May 2017, 22(3): 947-976. doi: 10.3934/dcdsb.2017048

A two patch prey-predator model with multiple foraging strategies in predator: Applications to insects

1. 

Simon A. Levin Mathematical and Computational Modeling Sciences Center, Arizona State University, Tempe, AZ 85287, USA

2. 

Sciences and Mathematics Faculty, College of Integrative Sciences and Arts, Arizona State University, Mesa, AZ 85212, USA

* Corresponding author: Yun Kang

Received  September 2015 Revised  April 2016 Published  January 2017

Fund Project: K.S.M is partially supported by the Department of Education GAANN (P200A120192). This research of Y.K. is partially supported by NSF-DMS (1313312); NSF-IOS/DMS (1558127) and The James S. McDonnell Foundation 21st Century Science Initiative in Studying Complex Systems Scholar Award (UHC Scholar Award 220020472)

We propose and study a two patch Rosenzweig-MacArthur prey-predator model with immobile prey and predator using two dispersal strategies. The first dispersal strategy is driven by the prey-predator interaction strength, and the second dispersal is prompted by the local population density of predators which is referred as the passive dispersal. The dispersal strategies using by predator are measured by the proportion of the predator population using the passive dispersal strategy which is a parameter ranging from 0 to 1. We focus on how the dispersal strategies and the related dispersal strengths affect population dynamics of prey and predator, hence generate different spatial dynamical patterns in heterogeneous environment. We provide local and global dynamics of the proposed model. Based on our analytical and numerical analysis, interesting findings could be summarized as follow: (1) If there is no prey in one patch, then the large value of dispersal strength and the large predator population using the passive dispersal in the other patch could drive predator extinct at least locally. However, the intermediate predator population using the passive dispersal could lead to multiple interior equilibria and potentially stabilize the dynamics; (2) The large dispersal strength in one patch may stabilize the boundary equilibrium and lead to the extinction of predator in two patches locally when predators use two dispersal strategies; (3) For symmetric patches (i.e., all the life history parameters are the same except the dispersal strengths), the large predator population using the passive dispersal can generate multiple interior attractors; (4) The dispersal strategies can stabilize the system, or destabilize the system through generating multiple interior equilibria that lead to multiple attractors; and (5) The large predator population using the passive dispersal could lead to no interior equilibrium but both prey and predator can coexist through fluctuating dynamics for almost all initial conditions.

Citation: Komi Messan, Yun Kang. A two patch prey-predator model with multiple foraging strategies in predator: Applications to insects. Discrete & Continuous Dynamical Systems - B, 2017, 22 (3) : 947-976. doi: 10.3934/dcdsb.2017048
References:
[1]

P. Auger and J. Poggiale, Emergence of population growth models: Fast migration and slow growth, Journal of Theoretical Biology, 182 (1996), 99-108. doi: 10.1006/jtbi.1996.0145.

[2]

D. E. Bowler and T. G. Benton, Causes and consequences of animal dispersal strategies: Relating individual behaviour to spatial dynamics, Biological Reviews, 80 (2005), 205-225. doi: 10.1017/S1464793104006645.

[3]

J. Colbert, E. Danchin, A. A. Dhondt and J. D. Nichols, Dispersal Oxford University Press, New York, (2001).

[4]

R. Cressman and K. Vlastimil, Two-patch population models with adaptive dispersal: The effects of varying dispersal speeds, Journal of Mathematical Biology, 67 (2013), 329-358. doi: 10.1007/s00285-012-0548-3.

[5]

G. K. DavorenW. A. Montevecchi and J. T. Anderson, Distributional patterns of a marine bird and its prey: Habitat selection based on prey and conspecific behaviour, Marine Ecology Progress Series, 256 (2003), 229-242. doi: 10.3354/meps256229.

[6]

D. F. Fraser and R. D. Cerri, Experimental evaluation of predator-prey relationships in a patchy environment: consequences for habitat use patterns in minnows, Ecology, 62 (1982), 307-313. doi: 10.2307/1938947.

[7]

J. F. Fujita, M. S. and R. V. Bowen, Ant foraging behavior: Ambient temperature influences prey selection, Behavioral Ecology and Sociobiology, 15 65–68.

[8]

S. Ghosh and S. Bhattacharyya, A two-patch prey-predator model with food-gathering activity, Journal of Applied Mathematics and Computing, 37 (2011), 497-521. doi: 10.1007/s12190-010-0446-z.

[9]

D. Grünbaum and V. R. Richard, Black-browed albatrosses foraging on antarctic krill: density-dependence through local enhancement?, Ecology, 84 (2003), 3265-3275.

[10]

I. Hanski, Metapopulation dynamics, Nature, 396 (1998), 41-49.

[11]

I. Hanski, Habitat connectivity, habitat continuity, and metapopulations in dynamic landscapes, Oikos, 87 (1999), 209-219. doi: 10.2307/3546736.

[12]

I. Hanski, Metapopulation Ecology Oxford University Press, Oxford, 1999.

[13]

I. Hanski and M. Gilpin, Metapopulation dynamics: Brief history and conceptual domain, Biological Journal of the Linnean Society, 42 (1991), 3-16. doi: 10.1016/B978-0-12-284120-0.50004-8.

[14]

L. Hansson, Dispersal and connectivity in metapopulations, Biological Journal of the Linnean Society, 42 (1991), 89-103. doi: 10.1016/B978-0-12-284120-0.50009-7.

[15]

A. Hastings, Can spatial variation alone lead to selection for dispersal?, Theoretical Population Biology, 24 (1983), 244-251. doi: 10.1016/0040-5809(83)90027-8.

