufficiently long time. We derive some sufficient conditions for the existence of stable periodic oscillations, which indicate that the plankton populations will behave periodically. The numerical results show that the plankton system experiences a transition from extinction to the coexistence of species due to the emergence of impulsive control. Additionally, we observe that the nutrient pulse has a stronger relationship with phytoplankton growth than the zooplankton pulse. Although the frequency of impulsive control and appropriate environmental fluctuations can promote the coexistence of plankton populations, an excessive intensity of noise can result in the collapse of the entire ecosystem. Our findings may provide some insights into the relationships among nutrients, phytoplankton and zooplankton in a stochastic environment.

    Citation: Xin Zhao, Lijun Wang, Pankaj Kumar Tiwari, He Liu, Yi Wang, Jianbing Li, Min Zhao, Chuanjun Dai, Qing Guo. Investigation of a nutrient-plankton model with stochastic fluctuation and impulsive control[J]. Mathematical Biosciences and Engineering, 2023, 20(8): 15496-15523. doi: 10.3934/mbe.2023692

    Related Papers:

  • In this paper, we investigate a stochastic nutrient-plankton model with impulsive control of the nutrient concentration and zooplankton population. Analytically, we find that the population size is nonnegative for a sufficiently long time. We derive some sufficient conditions for the existence of stable periodic oscillations, which indicate that the plankton populations will behave periodically. The numerical results show that the plankton system experiences a transition from extinction to the coexistence of species due to the emergence of impulsive control. Additionally, we observe that the nutrient pulse has a stronger relationship with phytoplankton growth than the zooplankton pulse. Although the frequency of impulsive control and appropriate environmental fluctuations can promote the coexistence of plankton populations, an excessive intensity of noise can result in the collapse of the entire ecosystem. Our findings may provide some insights into the relationships among nutrients, phytoplankton and zooplankton in a stochastic environment.



    加载中


    [1] D. M. Anderson, Prevention, control and mitigation of harmful algal blooms: Multiple approaches to HAB management, Harmful Algae Manage. Mitigation, 2004 (2004), 123–130. https://doi.org/10.1007/s10311-022-01457-2 doi: 10.1007/s10311-022-01457-2
    [2] B. Balaji-Prasath, Y. Wang, Y. P. Su, D. P. Hamilton, H. Lin, L. Zheng, et al., Methods to control harmful algal blooms: A review, Environ. Chem. Lett., 20 (2022), 3133–3152. https://doi.org/10.1007/s10311-022-01457-2 doi: 10.1007/s10311-022-01457-2
    [3] J. J. Gallardo-Rodríguez, A. Astuya-Villalón, A. Llanos-Rivera, V. Avello-Fontalba, V. Ulloa-Jofré, A critical review on control methods for harmful algal blooms, Rev. Aquacult., 11 (2019), 661–684. https://doi.org/10.1111/raq.12251 doi: 10.1111/raq.12251
    [4] D. M. Anderson, Approaches to monitoring, control and management of harmful algal blooms (HABs), Ocean Coastal Manage., 52 (2009), 342–347. https://doi.org/10.1016/j.ocecoaman.2009.04.006 doi: 10.1016/j.ocecoaman.2009.04.006
