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On the influence of cross-diffusion in pattern formation

  • * Corresponding author: Maxime Breden

    * Corresponding author: Maxime Breden 

MB and CK have been supported by a Lichtenberg Professorship of the VolkswagenStiftung. CS has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska–Curie grant agreement No. 754462. Support by INdAM-GNFM is gratefully acknowledged by CS

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  • In this paper we consider the Shigesada-Kawasaki-Teramoto (SKT) model to account for stable inhomogeneous steady states exhibiting spatial segregation, which describe a situation of coexistence of two competing species. We provide a deeper understanding on the conditions required on both the cross-diffusion and the reaction coefficients for non-homogeneous steady states to exist, by combining a detailed linearized analysis with advanced numerical bifurcation methods via the continuation software $\mathtt{pde2path}$. We report some numerical experiments suggesting that, when cross-diffusion is taken into account, there exist positive and stable non-homogeneous steady states outside of the range of parameters for which the coexistence homogeneous steady state is positive. Furthermore, we also analyze the case in which self-diffusion terms are considered.

    Mathematics Subject Classification: Primary: 35Q92, 92D25; Secondary: 35K59, 65P30.

    Citation:

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  • Figure 1.  Sign of the quantities $ \alpha $ and $ \beta $ in the weak competition regime (2), depending on the value of $ r_1/r_2 $

    Figure 2.  Sign of the quantities $ \alpha $ and $ \beta $ in the strong competition casek__ge (3), depending on the value of $ r_1/r_2 $

    Figure 3.  Bifurcation diagrams represented with different quantities in the weak competition case (first parameter set in Table 1, with $ d_{12} = 3 $ and $ d_{21} = 0 $). (A) "Usual" bifurcation diagram with respect to $ v(0) $. (B) Bifurcation diagram with respect to $ ||u||_{L^2} $

    Figure 4.  Different types of stable solutions on the blue and red branches of the bifurcation diagram 3. The densities of $ u $ and $ v $ on the domain are denoted with black and blue lines respectively

    Figure 5.  Bifurcation diagrams for different values of the cross-diffusion coefficient $ d_{21} $ in the weak competition case (first parameter set in Table 1, with $ d_{12} = 3 $). Notice that the range of values of $ d $ for which non-trivial solutions exist is getting smaller and smaller when $ d_{21} $ increases (especially compared to Figure 3b)

    Figure 6.  Different types of stable solutions coexisting with the homogeneous one. Species $ u $ and $ v $ on the domain are denoted with black and blue lines respectively. Figures (A) and (C) refer to $ d_{21} = 0.045 $ and correspond to solutions on the pastel blue and red branches of the bifurcation diagram in Figure 5a. Figures (B) and (D) refer to $ d_{21} = 0.055 $ and correspond to solutions on the pastel blue and pastel red branches of the bifurcation diagram in Figure 5b

    Figure 7.  Bifurcation diagrams for different values of the cross-diffusion coefficient $ d_{21} $ in the weak competition case (first parameter set in Table 1, with $ d_{12} = 3 $). We start in Figure 7a with a zoom in of Figure 3b, and then slowly increase $ d_{21} $

    Figure 8.  Solutions belonging to different branches when $ d_{12} = 1000 $. The species $ u $ and $ v $ are denoted with black and blue lines respectively, while the red line corresponds to $ uv $. As in the bifurcation diagrams, thin lines indicate unstable solutions, while thicker lines corresponds to stable ones. The red dotted line represents the product $ uv $

    Figure 9.  Bifurcation diagrams for different values of the cross-diffusion parameter $ d_{21} $ in the strong competition case (second parameter set in Table 1, with $ d_{12} = 3 $)

    Figure 10.  Evolution of the first bifurcation branch for increasing values of $ d_{21}\in [0,1000] $ (darker the blue, greater the value), corresponding to the second parameter set in Table 1, with $ d_{12} = 3 $

    Figure 11.  Bifurcation diagram and some solutions (third parameter set in Table 1 and $ d_{12}\; = \; d_{21} = 100 $). (A) Bifurcation diagram with respect to the parameter $ d $. Yellow points indicate the positions on the branches at which solutions are shown. (B)–(D) Solutions $ u(x),\;v(x) $ for different values of $ d $ (black lines correspond to species $ u $, while blue lines to species $ v $)