[16]

S. HsuS. Hubbell and P. Wlatman, A mathematical theory for single-nutrient competition in continuous cultures of micro-organisms, SIAM Journal on Applied Mathematics, 32 (1977), 366-383. doi: 10.1137/0132030.

[17]

S. Hsu, On global stability of a predator-prey system, Mathematical Biosciences, 39 (1978), 1-10. doi: 10.1016/0025-5564(78)90025-1.

[18]

Y. Huang and O. Diekmann, Predator migration in response to prey density: What are the consequences?, Journal of Mathematical Biology, 43 (2001), 561-581. doi: 10.1007/s002850100107.

[19]

V. Hutson, A theorem on average liapunov functions, Monatshefte für Mathematik, 98 (1984), 267-275. doi: 10.1007/BF01540776.

[20]

R. A. Ims and D. Hjermann, Condition-dependent dispersal, in Dispersal, Oxford University Press, Oxford, (2001), 203–216.

[21]

I. M. J{á}nosi and I. Scheuring, On the evolution of density dependent dispersal in a spatially structured population model, Journal of Theoretical Biology, 187 (1997), 397-408.

[22]

V. A. Jansen, Regulation of predator-prey systems through spatial interactions: A possible solution to the paradox of enrichment, Oikos, 74 (1995), 384-390. doi: 10.2307/3545983.

[23]

V. A. Jansen, The dynamics of two diffusively coupled predator-prey populations, Theoretical Population Biology, 59 (2001), 119-131. doi: 10.1006/tpbi.2000.1506.

[24]

Y. Kang, S. K. Sasmal and K. Messan, A two-patch prey-predator model with dispersal in predators driven by the strength of predation, preprint, arXiv: 1505.03820.

[25]

P. Kareiva and G. Odell, Swarms of predators exhibit ''prey-taxis" if individual predators use area-restricted search, American Naturalist,, 130 (1987), 233-270.

[26]

A. Kiester and M. Slatkin, A strategy of movement and resource utilization, Theoretical Population Biology, 6 (1974), 1-20. doi: 10.1016/0040-5809(74)90028-8.

[27]

V. Krivan, Dispersal dynamics: Distribution of lady beetles (coleoptera: Coccinellidae), European Journal of Entomology, 105 (2008), 405-409. doi: 10.14411/eje.2008.051.

[28]

M. KummelD. Brown and A. Bruder, How the aphids got their spots: Predation drives self-organization of aphid colonies in a patchy habitat, Oikos, 122 (2013), 896-906. doi: 10.1111/j.1600-0706.2012.20805.x.

[29]

X. Liu and L. Chen, Complex dynamics of Holling type Ⅱ Lotka--Volterra predator--prey system with impulsive perturbations on the predator, Chaos, Solitons & Fractals, 16 (2003), 311-320. doi: 10.1016/S0960-0779(02)00408-3.

[30]

Y. Liu, The Dynamical Behavior of a two Patch Predator-prey Model Honor Thesis, from The College of William and Mary, 2010.

[31]

G. P. Markin, Foraging behavior of the argentine ant in a california citrus grove, Journal of Economic Entomology, 63 (1970), 740-744. doi: 10.1093/jee/63.3.740.

[32]

M. MassotJ. ClobertP. Lorenzon and J.-M. Rossi, Condition-dependent dispersal and ontogeny of the dispersal behaviour: An experimental approach, Journal of Animal Ecology, 71 (2002), 253-261. doi: 10.1046/j.1365-2656.2002.00592.x.

[33]

E. Matthysen, Density-dependent dispersal in birds and mammals, Ecography, 28 (2005), 403-416. doi: 10.1111/j.0906-7590.2005.04073.x.

[34]

T. Namba, Density-dependent dispersal and spatial distribution of a population, Journal of Theoretical Biology, 86 (1980), 351-363. doi: 10.1016/0022-5193(80)90011-9.

[35]

D. Nguyen-NgocT. Nguyen-Huu and P. Auger, Effects of fast density dependent dispersal on pre-emptive competition dynamics, Ecological Complexity, 10 (2012), 26-33. doi: 10.1016/j.ecocom.2011.12.003.

[36]

J. Poggiale, From behavioural to population level: Growth and competition, Mathematical and Computer Modelling, 27 (1998), 41-49. doi: 10.1016/S0895-7177(98)00004-1.

[37]

J. D. Reeve, Environmental variability, migration, and persistence in host-parasitoid systems, American Naturalist, 132 (1988), 810-836. doi: 10.1086/284891.

[38] O. RonceI. OlivieriJ. Clobert and E. G. Danchin, Perspectives on the study of dispersal evolution, Oxford University Press, Oxford, 2001.
[39]

M. L. Rosenzweig and R. H. MacArthur, Graphical representation and stability conditions of predator-prey interactions, American Naturalist, 97 (1963), 209-223. doi: 10.1086/282272.

[40]

J. F. Savino and R. A. Stein, Behavioural interactions between fish predators and their prey: effects of plant density, Animal Behaviour, 37 (1989), 311-321. doi: 10.1016/0003-3472(89)90120-6.

[41]

J. A. SilvaM. L. De Castro and D. A. Justo, Stability in a metapopulation model with density-dependent dispersal, Bulletin of Mathematical Biology, 63 (2001), 485-505. doi: 10.1006/bulm.2000.0221.

[42]

A. T. Smith and M. M. Peacock, Conspecific attraction and the determination of metapopulation colonization rates, Conservation Biology, 4 (1990), 320-323. doi: 10.1111/j.1523-1739.1990.tb00294.x.

[43]

J. Stamps, Conspecific attraction and aggregation in territorial species, American Naturalist, 131 (1988), 329-347. doi: 10.1086/284793.

[44]

Foraging behavior of ants: experiments with two species of myrmecine ants, Behavioral Ecology and Sociobiology, 2 (1977), 147–167.