    [5] A. Burson, M. Stomp, E. Greenwell, J. Grosse, J. Huisman, Competition for nutrients and light: testing advances in resource competition with a natural phytoplankton community, Ecology, 99 (2018), 1108–1118. https://doi.org/10.1002/ecy.2187 doi: 10.1002/ecy.2187
    [6] D. W. Schindler, R. E. Hecky, D. L. Findlay, M. P. Stainton, B. R. Parker, M. J. Paterson, et al., Eutrophication of lakes cannot be controlled by reducing nitrogen input: Results of a 37-year whole-ecosystem experiment, Proc. Natl. Acad. Sci., 105 (2008), 11254–11258. https://doi.org/10.1073/pnas.0805108105 doi: 10.1073/pnas.0805108105
    [7] M. J. Vanni, Effects of nutrients and zooplankton size on the structure of a phytoplankton community, Ecology, 68 (1987), 624–635. https://doi.org/10.2307/1938467 doi: 10.2307/1938467
    [8] X. H. Ji, S. L. Yuan, T. H. Zhang, H. P. Zhu, Stochastic modeling of algal bloom dynamics with delayed nutrient recycling, Math. Biosci. Eng., 16 (2019), 1–24. https://doi.org/10.3934/mbe.2019001 doi: 10.3934/mbe.2019001
    [9] G. D. Liu, X. Z. Meng, S. Y. Liu, Dynamics for a tritrophic impulsive periodic plankton–fish system with diffusion in lakes, Math. Methods Appl. Sci., 44 (2021), 3260–3279. https://doi.org/10.1002/mma.6938 doi: 10.1002/mma.6938
    [10] P. M. Glibert, V. Kelly, J. Alexander, L. A. Codispoti, W. C. Boicourt, T. M. Trice, et al., In situ nutrient monitoring: A tool for capturing nutrient variability and the antecedent conditions that support algal blooms, Harmful Algae, 8 (2008), 175–181. https://doi.org/10.1016/j.hal.2008.08.013 doi: 10.1016/j.hal.2008.08.013
    [11] H. W. Han, R. S. Xiao, G. D. Gao, B. S. Yin, S. K. Liang, X. Q. lv, Influence of a heavy rainfall event on nutrients and phytoplankton dynamics in a well-mixed semi-enclosed bay, J. Hydrol., 617 (2023), 128932. https://doi.org/10.1016/j.jhydrol.2022.128932 doi: 10.1016/j.jhydrol.2022.128932
    [12] X. D. Li, X. Y. Yang, T. W. Huang, Persistence of delayed cooperative models: Impulsive control method, Appl. Math. Comput., 342 (2019), 130–146. https://doi.org/10.1016/j.amc.2018.09.003 doi: 10.1016/j.amc.2018.09.003
    [13] M. K. Alijani, H. Wang, J. J. Elser, Modeling the bacterial contribution to planktonic community respiration in the regulation of solar energy and nutrient availability, Ecol. Complexity, 23 (2015), 25–33. https://doi.org/10.1016/j.ecocom.2015.05.002 doi: 10.1016/j.ecocom.2015.05.002
    [14] C. J. Dai, M. Zhao, H. G. Yu, Dynamics induced by delay in a nutrient–phytoplankton model with diffusion, Ecol. Complexity, 26 (2016), 29–36. https://doi.org/10.1016/j.ecocom.2016.03.001 doi: 10.1016/j.ecocom.2016.03.001
    [15] P. Feketa, V. Klinshov, L. Lücken, A survey on the modeling of hybrid behaviors: How to account for impulsive jumps properly, Commun. Nonlinear Sci. Numer. Simul., 103 (2021), 105955. https://doi.org/10.1016/j.cnsns.2021.105955 doi: 10.1016/j.cnsns.2021.105955
    [16] A. M. Samoilenko, N. A. Perestyuk, Impulsive Differential Equations, World Scientific, 1995. https://doi.org/10.1142/2892
    [17] T. Yang, Impulsive Control Theory, Springer Science & Business Media, 2001.
    [18] V. Lakshmikantham, P. S. Simeonov, Theory of Impulsive Differential Equations, World Scientific, 1989.