    Figure 12.  Evolution of the first bifurcation branch for increasing values of $ d_{12}\in [0,3] $ (darker the blue, greater the value), corresponding to the fourth parameter set in Table 1, with $ d_{21} = 0 $

    Figure 13.  Bifurcation diagrams with bifurcation parameter $ r_1 $. (A) Weak competition case with $ d = 0.005 $ and the other parameter values as in the first parameter set in Table 1, with $ d_{12} = 3 $ and $ d_{21} = 0 $. (B) Strong competition case with $ d = 0.05 $ and the other parameter values as in the second parameter set in Table 1, with $ d_{12} = 3 $ and $ d_{21} = 0 $

    Figure 14.  Stable non-homogeneous solutions appearing beyond the usual weak or strong competitions regimes (species $ u $ and $ v $ are denoted with black and blue lines respectively). Weak competition case ($ d = 0.005 $, the other parameter values as in the first parameter set in Table 1, with $ d_{12} = 3 $ and $ d_{21} = 0 $): (A), (C) stable solutions with $ r_1 = 7.5 $, (E) stable solution with $ r_1 = 15 $. Strong competition case ($ d = 0.05 $, the other parameter values as in the second parameter set in Table 1, with $ d_{12} = 3 $ and $ d_{21} = 0 $): (B), (D) stable solutions with $ r_1 = 10 $, (F) stable solution with $ r_1 = 18 $. The colors of the branches refer to Figure 13

    Figure 15.  Bifurcation diagrams with bifurcation parameter $ d $ obtained with the first parameter set in Table 1 (weak competition case), with $ d_{12} = 3 $, $ d_{21} = 0 $, $ d_{11} = 0 $ and different values of the self-diffusion coefficient $ d_{22} $

    Table 1.  The parameter sets used in the numerical simulations presented in this section

    $ r_1 $ $ r_2 $ $ a_1 $ $ a_2 $ $ b_1 $ $ b_2 $ regime α β
    5 2 3 3 1 1 weak competition + - [5,24,27]
    2 5 1 1 0.5 3 strong competition + 0 [27]
    15/2 16/7 4 2 6 1 weak competition - +
    5 5 2 3 5 4 strong competition + +
     | Show Table
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    Table 2.  Convergence of the first three bifurcation points, numerically detected, to the predicted limited values $ \alpha/(v_*\lambda_k) $ when $ d_{12} $ increases ($ d_{21} = 0 $)

    $ d_{12} $ $ d^1_{ \text{bif}} $ $ d^2_{ \text{bif}} $ $ d^3_{ \text{bif}} $
    3 0.0328 0.0205 0.0113
    10 0.0762 0.0273 0.0131
    100 0.1190 0.0311 0.0139
    1000 0.1258 0.0315 0.0140
    $ d^k_{ \text{bif},\infty} $ 0.1267 0.0317 0.0141
     | Show Table
    DownLoad: CSV
  • [1] H. Amann, Dynamic theory of quasilinear parabolic equations. Ⅰ. Abstract evolution equations, Nonlinear Anal., 12 (1988), 895-919.  doi: 10.1016/0362-546X(88)90073-9.
    [2] H. Amann, Dynamic theory of quasilinear parabolic equations. Ⅱ. Reaction–diffusion systems, Differential and Integral Equations, 3 (1990), 13-75. 
    [3] J. Benson and B. Patterson, Inter-specific territoriality in a Canis hybrid zone: Spatial segregation between wolves, coyotes, and hybrids, Oecologia, 173 (2013), 1539-1550.  doi: 10.1007/s00442-013-2730-8.
    [4] V. Biktashev and M. Tsyganov, Quasisolitons in self-diffusive excitable systems, or Why asymmetric diffusivity obeys the Second Law, Scientific Reports, 6 (2016), 1-8.  doi: 10.1038/srep30879.
    [5] M. Breden and R. Castelli, Existence and instability of steady states for a triangular cross-diffusion system: A computer-assisted proof, J. Differential Equations, 264 (2018), 6418-6458.  doi: 10.1016/j.jde.2018.01.033.
    [6] M. Breden, C. Kuehn and C. Soresina, On the influence of cross-diffusion in pattern formation, supplementary material., Available from: https://github.com/soresina/fullSKT.
    [7] M. BredenJ.-P. Lessard and M. Vanicat, Global bifurcation diagrams of steady states of systems of PDEs via rigorous numerics: A 3-component reaction–diffusion system, Acta Appl. Math., 128 (2013), 113-152.  doi: 10.1007/s10440-013-9823-6.
    [8] J. Cecere, S. Bondì, S. Podofillini, S. Imperio and M. Griggio, et al., Spatial segregation of home ranges between neighbouring colonies in a diurnal raptor, Scientific Reports, 8 (2018). doi: 10.1038/s41598-018-29933-2.
    [9] L. Chen and A. Jüngel, Analysis of a parabolic cross-diffusion population model without self-diffusion, J. Differential Equations, 224 (2006), 39-59.  doi: 10.1016/j.jde.2005.08.002.
    [10] F. Conforto, L. Desvillettes and C. Soresina, About reaction–diffusion systems involving the Holling-type Ⅱ and the Beddington–DeAngelis functional responses for predator–prey models, NoDEA Nonlinear Differential Equations Appl., 25 (2018), 39pp. doi: 10.1007/s00030-018-0515-9.
    [11] L. DesvillettesT. LepoutreA. Moussa and A. Trescases, On the entropic structure of reaction-cross diffusion systems, Comm. Partial Differential Equations, 40 (2015), 1705-1747.  doi: 10.1080/03605302.2014.998837.
    [12] L. Desvillettes and C. Soresina, Non-triangular cross-diffusion systems with predator–prey reaction terms, Ric. Mat., 68 (2019), 295-314.  doi: 10.1007/s11587-018-0403-y.
    [13] L. Desvillettes and A. Trescases, New results for triangular reaction cross diffusion system, J. Math. Anal. Appl., 430 (2015), 32-59.  doi: 10.1016/j.jmaa.2015.03.078.
    [14] J. Diamond, Assembly of species communities, in Ecology and Evolution of Communities, Harvard Univ Press, Cambridge, MA, 1975, 342-444.
    [15] T. Dohnal, J. Rademacher, H. Uecker and D. Wetzel, pde2path 2.0: Multi-parameter continuation and periodic domains, in Proceedings of the 8th European Nonlinear Dynamics Conference, ENOC, 2014 (2014).
    [16] S.-I. Ei and M. Mimura, Pattern formation in heterogeneous reaction–diffusion–advection systems with an application to population dynamics, SIAM J. Math. Anal., 21 (1990), 346-361.  doi: 10.1137/0521019.
    [17] G. GalianoM. L. Garzón and A. Jüngel, Semi-discretization in time and numerical convergence of solutions of a nonlinear cross-diffusion population model, Numer. Math., 93 (2003), 655-673.  doi: 10.1007/s002110200406.
    [18] G. GambinoM. C. Lombardo and M. Sammartino, Pattern formation driven by cross-diffusion in a 2D domain, Nonlinear Anal. Real World Appl., 14 (2013), 1755-1779.  doi: 10.1016/j.nonrwa.2012.11.009.
    [19] G. GambinoM. C. Lombardo and M. Sammartino, Turing instability and traveling fronts for a nonlinear reaction–diffusion system with cross-diffusion, Math. Comput. Simulation, 82 (2012), 1112-1132.  doi: 10.1016/j.matcom.2011.11.004.
    [20] D. Henry, Geometric Theory of Semilinear Parabolic Equations, Lecture Notes in Mathematics, 840, Springer-Verlag, Berlin-New York, 1981. doi: 10.1007/BFb0089647.
    [21] L. T. HoangT. V. Nguyen and T. V. Phan, Gradient estimates and global existence of smooth solutions to a cross-diffusion system, SIAM J. Math. Anal., 47 (2015), 2122-2177.  doi: 10.1137/140981447.
    [22] R. HoffmanG. Larson and B. Brokes, Habitat segregation of Ambystoma gracile and Ambystoma macrodactylum in mountain ponds and lakes, Mount Rainier National Park, Washington, USA, J. Herpetology, 37 (2003), 24-34.  doi: 10.1670/0022-1511(2003)037[0024:HSOAGA]2.0.CO;2.
    [23] H. HoiT. Eichler and J. Dittami, Territorial spacing and interspecific competition in three species of reed warblers, Oecologia, 87 (1991), 443-448.  doi: 10.1007/BF00634604.
    [24] M. IidaM. Mimura and H. Ninomiya, Diffusion, cross-diffusion and competitive interaction, J. Math. Biol., 53 (2006), 617-641.  doi: 10.1007/s00285-006-0013-2.
    [25] M. IidaH. Ninomiya and H. Yamamoto, A review on reaction–diffusion approximation, J. Elliptic Parabol. Equ., 4 (2018), 565-600.  doi: 10.1007/s41808-018-0029-y.
    [26] H. Izuhara and S. Kobayashi, Spatio-temporal coexistence in the cross-diffusion competition system, Discrete Contin. Dyn. Syst. Ser. S, 14 (2021), 919-933.  doi: 10.3934/dcdss.2020228.
    [27] H. Izuhara and M. Mimura, Reaction-diffusion system approximation to the cross-diffusion competition system, Hiroshima Math. J., 38 (2008), 315-347.  doi: 10.32917/hmj/1220619462.
    [28] A. Jüngel, Diffusive and nondiffusive population models, in Mathematical Modeling of Collective Behavior in Socio-Economic and Life Sciences, Model. Simul. Sci. Eng. Technol., Birkhäuser Boston, Boston, MA, 2010,397–425. doi: 10.1007/978-0-8176-4946-3_15.
    [29] A. Jüngel, Entropy Methods for Diffusive Partial Differential Equations, SpringerBriefs in Mathematics, Springer, Cham, 2016. doi: 10.1007/978-3-319-34219-1.
    [30] A. JüngelC. Kuehn and L. Trussardi, A meeting point of entropy and bifurcations in cross-diffusion herding, European J. Appl. Math., 28 (2017), 317-356.  doi: 10.1017/S0956792516000346.
    [31] Y. Kan-On, On the limiting system in the Shigesada, Kawasaki and Teramoto model with large cross-diffusion rates, Discrete Contin. Dyn. Syst., 40 (2020), 3561-3570.  doi: 10.3934/dcds.2020161.
    [32] Y. Kan-On, Stability of singularly perturbed solutions to nonlinear diffusion systems arising in population dynamics, Hiroshima Math. J., 23 (1993), 509-536.  doi: 10.32917/hmj/1206392779.
    [33] C. Kennedy, Site segregation by species of Acanthocephala in fish, with special reference to eels, Anguilla anguilla, Parasitology, 90 (1985), 375-390.  doi: 10.1017/S0031182000051076.
    [34] K. Kishimoto and H. F. Weinberger, The spatial homogeneity of stable equilibria of some reaction–diffusion systems on convex domains, J. Differential Equations, 58 (1985), 15-21.  doi: 10.1016/0022-0396(85)90020-8.
    [35] C. Kuehn, Efficient gluing of numerical continuation and a multiple solution method for elliptic PDEs, Appl. Math. Comput., 266 (2015), 656-674.  doi: 10.1016/j.amc.2015.05.120.
    [36] C. Kuehn, PDE Dynamics. An Introduction, Mathematical Modeling and Computation, 23, Society for Industrial and Applied Mathematics (SIAM), Philadelphia, PA, 2019.