[45]

H. R. Thieme, Mathematics in Population Biology Princeton University Press, 2003.

show all references

References:
[1]

P. Auger and J. Poggiale, Emergence of population growth models: Fast migration and slow growth, Journal of Theoretical Biology, 182 (1996), 99-108. doi: 10.1006/jtbi.1996.0145.

[2]

D. E. Bowler and T. G. Benton, Causes and consequences of animal dispersal strategies: Relating individual behaviour to spatial dynamics, Biological Reviews, 80 (2005), 205-225. doi: 10.1017/S1464793104006645.

[3]

J. Colbert, E. Danchin, A. A. Dhondt and J. D. Nichols, Dispersal Oxford University Press, New York, (2001).

[4]

R. Cressman and K. Vlastimil, Two-patch population models with adaptive dispersal: The effects of varying dispersal speeds, Journal of Mathematical Biology, 67 (2013), 329-358. doi: 10.1007/s00285-012-0548-3.

[5]

G. K. DavorenW. A. Montevecchi and J. T. Anderson, Distributional patterns of a marine bird and its prey: Habitat selection based on prey and conspecific behaviour, Marine Ecology Progress Series, 256 (2003), 229-242. doi: 10.3354/meps256229.

[6]

D. F. Fraser and R. D. Cerri, Experimental evaluation of predator-prey relationships in a patchy environment: consequences for habitat use patterns in minnows, Ecology, 62 (1982), 307-313. doi: 10.2307/1938947.

[7]

J. F. Fujita, M. S. and R. V. Bowen, Ant foraging behavior: Ambient temperature influences prey selection, Behavioral Ecology and Sociobiology, 15 65–68.

[8]

S. Ghosh and S. Bhattacharyya, A two-patch prey-predator model with food-gathering activity, Journal of Applied Mathematics and Computing, 37 (2011), 497-521. doi: 10.1007/s12190-010-0446-z.

[9]

D. Grünbaum and V. R. Richard, Black-browed albatrosses foraging on antarctic krill: density-dependence through local enhancement?, Ecology, 84 (2003), 3265-3275.

[10]

I. Hanski, Metapopulation dynamics, Nature, 396 (1998), 41-49.

[11]

I. Hanski, Habitat connectivity, habitat continuity, and metapopulations in dynamic landscapes, Oikos, 87 (1999), 209-219. doi: 10.2307/3546736.

[12]

I. Hanski, Metapopulation Ecology Oxford University Press, Oxford, 1999.

[13]

I. Hanski and M. Gilpin, Metapopulation dynamics: Brief history and conceptual domain, Biological Journal of the Linnean Society, 42 (1991), 3-16. doi: 10.1016/B978-0-12-284120-0.50004-8.

[14]

L. Hansson, Dispersal and connectivity in metapopulations, Biological Journal of the Linnean Society, 42 (1991), 89-103. doi: 10.1016/B978-0-12-284120-0.50009-7.

[15]

A. Hastings, Can spatial variation alone lead to selection for dispersal?, Theoretical Population Biology, 24 (1983), 244-251. doi: 10.1016/0040-5809(83)90027-8.

[16]

S. HsuS. Hubbell and P. Wlatman, A mathematical theory for single-nutrient competition in continuous cultures of micro-organisms, SIAM Journal on Applied Mathematics, 32 (1977), 366-383. doi: 10.1137/0132030.

[17]

S. Hsu, On global stability of a predator-prey system, Mathematical Biosciences, 39 (1978), 1-10. doi: 10.1016/0025-5564(78)90025-1.

[18]

Y. Huang and O. Diekmann, Predator migration in response to prey density: What are the consequences?, Journal of Mathematical Biology, 43 (2001), 561-581. doi: 10.1007/s002850100107.

[19]

V. Hutson, A theorem on average liapunov functions, Monatshefte für Mathematik, 98 (1984), 267-275. doi: 10.1007/BF01540776.

[20]

R. A. Ims and D. Hjermann, Condition-dependent dispersal, in Dispersal, Oxford University Press, Oxford, (2001), 203–216.

[21]

I. M. J{á}nosi and I. Scheuring, On the evolution of density dependent dispersal in a spatially structured population model, Journal of Theoretical Biology, 187 (1997), 397-408.

[22]

V. A. Jansen, Regulation of predator-prey systems through spatial interactions: A possible solution to the paradox of enrichment, Oikos, 74 (1995), 384-390. doi: 10.2307/3545983.

[23]

V. A. Jansen, The dynamics of two diffusively coupled predator-prey populations, Theoretical Population Biology, 59 (2001), 119-131. doi: 10.1006/tpbi.2000.1506.

[24]

Y. Kang, S. K. Sasmal and K. Messan, A two-patch prey-predator model with dispersal in predators driven by the strength of predation, preprint, arXiv: 1505.03820.

[25]

P. Kareiva and G. Odell, Swarms of predators exhibit ''prey-taxis" if individual predators use area-restricted search, American Naturalist,, 130 (1987), 233-270.

[26]

A. Kiester and M. Slatkin, A strategy of movement and resource utilization, Theoretical Population Biology, 6 (1974), 1-20. doi: 10.1016/0040-5809(74)90028-8.

[27]

V. Krivan, Dispersal dynamics: Distribution of lady beetles (coleoptera: Coccinellidae), European Journal of Entomology, 105 (2008), 405-409. doi: 10.14411/eje.2008.051.

[28]

M. KummelD. Brown and A. Bruder, How the aphids got their spots: Predation drives self-organization of aphid colonies in a patchy habitat, Oikos, 122 (2013), 896-906. doi: 10.1111/j.1600-0706.2012.20805.x.

