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April 2019, 24(4): 1943-1960. doi: 10.3934/dcdsb.2018256
Lyapunov type inequalities for Hammerstein integral equations and applications to population dynamics
Kunquan Lan 1,, and Wei Lin 2,
Department of Mathematics, Ryerson University, Toronto, Ontario, Canada M5B 2K3
School of Mathematical Sciences and Centre for Computational Systems Biology, Fudan University, Shanghai 200433, China
* Corresponding author: Kunquan Lan
Received February 2017 Revised June 2018 Published April 2019 Early access October 2018
Fund Project: The first author was supported in part by the Natural Sciences and Engineering Research Council (NSERC) of Canada under grant no. 250187-2013 and 135752-2018, and the Shanghai Key Laboratory of Contemporary Applied Mathematics, and the second author was supported in part by the NNSF of China under grants no. 11322111 and no. 61773125.
Figure(2)
Lyapunov type inequalities for (linear or nonlinear) Hammerstein integral equations are established and applied to second order differential equations (ODEs) with general separated boundary conditions. These new inequalities provide necessary conditions for the Hammerstein integral equations and these boundary value problems to have nonzero nonnegative solutions. As applications of these inequalities for nonlinear ODEs, we obtain extinction criteria and optimal locations of favorable habitats for populations inhabiting one dimensional heterogeneous environments governed by reaction-diffusion equations with spatially varying growth rates and external forcing.
Keywords: Hammerstein integral equations, Lyapunov type inequalities, population models, extinction criteria, Allee effects, optimal favorable habitats.
Mathematics Subject Classification: Primary: 47H30; Secondary: 35P30, 45A05, 47H10, 92D25.
Citation: Kunquan Lan, Wei Lin. Lyapunov type inequalities for Hammerstein integral equations and applications to population dynamics. Discrete & Continuous Dynamical Systems - B, 2019, 24 (4) : 1943-1960. doi: 10.3934/dcdsb.2018256
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Figure 1. (a) The optimal position with $m_{a, k}$ when $(\beta, \delta, T)\in \mathcal{D}_{1}$, where $k = 3$, $a = 0$, $\beta = 4$, $\delta = 1$, and $T = 0.8$. (b) The optimal position with $m_{a, k}$ when $(\beta, \delta, T)\in \mathcal{D}_{2}$, where $k = 3$, $a = 0$, $\beta = 2.5$, $\delta = 2$, and $T = 0.8$. (c) The monotonically decreasing curve of $\eta_{1}$, as defined in (4.12), with respect to the variable $a\in[0, 1-T] = [0, 0.2]$ for $(\beta, \delta, T)$ in different area. Here, the curve, plotted by squares, corresponds to the parameters in (a) and the curve, plotted by dots, to the parameters in (b)
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Figure 2. (a) The optimal position with $m_{a, k}$ when $(\beta, \delta, T)\in \mathcal{D}_{3}$, where $k = 3$, $a = 1-T$, $\beta = 2.5$, $\delta = 2$, $T = 0.3$, and $a_{1}(T) = 0.1$. (b) The optimal position with $m_{a, k}$ when $(\beta, \delta, T)\in \mathcal{D}_{4}$, where $k = 3$, $a = 1-T$, $\beta = \delta = 3$, $T = 0.3$, and $a_{1}(T) = \frac{1-T}{2} = 0.35$. (c) The optimal position with $m_{a, k}$ when $(\beta, \delta, T)\in \mathcal{D}_{5}$, where $k = 3$, $a = 1-T$, $\beta = 3$, $\delta = 3.5$, $T = 0.3$, and $a_{1}(T) = 0.6$. (d) The unimodal curve of $\eta_{1}$, as defined in (4.12), with respect to the variable $a\in[0, 1-T] = [0, 0.7]$ for $(\beta, \delta, T)$ in different area. Here, the curve, plotted by the dash line, corresponds to the parameters in (a), the curve, plotted by the solid line, to the parameters in (b), and the curve, plotted by the dotted line, to the parameters in (c)
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