    [19] H. Liu, C. J. Dai, H. G. Yu, Q. Guo, J. B. Li, A. M. Hao, et al., Dynamics of a stochastic non-autonomous phytoplankton–zooplankton system involving toxin-producing phytoplankton and impulsive perturbations, Math. Comput. Simul., 203 (2023), 368–386. https://doi.org/10.1016/j.matcom.2022.06.012 doi: 10.1016/j.matcom.2022.06.012
    [20] X. W. Yu, S. L. Yuan, T. H. Zhang, Survival and ergodicity of a stochastic phytoplankton–zooplankton model with toxin-producing phytoplankton in an impulsive polluted environment, Appl. Math. Comput., 347 (2019), 249–264. https://doi.org/10.1016/j.amc.2018.11.005 doi: 10.1016/j.amc.2018.11.005
    [21] D. Z. Li, Y. Liu, H. D. Cheng, Dynamic complexity of a phytoplankton-fish model with the impulsive feedback control by means of Poincaré map, Complexity, 2020 (2020), 8974763. https://doi.org/10.1155/2020/8974763 doi: 10.1155/2020/8974763
    [22] J. Yang, M. Zhao, A mathematical model for the dynamics of a fish algae consumption model with impulsive control strategy, J. Appl. Math., 2012 (2012), 452789. https://doi.org/10.1155/2012/452789 doi: 10.1155/2012/452789
    [23] W. Li, T. H. Zhang, Y. F. Wang, H. D. Cheng, Dynamic analysis of a plankton–herbivore state-dependent impulsive model with action threshold depending on the density and its changing rate, Nonlinear Dyn., 107 (2022), 2951–2963. https://doi.org/10.1007/s11071-021-07022-w doi: 10.1007/s11071-021-07022-w
    [24] S. Spatharis, G. Tsirtsis, D. B. Danielidis, T. Do Chi, D. Mouillot, Effects of pulsed nutrient inputs on phytoplankton assemblage structure and blooms in an enclosed coastal area, Estuarine Coastal Shelf Sci., 73 (2007), 807–815. https://doi.org/10.1016/j.ecss.2007.03.016 doi: 10.1016/j.ecss.2007.03.016
    [25] K. L. Cottingham, S. Glaholt, A. C. Brown, Zooplankton community structure affects how phytoplankton respond to nutrient pulses, Ecology, 85 (2004), 158–171. https://doi.org/10.1890/02-0570 doi: 10.1890/02-0570
    [26] J. dos Santos Severiano, V. L. dos Santos Almeida-Melo, E. M. de Melo-Magalhães, M. do Carmo Bittencourt-Oliveira, A. do Nascimento Moura, Effects of zooplankton and nutrients on phytoplankton: An experimental analysis in a eutrophic tropical reservoir, Mar. Freshwater Res., 68 (2016), 1061–1069. https://doi.org/10.1071/MF15393 doi: 10.1071/MF15393
    [27] Z. Zhao, C. G. Luo, L. Y. Pang, Y. Chen, Nonlinear modelling of the interaction between phytoplankton and zooplankton with the impulsive feedback control, Chaos Solitons Fractals, 87 (2016), 255–261. https://doi.org/10.1016/j.chaos.2016.04.011 doi: 10.1016/j.chaos.2016.04.011
    [28] H. J. Guo, L. S. Chen, X. Y. Song, Qualitative analysis of impulsive state feedback control to an algae-fish system with bistable property, Appl. Math. Comput., 271 (2015), 905–922. https://doi.org/10.1016/j.amc.2015.09.046 doi: 10.1016/j.amc.2015.09.046
    [29] C. R. Tian, S. G. Ruan, Pattern formation and synchronism in an allelopathic plankton model with delay in a network, SIAM J. Appl. Dyn. Syst., 18 (2019), 531–557. https://doi.org/10.1137/18M1204966 doi: 10.1137/18M1204966
