doi: 10.3934/dcdsb.2021077

Dynamics of consumer-resource systems with consumer's dispersal between patches

School of Mathematics, Sun Yat-sen University, Guangzhou 510275, China

Received  August 2020 Revised  December 2020 Published  March 2021

Fund Project: The second author is supported by NSF grant of China (12071495, 11571382)

This paper considers consumer-resource systems with Holling II functional response. In the system, the consumer can move between a source and a sink patch. By applying dynamical systems theory, we give a rigorous analysis on persistence of the system. Then we show local/global stability of equilibria and prove Hopf bifurcation by the Kuznetsov Theorem. It is shown that dispersal in the system could lead to results reversing those without dispersal. Varying a dispersal rate can change species' interaction outcomes from coexistence in periodic oscillation, to persistence at a steady state, to extinction of the predator, and even to extinction of both species. By explicit expressions of stable equilibria, we prove that dispersal can make the consumer reach overall abundance larger than if non-dispersing, and there exists an optimal dispersal rate that maximizes the abundance. Asymmetry in dispersal can also lead to those results. It is proven that the overall abundance is a ridge-like function (surface) of dispersal rates, which extends both previous theory and experimental observation. These results are biologically important in protecting endangered species.

Citation: Kun Hu, Yuanshi Wang. Dynamics of consumer-resource systems with consumer's dispersal between patches. Discrete & Continuous Dynamical Systems - B, doi: 10.3934/dcdsb.2021077
References:
[1]

R. ArditiC. Lobry and T. Sari, Is dispersal always beneficial to carrying capacity? New insights from the multi-patch logistic equation, Theor. Popul. Biol., 106 (2015), 45-59.   Google Scholar

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R. ArditiC. Lobry and T. Sari, Asymmetric dispersal in the multi-patch logistic equation, Theor. Popul. Biol., 120 (2018), 11-15.   Google Scholar

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W. FengB. Rock and J. Hinson, On a new model of two-patch predator-prey system with migration of both species, J. Appl. Anal. Comput., 1 (2011), 193-203.  doi: 10.11948/2011013.  Google Scholar

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Y. KangS. K. Sasmal and K. Messan, A two-patch prey-predator model with predator dispersal driven by the predation strength, Math. Biosci. Eng., 14 (2017), 843-880.  doi: 10.3934/mbe.2017046.  Google Scholar

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Y. WangH. WuY. HeZ. Wang and K. Hu, Population abundance of two-patch competitive systems with asymmetric dispersal, J. Math. Biol., 81 (2020), 315-341.  doi: 10.1007/s00285-020-01511-z.  Google Scholar

[27]

H. WuY. WangY. Li and D. L. DeAngelis, Dispersal asymmetry in a two-patch system with source-sink populations, Theor. Popul. Biol., 131 (2020), 54-65.   Google Scholar

[28]

B. ZhangK. AlexM. L. KeenanZ. LuL. R. ArrixW.-M. NiD.L. DeAngelis and J. D. Dyken, Carrying capacity in a heterogeneous environment with habitat connectivity, Ecol. Lett., 20 (2017), 1118-1128.   Google Scholar

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B. ZhangD. L. DeAngelisW. M. NiY. WangL. ZhaiA. KulaS. Xu and J. D. Van Dyken, Effect of stressors on the carrying capacity of spatially-distributed metapopulations, The American Naturalis, 196 (2020), 46-60.   Google Scholar

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B. ZhangX. LiuD. L. DeAngelisW.-M. Ni and G. G. Wang, Effects of dispersal on total biomass in a patchy, heterogeneous system: Analysis and experiment, Math. Biosci., 264 (2015), 54-62.  doi: 10.1016/j.mbs.2015.03.005.  Google Scholar

show all references

References:
[1]

R. ArditiC. Lobry and T. Sari, Is dispersal always beneficial to carrying capacity? New insights from the multi-patch logistic equation, Theor. Popul. Biol., 106 (2015), 45-59.   Google Scholar

[2]

R. ArditiC. Lobry and T. Sari, Asymmetric dispersal in the multi-patch logistic equation, Theor. Popul. Biol., 120 (2018), 11-15.   Google Scholar

[3]

J. Astr$\ddot{o}$m and T. P$\ddot{a}$rt, Negative and matrix-dependent effects of dispersal corridors in an experimental metacommunity., Ecology, 94 2013), 1939-1970. Google Scholar

[4]

G. ButlerH. I. Freedman and P. Waltman, Uniformly persistent systems, Proc. Amer. Math. Soc., 96 (1986), 425-430.  doi: 10.1090/S0002-9939-1986-0822433-4.  Google Scholar

[5]

L. Fahrig, Effect of habitat fragmentation on the extinction threshold: A synthesis, Ecol. Appl., 12 (2002), 346-353.   Google Scholar