    [37] C. Kuehn and C. Soresina, Numerical continuation for a fast reaction system and its cross-diffusion limit, SN Partial Differential Equations Appl., 1 (2020). doi: 10.1007/s42985-020-0008-7.
    [38] S. Levin, Dispersion and population interactions, Amer. Naturalist, 108 (1974), 207-228.  doi: 10.1086/282900.
    [39] Y. Lou and W.-M. Ni, Diffusion, self-diffusion and cross-diffusion, J. Differential Equations, 131 (1996), 79-131.  doi: 10.1006/jdeq.1996.0157.
    [40] Y. Lou and W.-M. Ni, Diffusion vs cross-diffusion: An elliptic approach, J. Differential Equations, 154 (1999), 157-190.  doi: 10.1006/jdeq.1998.3559.
    [41] Y. LouW.-M. Ni and S. Yotsutani, On a limiting system in the Lotka–Volterra competition with cross-diffusion, Discrete Contin. Dyn. Syst., 10 (2004), 435-458.  doi: 10.3934/dcds.2004.10.435.
    [42] Y. LouW.-M. Ni and S. Yotsutani, Pattern formation in a cross-diffusion system, Discrete Contin. Dyn. Syst., 35 (2015), 1589-1607.  doi: 10.3934/dcds.2015.35.1589.
    [43] H. Matano and M. Mimura, Pattern formation in competition-diffusion systems in nonconvex domains, Publ. Res. Inst. Math. Sci., 19 (1983), 1049-1079.  doi: 10.2977/prims/1195182020.
    [44] M. Mimura, Stationary pattern of some density-dependent diffusion system with competitive dynamics, Hiroshima Math. J., 11 (1981), 621-635.  doi: 10.32917/hmj/1206133994.
    [45] M. Mimura and K. Kawasaki, Spatial segregation in competitive interaction-diffusion equations, J. Math. Biol., 9 (1980), 49-64.  doi: 10.1007/BF00276035.
    [46] T. MoriT. Suzuki and S. Yotsutani, Numerical approach to existence and stability of stationary solutions to a SKT cross-diffusion equation, Math. Models Methods Appl. Sci., 28 (2018), 2191-2210.  doi: 10.1142/S0218202518400122.
    [47] W.-M. Ni, Diffusion, cross-diffusion, and their spike-layer steady states, Notices Amer. Math. Soc., 45 (1998), 9-18. 
    [48] W.-M. NiY. Wu and Q. Xu, The existence and stability of nontrivial steady states for S-K-T competition model with cross diffusion, Discrete Contin. Dyn. Syst., 34 (2014), 5271-5298.  doi: 10.3934/dcds.2014.34.5271.
    [49] F. Palomares, N. Fernández, S. Roques, C. Chávez, L. Silveira, C. Keller and B. Adrados, Fine-scale habitat segregation between two ecologically similar top predators, PLoS one, 11 (2016). doi: 10.1371/journal.pone.0155626.
    [50] N. ShigesadaK. Kawasaki and E. Teramoto, Spatial segregation of interacting species, J. Theoret. Biol., 79 (1979), 83-99.  doi: 10.1016/0022-5193(79)90258-3.
    [51] U. Suwanvecho and W. Brockelman, Interspecific territoriality in gibbons (Hylobates lar and H. pileatus) and its effects on the dynamics of interspecies contact zones, Primates, 53 (2012), 97-108.  doi: 10.1007/s10329-011-0284-0.
    [52] C. TianZ. Lin and M. Pedersen, Instability induced by cross-diffusion in reaction-diffusion systems, Nonlinear Anal. Real World Appl., 11 (2010), 1036-1045.  doi: 10.1016/j.nonrwa.2009.01.043.
    [53] H. Uecker, Hopf bifurcation and time periodic orbits with pde2path – Algorithms and applications, Commun. Comput. Phys., 25 (2019), 812-852.  doi: 10.4208/cicp.oa-2017-0181.
    [54] H. UeckerD. Wetzel and J. D. M. Rademacher, pde2path - A Matlab package for continuation and bifurcation in 2D elliptic systems, Numer. Math. Theory Methods Appl., 7 (2014), 58-106.  doi: 10.4208/nmtma.2014.1231nm.
    [55] L. Wauters, G. Tosi and J. Gurnell, A review of the competitive effects of alien grey squirrels on behaviour, activity and habitat use of red squirrels in mixed, deciduous woodland in Italy, Hystrix Italian J. Mammalogy, 16 (2005). doi: 10.4404/hystrix-16.1-4340.
    [56] E. Wilson, Sociobiology: The New Synthesis, Cambridge, 1975.
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