Most of the species currently threatened with extinction seem to be under the pressure of unsuitable environmental conditions; e.g., climate change, scarce food resource, habitat fragmentation. One should expect species to have forms of resilience against such extinction. The point here is to examine the effect of spatial gradients on species survival against increasing temperature arising from climate change. Therefore, we start with the question of whether, when faced with extinction stemming from climate change, a spatial gradient and a beachhead have the power to prevent extinction. This problem is addressed theoretically using a coupled reaction diffusion equation for a predator-prey system in which the prey experiences an Allee effect. It is demonstrated that there exists a relationship between the slope of the gradient and the beachhead at which the predator-prey system can stably survive. The tendency of the system can be defined by a function where the system includes the threshold point for extinction, that separates the areas of extinction and survival. The findings reveal that spatial gradient can be used as a precaution, when the species faces to extinction, for species to create new habitat and sustain its persistence. Therefore, in this paper, it is shown that, in theory, the recovery of species from unsuitable environmental conditions can be achieved. This can be possible by taking into account the spatial gradient to slow down the forthcoming ecological extinction, and thus extend the system a while as an adaptation mechanism.
Citation: Yadigar Sekerci. Adaptation of species as response to climate change: Predator-prey mathematical model[J]. AIMS Mathematics, 2020, 5(4): 3875-3898. doi: 10.3934/math.2020251
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Abstract
Most of the species currently threatened with extinction seem to be under the pressure of unsuitable environmental conditions; e.g., climate change, scarce food resource, habitat fragmentation. One should expect species to have forms of resilience against such extinction. The point here is to examine the effect of spatial gradients on species survival against increasing temperature arising from climate change. Therefore, we start with the question of whether, when faced with extinction stemming from climate change, a spatial gradient and a beachhead have the power to prevent extinction. This problem is addressed theoretically using a coupled reaction diffusion equation for a predator-prey system in which the prey experiences an Allee effect. It is demonstrated that there exists a relationship between the slope of the gradient and the beachhead at which the predator-prey system can stably survive. The tendency of the system can be defined by a function where the system includes the threshold point for extinction, that separates the areas of extinction and survival. The findings reveal that spatial gradient can be used as a precaution, when the species faces to extinction, for species to create new habitat and sustain its persistence. Therefore, in this paper, it is shown that, in theory, the recovery of species from unsuitable environmental conditions can be achieved. This can be possible by taking into account the spatial gradient to slow down the forthcoming ecological extinction, and thus extend the system a while as an adaptation mechanism.
1.
Introduction
In recent decades, importance of fractional order models is well disclosed fact in many fields of engineering and science. Numerous fractional order partial differential equations(FPDEs) have been used by many authors to describe various important biological and physical processes like in the fields of chemistry, biology, mechanics, polymer, economics, biophysics control theory and aerodynamics. In this concern, many researchers have studied various schemes and aspects of PDEs and FPDEs as well, see [1,2,3,4,5,6,7,8,9,10]. However, the great attention has been given very recently to obtaining the solution of fractional models of the physical interest. Keeping in views, the computation complexities involved in fractional order models is very crucial and is the difficulty in solving these fractional models. Some times, the exact analytic solution of each and every FPDE can not be obtained using the traditional schemes and methods. However, there exists some schemes and methods, which have been proved to be efficient in obtaining the approximation to solution of the fractional order problems. Among them, we bring the attention of readers to these methods and schemes [11,12,13,14,15,16,17,18,19,20,21] which are used successfully. These methods and schemes have their own demerits and merits. Some of them provide a very good approximation with convenient way. For example, see the methods and schemes in the articles [22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39].
The main aim of this work is to develop a new procedure which is easy with respect to application and more efficient as compare with existing procedures. In this concern, we introduced asymptotic homotopy perturbation method (AHPM) to obtain the solution of nonlinear fractional order models. It is a new version of perturbation techniques. In simulation section, we have testified our proposed procedure by considering the test problems of non linear fractional order Zakharov-Kuznetsov ZK(m,n,r) equations of the form [11,12]
Where a0, a1, a2 are arbitrary constants and m,n,r are non zero integers. If α=1, then equation (1.1) becomes classical Zakharov-Kuznetsov ZK(m,n,r) equation given as:
The ZK equation has been firstly modelized for depicting weakly nonlinear ion-acoustic waves in strongly magnetized lossless plasma [40]. The ZK equation governs the behavior of weakly nonlinear ion-acoustic waves in plasma comprising cold ions and hot isothermal electrons in the presence of a uniform magnetic field [41,42].