    [30] N. K. Thakur, A. Ojha, D. Jana, R. K. Upadhyay, Modeling the plankton–fish dynamics with top predator interference and multiple gestation delays, Nonlinear Dyn., 100 (2020), 4003–4029. https://doi.org/10.1007/s11071-020-05688-2 doi: 10.1007/s11071-020-05688-2
    [31] Q. Guo, C. J. Dai, H. G. Yu, H. Liu, X. X. Sun, J. B. Li, et al., Stability and bifurcation analysis of a nutrient-phytoplankton model with time delay, Math. Methods Appl. Sci., 43 (2020), 3018–3039. https://doi.org/10.1002/mma.6098 doi: 10.1002/mma.6098
    [32] S. Q. Zhang, T. H. Zhang, S. L. Yuan, Dynamics of a stochastic predator-prey model with habitat complexity and prey aggregation, Ecol. Complexity, 45 (2021), 100889. https://doi.org/10.1016/j.ecocom.2020.100889 doi: 10.1016/j.ecocom.2020.100889
    [33] X. R. Mao, M. Glenn, R. Eric, Environmental Brownian noise suppresses explosion in population dynamics, Stochastic Process Their Appl., 97 (2002), 95–110. https://doi.org/10.1016/s0304-4149(01)00126-0 doi: 10.1016/s0304-4149(01)00126-0
    [34] F. Q. Deng, Q. Luo, X. R. Mao, S. L. Pang, Noise suppresses or expresses exponential growth, Syst. Control Lett., 57 (2008), 262–270. https://doi.org/10.1016/j.sysconle.2007.09.002 doi: 10.1016/j.sysconle.2007.09.002
    [35] Q. Guo, Y. Wang, C. J. Dai, L. J. Wang, H. Liu, J. B. Li, et al., Dynamics of a stochastic nutrient–plankton model with regime switching, Ecol. Modell., 477 (2023), 110249. https://doi.org/10.1016/j.ecolmodel.2022.110249 doi: 10.1016/j.ecolmodel.2022.110249
    [36] J. A. Freund, S. Mieruch, B. Scholze, K. Wiltshire, U. Feudel, Bloom dynamics in a seasonally forced phytoplankton–zooplankton model: Trigger mechanisms and timing effects, Ecol. Complexity, 3 (2006), 129–139. https://doi.org/10.1016/j.ecocom.2005.11.001 doi: 10.1016/j.ecocom.2005.11.001
    [37] J. P. DeLong, C. E. Cressler, Stochasticity directs adaptive evolution toward nonequilibrium evolutionary attractors, Ecology, 104 (2022), e3873. https://doi.org/10.1002/ecy.3873 doi: 10.1002/ecy.3873
    [38] H. Liu, C. J. Dai, H. G. Yu, Q, Guo, J. B. Li, A. M. Hao, et al., Dynamics induced by environmental stochasticity in a phytoplankton-zooplankton system with toxic phytoplankton, Math. Biosci. Eng., 18 (2021), 4101–4126. https://doi.org/10.3934/mbe.2021206 doi: 10.3934/mbe.2021206
    [39] X. W. Yu, S. L. Yuan, T. H. Zhang, Asymptotic properties of stochastic nutrient-plankton food chain models with nutrient recycling, Nonlinear Anal. Hybrid Syst., 34 (2019), 209–225. https://doi.org/10.1016/j.nahs.2019.06.005 doi: 10.1016/j.nahs.2019.06.005
    [40] X. M. Feng, J. X. Sun, L. Wang, F. Q. Zhang, S. L. Sun, Periodic solutions for a stochastic chemostat model with impulsive perturbation on the nutrient, J. Biol. Syst., 29 (2021), 849–870. https://doi.org/10.1142/S0218339021500200 doi: 10.1142/S0218339021500200
    [41] Q. Guo, C. J. Dai, L. J. Wang, H. Liu, Y. Wang, J. B. Li, et al., Stochastic periodic solution of a nutrient–plankton model with seasonal fluctuation, J. Biol. Syst., 30 (2022), 695–720. https://doi.org/10.1142/S0218339022500255 doi: 10.1142/S0218339022500255
    [42] C. J. Dai, H. G. Yu, Q. Guo, H. Liu, Q. Wang, Z. L. Ma, et al., Dynamics induced by delay in a nutrient-phytoplankton model with multiple delays, Complexity, 2019 (2019), 3879626. https://doi.org/10.1155/2019/3879626 doi: 10.1155/2019/3879626
