The behavior of population dispersion employing various numerical techniques

Main Article Content

Imran Abbas*
Asad Ejaz

Abstract

The exploration of population diversity motivated us to present this paper. A mathematical model for the ecological process of population dispersion is finally considered by us to figure out the dispersion of population along the area. The dispersal from one's home site to the next is considered the most important phenomenon in the demographic and evolutionary dynamics of the population. The most important factor regarding dispersal is the spatial distribution of individuals. This dispersal may result in enhanced clamping, huge randomness, or even more spacing. The Adomian Decomposition method has opted to work out the problem analytically. Numerical schemes brought an approximate solution by incorporating the Forward-in-Time and Central-In-Space (FTCS) scheme, the Crank Nicolson (CN) scheme, and Numerov’s method. The validity and efficiency of schemes employed for the proposed model are supported by core properties like stability, consistency, and convergence. A comparison is made between the results calculated via schemes and the one analytically.

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Abbas, I., & Ejaz, A. (2023). The behavior of population dispersion employing various numerical techniques. Annals of Mathematics and Physics, 6(2), 126–140. https://doi.org/10.17352/amp.000092
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Copyright (c) 2023 Abbas I, et al.

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Gray BF, Kirwan NA. Growth rates of yeast colonies on solid media. Biophys Chem. 1974 Feb;1(3):204-13. doi: 10.1016/0301-4622(74)80006-2. PMID: 4609508.

Chen SC. Effect of habitat fragmentation on seed dispersal ability of a wind-dispersed annual in an agroecosystem. Agriculture. Ecol Evol. 2020; 304: 107138.

James F, Mansur ET, Sacerdote B. Geographic dispersion of economic shocks: Evidence from the fracking revolution. Am Econ Rev. 2017; 107: 1313-1334.

Macfarlan SJ, Schacht R, Schniter E, Garcia JJ, Guevara Beltran D, Lerback J. The role of dispersal and school attendance on reproductive dynamics in small, dispersed populations: Choyeros of Baja California Sur, Mexico. PLoS One. 2020 Oct 7;15(10):e0239523. doi: 10.1371/journal.pone.0239523. PMID: 33027256; PMCID: PMC7540897.

Shoemaker LG, Sullivan LL, Donohue I, Cabral JS, Williams RJ, Mayfield MM, Chase JM, Chu C, Harpole WS, Huth A, HilleRisLambers J, James ARM, Kraft NJB, May F, Muthukrishnan R, Satterlee S, Taubert F, Wang X, Wiegand T, Yang Q, Abbott KC. Integrating the underlying structure of stochasticity into community ecology. Ecology. 2020 Feb;101(2):e02922. doi: 10.1002/ecy.2922. Epub 2019 Dec 26. PMID: 31652337; PMCID: PMC7027466.

Soares J, Fernandes R, Brito D, Oliveira H, Neuparth T, Martins I, Santos MM. Environmental risk assessment of accidental marine spills: A new approach combining an online dynamic Hazardous and Noxious substances database with numerical dispersion, risk and population modelling. Sci Total Environ. 2020 May 1;715:136801. doi: 10.1016/j.scitotenv.2020.136801. Epub 2020 Jan 18. PMID: 32007875.

Koki T, Tokuda M. Negative correlation between dispersal investment and canopy openness among populations of the ant-dispersed sedge, Carex lanceolata. Plant Ecol. 2020; 221: 1105-1115.

Israel G. The emergence of biomathematics and the case of population dynamics: a revival of mechanical reductionism and Darwinism. Sci Context. 1993 Autumn;6(2):469-509. doi: 10.1017/s0269889700001484. PMID: 11623401.

Shaher M, Qaralleh R. Numerical approximations and Padé approximants for a fractional population growth model. Appl Math Model. 2007; 31: 1907-1914.

Ismail HNA, Kamal RR, Ghada SES. Solitary wave solutions for the general KDV equation by Adomian decomposition method. Appl Math Comput. 2004; 154: 17-29.

Segel LA. Mathematical models in biology (Leah Edelstein-Keshet). 1988; 679.

Seng VKA, Cherruault Y. Adomian's polynomials for nonlinear operators. Math Comput Model. 1996; 24: 59-65.

Yves C. Convergence of Adomian's method. Kybernetes. 1989; 18: 31-38.

Rèpaci A. Nonlinear dynamical systems: on the accuracy of Adomian's decomposition method. Appl Math Lett. 1990; 3: 35-39.