The plan of the rest paper is as follows: Section 2 provides theory of the AHPM; Section 3 provides implementation of AHPM. Finally, a brief conclusion and the further work has been listed.
2.
Basic idea of AHPM
Here, we provide that the Caputo type fractional order derivative will be used throughout this paper for the computation of derivative.
Let us consider the nonlinear problem in the form as
T(u(x,y,t))+g(x,y,t)=0,
(2.1)
B(u(x,y,t),∂u(x,y,t)∂t)=0.
(2.2)
Where T(u(x,y,t)) denotes a differential operator which may consists ordinary, partial or space- fractional or time-fractional differential derivative. T(u(x,y,t)) can be expressed for fractional model as follows:
∂αu(x,y,t)∂tα+N(u(x,y,t))+g(x,y,t)=0
(2.3)
subject to the condition
B(u(x,y,t),∂u(x,y,t)∂t)=0,
(2.4)
where the operator ∂α∂tα denotes the Caputo derivative operator, N is non linear operator and B denotes a boundary operator, u(x,y,t) is unknown exact solution of Eq. (2.1), g(x,y,t) denotes known function and x,y and t denote special and temporal variables respectively. Let us construct a homotopy Φ(x,y,t;p):Ω×[0,1]→R which satisfies
∂αΦ(x,y,t;p)∂tα+g(x,y,t)−p[N(Φ(x,y,t;p)]=0,
(2.5)
where p∈[0,1] is said to be an embedding parameter. At this phase of our work it is pertinent that our proposed deformation Eq. (2.5) is an alternate form of the deformation equations as
in HPM, HAM, OHAM proposed by Liao in [43], He in [44] and Marinca in [45] respectively.
Basically, according to homotopy definition, when p=0 and p=1 we have
Φ(x,y,t;p)=u0(x,y,t),ϕ(x,y,t;p)=u(x,y,t).
Obviously, when the embedding parameter p varies from 0 to 1, the defined homotopy ensures the convergence of ϕ(x,y,t;p) to the exact solution u(x,y,t). Consider ϕ(x,y,t;p) in the form
It is obvious that the construction of introduced auxiliary function in Eq. (2.10) is different from the auxiliary functions that are proposed in articles [43,44,45]. Hence the procedure proposed in our paper is different from the procedures proposed by Liao, He, Marinca in aforesaid papers [43,44,45] as well as Optimal Homotopy perturbation method (OHPM) in [46].
Furthermore, when we substitute Eq. (2.9) and Eq. (2.10) in Eq. (2.5) and equate like power of p, the obtained series of simpler linear problems are
We obtain the series solutions by using the integral operator Jα on both sides of the above each simple fractional differential equation. The convergence of the series solution Eq. (2.9) to the exact solution depends upon the auxiliary parameters (functions) Bi(x,y,t,ci). The choice of Bi(x,y,t,ci) is purely on the basis of terms appear in nonlinear part of the Eq. (2.1). The Eq.(2.9) converges to the exact solution of Eq. (2.1) at p=1:
˜u(x,y,t)=u0(x,y,t)+∞∑k=1uk(x,y,t;ci),i=1,2,3,….
(2.12)
Particularly, we can truncate the Eq. (2.12) into finite m-terms to obtain the solution of nonlinear problem. The auxiliary convergence control constants c1,c2,c3,… can be found by solving the system
in Eq. (2.10), we obtain exactly the series problems which are obtained by OHAM after expanding and equating the like power of p in deformation equation. Furthermore, concerning the Optimal Homotopy Asymptotic Method (OHAM) mentioned in this manuscript and presented in [45], that the version of OHAM proposed in 2008 was improved in time and the most recent improvement, which also contains an auxiliary functions, are presented in the papers [47,48]. We also have improved the version of OHAM by introducing a very new auxiliary function in Eq. (2.10). Our method proposed in this paper uses a very new and more general form of auxiliary function
N(ϕ(x,y,t;p))=B1N0+∞∑i=1(i∑m=0Bi+1−mNm)pi
which depends on arbitrary parameters B1,B2,B3,… and is useful for adjusting and controlling the convergence of nonlinear part as well as linear part of the problem with simple way.