    [43] D. Song, M. Fan, S. H. Yan, M. Liu, Dynamics of a nutrient-phytoplankton model with random phytoplankton mortality, J. Theor. Biol., 488 (2020), 110119. https://doi.org/10.1016/j.jtbi.2019.110119 doi: 10.1016/j.jtbi.2019.110119
    [44] H. Wang, M. Liu, Stationary distribution of a stochastic hybrid phytoplankton–zooplankton model with toxin-producing phytoplankton, Appl. Math. Lett., 101 (2020), 106077. https://doi.org/10.1016/j.aml.2019.106077 doi: 10.1016/j.aml.2019.106077
    [45] A. Mandal, P. K. Tiwari, S. Pal, A nonautonomous model for the effects of refuge and additional food on the dynamics of phytoplankton-zooplankton system, Ecol. Complexity, 46 (2021), 100927. https://doi.org/10.1016/j.ecocom.2021.100927 doi: 10.1016/j.ecocom.2021.100927
    [46] J. Chattopadhayay, R. R. Sarkar, S. Mandal, Toxin-producing plankton may act as a biological control for planktonic blooms—field study and mathematical modelling, J. Theor. Biol., 215 (2002), 333–344. https://doi.org/10.1006/jtbi.2001.2510 doi: 10.1006/jtbi.2001.2510
    [47] C. S. Holling, The components of predation as revealed by a study of small-mammal predation of the European pine sawfly, Can. Entomol., 91 (1959), 293–320. https://doi.org/10.4039/Ent91293-5 doi: 10.4039/Ent91293-5
    [48] A. M. Siepielski, A. Nemirov, M. Cattivera, A. Nickerson, Experimental evidence for an eco-evolutionary coupling between local adaptation and intraspecific competition, Am. Nat., 187 (2016), 447–456. https://doi.org/10.1086/685295 doi: 10.1086/685295
    [49] R. M. May, Stability and Complexity in Model Ecosystems, Princeton university press, 2019.
    [50] Y. Zhang, S. H. Chen, S. J. Gao, X. Wei, Stochastic periodic solution for a perturbed non-autonomous predator–prey model with generalized nonlinear harvesting and impulses, Phys. A, 486 (2017), 347–366. http://dx.doi.org/10.1016/j.physa.2017.05.058 doi: 10.1016/j.physa.2017.05.058
    [51] W. J. Zuo, D. Q. Jiang, Periodic solutions for a stochastic non-autonomous Holling–Tanner predator–prey system with impulses, Nonlinear Anal. Hybrid Syst., 22 (2016), 191–201. http://dx.doi.org/10.1016/j.nahs.2016.03.004 doi: 10.1016/j.nahs.2016.03.004
    [52] S. W. Zhang, D. J. Tan, Dynamics of a stochastic predator–prey system in a polluted environment with pulse toxicant input and impulsive perturbations, Appl. Math. Modell., 39 (2015), 6319–6331. https://doi.org/10.1016/j.apm.2014.12.020 doi: 10.1016/j.apm.2014.12.020
    [53] N. Dalal, D. Greenhalgh, X. R. Mao, A stochastic model for internal HIV dynamics, J. Math. Anal. Appl., 341 (2008), 1084–1101. https://doi.org/10.1016/j.hal.2020.101845 doi: 10.1016/j.hal.2020.101845
    [54] Y. Zhang, S. H. Chen, S. J. Gao, X. Wei, Stochastic periodic solution for a perturbed non-autonomous predator–prey model with generalized nonlinear harvesting and impulses, Phys. A, 486 (2017), 347–366. https://doi.org/10.1016/j.physa.2017.05.058 doi: 10.1016/j.physa.2017.05.058
    [55] J. X. Zhao, Y. F. Shao, Stochastic periodic solution and permanence of a holling–leslie predator-prey system with impulsive effects, J. Math., 2021 (2021), 6694479. https://doi.org/10.1155/2021/6694479 doi: 10.1155/2021/6694479
