Pattern formation in a predator-mediated coexistence model with prey-taxis

Evan C. Haskell; Jonathan Bell

doi:10.3934/dcdsb.2020045

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August 2020, 25(8): 2895-2921. doi: 10.3934/dcdsb.2020045

Pattern formation in a predator-mediated coexistence model with prey-taxis

Evan C. Haskell ^1, and Jonathan Bell ^2,

1.	Department of Mathematics, Nova Southeastern University, Fort Lauderdale, FL, USA
2.	Department of Mathematics and Statistics, University of Maryland Baltimore County, Baltimore, MD, USA

Received October 2018 Revised September 2019 Published August 2020 Early access February 2020

Can foraging by predators or a repulsive prey defense mechanism upset predator-mediated coexistence? This paper investigates one scenario involving a prey-taxis by a prey species. We study a system of three populations involving two competing species with a common predator. All three populations are mobile via random dispersal within a bounded spatial domain $ \Omega $, but the predator's movement is influenced by one prey's gradient representing a repulsive effect on the predator. We prove existence of positive solutions, and investigate pattern formation through bifurcation analysis and numerical simulation.

Keywords: Competing species, predator prey, prey-taxis, chemorepulsion, pattern formation, bifurcation.

Mathematics Subject Classification: 35K59, 35Q92, 92D25.

Citation: Evan C. Haskell, Jonathan Bell. Pattern formation in a predator-mediated coexistence model with prey-taxis. Discrete and Continuous Dynamical Systems - B, 2020, 25 (8) : 2895-2921. doi: 10.3934/dcdsb.2020045

References:

[1]	B. E. Ainseba, M. Bendahmane and A. Noussair, A reaction-diffusion system modeling predator-prey with prey-taxis, Nonlinear Anal. Real World Appl., 9 (2008), 2086-2105. doi: 10.1016/j.nonrwa.2007.06.017.
[2]	N. D. Alikakos, $L^{p}$ bounds of solutions of reaction-diffusion equations, Comm. Partial Differential Equations, 4 (1979), 827-868. doi: 10.1080/03605307908820113.
[3]	M. S. Alnæs, J. Blechta, J. Hake, A. Johansson and B. Kehlet, et al., The FEniCS project, version 1.5, Archive Numerical Software, 3. doi: 10.11588/ans.2015.100.20553.
[4]	H. Amann, Nonhomogeneous linear and quasilinear elliptic and parabolic boundary value problems, in Function Spaces, Differential Operators and Nonlinear Analysis, Teubner-Texte Math., 133, Teubner, Stuttgart, 1993, 9–126. doi: 10.1007/978-3-663-11336-2_1.
[5]	H. Amann, Dynamic theory of quasilinear parabolic equations. Ⅱ: Reaction-diffusion systems, Differential Integral Equations, 3 (1990), 13-75.
[6]	B. Ayuso and L. D. Marini, Discontinuous Galerkin methods for advection-diffusion-reaction problems, SIAM J. Numer. Anal., 47 (2009), 1391-1420. doi: 10.1137/080719583.
[7]	M. Bendahmane, Analysis of a reaction-diffusion system modeling predator-prey with prey-taxis, Netw. Heterog. Media, 3 (2008), 863-879. doi: 10.3934/nhm.2008.3.863.
[8]	M. Burger and J.-F. Pietschmann, Flow characteristics in a crowded transport model, Nonlinearity, 29 (2016), 3528-3550. doi: 10.1088/0951-7715/29/11/3528.
[9]	G. Caristi, K. P. Rybakowski and T. Wessolek, Persistence and spatial patterns in a onepredator-two-prey Lotka-Volterra model with diffusion, Ann. Mat. Pura Appl. (4), 161 (1992), 345–377. doi: 10.1007/BF01759645.
[10]	J. M. Chase, P. A. Abrams, J. P. Grover, S. Diehl and P. Chesson, et al., The interaction between predation and competition: A review and synthesis, Ecology Lett., 5 (2002), 302–315. doi: 10.1046/j.1461-0248.2002.00315.x.
[11]	A. Chertock, A. Kurganov, X. Wang and Y. Wu, On a chemotaxis model with saturated chemotactic flux, Kinet. Relat. Models, 5 (2012), 51-95. doi: 10.3934/krm.2012.5.51.
[12]	C. Cosner, Reaction-diffusion-advection models for the effects and evolution of dispersal, Discrete Contin. Dyn. Syst., 34 (2014), 1701-1745. doi: 10.3934/dcds.2014.34.1701.
[13]	N. Cramer and R. May, Interspecific competition, predation and species diversity: A comment, J. Theoretical Biology, 34 (1972), 289-293. doi: 10.1016/0022-5193(72)90162-2.
[14]	D. A. Di Pietro and A. Ern, Mathematical Aspects of Discontinuous Galerkin Methods, Mathematics & Applications, 69, Springer, Heidelberg, 2012. doi: 10.1007/978-3-642-22980-0.
[15]	W. Feng, Coexistence, stability, and limiting behavior in a one-predator-two-prey model, J. Math. Anal. Appl., 179 (1993), 592-609. doi: 10.1006/jmaa.1993.1371.
[16]	C. Gai, Q. Wang and J. Yan, Qualitative analysis of a Lotka-Volterra competition system with advection, Discrete Contin. Dyn. Syst., 35 (2015), 1239-1284. doi: 10.3934/dcds.2015.35.1239.
[17]	T. Hillen and K. J. Painter, A user's guide to PDE models for chemotaxis, J. Math. Biol., 58 (2009), 183-217. doi: 10.1007/s00285-008-0201-3.
[18]	D. Horstmann et al., From 1970 until present: The Keller-Segel model in chemotaxis and its consequences, I, Jahresber. Deutsch. Math.-Verein., 105 (2003), 103–165.
[19]	S. Hsu, Predator-mediated coexistence and extinction, Math. Biosci., 54 (1981), 231-248. doi: 10.1016/0025-5564(81)90088-2.
[20]	G. E. Hutchinson, The paradox of the plankton, Amer. Naturalist, 95 (1961), 137-145. doi: 10.1086/282171.
[21]	H.-Y. Jin and Z.-A. Wang, Boundedness, blowup and critical mass phenomenon in competing chemotaxis, J. Differential Equations, 260 (2016), 162-196. doi: 10.1016/j.jde.2015.08.040.
[22]	P. Kareiva and G. Odell, Swarms of predators exhibit "preytaxis" if individual predators use area-restricted search, Amer. Naturalist, 130 (1987), 233-270. doi: 10.1086/284707.
[23]	P. Korman and A. W. Leung, A general monotone scheme for elliptic systems with applications to ecological models, Proc. Roy. Soc. Edinburgh Sect. A, 102 (1986), 315-325. doi: 10.1017/S0308210500026391.
[24]	A. Kurganov and M. Lukáčová-Medvid'ová, Numerical study of two-species chemotaxis models, Discrete Contin. Dyn. Syst. Ser. B, 19 (2014), 131-152. doi: 10.3934/dcdsb.2014.19.131.
[25]	N. Lakos, Existence of steady-state solutions for a one-predator-two-prey system, SIAM J. Math. Anal., 21 (1990), 647-659. doi: 10.1137/0521034.
[26]	J. Lee, T. Hillen and M. Lewis, Pattern formation in prey-taxis systems, J. Biol. Dyn., 3 (2009), 551-573. doi: 10.1080/17513750802716112.
[27]	C. Li and Z. Hong, Global existence of classical solutions to a three-species predator-prey model with two prey-taxes, J. Appl. Math., 2012, 12pp. doi: 10.1155/2012/702603.
[28]	Y. Li, K. Lin and C. Mu, Asymptotic behavior for small mass in an attraction-repulsion chemotaxis system, Electronic J. Differential Equations, 2015 (2015), 1-13.
[29]	A. Logg, K.-A. Mardal and G. N. Wells, Automated Solution of Differential Equations by the Finite Element Method. The FEniCS Book, Lecture Notes in Computational Science and Engineering, 84, Springer, Heidelberg, 2012. doi: 10.1007/978-3-642-23099-8.
[30]	R. M. May, Stability in multispecies community models, Math. Biosci., 12 (1971), 59-79. doi: 10.1016/0025-5564(71)90074-5.
[31]	J. Murray, Mathematical Biology. I: An Introduction, Interdisciplinary Applied Mathematics, 17, Springer-Verlag, New York, 2002. doi: 10.1007/b98868.
[32]	R. T. Paine, Food web complexity and species diversity, Amer. Naturalist, 100 (1966), 65-75. doi: 10.1086/282400.
[33]	J. Parrish and S. Saila, Interspecific competition, predation and species diversity, J. Theoretical Biology, 27 (1970), 207-220. doi: 10.1016/0022-5193(70)90138-4.
[34]	J. I. Tello and D. Wrzosek, Predator-prey model with diffusion and indirect prey-taxis, Math. Models Methods Appl. Sci., 26 (2016), 2129-2162. doi: 10.1142/S0218202516400108.
[35]	J. I. Tello and D. Wrzosek, Inter-species competition and chemorepulsion, J. Math. Anal. Appl., 459 (2018), 1233-1250. doi: 10.1016/j.jmaa.2017.11.021.
[36]	S. Vȧge, G. Bratbak, J. Egge, M. Heldal and A. Larsen, et al., Simple models combining competition, defence and resource availability have broad implications in pelagic microbial food webs, Ecology Lett., 21 (2018), 1440–1452. doi: 10.1111/ele.13122.
[37]	X. Wang, W. Wang and G. Zhang, Global bifurcation of solutions for a predator–prey model with prey-taxis, Math. Methods Appl. Sci., 38 (2015), 431-443. doi: 10.1002/mma.3079.
[38]	X. Wang and X. Zou, Pattern formation of a predator-prey model with the cost of anti-predator behaviors, Math. Biosci. Eng., 15 (2018), 775-805. doi: 10.3934/mbe.2018035.
[39]	Z. Wang and T. Hillen, Classical solutions and pattern formation for a volume filling chemotaxis model, Chaos, 17 (2007), 13pp. doi: 10.1063/1.2766864.
[40]	M. Winkler, Aggregation vs. global diffusive behavior in the higher-dimensional Keller–Segel model, J. Differential Equations, 248 (2010), 2889-2905. doi: 10.1016/j.jde.2010.02.008.
[41]	D. Wrzosek, Global attractor for a chemotaxis model with prevention of overcrowding, Nonlinear Anal., 59 (2004), 1293-1310. doi: 10.1016/j.na.2004.08.015.
[42]	S. Wu, J. Shi and B. Wu, Global existence of solutions and uniform persistence of a diffusive predator-prey model with prey-taxis, J. Differential Equations, 260 (2016), 5847-5874. doi: 10.1016/j.jde.2015.12.024.

show all references

References:

[1]	B. E. Ainseba, M. Bendahmane and A. Noussair, A reaction-diffusion system modeling predator-prey with prey-taxis, Nonlinear Anal. Real World Appl., 9 (2008), 2086-2105. doi: 10.1016/j.nonrwa.2007.06.017.
[2]	N. D. Alikakos, $L^{p}$ bounds of solutions of reaction-diffusion equations, Comm. Partial Differential Equations, 4 (1979), 827-868. doi: 10.1080/03605307908820113.
[3]	M. S. Alnæs, J. Blechta, J. Hake, A. Johansson and B. Kehlet, et al., The FEniCS project, version 1.5, Archive Numerical Software, 3. doi: 10.11588/ans.2015.100.20553.
[4]	H. Amann, Nonhomogeneous linear and quasilinear elliptic and parabolic boundary value problems, in Function Spaces, Differential Operators and Nonlinear Analysis, Teubner-Texte Math., 133, Teubner, Stuttgart, 1993, 9–126. doi: 10.1007/978-3-663-11336-2_1.
[5]	H. Amann, Dynamic theory of quasilinear parabolic equations. Ⅱ: Reaction-diffusion systems, Differential Integral Equations, 3 (1990), 13-75.
[6]	B. Ayuso and L. D. Marini, Discontinuous Galerkin methods for advection-diffusion-reaction problems, SIAM J. Numer. Anal., 47 (2009), 1391-1420. doi: 10.1137/080719583.
[7]	M. Bendahmane, Analysis of a reaction-diffusion system modeling predator-prey with prey-taxis, Netw. Heterog. Media, 3 (2008), 863-879. doi: 10.3934/nhm.2008.3.863.
[8]	M. Burger and J.-F. Pietschmann, Flow characteristics in a crowded transport model, Nonlinearity, 29 (2016), 3528-3550. doi: 10.1088/0951-7715/29/11/3528.
[9]	G. Caristi, K. P. Rybakowski and T. Wessolek, Persistence and spatial patterns in a onepredator-two-prey Lotka-Volterra model with diffusion, Ann. Mat. Pura Appl. (4), 161 (1992), 345–377. doi: 10.1007/BF01759645.
[10]	J. M. Chase, P. A. Abrams, J. P. Grover, S. Diehl and P. Chesson, et al., The interaction between predation and competition: A review and synthesis, Ecology Lett., 5 (2002), 302–315. doi: 10.1046/j.1461-0248.2002.00315.x.
[11]	A. Chertock, A. Kurganov, X. Wang and Y. Wu, On a chemotaxis model with saturated chemotactic flux, Kinet. Relat. Models, 5 (2012), 51-95. doi: 10.3934/krm.2012.5.51.
[12]	C. Cosner, Reaction-diffusion-advection models for the effects and evolution of dispersal, Discrete Contin. Dyn. Syst., 34 (2014), 1701-1745. doi: 10.3934/dcds.2014.34.1701.
[13]	N. Cramer and R. May, Interspecific competition, predation and species diversity: A comment, J. Theoretical Biology, 34 (1972), 289-293. doi: 10.1016/0022-5193(72)90162-2.
[14]	D. A. Di Pietro and A. Ern, Mathematical Aspects of Discontinuous Galerkin Methods, Mathematics & Applications, 69, Springer, Heidelberg, 2012. doi: 10.1007/978-3-642-22980-0.
[15]	W. Feng, Coexistence, stability, and limiting behavior in a one-predator-two-prey model, J. Math. Anal. Appl., 179 (1993), 592-609. doi: 10.1006/jmaa.1993.1371.
[16]	C. Gai, Q. Wang and J. Yan, Qualitative analysis of a Lotka-Volterra competition system with advection, Discrete Contin. Dyn. Syst., 35 (2015), 1239-1284. doi: 10.3934/dcds.2015.35.1239.
[17]	T. Hillen and K. J. Painter, A user's guide to PDE models for chemotaxis, J. Math. Biol., 58 (2009), 183-217. doi: 10.1007/s00285-008-0201-3.
[18]	D. Horstmann et al., From 1970 until present: The Keller-Segel model in chemotaxis and its consequences, I, Jahresber. Deutsch. Math.-Verein., 105 (2003), 103–165.
[19]	S. Hsu, Predator-mediated coexistence and extinction, Math. Biosci., 54 (1981), 231-248. doi: 10.1016/0025-5564(81)90088-2.
[20]	G. E. Hutchinson, The paradox of the plankton, Amer. Naturalist, 95 (1961), 137-145. doi: 10.1086/282171.
[21]	H.-Y. Jin and Z.-A. Wang, Boundedness, blowup and critical mass phenomenon in competing chemotaxis, J. Differential Equations, 260 (2016), 162-196. doi: 10.1016/j.jde.2015.08.040.