3.
Applications
In this portion, we apply AHPM to obtain solution of the following problems to show the accuracy and appropriateness of the new procedure for to solve nonlinear problems.
Problem 3.1.Let us consider FZK(2,2,2) in the form:
In similar way, we can compute the solution of the next simpler linear problems which are difficult to compute by using OHAM procedure. we choose B1=c1,B2=c2,B3=c3,B4=c4 and consider
We obtain number of optimal values of auxiliary constants by using the Eq. (2.13) and choose those optimal values whose sum is in [−1,0). Now, substituting the optimal values of auxiliary constants (from the Table 1) into the Eq. (3.12), we obtain the AHPM solutions for different values of α at k=0.001
Table 1.
The auxiliary control constants for the problem 3.1.
Tables 2 and 3 show the AHPM solution, VIM solution, exact solution and absolute error of AHPM solution. It is obvious from Tables 2 and 3 that AHPM solution results are more accurate to the exact solution results as compare with VIM [11] solution results. The AHPM solution, exact solution and absolute error of AHPM solution are plotted for different values of α, x, y and t in Figures 1 and 2. The curves of AHPM and exact solution are exactly matching as compare with homotopy perturbation transform method (HPTM)[12]. It is obvious from the Tables 2 and 3, Figures 1 and 2, that the AHPM solution of the problem 3.1 is in very good agreement with exact solution.
Table 2.
Solution of the problem 3.1 for various values of α, x, y and t at k=0.001.
We obtain number of optimal values of auxiliary constants by using the Eq. (2.13) and choose those optimal values whose sum is in [−1,0). Now, substituting the optimal values of auxiliary constants (from Table 4) into the Eq. (3.20), we obtain the AHPM solutions for different values of α at k=0.001.
Table 4.
The auxiliary control constants for the problem 3.2.
Tables 5–7 show the AHPM solution, VIM solution, exact solution and absolute error of AHPM solution. The AHPM solution, exact solution and absolute error of AHPM solution are plotted for different values of α, x, y and t in Figures 3 and 4. It is obvious from the Tables 5–7, Figures 3 and 4, that the AHPM solution of the problem 3.2 is in very good agreement with exact solution.
Table 5.
Solution of the problem 3.2 for varios values of α, x, y and t at k=0.001.
In this article, asymptotic homotopy perturbation method (AHPM) is developed to solve non-linear fractional models. It is a different procedure from the procedures of HAM, HPM and OHAM. The two special cases, ZK(2,2,2) and ZK(3,3,3) of fractional Zakharov-Kuznetsov model are considered to illustrate a very simple procedure of the homotopy methods. The numerical results in simulation section of AHPM solutions are more accurate to the exact solutions as comparing with fractional complex transform (FCT) using variational iteration method (VIM). In the field of fractional calculus, it is necessary to introduce various procedures and schemes to compute the solution of non-linear fractional models. In this concern, we expect that this new proposed procedure is a best effort. The best improvement and the application of this new procedures to the solution of advanced non-linear fractional models with computer software codes will be our further consideration.
Acknowledgments
The authors would like to thank anonymous referees for their careful corrections to and valuable comments on the original version of this paper.
Conflict of interest
The authors declare no conflict of interest.
References
[1]
W. C. Allee, Animal Aggregations: A Study in General Sociology, The University of Chicago Press, Chicago, IL, USA, 1931.
[2]
A. Morozov, S. Petrovskii, B. L. Li, Spatiotemporal complexity of patchy invasion in a predatorprey system with the Allee effect, J. Theor. Biol., 238 (2006), 18-35. doi: 10.1016/j.jtbi.2005.05.021
[3]
S. Petrovskii, A. Morozov, E. Venturino, Allee effect makes possible patchy invasion in a predatorprey system, Ecol. Lett., 5 (2002), 345-352. doi: 10.1046/j.1461-0248.2002.00324.x
[4]
S. W. Yao, Z. P. Ma, Z. B. Cheng, Pattern formation of a diffusive predator-prey model with strongAllee effect and nonconstant death rate, Physica A, 527 (2019), 1-11.
[5]
R. Han, B. Dai, Spatiotemporal pattern formation and selection induced by nonlinear crossdiffusion in a toxic-phytoplankton-zooplankton model with Allee effect, Nonlinear Anal.: Real World Appl., 45 (2019), 822-853. doi: 10.1016/j.nonrwa.2018.05.018
[6]
S. Yan, D. Jia, T. Zhang, et al. Pattern dynamics in a diffusive predator-prey model with huntingcooperations, Chaos, Soliton. Fract., 130 (2020), 1-12.
[7]
S. Petrovskii, A. Morozov, B. L. Li, Regimes of biological invasion in a predator-prey system withthe Allee effect, B. Math. Biol., 67 (2005), 637-661. doi: 10.1016/j.bulm.2004.09.003
[8]
H. Berestycki, L. Desvillettes, O. Diekmann, Can climate change lead to gap formation?, Ecol. Complex., 20 (2014), 264-270. doi: 10.1016/j.ecocom.2014.10.006
[9]
C. Cosner, Challenges in modelling biological invasions and population distributions in a changingclimate, Ecol. Complex., 20 (2014), 258-263. doi: 10.1016/j.ecocom.2014.05.007
[10]
Y. Sekerci, Climate change effects on fractional order prey-predator model, Chaos, Soliton. Fract., 134 (2020), 1-16. doi: 10.1016/j.chaos.2020.109690
[11]
O. Bonnefon, J. Coville, J. Garnier, et al. The spatio-temporal dynamics of neutral genetic diversity, Ecol. Complex., 20 (2014), 282-292. doi: 10.1016/j.ecocom.2014.05.003
[12]
P. Kyriazopoulos, J. Nathan, E. Meron, Species coexistence by front pinning, Ecol. Complex., 20 (2014), 271-281. doi: 10.1016/j.ecocom.2014.05.001
[13]
J. T. Curtis, The Vegetation of Wisconsin: An Ordination of Plant Communities, University of Wisconsin Press, 1959.
[14]
R. H. Whittaker, Vegetation of the great smoky mountains, Ecol. Monogr., 26 (1956), 1-80. doi: 10.2307/1943577
[15]
M. Doebeli, U. Dieckmann, Speciation along environmental gradients, Nature, 421 (2003), 259-264. doi: 10.1038/nature01274
[16]
H. A. Gleason, The structure and development of the plant association, Bull. Torrey Bot. Club, 44 (1917), 463-81. doi: 10.2307/2479596
[17]
H. A. Gleason, The individualist concept of the plant association, Bull. Torrey Bot. Club, 53 (1926), 7-26. doi: 10.2307/2479933
[18]
H. A. Gleason, The individualistic concept of the plant association, Am. Midl. Nat., 21 (1939), 92-110. doi: 10.2307/2420377
[19]
M. Pascual, Diffusion-induced chaos in a spatial predator-prey system, Proc. R. Soc. Lond. B., 251 (1993), 1-7. doi: 10.1098/rspb.1993.0001
[20]
E. Post, Large-scale spatial gradients in herbivore population dynamics, Ecology, 86 (2005), 2320-2328. doi: 10.1890/04-0823
[21]
J. Z. Farkas, A. Y. Morozov, E. G. Arashkevich, et al. Revisiting the stability of spatiallyheterogeneous predator-prey systems under eutrophication, B. Math. Biol., 77 (2015), 1886-1908. doi: 10.1007/s11538-015-0108-2
[22]
L. Jonkers, H. Hillebrand, M. Kucera, Global change drives modern plankton communities awayfrom the pre-industrial state, Nature, 570 (2019), 372-375. doi: 10.1038/s41586-019-1230-3
[23]
M. Siccha, M. Kucera, ForCenS, a curated database of planktonic foraminifera census counts inmarine surface sediment samples, Sci. Data, 4 (2017), 1-12. doi: 10.1007/s40745-016-0096-6
[24]
G. T. Pecl, M. B. Araújo, J. D. Bell, et al. Biodiversity redistribution under climate change: Impactson ecosystems and human well-being, Science, 355 (2017), 1-9.
[25]
S. Levin, L. A. Segel, Hypothesis for origin of planktonic patchiness, Nature, 259 (1976), 659. doi: 10.1038/259659a0
[26]
H. Malchow, S. V. Petrovskii, E. Venturino, Spatiotemporal Patterns in Ecology and Epidemiology:Theory, Models, and Simulation, CRC Press, 2007.