[29]

X. Liu and L. Chen, Complex dynamics of Holling type Ⅱ Lotka--Volterra predator--prey system with impulsive perturbations on the predator, Chaos, Solitons & Fractals, 16 (2003), 311-320. doi: 10.1016/S0960-0779(02)00408-3.

[30]

Y. Liu, The Dynamical Behavior of a two Patch Predator-prey Model Honor Thesis, from The College of William and Mary, 2010.

[31]

G. P. Markin, Foraging behavior of the argentine ant in a california citrus grove, Journal of Economic Entomology, 63 (1970), 740-744. doi: 10.1093/jee/63.3.740.

[32]

M. MassotJ. ClobertP. Lorenzon and J.-M. Rossi, Condition-dependent dispersal and ontogeny of the dispersal behaviour: An experimental approach, Journal of Animal Ecology, 71 (2002), 253-261. doi: 10.1046/j.1365-2656.2002.00592.x.

[33]

E. Matthysen, Density-dependent dispersal in birds and mammals, Ecography, 28 (2005), 403-416. doi: 10.1111/j.0906-7590.2005.04073.x.

[34]

T. Namba, Density-dependent dispersal and spatial distribution of a population, Journal of Theoretical Biology, 86 (1980), 351-363. doi: 10.1016/0022-5193(80)90011-9.

[35]

D. Nguyen-NgocT. Nguyen-Huu and P. Auger, Effects of fast density dependent dispersal on pre-emptive competition dynamics, Ecological Complexity, 10 (2012), 26-33. doi: 10.1016/j.ecocom.2011.12.003.

[36]

J. Poggiale, From behavioural to population level: Growth and competition, Mathematical and Computer Modelling, 27 (1998), 41-49. doi: 10.1016/S0895-7177(98)00004-1.

[37]

J. D. Reeve, Environmental variability, migration, and persistence in host-parasitoid systems, American Naturalist, 132 (1988), 810-836. doi: 10.1086/284891.

[38] O. RonceI. OlivieriJ. Clobert and E. G. Danchin, Perspectives on the study of dispersal evolution, Oxford University Press, Oxford, 2001.
[39]

M. L. Rosenzweig and R. H. MacArthur, Graphical representation and stability conditions of predator-prey interactions, American Naturalist, 97 (1963), 209-223. doi: 10.1086/282272.

[40]

J. F. Savino and R. A. Stein, Behavioural interactions between fish predators and their prey: effects of plant density, Animal Behaviour, 37 (1989), 311-321. doi: 10.1016/0003-3472(89)90120-6.

[41]

J. A. SilvaM. L. De Castro and D. A. Justo, Stability in a metapopulation model with density-dependent dispersal, Bulletin of Mathematical Biology, 63 (2001), 485-505. doi: 10.1006/bulm.2000.0221.

[42]

A. T. Smith and M. M. Peacock, Conspecific attraction and the determination of metapopulation colonization rates, Conservation Biology, 4 (1990), 320-323. doi: 10.1111/j.1523-1739.1990.tb00294.x.

[43]

J. Stamps, Conspecific attraction and aggregation in territorial species, American Naturalist, 131 (1988), 329-347. doi: 10.1086/284793.

[44]

Foraging behavior of ants: experiments with two species of myrmecine ants, Behavioral Ecology and Sociobiology, 2 (1977), 147–167.

[45]

H. R. Thieme, Mathematics in Population Biology Princeton University Press, 2003.

Figure 1.  One parameter bifurcation diagrams of Model (4) with $y$-axis representing the population size of predator at Patch 1 and $x$-axis represent the proportion of predator using the passive dispersal. Figure 1(a) describes the number of interior equilibria $(\hat{x}_1^*, \hat{y}_1^*, \hat{y}_2^*)$ when $x_2 = 0$ in Model (3) and their stability with respect to variation in $s$. Figure 1(b) and 1(c) describe the number of interior equilibria $(y_1^*, x_2^*, y_2^*)$ of the submodel $x_2 = 0$ of Model (3) and their stability when $s$ varies from $0$ to $1$. Blue represents the sink and green represents the saddle.
Figure 2.  Boundary equilibria $E_{1\ell}^b = (x_{1\ell}^*, y_{1\ell}^*, 0, y_{2\ell}^*)$ and $E_{2\ell}^b = (0, \hat{y}_{1\ell}^*, \hat{x}_{2\ell}^*, \hat{y}_{2\ell}^*)$. Figure 2(a) and 2(b) are the cases when $s = 0.65$, $r_1=1$, $r_2=0.54$, $d_1=0.45$, $d_2=0.105$, $K_1=10$, $K_2=8$, $a_1=0.6$, $a_2=0.35$, $\rho_1 = 1.75$, and $\rho_2=1.2$. The solid lines are $f_1(x_1)$ and $f_2(x_2)$ while the dashed lines are $K_1$ and $K_2$ which illustrates the existence of boundary equilibria when $K_1> x_{1\ell}^* \mbox{ or } K_2> \hat{x}_{2\ell}^*, ~\ell=1, 2$. The black dots represent real positive $x_{1\ell}^*$ and $\hat{x}_{2\ell}^*$ that satisfy existence of boundary equilibria, respectively.
Figure 3.  One parameter bifurcation diagrams of Model (3) with $y$-axis representing the population size of predator at Patch 1 and $x$-axis represent the proportion of predator using the passive dispersal. Figure 3(a) describes the number of boundary equilibria $E_{1\ell}^b = (x_{1\ell}^*, y_{1\ell}^*, 0, y_{2\ell}^*), \ell=1, 2$ from Model (3) and their stability with respect to variation in $s$ when $d_1 = 0.85, ~a_1=1$. Figure 3(b) and 3(c) describes the number of boundary equilibria $E_{2\ell}^b = (0, \hat{y}_{1\ell}^*, \hat{x}_{2\ell}^*, \hat{y}_{2\ell}^*), \ell =1, 2$ from Model (3) and their change in stability when $s$ varies from $0$ to $1$ with $d_1 = 0.85, ~a_1=1$ and $d_1 = 2, ~a_1=2.1$ respectively. Blue represents the sink and green represents the saddle.
Figure 4.  One and two parameter bifurcation diagrams of symmetric Model (3) with $y$-axis representing the population size of predator at Patch 1 in figure 4(a). We used the following parameters $r=1$, $d=5$, $K=10$, and $a=6$. Figure 4(a) describes the number of interior equilibria and their change in stability when $s$ varies from $0$ to $1$. Blue line represents sink and green line represents saddle in Figure 4(a). Figure 4(b) describes how the number of interior equilibria change for different values of dispersal strategy $s$ and dispersal rate $\rho_1$. Black region have three interior equilibria; red regions have two interior equilibria; and blue regions have one interior equilibrium in Figure 4(b)
Figure 5.  One and two parameter bifurcation diagrams of Model (3) with $y$-axis representing the population size of predator at Patch 1 in Figure 5(a). The following parameters are used: $r=1.8$, $d_2=0.35$, $K_1=10$, $K_2=7$, and $a_2=1.4$. Figure 5(a) describes the number of interior equilibria and their change in stability when $s$ varies from $0$ to $1$. Blue line represents sink, green line represents saddle, and red line represents source in Figure 5(a). Figure 5(b) describes how the number of interior equilibria change for different values of $s$ and dispersal rate $\rho_1$. Black region have three interior equilibria; red regions have two interior equilibria; blue regions have one interior equilibrium, and white regions have no interior equilibria in Figure 5(b).
Figure 6.  One and two parameter bifurcation diagrams of Model (3) with $y$-axis representing the population size of predator at Patch 1 in figure 6(a). We used the following parameters $r=1.8$, $d_2=0.35$, $K_1=10$, $K_2=7$, and $a_2=1.4$. Figure 6(a) describes the number of interior equilibria and their change in stability when $s$ varies from $0$ to $1$ under the parameters $d_1 =2$ and $a_1=2.1$. Blue represents sink, green represents saddle, and red represents source. Figure 6(b) describes how the number of interior equilibria change for different values of dispersal strategy $s$ and dispersal rate $\rho_1$. Black region have three interior equilibria; red regions have two interior equilibria; blue regions have one interior equilibrium, and white regions have no interior equilibria
Figure 7.  Two parameters bifurcation diagrams of Model (3) with $y$-axis representing the relative dispersal rate $\rho_2$ and $x$-axis represent the strength of dispersal mode $s$. The following parameters are used: $r=1.8$, $d_2=0.35$, $K_1=10$, $K_2=7$, $a_2=1.4$, and $\rho_1 = 1$. Both figures 7(a) and 7(b) describes how the number of interior equilibria change for different values of dispersal strategy $s$ and dispersal rate $\rho_2$ where the parameters $d_1 =0.85, ~a_1=1$ are used for the left figure 7(a) while $d_1 =2, ~a_1=2.1$ are used for the right figure 7(b) in addition to the fixed parameters. Black region have three interior equilibria; red regions have two interior equilibria; blue regions have one interior equilibrium, and white regions have no interior equilibria in Figures 7(a) and 7(b)
Figure 8.  Time series of Model 3 when $r=1.8$, $d_2=0.35$, $K_1=10$, $K_2=7$, and $a_2=1.4$. Figures 8(a) and 8(b) illustrate the coexistence of prey and predator through fluctuating dynamics while Model 3 has no interior equilibria. The blue dashed lines represent the prey population in patch 1, the dashed red lines represent the predator population in patch 1, the blue solid lines is the the prey population in patch 2, and the red solid lines represent predator population in patch 2
Figure 9.  Time series of Model 3 when $r=1.8$, $d_2=0.35$, $K_1=10$, $K_2=7$, $a_2=1.4$, $\rho_1 = 1$, and $\rho_2 = 2.5$. Figures 9(a) and 9(b) represent the dynamical pattern generated by two interior saddles, one boundary sink and one boundary saddle. The blue dashed lines represent the prey population in patch 1, the dashed red lines represent the predator population in patch 1, the blue solid lines is the the prey population in patch 2, and the red solid lines represent predator population in patch 2.
Figure 10.  Time series of Model 3 when $r=1.8$, $d_2=0.35$, $K_1=10$, $K_2=7$, $a_2=1.4$, $\rho_1 = 1$, and $\rho_2 = 2.5$. Figures 10(a) and 10(b) describe the dynamical pattern generated by two interior saddles and one interior that is locally stable. The blue dashed lines represent the prey population in patch 1, the dashed red lines represent the predator population in patch 1, the blue solid lines is the the prey population in patch 2, and the red solid lines represent predator population in patch 2
Table 1.  Summary of the effect of the proportion of predators using the passive dispersal on Model (4) From Figures 1(a), 1(b), and 1(c). LAS refers to local asymptotical stability and ✗ implies the equilibrium does not exist
Scenarios $\mathbf{a_1= 1 \mbox{ and } d_1 =0.85 }$ $\mathbf{a_1= 2.1 \mbox{ and } d_1 = 2 }$
$\mathbf{E_{x_1y_1y_2}^{1} }$ $\mathbf{E_{x_1y_1y_2}^{2} }$ $\mathbf{ E_{y_1x_2y_2}^{1}}$ $\mathbf{E_{y_1x_2y_2}^{2} }$ $\mathbf{E_{x_1y_1y_2}^{1, 2} }$ $\mathbf{ E_{y_1x_2y_2}^{1}}$ $\mathbf{E_{y_1x_2y_2}^{2} }$
$s\leq0.1$ LAS Saddle Saddle
$ 0.15 \leq s \leq 0.45$ LAS Saddle Saddle Saddle
$ 0.55 \leq s \leq 0.62$ Saddle LAS Saddle
$ 0.68 < s < 0.82$ LAS Saddle
$ s \geq 0.82$
Scenarios $\mathbf{a_1= 1 \mbox{ and } d_1 =0.85 }$ $\mathbf{a_1= 2.1 \mbox{ and } d_1 = 2 }$
$\mathbf{E_{x_1y_1y_2}^{1} }$ $\mathbf{E_{x_1y_1y_2}^{2} }$ $\mathbf{ E_{y_1x_2y_2}^{1}}$ $\mathbf{E_{y_1x_2y_2}^{2} }$ $\mathbf{E_{x_1y_1y_2}^{1, 2} }$ $\mathbf{ E_{y_1x_2y_2}^{1}}$ $\mathbf{E_{y_1x_2y_2}^{2} }$
$s\leq0.1$ LAS Saddle Saddle
$ 0.15 \leq s \leq 0.45$ LAS Saddle Saddle Saddle
$ 0.55 \leq s \leq 0.62$ Saddle LAS Saddle
$ 0.68 < s < 0.82$ LAS Saddle
$ s \geq 0.82$
Table 2.  Summary of the effect of the proportion of predators using the passive dispersal on Model (4) From Figures 3(a), 3(b), and 3(c). LAS refers to local asymptotical stability and ✗ implies the equilibrium does not exist.
Scenarios $\mathbf{a_1= 1 \mbox{ and } d_1 =0.85 }$ $\mathbf{a_1= 2.1 \mbox{ and } d_1 = 2 }$
E11b E12b E21b E22b E11, 12b E21b E22b
s ≤ 0.1 Saddle Saddle Saddle
0.15 ≤ s ≤ 0.45 Saddle Saddle Saddle Saddle
0.55 ≤ s ≤ 0.62 Saddle Saddle Saddle
0.68 < s < 0.82 LAS Saddle
s ≥ 0.82
Scenarios $\mathbf{a_1= 1 \mbox{ and } d_1 =0.85 }$ $\mathbf{a_1= 2.1 \mbox{ and } d_1 = 2 }$
E11b E12b E21b E22b E11, 12b E21b E22b
s ≤ 0.1 Saddle Saddle Saddle
0.15 ≤ s ≤ 0.45 Saddle Saddle Saddle Saddle
0.55 ≤ s ≤ 0.62 Saddle Saddle Saddle
0.68 < s < 0.82 LAS Saddle
s ≥ 0.82
Table 3.  Summary of the local and global dynamic of Model (3). LAS refers to the local asymptotical stability, GAS refers to the global stability, and Cond. refers to condition.
Scenarios Existence condition, Local and Global stability of Model (3)