[22]	P. Kareiva and G. Odell, Swarms of predators exhibit "preytaxis" if individual predators use area-restricted search, Amer. Naturalist, 130 (1987), 233-270. doi: 10.1086/284707.
[23]	P. Korman and A. W. Leung, A general monotone scheme for elliptic systems with applications to ecological models, Proc. Roy. Soc. Edinburgh Sect. A, 102 (1986), 315-325. doi: 10.1017/S0308210500026391.
[24]	A. Kurganov and M. Lukáčová-Medvid'ová, Numerical study of two-species chemotaxis models, Discrete Contin. Dyn. Syst. Ser. B, 19 (2014), 131-152. doi: 10.3934/dcdsb.2014.19.131.
[25]	N. Lakos, Existence of steady-state solutions for a one-predator-two-prey system, SIAM J. Math. Anal., 21 (1990), 647-659. doi: 10.1137/0521034.
[26]	J. Lee, T. Hillen and M. Lewis, Pattern formation in prey-taxis systems, J. Biol. Dyn., 3 (2009), 551-573. doi: 10.1080/17513750802716112.
[27]	C. Li and Z. Hong, Global existence of classical solutions to a three-species predator-prey model with two prey-taxes, J. Appl. Math., 2012, 12pp. doi: 10.1155/2012/702603.
[28]	Y. Li, K. Lin and C. Mu, Asymptotic behavior for small mass in an attraction-repulsion chemotaxis system, Electronic J. Differential Equations, 2015 (2015), 1-13.
[29]	A. Logg, K.-A. Mardal and G. N. Wells, Automated Solution of Differential Equations by the Finite Element Method. The FEniCS Book, Lecture Notes in Computational Science and Engineering, 84, Springer, Heidelberg, 2012. doi: 10.1007/978-3-642-23099-8.
[30]	R. M. May, Stability in multispecies community models, Math. Biosci., 12 (1971), 59-79. doi: 10.1016/0025-5564(71)90074-5.
[31]	J. Murray, Mathematical Biology. I: An Introduction, Interdisciplinary Applied Mathematics, 17, Springer-Verlag, New York, 2002. doi: 10.1007/b98868.
[32]	R. T. Paine, Food web complexity and species diversity, Amer. Naturalist, 100 (1966), 65-75. doi: 10.1086/282400.
[33]	J. Parrish and S. Saila, Interspecific competition, predation and species diversity, J. Theoretical Biology, 27 (1970), 207-220. doi: 10.1016/0022-5193(70)90138-4.
[34]	J. I. Tello and D. Wrzosek, Predator-prey model with diffusion and indirect prey-taxis, Math. Models Methods Appl. Sci., 26 (2016), 2129-2162. doi: 10.1142/S0218202516400108.
[35]	J. I. Tello and D. Wrzosek, Inter-species competition and chemorepulsion, J. Math. Anal. Appl., 459 (2018), 1233-1250. doi: 10.1016/j.jmaa.2017.11.021.
[36]	S. Vȧge, G. Bratbak, J. Egge, M. Heldal and A. Larsen, et al., Simple models combining competition, defence and resource availability have broad implications in pelagic microbial food webs, Ecology Lett., 21 (2018), 1440–1452. doi: 10.1111/ele.13122.
[37]	X. Wang, W. Wang and G. Zhang, Global bifurcation of solutions for a predator–prey model with prey-taxis, Math. Methods Appl. Sci., 38 (2015), 431-443. doi: 10.1002/mma.3079.
[38]	X. Wang and X. Zou, Pattern formation of a predator-prey model with the cost of anti-predator behaviors, Math. Biosci. Eng., 15 (2018), 775-805. doi: 10.3934/mbe.2018035.
[39]	Z. Wang and T. Hillen, Classical solutions and pattern formation for a volume filling chemotaxis model, Chaos, 17 (2007), 13pp. doi: 10.1063/1.2766864.
[40]	M. Winkler, Aggregation vs. global diffusive behavior in the higher-dimensional Keller–Segel model, J. Differential Equations, 248 (2010), 2889-2905. doi: 10.1016/j.jde.2010.02.008.
[41]	D. Wrzosek, Global attractor for a chemotaxis model with prevention of overcrowding, Nonlinear Anal., 59 (2004), 1293-1310. doi: 10.1016/j.na.2004.08.015.
[42]	S. Wu, J. Shi and B. Wu, Global existence of solutions and uniform persistence of a diffusive predator-prey model with prey-taxis, J. Differential Equations, 260 (2016), 5847-5874. doi: 10.1016/j.jde.2015.12.024.