[27]
T. Singh, R. Dubey, V. N. Mishra, Spatial dynamics of predator-prey system with huntingcooperation in predators and type I functional response, AIMS Mathematics, 5 (2019), 673-684. doi: 10.3934/math.2020045
[28]
A. M. Turing, The chemical basis of morphogenesis, Philos. T. R. Soc. B., 237 (1952), 37-72.
[29]
S. A. Levin, T. M. Powell, J. H. Steele, Patch Dynamics, Springer Science & Business Media, 2012.
[30]
A. Morozov, K. Abbott, K. Cuddington, et al. Long transients in ecology: Theory and applications, Phys. Life Rev., 2019.
[31]
S. Petrovskii, Y. Sekerci, E. Venturino, Regime shifts and ecological catastrophes in a model ofplankton-oxygen dynamics under the climate change, J. Theor. Biol., 424 (2017), 91-109. doi: 10.1016/j.jtbi.2017.04.018
[32]
Y. Sekerci, S. Petrovskii, Global warming can lead to depletion of oxygen by disruptingphytoplankton photosynthesis: a mathematical modelling approach, Geosciences, 8 (2018), 1-21. doi: 10.3390/geosciences8060201
[33]
Y. Sekerci, S. Petrovskii, Mathematical modelling of plankton-oxygen dynamics under the climatechange, B. Math. Biol., 77 (2015), 2325-2353. doi: 10.1007/s11538-015-0126-0
[34]
A. S. Ackleh, L. J. Allen, J. Carter, Establishing a beachhead: a stochastic population model withan Allee effect applied to species invasion, Theor. Popul. Biol., 71 (2007), 290-300. doi: 10.1016/j.tpb.2006.12.006
[35]
Y. Sekerci, S. Petrovskii, Pattern formation in a model oxygen-plankton system, Computation, 6 (2018), 59. doi: 10.3390/computation6040059
[36]
J. Wang, J. Shi, J. Wei, Predator-prey system with strong Allee effect in prey, J. Math. Biol., 62 (2011), 291-331. doi: 10.1007/s00285-010-0332-1
[37]
J. Wang, J. Shi, J. Wei, Nonexistence of periodic orbits for predator-prey system with strong Alleeeffect in prey populations, Electron. J. Differ. Eq., 2013 (2013), 1-14. doi: 10.1186/1687-1847-2013-1
[38]
G. A. Van Voorn, L. Hemerik, M. P. Boer, et al., Heteroclinic orbits indicate overexploitation inpredator-prey systems with a strong Allee effect, Math. Biosci., 209 (2007), 451-469. doi: 10.1016/j.mbs.2007.02.006
[39]
R. M. Nisbet, W. Gurney, Modelling Fluctuating Populations, Chichester, 1982.
[40]
J. D. Murray, Mathematical Biology, Springer, Berlin, 1993.
[41]
J. A. Sherratt, Periodic travelling waves in cyclic predator-prey systems, Ecol. Lett., 4 (2001), 30-37. doi: 10.1046/j.1461-0248.2001.00193.x
[42]
M. H. Wang, M. Kot, Speeds of invasion in a model with strong or weak Allee effects, Math. Biosci., 171 (2001), 83-97. doi: 10.1016/S0025-5564(01)00048-7
[43]
S. Chen, H. Yang, J. Wei, Global dynamics of two phytoplankton-zooplankton models with toxicsubstances effect, J. Appl. Anal. Comput., 9 (2019), 796-809.
[44]
H. Liu, T. Li, F. Zhang, A prey-predator model with Holling II functional response and the carryingcapacity of predator depending on its prey, J. Appl. Anal. Comput., 8 (2018), 1464-1474.
[45]
M. A. Lewis, P. Kareiva, Allee dynamics and the spread of invading organisms, Theor. Popul. Biol., 43 (1993), 141-158. doi: 10.1006/tpbi.1993.1007
[46]
S. V. Petrovskii, H. Malchow, Wave of chaos: New mechanism of pattern formation in spatiotemporal population dynamics, Theor. Popul. Biol., 59 (2001), 157-174. doi: 10.1006/tpbi.2000.1509
[47]
W. F. Fagan, J. G. Bishop, Trophic interactions during primary succession: Herbivores slow aplant reinvasion at Mount St. Helens, Am. Nat., 155 (2000), 238-251. doi: 10.1086/303320
[48]
M. R. Owen, M. A. Lewis, How predation can slow, stop or reverse a prey invasion, B. Math. Biol., 63 (2001), 655-684. doi: 10.1006/bulm.2001.0239
[49]
S. Petrovskii, H. Malchow, B. L. Li, An exact solution of a diffusive predator-prey system, Proc. R. Soc. A., 461 (2005), 1029-1053. doi: 10.1098/rspa.2004.1404
[50]
S. Petrovskii, A. Morozov, M. Sieber Noise can prevent onset of chaos in spatiotemporalpopulation dynamics, Eur. Phys. J. B., 78 (2010), 253-264.