    [56] R. Khasminskii, Stochastic stability of differential equations, 2$^{nd}$ edition, Springer Science and Business Media, 2011. https://doi.org/10.1007/978-3-642-23280-0
    [57] J. J. Elser, M. Kyle, L. Steger, K. R. Nydick, J. S. Baron, Nutrient availability and phytoplankton nutrient limitation across a gradient of atmospheric nitrogen deposition, Ecology, 90 (2009), 3062–3073. https://doi.org/10.1890/08-1742.1 doi: 10.1890/08-1742.1
    [58] G. Borics, I. Grigorszky, S. Szabó, J. Padisák, Phytoplankton associations in a small hypertrophic fishpond in East Hungary during a change from bottom-up to top-down control, in Developments in Hydrobiology (eds. C. S. Reynolds, M. Dokulil and J. Padisák), 150 (2000), 79–90. https://doi.org/10.1007/978-94-017-3488-2_7
    [59] Y. Kang, F. Koch, C. J. Gobler, The interactive roles of nutrient loading and zooplankton grazing in facilitating the expansion of harmful algal blooms caused by the pelagophyte, Aureoumbra lagunensis, to the Indian River Lagoon, FL, USA, Harmful Algae, 49 (2015), 162–173. https://doi.org/10.1016/j.hal.2015.09.005 doi: 10.1016/j.hal.2015.09.005
    [60] X. D. Wang, B. Q. Qin, G. Gao, H. W. Paerl, Nutrient enrichment and selective predation by zooplankton promote Microcystis (Cyanobacteria) bloom formation, J. Plankton Res., 32 (2010), 457–470. https://doi.org/10.1093/plankt/fbp143 doi: 10.1093/plankt/fbp143
    [61] A. P. Belfiore, R. P. Buley, E. G. Fernandez-Figueroa, M. F. Gladfelter, A. E. Wilson, Zooplankton as an alternative method for controlling phytoplankton in catfish pond aquaculture, Aquacult. Rep., 21 (2021), 100897. https://doi.org/10.1016/j.aqrep.2021.100897 doi: 10.1016/j.aqrep.2021.100897
    [62] D. M. Anderson, A. D. Cembella, G. M. Hallegraeff, Progress in understanding harmful algal blooms: paradigm shifts and new technologies for research, monitoring, and management, Ann. Rev. Mar. Sci., 4 (2012), 143–176. https://doi.org/10.1146/annurev-marine-120308-081121 doi: 10.1146/annurev-marine-120308-081121
    [63] V. Ittekkot, U. Brockmann, W. Michaelis, E. T. Degens, Dissolved free and combined carbohydrates during a phytoplankton bloom in the northern North Sea, Mar. Ecol. Progress Ser., 4 (1981), 299–305. https://doi.org/10.1016/j.amc.2015.09.046 doi: 10.1016/j.amc.2015.09.046
    [64] M. Rehim, Z. Z. Zhang, A. Muhammadhaji, Mathematical analysis of a nutrient–plankton system with delay, SpringerPlus, 5 (2016), 1055. https://doi.org/10.1186/s40064-016-2435-7 doi: 10.1186/s40064-016-2435-7
    [65] N. K. Thakur, A. Ojha, P. K. Tiwari, R. K. Upadhyay, An investigation of delay induced stability transition in nutrient-plankton systems, Chaos Solitons Fractals, 142 (2021), 110474. https://doi.org/10.1016/j.chaos.2020.110474 doi: 10.1016/j.chaos.2020.110474
    [66] S. R. J. Jang, J. Baglama, J. Rick, Nutrient-phytoplankton-zooplankton models with a toxin, Math. Comput. Modell., 43 (2006), 105–118. https://doi.org/10.1016/j.mcm.2005.09.030 doi: 10.1016/j.mcm.2005.09.030
  • Reader Comments
  • © 2023 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Metrics

Article views(1126) PDF downloads(74) Cited by(2)

Article outline

Figures and Tables

Figures(6)  /  Tables(1)

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog