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July  2021, 14(7): 2163-2181. doi: 10.3934/dcdss.2021055

Application of aggregation of variables methods to a class of two-time reaction-diffusion-chemotaxis models of spatially structured populations with constant diffusion

1. 

Laboratoire MAE2D, Université A. ESSAADI, Faculté Polydisciplinaire - B.P 745, Larache, Maroc

2. 

Laboratoire LAREFAG, Ecole Nationale de Commerce et de Gestion, B.P. 1255, Tanger, Maroc

3. 

Sorbonne University, IRD, UMMISCO, F-93143, Bondy, France, LMDP, Université Cadi Ayyad, Faculté des Sciences de Semlalia, Marrakech, Maroc

4. 

IXXI, ENS Lyon, 46 allée d'Italie, 69364 Lyon cedex 7, France

5. 

Sorbonne University, IRD, UMMISCO, F-93143, Bondy, France

* Corresponding author: anouarelharrak1@gmail.com

Received  July 2019 Revised  September 2020 Published  May 2021

The main goal of this paper is to adapt a class of complexity reduction methods called aggregation of variables methods to the construction of reduced models of two-time reaction-diffusion-chemotaxis models of spatially structured populations and to provide an error bound of the approximate dynamics. Aggregation of variables methods are general techniques that allow reducing the dimension of a mathematical dynamical system. Here we reduce a system of Partial Differential Equations to a simpler Ordinary Differential Equation system, provided that the evolution processes occur at two different time scales: a slow one for the demography and a fast one for migrations and chemotaxis, with a ratio $ \varepsilon>0 $ small enough. We give an approximation of the error between solutions of both original and reduced model for a generic function representing the demography. Finally, we provide an optimization of the error bound and validate numerically this result for a spatial inter-specific model with constant diffusion and population growth given by a logistic law in population dynamics.

Citation: Anouar El Harrak, Amal Bergam, Tri Nguyen-Huu, Pierre Auger, Rachid Mchich. Application of aggregation of variables methods to a class of two-time reaction-diffusion-chemotaxis models of spatially structured populations with constant diffusion. Discrete & Continuous Dynamical Systems - S, 2021, 14 (7) : 2163-2181. doi: 10.3934/dcdss.2021055
References:
[1]

W. Arendt, A. Grabosch, G. Greiner, U, MoustakasRainer, N. Schlotterbeck, U. Groh, H. P. Lotz and F. Neubrander, One-Parameter Semigroups of Positive Operators, Lecture Notes in Mathematics, Springer, 2006. doi: 10.1007/BFb0074922.  Google Scholar

[2]

P. Auger and R. Bravo de la Parra, Methods of aggregation of variables in population dynamics Méthodes d'agrégation de variables en dynamique de population, C. R. Acad. Sci. Paris Sér. Ⅲ Sci. Vie, 323 (2000), 665 – 674. doi: 10.1016/S0764-4469(00)00182-7.  Google Scholar

[3]

P. Auger and J.-C. Poggiale, Aggregation and emergence in systems of ordinary differential equations, Math. Comput. Modelling, 27 (1998), 1-21.  doi: 10.1016/S0895-7177(98)00002-8.  Google Scholar

[4]

P. Auger, R. Bravo de la Parra, J.-C. Poggiale, E. Sánchez and T. Nguyen-Huu, Aggregation of variables and applications to population dynamics, Lect. Notes Math., Springer Berlin Heidelberg, (2008), 209–263. doi: 10.1007/978-3-540-78273-5_5.  Google Scholar

[5]

P. M. Auger and R. Roussarie, Complex ecological models with simple dynamics: From individuals to populations, Acta Biotheor., 42 (1994), 111-136.  doi: 10.1007/BF00709485.  Google Scholar

[6]

T. BrochierP. AugerD. ThiaoA. BahS. LyT. Nguyen-Huu and P. Brehmer, Can overexploited fisheries recover by self-organization? reallocation of fishing effort as an emergent form of governance, Mar. Policy, 95 (2018), 46-56.  doi: 10.1016/j.marpol.2018.06.009.  Google Scholar

[7]

M. Eisenbach, Chemotaxis, World Scientific Publishing Company, 2004. Google Scholar

[8]

R. Eymard, T. Gallouët and R. Herbin, Finite volume methods, Handb. Numer. Anal., North-Holland, Amsterdam, VII (2000), 713–1020. doi: 10.1086/phos.67.4.188705.  Google Scholar

[9] S. D. Fretwell, Populations in a Seasonal Environment, Princeton University Press, 1972.   Google Scholar
[10]

M. F. Goy, M. S. Springer and G. L. Hazelbauer, Taxis and behaviour, elementary sensory systems in biology, in Receptors and Recognition, Ser. B, Chapman and Hall London, 5 (1978), 1–34. Google Scholar

[11] W. Gurney and R. M. Nisbet, Ecological Dynamics, Oxford University Press, 1998.   Google Scholar
[12]

E. F. Keller and L. A. Segel, Model for chemotaxis, J. Theoret. Biol., 30 (1971), 225-234.  doi: 10.1016/0022-5193(71)90050-6.  Google Scholar

[13]

X. Mora, Semilinear parabolic problems define semiflows on $ {C}^k $ spaces, Trans. Amer. Math. Soc., 278 (1983), 21-55.  doi: 10.2307/1999300.  Google Scholar

[14]

K. W. Morton and E. Sūli, Finite volume methods and their analysis, IMA J. Numer. Anal., 11 (1991), 241-260.  doi: 10.1093/imanum/11.2.241.  Google Scholar

[15]

V. N. MoseT. Nguyen-HuuP. Auger and D. Western, Modelling herbivore population dynamics in the amboseli national park, kenya: Application of spatial aggregation of variables to derive a master model, Ecol. Complex., 10 (2012), 42-51.  doi: 10.1016/j.ecocom.2012.02.002.  Google Scholar

[16]

A. MoussaouiM. BensenaneP. Auger and A. Bah, On the optimal size and number of reserves in a multi-site fishery model. journal of biological systems, J. Biol. Systems, 23 (2015), 31-47.  doi: 10.1142/S0218339015500023.  Google Scholar

[17]

J. D. Murray, Mathematical Biology, Springer-Verlag, Berlin, 1989. doi: 10.1007/978-3-662-08539-4.  Google Scholar

[18]

T. H. NguyenT. BrochierP. AugerV. D. Trinh and P. Brehmer, Competition or cooperation in transboundary fish stocks management: Insight from a dynamical model, J. Theoret. Biol., 447 (2018), 1-11.  doi: 10.1016/j.jtbi.2018.03.017.  Google Scholar

[19]

A. Pazy, Semigroups of Linear Operators and Applications to Partial Differential Equations, Applied Mathematical Sciences, 44. Springer-Verlag, New York, 1983. doi: 10.1007/978-1-4612-5561-1.  Google Scholar

[20]

E. SánchezP. Auger and J. C. Poggiale, Two-time scales in spatially structured models of population dynamics: A semigroup approach, J. Math. Anal. Appl., 375 (2011), 149-165.  doi: 10.1016/j.jmaa.2010.08.014.  Google Scholar

[21]

E. SánchezO. ArinoP. Auger and R. Bravo de la Parra, A singular perturbation in an age-structured population model, SIAM J. Appl. Math., 60 (2000), 408-436.  doi: 10.1137/S0036139998337966.  Google Scholar

[22]

E. SánchezR. Bravo de la ParraP. Auger and P. Gómez-Mourelo, Time scales in linear delayed differential equations, J. Math. Anal. Appl., 323 (2006), 680-699.  doi: 10.1016/j.jmaa.2005.10.074.  Google Scholar

[23] D. Tilman and P. Kareiva, Spatial Ecology: The Role of Space in Population Dynamics and Interspecific Interactions (MPB-30), Princeton University Press, 1997.  doi: 10.1515/9780691188362.  Google Scholar
[24]

R. Vilsmeier, F. Benkhaldoun and D. Hänel, Finite Volumes for Complex Applications II, Hermes Science Publications, Paris, 1999.  Google Scholar

show all references

References:
[1]

W. Arendt, A. Grabosch, G. Greiner, U, MoustakasRainer, N. Schlotterbeck, U. Groh, H. P. Lotz and F. Neubrander, One-Parameter Semigroups of Positive Operators, Lecture Notes in Mathematics, Springer, 2006. doi: 10.1007/BFb0074922.  Google Scholar

[2]

P. Auger and R. Bravo de la Parra, Methods of aggregation of variables in population dynamics Méthodes d'agrégation de variables en dynamique de population, C. R. Acad. Sci. Paris Sér. Ⅲ Sci. Vie, 323 (2000), 665 – 674. doi: 10.1016/S0764-4469(00)00182-7.  Google Scholar

[3]

P. Auger and J.-C. Poggiale, Aggregation and emergence in systems of ordinary differential equations, Math. Comput. Modelling, 27 (1998), 1-21.  doi: 10.1016/S0895-7177(98)00002-8.  Google Scholar

[4]

P. Auger, R. Bravo de la Parra, J.-C. Poggiale, E. Sánchez and T. Nguyen-Huu, Aggregation of variables and applications to population dynamics, Lect. Notes Math., Springer Berlin Heidelberg, (2008), 209–263. doi: 10.1007/978-3-540-78273-5_5.  Google Scholar

[5]

P. M. Auger and R. Roussarie, Complex ecological models with simple dynamics: From individuals to populations, Acta Biotheor., 42 (1994), 111-136.  doi: 10.1007/BF00709485.  Google Scholar

[6]

T. BrochierP. AugerD. ThiaoA. BahS. LyT. Nguyen-Huu and P. Brehmer, Can overexploited fisheries recover by self-organization? reallocation of fishing effort as an emergent form of governance, Mar. Policy, 95 (2018), 46-56.  doi: 10.1016/j.marpol.2018.06.009.  Google Scholar

[7]

M. Eisenbach, Chemotaxis, World Scientific Publishing Company, 2004. Google Scholar

[8]

R. Eymard, T. Gallouët and R. Herbin, Finite volume methods, Handb. Numer. Anal., North-Holland, Amsterdam, VII (2000), 713–1020. doi: 10.1086/phos.67.4.188705.  Google Scholar

[9] S. D. Fretwell, Populations in a Seasonal Environment, Princeton University Press, 1972.   Google Scholar
[10]

M. F. Goy, M. S. Springer and G. L. Hazelbauer, Taxis and behaviour, elementary sensory systems in biology, in Receptors and Recognition, Ser. B, Chapman and Hall London, 5 (1978), 1–34. Google Scholar

[11] W. Gurney and R. M. Nisbet, Ecological Dynamics, Oxford University Press, 1998.   Google Scholar
[12]

E. F. Keller and L. A. Segel, Model for chemotaxis, J. Theoret. Biol., 30 (1971), 225-234.  doi: 10.1016/0022-5193(71)90050-6.  Google Scholar

[13]

X. Mora, Semilinear parabolic problems define semiflows on $ {C}^k $ spaces, Trans. Amer. Math. Soc., 278 (1983), 21-55.  doi: 10.2307/1999300.  Google Scholar

[14]

K. W. Morton and E. Sūli, Finite volume methods and their analysis, IMA J. Numer. Anal., 11 (1991), 241-260.  doi: 10.1093/imanum/11.2.241.  Google Scholar

[15]

V. N. MoseT. Nguyen-HuuP. Auger and D. Western, Modelling herbivore population dynamics in the amboseli national park, kenya: Application of spatial aggregation of variables to derive a master model, Ecol. Complex., 10 (2012), 42-51.  doi: 10.1016/j.ecocom.2012.02.002.  Google Scholar

[16]

A. MoussaouiM. BensenaneP. Auger and A. Bah, On the optimal size and number of reserves in a multi-site fishery model. journal of biological systems, J. Biol. Systems, 23 (2015), 31-47.  doi: 10.1142/S0218339015500023.  Google Scholar

[17]

J. D. Murray, Mathematical Biology, Springer-Verlag, Berlin, 1989. doi: 10.1007/978-3-662-08539-4.  Google Scholar

[18]

T. H. NguyenT. BrochierP. AugerV. D. Trinh and P. Brehmer, Competition or cooperation in transboundary fish stocks management: Insight from a dynamical model, J. Theoret. Biol., 447 (2018), 1-11.  doi: 10.1016/j.jtbi.2018.03.017.  Google Scholar

[19]

A. Pazy, Semigroups of Linear Operators and Applications to Partial Differential Equations, Applied Mathematical Sciences, 44. Springer-Verlag, New York, 1983. doi: 10.1007/978-1-4612-5561-1.  Google Scholar

[20]

E. SánchezP. Auger and J. C. Poggiale, Two-time scales in spatially structured models of population dynamics: A semigroup approach, J. Math. Anal. Appl., 375 (2011), 149-165.  doi: 10.1016/j.jmaa.2010.08.014.  Google Scholar

[21]

E. SánchezO. ArinoP. Auger and R. Bravo de la Parra, A singular perturbation in an age-structured population model, SIAM J. Appl. Math., 60 (2000), 408-436.  doi: 10.1137/S0036139998337966.  Google Scholar

[22]

E. SánchezR. Bravo de la ParraP. Auger and P. Gómez-Mourelo, Time scales in linear delayed differential equations, J. Math. Anal. Appl., 323 (2006), 680-699.  doi: 10.1016/j.jmaa.2005.10.074.  Google Scholar

[23] D. Tilman and P. Kareiva, Spatial Ecology: The Role of Space in Population Dynamics and Interspecific Interactions (MPB-30), Princeton University Press, 1997.  doi: 10.1515/9780691188362.  Google Scholar
[24]

R. Vilsmeier, F. Benkhaldoun and D. Hänel, Finite Volumes for Complex Applications II, Hermes Science Publications, Paris, 1999.  Google Scholar

Figure 1.  Plot of the numerical solution of the perturbed problem (11), (A) and (C), and its approximate solution using aggregation of variables methods, (B) and (D), at two times $ t = 0.2 $ and $ t = 25 $ for $ \varepsilon = 1e-1 $
Figure 2.  Plot of total population of the global model and its approximate solution using aggregation of variables methods for for $ \varepsilon = 1e-1 $. $ K_T: = \int_{\Omega}K(x)dx $ stands for the total carrying capacity of the environment and $ K^\ast $ for the new homogeneous one
Figure 3.  Plot of errors, $ |N_{\varepsilon}(t)-N(t)| $, (A), and $ {\Vert n_{\varepsilon}(.,t)-\lambda(.)N(t) \Vert_\infty} $ with $ t\geq 1 $, (B), with respect to time $ t $ for different values of $ \varepsilon $; $ \varepsilon = 1e-1 $, $ \varepsilon = 1e-2 $, $ \varepsilon = 1e-3 $, and $ \varepsilon = 1e-4 $
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