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Table 2 Parameters of Figs. 111

From: Numerical simulation for a class of predator–prey system with homogeneous Neumann boundary condition based on a sinc function interpolation method

Figure

Parameters

Fig. 1 (a)

r = 0.1, \(\alpha _{0}=0.6\), \(\alpha _{1}=0.3\), \(\beta _{0}=0.3\), \(\beta _{1}=0.1\), \(\gamma _{0}=0.1\), \(\gamma _{1}=0.08\), \(m_{1}=0.15\), \(m_{2}=0.2\), k = 20

Fig. 1 (b)

r = 0.1, \(\alpha _{0}=0.6\), \(\alpha _{1}=0.3\), \(\beta _{0}=0.3\), \(\beta _{1}=0.1\), \(\gamma _{0}=0.1\), \(\gamma _{1}=0.08\), \(m_{1}=0.15\), \(m_{2}=0.2\), k = 200

Fig. 1 (c)

r = 0.1, \(\alpha _{0}=0.6\), \(\alpha _{1}=0.3\), \(\beta _{0}=0.3\), \(\beta _{1}=0.1\), \(\gamma _{0}=0.1\), \(\gamma _{1}=0.1\), \(m_{1}=0.08\), \(m_{2}=0.1\), k = 50

Fig. 2 (a), 3

r = 0.6, \(\alpha _{0}=0.6\), \(\alpha _{1}=0.3\), \(\beta _{0}=0\), \(\beta _{1}=0\), \(\gamma _{0}=0.1\), \(\gamma _{1}=0.1\), \(m_{1}=0.08\), \(m_{2}=0.1\), k = 200

Fig. 2 (b)

r = 0.6, \(\alpha _{0}=0.6\), \(\alpha _{1}=0.3\), \(\beta _{0}=0\), \(\beta _{1}=0\), \(\gamma _{0}=0.1\), \(\gamma _{1}=0.1\), \(m_{1}=0.08\), \(m_{2}=0.1\), k = 50

Fig. 2 (c), 4

r = 0.5, \(\alpha _{0}=0.3\), \(\alpha _{1}=0.25\), \(\beta _{0}=0\), \(\beta _{1}=0\), \(\gamma _{0}=0.2\), \(\gamma _{1}=0.1\), \(m_{1}=0.1\), \(m_{2}=0.15\), k = 200

Figs. 57

r = 0.1, \({\alpha _{0}} = 0.6\), \({\alpha _{1}} = 0.3\), \({\beta _{0}} = 0.3\), \({\beta _{1}} = 0.1\), \({\gamma _{0}} = 0.1\), \({\gamma _{1}} = 1\), \({m_{1}} = 0.3\), \({m_{2}} = 0.5\), k = 1

Figs. 811

r = 0.8, \({\alpha _{0}} = 0.4\), \({\alpha _{1}} = 0.4\), \({\beta _{0}} = 0.5\), \(\beta _{1}=0.4\), \({\gamma _{0}} = 0.2\), \(\gamma _{1}=0.5\), \({m_{1}} = 0.1\), \({m_{2}} = 0.2\), k = 2.4