一类具有Holling-II型功能反应的食饵带恐惧效应与捕食者非线性收获的捕食者-食饵模型的动力学性态
Dynamics of a Class of Prey-Predator Interaction Model with Holling Type-II Functional Response Incorporatingthe Effect of Fear on Prey and Non-Linear Predator Harvest
DOI: 10.12677/AAM.2024.137287, PDF, 下载: 7  浏览: 15 
作者: 李立:西北师范大学数学与统计学院,甘肃 兰州
关键词: Holling-II 型功能反应平衡点稳定性Hopf分支恐惧效应Turing不稳定性Holling-II Type Functional Response Equilibrium Stability Hopf Branch Fear Effect Turing Instability
摘要: 本文研究了具有Holling-II型功能反应的捕食者-食饵模型的恐惧效应和对捕食者种群的非线性收获。对捕食者种群的恐惧增强了食饵种群的生存率,同时也大大减少了食饵种群的繁衍。对捕食者种群的捕获不仅可以获取经济收益,还可以调节捕食者与食饵的数量关系。文中研究了所有的生物可行平衡点并分析了可行平衡点的稳定时的模型参数。分析上,我们选择以捕食者种群的转化率为分支参数。模型系统经历了跨伍界分支,鞍结分支和Hopf分支。考虑捕食者和食饵种群在时空内的扩散效应,接着研究了正平衡点的局部稳定性,正平衡点和分支周期解的Turing不稳定性,Hopf分支的方向和分支周期解的稳定性。
Abstract: In this paper, we investigate the effects of fear effect and nonlinear predator harvest in predator-prey interaction models with Holling-II type functional response. Fear of the predator population increases the survival rate of the prey population and also greatly reduces the birth rate. The capture of the predator population can not only obtain economic benefits, but also regulate the quantitative relationship between predator and prey. In this paper, all viable equilibrium points are studied and the model parameters of stability of stability of viable equilibrium points are analyzed. In analysis, we have established that the conversion rate of predator population is the branch parameter and the model system undergoes transcritical branch, saddle- node branch and Hopf branch. Considering the diffusion effect of predator and prey population in space and time, we study the local stability of the positive equilibrium point, the positive equilibrium point and the Turing of the branch periodic solution instability, the direction of the Hopf branch and stability of the periodic solution of the branch.
文章引用:李立. 一类具有Holling-II型功能反应的食饵带恐惧效应与捕食者非线性收获的捕食者-食饵模型的动力学性态[J]. 应用数学进展, 2024, 13(7): 3015-3030. https://doi.org/10.12677/AAM.2024.137287

参考文献

[1] Holling, C.S. (1959) The Components of Predation as Revealed by a Study of Small-Mammal Predation of the European Pine Sawfly. The Canadian Entomologist, 91, 293-320.
https://doi.org/10.4039/ent91293-5
[2] Holling, C.S. (1959) Some Characteristics of Simple Types of Predation and Parasitism. The Canadian Entomologist, 91, 385-398.
https://doi.org/10.4039/ent91385-7
[3] Holling, C.S. (1965) The Functional Response of Predators to Prey Density and Its Role in Mimicry and Population Regulation. Memoirs of the Entomological Society of Canada, 97, 5-60.
https://doi.org/10.4039/entm9745fv
[4] Majumdar, P., Debnath, S., Mondal, B., Sarkar, S. and Ghosh, U. (2022) Complex Dynamics of a Prey-Predator Interaction Model with Holling Type-II Functional Response Incorporat- ing the Effect of Fear on Prey and Non-Linear Predator Harvesting. Rendiconti del Circolo Matematico di Palermo Series 2, 72, 1017-1048.
https://doi.org/10.1007/s12215-021-00701-y
[5] Dawes, J.H.P. and Souza, M.O. (2013) A Derivation of Holling’s Type I, II and III Functional Responses in Predator-Prey Systems. Journal of Theoretical Biology, 327, 11-22.
https://doi.org/10.1016/j.jtbi.2013.02.017
[6] Arsie, A., Kottegoda, C. and Shan, C. (2022) A Predator-Prey System with Generalized Holling Type IV Functional Response and Allee Effects in Prey. Journal of Differential Equations, 309, 704-740.
https://doi.org/10.1016/j.jde.2021.11.041
[7] Huang, J., Ruan, S. and Song, J. (2014) Bifurcations in a Predator-Prey System of Leslie Type with Generalized Holling Type III Functional Response. Journal of Differential Equations, 257, 1721-1752.
https://doi.org/10.1016/j.jde.2014.04.024
[8] Zaw Myint, A. and Wang, M. (2020) Dynamics of Holling-Type II Prey-Predator System with a Protection Zone for Prey. Applicable Analysis, 101, 1833-1847.
https://doi.org/10.1080/00036811.2020.1789595
[9] Li, X., Hu, G. and Lu, S. (2020) Pattern Formation in a Diffusive Predator-Prey System with Cross-Diffusion Effects. Nonlinear Dynamics, 100, 4045-4060.
https://doi.org/10.1007/s11071-020-05747-8
[10] Wang, X., Zanette, L. and Zou, X. (2016) Modelling the Fear Effect in Predator-Prey Inter- actions. Journal of Mathematical Biology, 73, 1179-1204.
https://doi.org/10.1007/s00285-016-0989-1
[11] Barman, D., Roy, J., Alrabaiah, H., Panja, P., Mondal, S.P. and Alam, S. (2021) Impact of Predator Incited Fear and Prey Refuge in a Fractional Order Prey Predator Model. Chaos, Solitons Fractals, 142, Article 110420.
https://doi.org/10.1016/j.chaos.2020.110420
[12] Sasmal, S.K. (2018) Population Dynamics with Multiple Allee Effects Induced by Fear Factors—A Mathematical Study on Prey-Predator Interactions. Applied Mathematical Mod- elling, 64, 1-14.
https://doi.org/10.1016/j.apm.2018.07.021
[13] Clark, C.W. (1979) Mathematical Models in the Economics of Renewable Resources. SIAM Review, 21, 81-99.
https://doi.org/10.1137/1021006
[14] Krishna, S. (1998) Conservation of an Ecosystem through Optimal Taxation. Bulletin of Math- ematical Biology, 60, 569-584.
https://doi.org/10.1006/bulm.1997.0023
[15] Perko, L. (1996) Differential Equations and Dynamical Systems. Springer, 7.
[16] Hassard, B.D., Kazarinoff, N.D. and Wan, Y.H. (1981) Theory and Applications of Hopf Bifurcation. Cambridge University Press.