Cherruault Y, Adomian G. Decomposition methods: a new proof of convergence. Math Comput Model. 1993; 18: 103-106.

Ames WF. Numerical methods for partial differential equations. Academic Press, 2014.

Adomaitis RA, Yi-hung L, Hsiao-Yung C. A computational framework for boundary-value problem-based simulations. Simulation. 2000; 74: 28-38.

Hasnain S, Muhammad S, Daoud SM. Two-Dimensional nonlinear reaction-diffusion equation with time efficient scheme. Am J Comput Math. 2017; 7: 183-194.

Hasnain S, Muhammad S. Numerical study of one-dimensional Fishers KPP equation with finite difference schemes. Am J Comput Math. 2017; 7: 70-83.

Abushaikha AS, Kirill MT. A fully implicit mimetic finite difference scheme for general purpose subsurface reservoir simulation with full tensor permeability. J Comput Phys. 2020; 406: 109194.

Xu D, Wenlin Q, Jing G. A compact finite difference scheme for the fourth‐order time‐fractional integro‐differential equation with a weakly singular kernel. Numer Methods Partial Differ Equ. 2020; 36: 439-458.

Safdari H. Convergence analysis of the space fractional-order diffusion equation based on the compact finite difference scheme. Comput Appl Math. 2020; 39: 1-15.

Saqib M, Shahid H, Daoud SM. Computational solutions of two-dimensional convection-diffusion equation using crank-nicolson and time efficient ADI. Am J Comput Math. 2017; 7: 208-227.

Li Z, Kazufumi I. The immersed interface method: numerical solutions of PDEs involving interfaces and irregular domains. Soc Indust Appl Math. 2006.

Hasnain S. Efficiency of numerical schemes for two dimensional Gray Scott model. AIP Advances. 2019; 9.

Hasnain S, Muhammad S, Nawaf AH. Finite Difference Implicit Schemes to Coupled Two-Dimension Reaction-Diffusion System. J Appl Math Phy. 2018; 6: 737-753.

Xue G, Yunjie G, Hui F. The splitting Crank–Nicolson scheme with intrinsic parallelism for solving parabolic equations. J Funct Space. 2020; 1-12.

Gan X, Dengguo X. On the convergence of a crank–nicolson fitted finite volume method for pricing American bond options. Math Probl Eng. 2020; 2020: 1-13.

Luo Z, Wenrui J. The Crank–Nicolson finite spectral element method and numerical simulations for 2D non‐stationary Navier–Stokes equations. Math Methods Appl Sci. 2020; 43: 2276-2288.

Hasnain S, Muhammad S, Daoud SM. Fourth-order Douglas implicit scheme for solving three dimensions reaction-diffusion equation with non-linear source term. AIP Advances. 2017; 7.

Craig IJD, Sneyd AD. An alternating-direction implicit scheme for parabolic equations with mixed derivatives. Comput Math Applicat. 1988; 16: 341-350.

Steidl G, Tanja T. Removing multiplicative noise by Douglas-Rachford splitting methods. J Math Imaging Vis. 2010; 36: 168-184.

Stinson DR, Reto S. Provably secure distributed Schnorr signatures and a (t, n) threshold scheme for implicit certificates. Australasian Conference on Information Security and Privacy. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001.

Ixaru LGR, Rizea M. Numerov method maximally adapted to the schr6dinger equation. J Comput Phy. 1987; 73: 306-324.

Alexander Z. The Numerov-Crank-Nicolson scheme on a non-uniform mesh for the time-dependent Schrödinger equation on the half-axis. Kin Relat Model. 2015; 8: 587-613.

Zlotnik A, Alla R. On a Numerov–Crank–Nicolson–Strang scheme with discrete transparent boundary conditions for the Schrödinger equation on a semi-infinite strip. Appl Num Math. 2015; 93: 279-294.

Babuska IB. Numerical Stability in Mathematical Analysis. IFIP Congress. North-Holland, Amsterdam. 1968; 11: 23.

Roache PJ. Computational fluid dynamics (Book- Computational fluid dynamics.). Albuquerque, N. Mex., Hermosa Publishers. 1972; 437.

Saqib M, Muhammad FA, Iqtadar H. Numerical study to coupled three-dimensional reaction-diffusion system. IEEE Access. 2019; 7: 46695-46705.

Çelik E, Durmuş A. Nonlinear regression applications in modeling over-dispersion of bird populations. J Anim Plant Sci. 2020; 30: 345-354.