In any ecosystem, chaotic situations may arise from equilibrium state for different reasons. To overcome these chaotic situations, sometimes the system itself exhibits some mechanisms of self-adaptability. In this paper, we explore an eco-epidemiological model consisting of three aquatic groups: phytoplankton, zooplankton, and marine free viruses. We assume that the phytoplankton population is infected by external free viruses and zooplankton get affected on consumption of infected phytoplankton; also, the infected phytoplankton do not compete for resources with the susceptible one. In addition, we model a mechanism by which zooplankton recognize and avoid infected phytoplankton, at least when susceptible phytoplankton are present. The zooplankton extinction chance increases on increasing the force of infection or decreasing the intensity of avoidance. Further, when the viral infection triggers chaotic dynamics, high zooplankton avoidance intensity can stabilize again the system. Interestingly, for high avoidance intensity, nutrient enrichment has a destabilizing effect on the system dynamics, which is in line with the paradox of enrichment. Global sensitivity analysis helps to identify the most significant parameters that reduce the infected phytoplankton in the system. Finally, we compare the dynamics of the system by allowing the infected phytoplankton also to share resources with the susceptible phytoplankton. A gradual increase of the virus replication factor turns the system dynamics from chaos to doubling state to limit cycle to stable state and the system finally settles down to the zooplankton-free equilibrium point. Moreover, on increasing the intensity of avoidance, the system shows a transcritical bifurcation from the zooplankton-free equilibrium to the coexistence steady state and remains stable thereafter.

Modeling the avoidance behavior of zooplankton on phytoplankton infected by free viruses

Bona F.;Venturino E.
2020-01-01

Abstract

In any ecosystem, chaotic situations may arise from equilibrium state for different reasons. To overcome these chaotic situations, sometimes the system itself exhibits some mechanisms of self-adaptability. In this paper, we explore an eco-epidemiological model consisting of three aquatic groups: phytoplankton, zooplankton, and marine free viruses. We assume that the phytoplankton population is infected by external free viruses and zooplankton get affected on consumption of infected phytoplankton; also, the infected phytoplankton do not compete for resources with the susceptible one. In addition, we model a mechanism by which zooplankton recognize and avoid infected phytoplankton, at least when susceptible phytoplankton are present. The zooplankton extinction chance increases on increasing the force of infection or decreasing the intensity of avoidance. Further, when the viral infection triggers chaotic dynamics, high zooplankton avoidance intensity can stabilize again the system. Interestingly, for high avoidance intensity, nutrient enrichment has a destabilizing effect on the system dynamics, which is in line with the paradox of enrichment. Global sensitivity analysis helps to identify the most significant parameters that reduce the infected phytoplankton in the system. Finally, we compare the dynamics of the system by allowing the infected phytoplankton also to share resources with the susceptible phytoplankton. A gradual increase of the virus replication factor turns the system dynamics from chaos to doubling state to limit cycle to stable state and the system finally settles down to the zooplankton-free equilibrium point. Moreover, on increasing the intensity of avoidance, the system shows a transcritical bifurcation from the zooplankton-free equilibrium to the coexistence steady state and remains stable thereafter.
2020
46
1
1
31
Avoidance behavior; Chaos; Free-virus; Global sensitivity; Phytoplankton; Zooplankton
Biswas S.; Tiwari P.K.; Bona F.; Pal S.; Venturino E.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2318/1743424
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