[6]

W. FengB. Rock and J. Hinson, On a new model of two-patch predator-prey system with migration of both species, J. Appl. Anal. Comput., 1 (2011), 193-203.  doi: 10.11948/2011013.  Google Scholar

[7]

D. Franco and A. Ruiz-Herrera, To connect or not to connect isolated patches, J. Theor. Biol., 370 (2015), 72-80.  doi: 10.1016/j.jtbi.2015.01.029.  Google Scholar

[8]

H. I. Freedman and D. Waltman, Mathematical models of population interactions with dispersal. I. Stability of two habitats with and without a predator, SIAM J. Appl. Math., 32 (1977), 631-648.  doi: 10.1137/0132052.  Google Scholar

[9]

E. E. Goldwyn and A. Hastings, When can dispersal synchronize populations?, Theor. Popul. Biol., 73 (2008), 395-402.   Google Scholar

[10]

J. K. Hale, Ordinary Differential Equations, Wiley-Interscience, 1969.  Google Scholar

[11] J. Hofbauer and K. Sigmund, Evolutionary Games and Population Dynamics, Cambridge University Press, Cambridge, UK, 1998.  doi: 10.1017/CBO9781139173179.  Google Scholar
[12]

R. D. Holt, Population dynamics in two-patch environments: Some anomalous consequences of an optimal habitat distribution, Theoret. Population Biol., 28 (1985), 181-208.  doi: 10.1016/0040-5809(85)90027-9.  Google Scholar

[13]

Y. Huang and O. Diekmann, Predator migration in response to prey density: What are the consequences?, J. Math. Biol., 43 (2001), 561-581.  doi: 10.1007/s002850100107.  Google Scholar

[14]

R. HuangY. Wang and H. Wu, Population abundance in predator-prey systems with predator's dispersal between two patches, Theor. Popu. Biol., 135 (2020), 1-8.   Google Scholar

[15]

V. HutsonY. Lou and K. Mischaikow, Convergence in competition models with small diffusion coefficients, J. Differential Equations, 211 (2005), 135-161.  doi: 10.1016/j.jde.2004.06.003.  Google Scholar

[16]

V. A. Jansen, The dynamics of two diffusively coupled predator-prey populations, Theo. Popu. Biol., 59 (2001), 119-131.   Google Scholar

[17]

Y. KangS. K. Sasmal and K. Messan, A two-patch prey-predator model with predator dispersal driven by the predation strength, Math. Biosci. Eng., 14 (2017), 843-880.  doi: 10.3934/mbe.2017046.  Google Scholar

[18]

Y. A. Kuznetsov, Elements of Applied Bifurcation Theory in: Applied Mathematical Sciences, Vol. 112, third ed., Springer-Verlag, New York, 2004. doi: 10.1007/978-1-4757-3978-7.  Google Scholar

[19]

Y. Liu, The Dynamical Behavior of a Two Patch Predator-Prey Model, Honor Thesis, from The College of William and Mary, 2010. Google Scholar

[20]

Y. Lou, On the effects of migration and spatial heterogeneity on single and multiple species, J. Differential Equations, 223 (2006), 400-426.  doi: 10.1016/j.jde.2005.05.010.  Google Scholar

[21]

A. Ruiz-Herrera and P. J. Torres, Effects of diffusion on total biomass in simple metacommunities, J. Theor. Biol., 447 (2018), 12-24.  doi: 10.1016/j.jtbi.2018.03.018.  Google Scholar

[22] H. L. Smith and P. Waltman, The Theory of the Chemostat, Cambridge University Press, New York, 1995.   Google Scholar
[23]

Y. Wang, Pollination-mutualisms in a two-patch system with dispersal, J. Theor. Biol., 476 (2019), 51-61.  doi: 10.1016/j.jtbi.2019.06.004.  Google Scholar

[24]

Y. Wang, Asymptotic state of a two-patch system with infinite diffusion, Bull. Math. Biol., 81 (2019), 1665-1686.  doi: 10.1007/s11538-019-00582-4.  Google Scholar

[25]

Y. Wang and D. L. DeAngelis, Comparison of effects of diffusion in heterogeneous and homogeneous with the same total carrying capacity on total realized population size, Theor. Popul. Biol., 125 (2019), 30-37.   Google Scholar

[26]

Y. WangH. WuY. HeZ. Wang and K. Hu, Population abundance of two-patch competitive systems with asymmetric dispersal, J. Math. Biol., 81 (2020), 315-341.  doi: 10.1007/s00285-020-01511-z.  Google Scholar

[27]

H. WuY. WangY. Li and D. L. DeAngelis, Dispersal asymmetry in a two-patch system with source-sink populations, Theor. Popul. Biol., 131 (2020), 54-65.   Google Scholar

[28]