$\mathbf{ s = 0}$ $\mathbf{ s \in (0, 1)}$ $\mathbf{ s = 1}$
$E_{0000}, $$E_{K_1000}, $$E_{00K_20}$ Always exist and always saddle Always exist and always saddle Always exist and always saddle
$E_{K_10K_20}$ Always exist; LAS and GAS if $\mu_i>K_i$ for both $i=1, 2$ Always exist; GAS if $\mu_i>K_i$ for both $i=1, 2$; while LAS if Equations 8 are satisfied Always exist; GAS if $\mu_i>K_i$ for both $i=1, 2$; LAS if condition (1) is satisfied
$E_{1\ell}^b$
$x_i=0$
$\ell = 1, ~2$$i =1, 2$
Do not exist One or two exist if $\frac{3\beta_j}{\mu_j+K_j}<\alpha_j<(\mu_j+K_j)^2$ with $i, j=1, 2$, $i\not=j$; Can be locally asymptotically stable or saddle as shown in Figures 3(a), 3(b), 3(c) Exist if $0<\hat{\mu}_i<K_i$; LAS if $\frac{K_i-1}{2}<\widehat{\mu}_i<K_i$ and $r_j<a_j\hat{\nu}_j^i$. GAS if $\frac{K_i-1}{2}<\widehat{\mu}_i<K_i$ and $\frac{r_j(K_j+1)^2}{4a_jK_j}<\widehat{\nu}_i^j$, $i, j=1, 2$, $i\not=j$}.
$E_{i2}^{b*}$
$i, j=1, 2$
$i\neq j$
Exist if $0<\mu_i<K_i$; LAS if $\frac{K_i-1}{2}<\mu_i<K_i$ and condition (2) is satisfied Do not exist Do not exist
Cond. 1:
Cond. 2:
$0<\frac{d_i}{a_j-d_i}<K_j<\mu_j$ and $\rho_j<\frac{d_j-K_j(a_j-d_j)}{\nu_i\left[K_j(a_j-d_i)-d_i\right]}$; $i, j=1, 2$, $i\not=j$
Scenarios Existence condition, Local and Global stability of Model (3)
$\mathbf{ s = 0}$ $\mathbf{ s \in (0, 1)}$ $\mathbf{ s = 1}$
$E_{0000}, $$E_{K_1000}, $$E_{00K_20}$ Always exist and always saddle Always exist and always saddle Always exist and always saddle
$E_{K_10K_20}$ Always exist; LAS and GAS if $\mu_i>K_i$ for both $i=1, 2$ Always exist; GAS if $\mu_i>K_i$ for both $i=1, 2$; while LAS if Equations 8 are satisfied Always exist; GAS if $\mu_i>K_i$ for both $i=1, 2$; LAS if condition (1) is satisfied
$E_{1\ell}^b$
$x_i=0$
$\ell = 1, ~2$$i =1, 2$
Do not exist One or two exist if $\frac{3\beta_j}{\mu_j+K_j}<\alpha_j<(\mu_j+K_j)^2$ with $i, j=1, 2$, $i\not=j$; Can be locally asymptotically stable or saddle as shown in Figures 3(a), 3(b), 3(c) Exist if $0<\hat{\mu}_i<K_i$; LAS if $\frac{K_i-1}{2}<\widehat{\mu}_i<K_i$ and $r_j<a_j\hat{\nu}_j^i$. GAS if $\frac{K_i-1}{2}<\widehat{\mu}_i<K_i$ and $\frac{r_j(K_j+1)^2}{4a_jK_j}<\widehat{\nu}_i^j$, $i, j=1, 2$, $i\not=j$}.
$E_{i2}^{b*}$
$i, j=1, 2$
$i\neq j$
Exist if $0<\mu_i<K_i$; LAS if $\frac{K_i-1}{2}<\mu_i<K_i$ and condition (2) is satisfied Do not exist Do not exist
Cond. 1:
Cond. 2:
$0<\frac{d_i}{a_j-d_i}<K_j<\mu_j$ and $\rho_j<\frac{d_j-K_j(a_j-d_j)}{\nu_i\left[K_j(a_j-d_i)-d_i\right]}$; $i, j=1, 2$, $i\not=j$
Table 4.  Summary of the effect of the proportion of predators using the passive dispersal on the interior equilibria of Model (3) From Figures 5(a), and 6(a). LAS refers to local asymptotical stability, ✗ implies the equilibrium does not exist, and $E_{x_1y_1x_2y_2}^{i}, i=1, 2, 3$ are the three possible interior equilibria of Model (3)
Scenarios $\mathbf{a_1= 1 \mbox{ and } d_1 =0.85 }$ $\mathbf{a_1= 2.1 \mbox{ and } d_1 = 2 }$
${\bf{E}}_{{{\bf{x}}_{\bf{1}}}{{\bf{y}}_{\bf{1}}}{{\bf{x}}_2}{{\bf{y}}_{\bf{2}}}}^{\bf{1}}$ ${\bf{E}}_{{{\bf{x}}_{\bf{1}}}{{\bf{y}}_{\bf{1}}}{{\bf{x}}_2}{{\bf{y}}_{\bf{2}}}}^2$ ${\bf{E}}_{{{\bf{x}}_{\bf{1}}}{{\bf{y}}_{\bf{1}}}{{\bf{x}}_2}{{\bf{y}}_{\bf{2}}}}^3$ ${\bf{E}}_{{{\bf{x}}_{\bf{1}}}{{\bf{y}}_{\bf{1}}}{{\bf{x}}_2}{{\bf{y}}_{\bf{2}}}}^{\bf{1}}$ ${\bf{E}}_{{{\bf{x}}_{\bf{1}}}{{\bf{y}}_{\bf{1}}}{{\bf{x}}_2}{{\bf{y}}_{\bf{2}}}}^2$ ${\bf{E}}_{{{\bf{x}}_{\bf{1}}}{{\bf{y}}_{\bf{1}}}{{\bf{x}}_2}{{\bf{y}}_{\bf{2}}}}^3$
s ≤ 0.07 Source Saddle Source LAS
0.9 ≤ s ≤ 0.15 Source Saddle Saddle LAS
0.2 ≤ s ≤ 0.43 Saddle LAS Saddle Saddle
0.68 < s < 0.82 LAS
s ≥ 0.82
0.83 ≤ s ≤ 0.84 Saddle Saddle LAS Saddle
s ≥ 0.84 Saddle Saddle Saddle Saddle
Scenarios $\mathbf{a_1= 1 \mbox{ and } d_1 =0.85 }$ $\mathbf{a_1= 2.1 \mbox{ and } d_1 = 2 }$
${\bf{E}}_{{{\bf{x}}_{\bf{1}}}{{\bf{y}}_{\bf{1}}}{{\bf{x}}_2}{{\bf{y}}_{\bf{2}}}}^{\bf{1}}$ ${\bf{E}}_{{{\bf{x}}_{\bf{1}}}{{\bf{y}}_{\bf{1}}}{{\bf{x}}_2}{{\bf{y}}_{\bf{2}}}}^2$ ${\bf{E}}_{{{\bf{x}}_{\bf{1}}}{{\bf{y}}_{\bf{1}}}{{\bf{x}}_2}{{\bf{y}}_{\bf{2}}}}^3$ ${\bf{E}}_{{{\bf{x}}_{\bf{1}}}{{\bf{y}}_{\bf{1}}}{{\bf{x}}_2}{{\bf{y}}_{\bf{2}}}}^{\bf{1}}$ ${\bf{E}}_{{{\bf{x}}_{\bf{1}}}{{\bf{y}}_{\bf{1}}}{{\bf{x}}_2}{{\bf{y}}_{\bf{2}}}}^2$ ${\bf{E}}_{{{\bf{x}}_{\bf{1}}}{{\bf{y}}_{\bf{1}}}{{\bf{x}}_2}{{\bf{y}}_{\bf{2}}}}^3$
s ≤ 0.07 Source Saddle Source LAS
0.9 ≤ s ≤ 0.15 Source Saddle Saddle LAS
0.2 ≤ s ≤ 0.43 Saddle LAS Saddle Saddle
0.68 < s < 0.82 LAS
s ≥ 0.82
0.83 ≤ s ≤ 0.84 Saddle Saddle LAS Saddle
s ≥ 0.84 Saddle Saddle Saddle Saddle
[1]