[51]
W. Alharbi, S. Petrovskii, Critical domain problem for the reaction-telegraph equation model ofpopulation dynamics, Mathematics, 6 (2018), 1-15. doi: 10.3390/math6040059
[52]
J. Martin, B. van Moorter, E. Revilla, et al. Reciprocal modulation of internal and external factorsdetermines individual movements, J. Anim. Ecol., 82 (2013), 290-300. doi: 10.1111/j.1365-2656.2012.02038.x
[53]
B. Chakraborty, N. Bairagi, Complexity in a prey-predator model with prey refuge and diffusion, Ecol. Complex., 37 (2019), 11-23. doi: 10.1016/j.ecocom.2018.10.004
[54]
V. Dakos, E. H. van Nes, R. Donangelo, et al. Spatial correlation as leading indicator ofcatastrophic shifts, Theor. Ecol., 3 (2010), 163-174. doi: 10.1007/s12080-009-0060-6
[55]
A. Hastings, K. C. Abbott, K. Cuddington, et al. Transient phenomena in ecology, Science, 361 (2018), 1-11. doi: 10.1126/science.aat6412
[56]
S. Kéfi, M. Rietkerk, C. L. Alados, et al. Spatial vegetation patterns and imminent desertificationin Mediterranean arid ecosystems, Nature, 449 (2007), 213-217. doi: 10.1038/nature06111
[57]
M. Pascual, F. Guichard, Critically and disturbance in spatial ecological system, Trends Ecol. Evol., 20 (2005), 88-95. doi: 10.1016/j.tree.2004.11.012
[58]
M. Rietkerk, S. C. Dekker, P. C. de Ruiter, et al. Self-organized patchiness and catastrophic shiftsin ecosystems, Science, 305 (2004), 1926-1929. doi: 10.1126/science.1101867
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2215,
10.3390/sym13112215
6.
Muhammad Farooq, Hijaz Ahmad, Dilber Uzun Ozsahin, Alamgeer Khan, Rashid Nawaz, Bandar Almohsen,
A study of heat and mass transfer flow of a variable viscosity couple stress fluid between inclined plates,
2024,
38,
0217-9849,
10.1142/S0217984923502317
7.
Murugesan Manigandan, Saravanan Shanmugam, Mohamed Rhaima, Elango Sekar,
Existence of Solutions for Caputo Sequential Fractional Differential Inclusions with Nonlocal Generalized Riemann–Liouville Boundary Conditions,
2024,
8,
2504-3110,
441,
10.3390/fractalfract8080441
8.