B. ZhangK. AlexM. L. KeenanZ. LuL. R. ArrixW.-M. NiD.L. DeAngelis and J. D. Dyken, Carrying capacity in a heterogeneous environment with habitat connectivity, Ecol. Lett., 20 (2017), 1118-1128.   Google Scholar

[29]

B. ZhangD. L. DeAngelisW. M. NiY. WangL. ZhaiA. KulaS. Xu and J. D. Van Dyken, Effect of stressors on the carrying capacity of spatially-distributed metapopulations, The American Naturalis, 196 (2020), 46-60.   Google Scholar

[30]

B. ZhangX. LiuD. L. DeAngelisW.-M. Ni and G. G. Wang, Effects of dispersal on total biomass in a patchy, heterogeneous system: Analysis and experiment, Math. Biosci., 264 (2015), 54-62.  doi: 10.1016/j.mbs.2015.03.005.  Google Scholar

Figure 1.  Effect of dispersal $ D_1 $ on dynamics of system (1). Fix $ r = 1, r_1 = 0.2, r_2 = 0.1, a_{12} = 0.9, a_{21} = 0.5, b = 1, K = 4, D_2 = 0.1 $. (a-b) Let $ D_1 = 0.03 $ and $ D_1 = 0.15 $, respectively. The resource and consumer coexist in periodic oscillations, while the amplitude decreases with the increase of $ D_1 $. (c) Let $ D_1 = 0.28 $. The resource and consumer coexist at a steady state $ P^*( 2.1250, 1.6272, 2.2787) $. (d) Let $ D_1 = 0.9 $. The consumer goes to extinction even though the resource species persists
Figure 2.  Dynamics of system (1). Let $ r = 1, r_1 = 0.2, r_2 = 0.1, a_{12} = 0.9, a_{21} = 0.8, b = 1, K = 4, D_1 = 0.3, D_2 = 0.1 $. Numerical simulations display that all positive solutions (except $ P^* $) of systems (1) converge to the unique limit cycle and exhibit periodic oscillations
Figure 3.  Comparison of $ T_1(D_1, D_2) $ and $ T_0 $. The red and black lines represent $ T_1 $ and $ T_0 $, respectively. Fix $ r = 1, r_1 = 0.2, r_2 = 0.1, a_{12} = 2, a_{21} = 0.8, b = 1, K = 2 $. (a) Fix $ D_2 = 0.1 $ but let $ D_1 $ vary. $ T_1 $ approaches its maximum $ T_{1max} = 1.5089 $ at $ \bar{D_1} = 0.4276 $, and the curve is hump-shaped. We have $ T_1>T_0 $ if $ D_1<0.6305 $; $ T_1<T_0 $ if $ D_1>0.6305 $ as shown in Proposition 4(ii). (b) Fix $ D_1 = 0.86 $ but let $ D_2 $ vary. $ T_1 $ approaches its maximum $ T_{1max} = 1.5089 $ at $ \bar{D_2} = 0.3023 $. The curve is hump-shaped if $ D_2<0.6014 $ but is convex if $ D_2>0.6014 $. We have $ T_1>T_0 $ if $ D_2>\hat{D_2} = 0.1728 $; $ T_1<T_0 $ if $ D_2<0.1728 $
Figs. 3a-b, i.e., when $ D_2 $ is fixed, the surface becomes Fig. 3a; when $ D_1 $ is fixed, the surface becomes Fig. 3b">Figure 4.  The surface of $ T_1(D_1, D_2) $ when both $ D_1 $ and $ D_2 $ vary. Fix $ r = 1, r_1 = 0.2, r_2 = 0.1, a_{12} = 2, a_{21} = 0.8, b = 1, K = 2 $. Then $ T_1 $ approaches its maximum $ T_{1max} = 1.5089 $ at a line $ D_1 = 0.213+2.137D_2 $, as shown in proposition 5(iii). This figure provides an intuition of the surface of $ T_1 = T_1(D_1, D_2) $, which is a combination of Figs. 3a-b, i.e., when $ D_2 $ is fixed, the surface becomes Fig. 3a; when $ D_1 $ is fixed, the surface becomes Fig. 3b
Figure 5.  Dynamics of system (1). Fix $ r = 1, r_1 = 0.2, r_2 = 0.1, a_{12} = 0.9, b = 1, K = 4, D_1 = 0.28, D_2 = 0.1 $. (a) Let $ a_{21} = 0.4 $. The consumer goes to extinction even though the resource species persists. (b) Let $ a_{21} = 0.5 $. The resource and consumer coexist at a steady state $ P^*(2.1250, 1.6272, 2.2787) $. (c-d) Let $ a_{21} = 0.65 $ and $ a_{21} = 0.8 $, respectively. The resource and consumer coexist in periodic oscillation, while the amplitude increases with the increase of $ a_{21} $
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