Stochastic Dynamics of Dual-Prey–Predator Interactions under Harvesting Pressure: Insights from the California Current Ecosystem
Abstract
This study presents a stochastic predator-prey model involving two harvested prey species—sardines and anchovies—and a common predator, the blacktip shark, under the influence of environmental noise. The model incorporates Holling type-II functional responses, harvesting efforts, and white noise perturbations representing environmental variability. Analytical investigations determine the boundedness and stability conditions of equilibria. Numerical simulations reveal that the predator population is highly sensitive to stochastic perturbations, particularly the noise intensity associated with predator mortality. Notably, a sufficiently large noise intensity in anchovy dynamics ($\alpha_2 > 72.06$) can stabilize the coexistence equilibrium, where higher values of $\alpha_1$ and $\alpha_2$ tend to destabilize the system. Phase portraits and bifurcation analyses illustrate the effects of harvesting rates and noise intensities on species persistence and extinction. These findings highlight critical thresholds for sustainable harvesting and noise tolerance, offering ecological insights into species coexistence within the California Current ecosystem.
References
Kaplan, I. C., Koehn, L. E., Hodgson, E. E., Marshall, K. N., & Essington, T. E. (2017). Modeling food web effects of low sardine and anchovy abundance in the California Current. Ecological Modelling, 359, 1–24. https://doi.org/10.1016/j.ecolmodel.2017.05.007
Kaplan, I. C., Francis, T. B., Punt, A. E., Koehn, L. E., Curchitser, E., Hurtado-Ferro, F., Johnson, K. F., Lluch-Cota, S. E., Sydeman, W. J., & Essington, T. E. (2019). A multi-model approach to understanding the role of Pacific sardine in the California Current food web. Marine Ecology Progress Series, 617, 307–321. https://doi.org/10.3354/meps12504
Uriarte, A., Alday, A., Santos, M., & Motos, L. (2012). A re-evaluation of the spawning fraction estimation procedures for Bay of Biscay anchovy, a species with short interspawning intervals. Fisheries Research, 117, 96–111. https://doi.org/10.1016/j.fishres.2011.03.002
Mantua, N. J., Hare, S. R., Zhang, Y., Wallace, J. M., & Francis, R. C. (1997). A Pacific interdecadal climate oscillation with impacts on salmon production. Bulletin of the American Meteorological Society, 78(6), 1069–1080. https://doi.org/10.1175/1520-0477(1997)078<1069:APICOW>2.0.CO;2
Checkley, D. M. Jr., Asch, R. G., & Rykaczewski, R. R. (2017). Climate, anchovy, and sardine. Annual Review of Marine Science, 9(1), 469–493. https://doi.org/10.1146/annurev-marine-122414-033819
Mao, X. (2007). Stochastic differential equations and applications. Elsevier. https://doi.org/10.1007/978-3-642-11079-5_2
Buffoni, G., Groppi, M., & Soresina, C. (2016). Dynamics of predator–prey models with a strong Allee effect on the prey and predator-dependent trophic functions. Nonlinear Analysis: Real World Applications, 30, 143–169. https://doi.org/10.1016/j.nonrwa.2015.12.001
Mahapatra, G. S., & Santra, P. (2016). Prey–predator model for optimal harvesting with functional response incorporating prey refuge. International Journal of Biomathematics, 9(1), 1650014. https://doi.org/10.1142/S1793524516500145
Das, K. P., Chatterjee, S., & Chattopadhyay, J. (2010). Occurrence of chaos and its possible control in a predator-prey model with density dependent disease-induced mortality on predator population. Journal of Biological Systems, 18(2), 399–435. https://doi.org/10.1142/S0218339010003391
Banerjee, C., Das, P., & Roy, A. B. (2015). Stability, bifurcations and chaotic dynamics in a delayed hybrid tri-trophic food chain model with Holling type-II and Leslie-Gower type functional responses. World Journal of Modelling and Simulation, 11(3), 174–198. https://www.researchgate.net/publication/283778884
May, R. M. (2019). Stability and complexity in model ecosystems. Princeton University Press. https://doi.org/10.2307/j.ctvs32rq4
Liu, Q., Zu, L., & Jiang, D. (2016). Dynamics of stochastic predator–prey models with Holling II functional response. Communications in Nonlinear Science and Numerical Simulation, 37, 62–76. https://doi.org/10.1016/j.cnsns.2016.01.005
Zuo, W., & Jiang, D. (2016). Stationary distribution and periodic solution for stochastic predator-prey systems with nonlinear predator harvesting. Communications in Nonlinear Science and Numerical Simulation, 36, 65–80. https://doi.org/10.1016/j.cnsns.2015.11.014
Jana, D., Agrawal, R., & Upadhyay, R. K. (2015). Dynamics of generalist predator in a stochastic environment: Effect of delayed growth and prey refuge. Applied Mathematics and Computation, 268, 1072–1094. https://doi.org/10.1016/j.amc.2015.06.098
Aguirre, P., González-Olivares, E., & Torres, S. (2013). Stochastic predator–prey model with Allee effect on prey. Nonlinear Analysis: Real World Applications, 14(1), 768–779. https://doi.org/10.1016/j.nonrwa.2012.07.032
Lv, J., & Wang, K. (2011). Asymptotic properties of a stochastic predator–prey system with Holling II functional response. Communications in Nonlinear Science and Numerical Simulation, 16(10), 4037–4048. https://doi.org/10.1016/j.cnsns.2011.01.015
Liu, Z., Shi, N., Jiang, D., & Ji, C. (2012). The asymptotic behavior of a stochastic predator-prey system with Holling II functional response. Abstract and Applied Analysis, 2012(1), 801812. https://doi.org/10.1155/2012/801812