Razia Begum, Sajjad Ali, Nahid Fatima, Kamal Shah, Thabet Abdeljawad,
Dynamical behavior of whooping cough SVEIQRP model via system of fractal fractional differential equations,
2024,
12,
26668181,
100990,
10.1016/j.padiff.2024.100990
Yadigar Sekerci. Adaptation of species as response to climate change: Predator-prey mathematical model[J]. AIMS Mathematics, 2020, 5(4): 3875-3898. doi: 10.3934/math.2020251
Yadigar Sekerci. Adaptation of species as response to climate change: Predator-prey mathematical model[J]. AIMS Mathematics, 2020, 5(4): 3875-3898. doi: 10.3934/math.2020251
Figure 1. Snapshots of the prey (dashed) and predator (solid) distributions over space at t=2000 obtained for parameters ω=0.0006, δL=0.57 and x1=0 for (a) initial condition given by Eqs. (2.12 and 2.13), (b) initial condition given by Eq. (2.14). Red line δ value for given parameter values
Figure 2. Snapshot of the prey (dashed) and predator (solid) distributions over space for given initial conditions as Figure 1a and parameters ω=0, δL=0.49 and at (a) t=2000, (b) t=5000. Red line shows δ for given parameter values
Figure 3. Snapshot of the prey (dashed) and predator (solid) distributions over space for given initial conditions as Figure 1a and parameters ω=0.0001, δL=0.49 and x1=133 at (a) t=2000, (b) t=5000. Red line shows δ for given parameter values
Figure 4. Snapshot of the prey (dashed) and predator (solid) distributions over space for given initial conditions as Figure 1a and parameters ω=0 for t=2000, (a) δL=0.46, (b) δL=0.45, (c) Power spectrum analyse of prey densities for δL=0.45. Red line shows δ for given parameter values
Figure 5. Snapshot of the prey (dashed) and predator (solid) distributions over space for given initial conditions as Figure 1a and parameters ω=0.001, δL=0.44 and x1=100 at (a) t=2000, (b) t=4000, (c) t=5000, (d) t=6000. Red line shows δ value for given parameter values. Note that, in the corresponding spatially uniform system, species persistence would not be possible for the values of δ on the right of the vertical blue line, i.e., outside of the beachhead
Figure 6. Snapshot of the prey (dashed) and predator (solid) distributions over space for given initial conditions as Figure 1a and parameters ω=0.0006, δL=0.44 and x1=100 at (a) t=2000, (b) t=4000, (c) t=5000, (d) t=6000. Red line shows δ value for given parameter values. The vertical blue dashed line shows the assumed value of beachhead. Note that, in the corresponding the spatially uniform system, species persistence would not be possible for the values of δ on the right of the vertical blue line, i.e., outside of the beachhead
Figure 7. The initial distribution is chosen as Figure 1a for given parameters δL=0.44 and x1=100 at t=6000. (a) ω=0.001, (b) ω=0.0006
Figure 8. Snapshot of the prey (dashed) and predator (solid) distributions over space for given initial conditions as Figure 1a and parameters ω=0.001, δL=0.44 and x1=150 at (a) t=2000, (b) t=4000, (c) t=5000, (d) t=6000. Red line shows δ for given parameter values. The vertical blue dashed line shows the assumed value of beachhead. Note that, in the corresponding spatially the uniform system, species persistence would not be possible for the values of δ on the right of the vertical blue line, i.e., outside of the beachhead
Figure 9. Snapshot of the prey (dashed) and predator (solid) distributions over space for given initial conditions as Figure 1a and parameters ω=0.0006, δL=0.44 and x1=150 at (a) t=2000, (b) t=4000, (c) t=5000, (d) t=6000. Note that, in the corresponding spatially uniform system, species persistence would not be possible for the values of δ on the right of the vertical blue line, i.e., outside of the beachhead
Figure 10. The initial distribution is chosen as Figure 1a for given parameters δL=0.44 and x1=150 at t=6000. (a) ω=0.001, (b) ω=0.0006
Figure 11. The critical points x1 with different ω. The species induces persistence above the dashed line, while extinction is below the threshold for t=2000, δL=0.44 and critical point (beachhead) x1 takes the values from corresponding ω. Dashed line shows the rational function is detailed in the text and diamonds show the obtained data points for extinction from corresponding ω and beachhead x1
Figure 12. Local dynamics of population densities and corresponding phase plane at a fixed spatial point for different parameter domains x1=50, x1=94 and x1=150 from left to right for δL=0.44, ω=6×10−4 and t=1000
Figure 13. Spatial distribution of prey densities obtained for (a) t=100, (b) t=300, (c) t=550, (d) t=2000 for ω=0.0006, δL=0.44 and x1=100, ϵ2=ϵ3=3.10−5. Other parameters are the same as above. Red dashed line shows the corresponding beachhead value. Note that, in the corresponding spatially uniform system, species persistence would not be possible for the values of δ above the horizontal red line, i.e., outside of the beachhead. The initial conditions are given by Eqs. (3.2 and 3.3) with u0=v0=1
Figure 14. Spatial distribution of prey densities obtained for (a) t=100, (b) t=300, (c) t=550, (d) t=2000 for ω=0.0006, δL=0.44 and x1=100, ϵ1=2.10−7, ϵ2=3.10−5, ϵ3=ϵ4=6.10−5. Red dashed line shows the corresponding beachhead value. Note that, in the corresponding spatially uniform system, species persistence would not be possible for the values of δ above the horizontal red line, i.e., outside of the beachhead. The initial conditions are given by Eqs. (3.4 and 3.5) with u0=v0=1