Figure 1. A plot of $ \chi $ versus $ k^{2} $. The red line is $ \chi_{c} $ as a function of $ k^{2} $. The blue dashed line shows $ a(k^{2})b(k^{2}, \chi)-c(k^{2}, \chi) = 0 $. Values of ($ k^{2}, \chi $) above (below) the blue dashed line correspond to $ a(k^{2})b(k^{2}, \chi)-c(k^{2}, \chi) $ positive (negative). This is an example where we use the parameter set: $ (a_{1}, a_{2}, b_{1}, b_{2}, r, \varepsilon, \mu, d) = (0.70, 0.70, 0.10, 0.30, 1.39, 0.30, 0.70, 0.25) $ Hence, $ a_{1}b_{2}-rb_{1} = 0.07 $, so $ k_{min}^{2} = 0.26 $

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Figure 2. A plot of $ \chi_{c_{1}} $ and $ \chi_{c_{2}} $ versus $ k^{2} $. $ \chi_{c_{1}} $ (red line) and $ \chi_{c_{2}} $ (blue dashed line) as functions of $ k^{2} $ are plotted for the parameter set: $ (a_{1}, a_{2}, b_{1}, b_{2}, r, \varepsilon, \mu) = (0.10, 0.20, 0.10, 1.50, 3.0, 0.6, 0.1) $

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Figure 3. Case 1: $ u $ "wins": In this example the parameters are chosen so that in the pure competition system $ u $ "wins". Note that while parameters are such that in the absence of a repulsive prey defense mechanism by $ u $, a predator mediated coexistence is expected; yet, once this state is disrupted $ v $ is driven to extinction in the presence of this taxis mechanism. Parameter set for this figure is: $ (a_{1}, a_{2}, b_{1}, b_{2}, r, \varepsilon, \mu, \chi, d) = (0.25, 0.75, 0.75, 0.25, 0.6875, 0.5, 0.25, 15, 0.5) $

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Figure 4. Case 2: $ v $ "wins": In this example the parameters are chosen so that in the pure competition system $ v $ "wins". Note that while parameters are such that in the absence of a repulsive prey defense mechanism by $ u $, a predator mediated coexistence is expected; yet, once this state is disrupted, the prey coexist with peak densities out of phase with the predator in the presence of this taxis mechanism. Parameter set for this figure is: $ (a_{1}, a_{2}, b_{1}, b_{2}, r, \varepsilon, \mu, \chi, d) = (1.1, 0.1, 1.4, 0.3, 0.4, 0.4, 0.2, 50, 0.25) $

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Figure 5. Spatio-temporal patterns: $ v $ "wins" case. The first column shows the initial dynamics from the perturbation of the homogeneous steady state and the second column shows the spatio-temporal pattern that is achieved. In the last row $ u(x, t) $, $ v(x, t) $, and $ w(x, t) $ are given by the blue, green, and red lines respectively. The parameter set for this figure is: $ (a_{1}, a_{2}, b_{1}, b_{2}, r, \varepsilon, \mu, \chi, d) = (1.25, 1.0, 0.25, 0.5, 1.25, 0.5, 0.25, 25, 0.1) $

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Figure 6. Spatio-temporal patterns: $ u $ "wins" case. The first column shows the initial dynamics from the perturbation of the homogeneous steady state and the second column shows the spatio-temporal pattern that is achieved. In the last row $ u(x, t) $, $ v(x, t) $, and $ w(x, t) $ are given by the blue, green, and red lines respectively. The parameter set for this figure is: $ (a_{1}, a_{2}, b_{1}, b_{2}, r, \varepsilon, \mu, \chi, d) = (0.9, 1.1, 0.2, 0.2, 1.09, 0.5, 1.0, 16, 0.25) $

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Figure 7. Attracting ($ \chi < 0 $) case: In this example the parameters are chosen so that in the pure competition system $ v $ "wins". Note that while parameters are such that in the absence of a attracing prey-taxis mechanism by $ u $, a predator mediated coexistence is expected; yet, once this state is disrupted, the prey coexist with peak densities out of phase with the predator in the presence of this taxis mechanism. Parameter set for this figure is: $ (a_{1}, a_{2}, b_{1}, b_{2}, r, \varepsilon, \mu, \chi, d) = (1.25, 1.00, 0.25, 0.50, 1.25, 0.5, 0.25, -0.1, 10) $

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2021 Impact Factor: 1.497

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2021 Impact Factor: 1.497

American Institute of Mathematical Sciences

Pattern formation in a predator-mediated coexistence model with prey-taxis

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American Institute of Mathematical Sciences

Pattern formation in a predator-mediated coexistence model with prey-taxis

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