Mandal, P. S., & Banerjee, M. (2012). Stochastic persistence and stationary distribution in a Holling–Tanner type prey–predator model. Physica A: Statistical Mechanics and its Applications, 391(4), 1216–1233. https://doi.org/10.1016/j.physa.2011.10.019
Gard, T. C. (2000). Transient effects of stochastic multi-population models. Preprint. https://www.researchgate.net/publication/26393530
Allen, E. (2007). Modeling with Itô stochastic differential equations. Springer. https://doi.org/10.1007/978-1-4020-5953-7
Wilkinson, D. J. (2018). Stochastic modelling for systems biology. Chapman & Hall/CRC. https://doi.org/10.1201/9781351000918
Gani, J., & Swift, R. J. (2008). An unexpected result in an approximate carrier-borne epidemic process. Statistics & Probability Letters, 78(14), 2116–2120. https://doi.org/10.1016/j.spl.2008.01.077
Baishya, M. C., & Chakraborti, C. G. (1987). Non-equilibrium fluctuation in Volterra-Lotka systems. Bulletin of Mathematical Biology, 49, 125–131. https://doi.org/10.1007/BF02459962
Bandyopadhyay, M., & Chakrabarti, C. G. (2003). Deterministic and stochastic analysis of a nonlinear prey-predator system. Journal of Biological Systems, 11(2), 161–172. https://doi.org/10.1142/S0218339003000816
Carletti, M. (2002). On the stability properties of a stochastic model for phage–bacteria interaction in open marine environment. Mathematical Biosciences, 175(2), 117–131. https://doi.org/10.1016/S0025-5564(01)00089-X
Allen, L. J. S. (2010). An introduction to stochastic processes with applications to biology. CRC Press. https://doi.org/10.1201/b12537
Lindegren, M., Checkley, D. M. Jr., Rouyer, T., MacCall, A. D., & Stenseth, N. C. (2013). Climate, fishing, and fluctuations of sardine and anchovy in the California Current. Proceedings of the National Academy of Sciences, 110(33), 13672–13677. https://doi.org/10.1073/pnas.1305733110
Turelli, M. (1977). Random environments and stochastic calculus. Theoretical Population Biology, 12(2), 140–178. https://doi.org/10.1016/0040-5809(77)90040-5
May, R. M. (1976). Simple mathematical models with very complicated dynamics. Nature, 261(5560), 459–467. https://www.nature.com/articles/261459a0
Clark, C. W. (2010). Mathematical bioeconomics: The mathematics of conservation. John Wiley & Sons.
Tuck, G. N., & Possingham, H. P. (2003). Bioeconomy – Economically optimal spatial and inter-temporal fishing patterns in a metapopulation. In Marine protected areas: A multidisciplinary approach (Chapter 4). Cambridge University Press. https://doi.org/10.1017/CBO9781139049382.007
Braumann, C. A. (2002). Variable effort harvesting models in random environments: Generalization to density-dependent noise intensities. Mathematical Biosciences, 177, 229–245. https://doi.org/10.1016/S0025-5564(01)00110-9
Ma, Y., & Yu, X. (2024). Stochastic analysis of survival and sensitivity in a competition model influenced by toxins under a fluctuating environment. AIMS Mathematics, 9(4), 8230–8249. https://doi.org/10.3934/math.2024400
Sha, A., Roy, S., Tiwari, P. K., & Chattopadhyay, J. (2024). Dynamics of a generalist predator–prey system with harvesting and hunting cooperation in deterministic/stochastic environment. Mathematical Methods in the Applied Sciences, 47(7), 5916–5938. https://doi.org/10.1002/mma.9897
Reis, M., Brites, N. M., Santos, C., & Dias, C. (2024). Comparison of optimal harvesting policies with general logistic growth and a general harvesting function. Mathematical Methods in the Applied Sciences, 47(10), 8076–8088. https://doi.org/10.1002/mma.10004
Edwards, H., & Penney, D. (1999). Differential equations and boundary value problems (2nd ed.). Prentice Hall.
Kolmanovskii, V. B., & Shaikhet, L. E. (2002). Some peculiarities of the general method of Lyapunov functionals construction. Applied Mathematics Letters, 15, 355–360. https://doi.org/10.1016/S0893-9659(01)00143-4
Kolmanovskii, V. B., & Shaikhet, L. E. (2002). Construction of Lyapunov functionals for stochastic hereditary systems: A survey of some recent results. Mathematical and Computer Modelling, 36, 691–716. https://doi.org/10.1016/S0895-7177(02)00168-1
Birkhoff, G., & Rota, G. C. (1982). Ordinary differential equations. Ginn.
Hasminskii, R. Z. (1980). Stochastic stability in differential equations. Sijthoff and Noordhoff. https://doi.org/10.1007/978-94-009-9121-7
Arnold, L. (1972). Stochastic differential equations: Theory and applications. Wiley.
Friedman, A. (1976). Stochastic differential equations and their applications. Academic Press.
Gard, T. C. (1987). Introduction to stochastic differential equations. Marcel Dekker.
Mao, X. (1997). Stochastic differential equations and applications. Horwood.
Liu, M., Wang, K., & Wu, Q. (2010). Survival analysis of stochastic competitive models in a polluted environment and stochastic competitive exclusion principle. Bulletin of Mathematical Biology. https://doi.org/10.1007/s11538-010-9569-5
Allen, L. J. S. (2003). An introduction to stochastic processes with applications to biology. Pearson Education.
Ackleh, A. S., Allen, L. J. S., & Carter, J. (2007). Establishing a beachhead: A stochastic population model with an Allee effect applied to species invasion. Theoretical Population Biology, 71(3), 290–300. https://doi.org/10.1016/j.tpb.2006.12.006
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