Yun Kang, Sourav Kumar Sasmal, Komi Messan. A two-patch prey-predator model with predator dispersal driven by the predation strength. Mathematical Biosciences & Engineering, 2017, 14 (4) : 843-880. doi: 10.3934/mbe.2017046

[2]

Jinfeng Wang, Hongxia Fan. Dynamics in a Rosenzweig-Macarthur predator-prey system with quiescence. Discrete & Continuous Dynamical Systems - B, 2016, 21 (3) : 909-918. doi: 10.3934/dcdsb.2016.21.909

[3]

Wei Feng, Nicole Rocco, Michael Freeze, Xin Lu. Mathematical analysis on an extended Rosenzweig-MacArthur model of tri-trophic food chain. Discrete & Continuous Dynamical Systems - S, 2014, 7 (6) : 1215-1230. doi: 10.3934/dcdss.2014.7.1215

[4]

Chiu-Yen Kao, Yuan Lou, Wenxian Shen. Random dispersal vs. non-local dispersal. Discrete & Continuous Dynamical Systems - A, 2010, 26 (2) : 551-596. doi: 10.3934/dcds.2010.26.551

[5]

Isam Al-Darabsah, Xianhua Tang, Yuan Yuan. A prey-predator model with migrations and delays. Discrete & Continuous Dynamical Systems - B, 2016, 21 (3) : 737-761. doi: 10.3934/dcdsb.2016.21.737

[6]

R. P. Gupta, Peeyush Chandra, Malay Banerjee. Dynamical complexity of a prey-predator model with nonlinear predator harvesting. Discrete & Continuous Dynamical Systems - B, 2015, 20 (2) : 423-443. doi: 10.3934/dcdsb.2015.20.423

[7]

Xinfu Chen, Yuanwei Qi, Mingxin Wang. Steady states of a strongly coupled prey-predator model. Conference Publications, 2005, 2005 (Special) : 173-180. doi: 10.3934/proc.2005.2005.173

[8]

Kousuke Kuto, Yoshio Yamada. Coexistence states for a prey-predator model with cross-diffusion. Conference Publications, 2005, 2005 (Special) : 536-545. doi: 10.3934/proc.2005.2005.536

[9]

Mingxin Wang, Peter Y. H. Pang. Qualitative analysis of a diffusive variable-territory prey-predator model. Discrete & Continuous Dynamical Systems - A, 2009, 23 (3) : 1061-1072. doi: 10.3934/dcds.2009.23.1061

[10]

Guirong Jiang, Qishao Lu. The dynamics of a Prey-Predator model with impulsive state feedback control. Discrete & Continuous Dynamical Systems - B, 2006, 6 (6) : 1301-1320. doi: 10.3934/dcdsb.2006.6.1301

[11]

J. Gani, R. J. Swift. Prey-predator models with infected prey and predators. Discrete & Continuous Dynamical Systems - A, 2013, 33 (11&12) : 5059-5066. doi: 10.3934/dcds.2013.33.5059

[12]

Wenjie Ni, Mingxin Wang. Dynamical properties of a Leslie-Gower prey-predator model with strong Allee effect in prey. Discrete & Continuous Dynamical Systems - B, 2017, 22 (9) : 3409-3420. doi: 10.3934/dcdsb.2017172

[13]

Song Liang, Yuan Lou. On the dependence of population size upon random dispersal rate. Discrete & Continuous Dynamical Systems - B, 2012, 17 (8) : 2771-2788. doi: 10.3934/dcdsb.2012.17.2771

[14]

Long Zhang, Gao Xu, Zhidong Teng. Intermittent dispersal population model with almost period parameters and dispersal delays. Discrete & Continuous Dynamical Systems - B, 2016, 21 (6) : 2011-2037. doi: 10.3934/dcdsb.2016034

[15]

Yang Lu, Xia Wang, Shengqiang Liu. A non-autonomous predator-prey model with infected prey. Discrete & Continuous Dynamical Systems - B, 2017, 22 (11) : 1-20. doi: 10.3934/dcdsb.2018082

[16]

Huiling Li, Peter Y. H. Pang, Mingxin Wang. Qualitative analysis of a diffusive prey-predator model with trophic interactions of three levels. Discrete & Continuous Dynamical Systems - B, 2012, 17 (1) : 127-152. doi: 10.3934/dcdsb.2012.17.127

[17]

Shanbing Li, Jianhua Wu. Effect of cross-diffusion in the diffusion prey-predator model with a protection zone. Discrete & Continuous Dynamical Systems - A, 2017, 37 (3) : 1539-1558. doi: 10.3934/dcds.2017063

[18]

Shuping Li, Weinian Zhang. Bifurcations of a discrete prey-predator model with Holling type II functional response. Discrete & Continuous Dynamical Systems - B, 2010, 14 (1) : 159-176. doi: 10.3934/dcdsb.2010.14.159

[19]

Meng Zhao, Wan-Tong Li, Jia-Feng Cao. A prey-predator model with a free boundary and sign-changing coefficient in time-periodic environment. Discrete & Continuous Dynamical Systems - B, 2017, 22 (9) : 3295-3316. doi: 10.3934/dcdsb.2017138

[20]

Alex John Quijano, Michele L. Joyner, Edith Seier, Nathaniel Hancock, Michael Largent, Thomas C. Jones. An aggregate stochastic model incorporating individual dynamics for predation movements of anelosimus studiosus. Mathematical Biosciences & Engineering, 2015, 12 (3) : 585-607. doi: 10.3934/mbe.2015.12.585

2017 Impact Factor: 0.972

Metrics

  • PDF downloads (4)
  • HTML views (1)
  • Cited by (1)

Other articles
by authors

[Back to Top]