# American Institute of Mathematical Sciences

• Previous Article
A proton therapy model using discrete difference equations with an example of treating hepatocellular carcinoma
• MBE Home
• This Issue
• Next Article
A chaotic bursting-spiking transition in a pancreatic beta-cells system: observation of an interior glucose-induced crisis
August  2017, 14(4): 843-880. doi: 10.3934/mbe.2017046

## A two-patch prey-predator model with predator dispersal driven by the predation strength

 1 Sciences and Mathematics Faculty, College of Integrative Sciences and Arts, Arizona State University, Mesa, AZ 85212, USA 2 Agricultural and Ecological Research Unit, Indian Statistical Institute, 203, B. T. Road, Kolkata 700108, India 3 Simon A. Levin Mathematical and Computational Modeling Sciences Center, Arizona State University, Mesa, AZ 85212, USA

* Corresponding author: Yun Kang

Received  August 30, 2016 Accepted  December 25, 2016 Published  February 2017

Fund Project: The first author 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)

Foraging movements of predator play an important role in population dynamics of prey-predator systems, which have been considered as mechanisms that contribute to spatial self-organization of prey and predator. In nature, there are many examples of prey-predator interactions where prey is immobile while predator disperses between patches non-randomly through different factors such as stimuli following the encounter of a prey. In this work, we formulate a Rosenzweig-MacArthur prey-predator two patch model with mobility only in predator and the assumption that predators move towards patches with more concentrated prey-predator interactions. We provide completed local and global analysis of our model. Our analytical results combined with bifurcation diagrams suggest that: (1) dispersal may stabilize or destabilize the coupled system; (2) dispersal may generate multiple interior equilibria that lead to rich bistable dynamics or may destroy interior equilibria that lead to the extinction of predator in one patch or both patches; (3) Under certain conditions, the large dispersal can promote the permanence of the system. In addition, we compare the dynamics of our model to the classic two patch model to obtain a better understanding how different dispersal strategies may have different impacts on the dynamics and spatial patterns.

Citation: 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
##### References:

show all references

##### References:
One and two bifurcation diagrams of Model (4) where $r=1.5$, $d_1=0.2$, $d_2=0.1$, $K_1=5$, $K_2=3$, $a_1=0.25$, and $a_2=0.15$. The left figure (1a) describes how number of interior equilibria changes for different dispersal values $\rho_i, i=1,2$: black regions have three interior equilibria; red regions have two interior equilibria; blue regions have unique interior equilibrium; yellow regions have no interior equilibrium and predator in Patch 2 dies out; white regions have no interior equilibrium and both predator die out. The right figure (1b) describes the number of interior equilibria and their stability when $\rho_2=0.025$ and $\rho_1$ changes from 0 to 0.5 where $y$-axis is the population size of predator at Patch 1: Blue represents the sink; green represents the saddle; and red represents the source
One dimensional bifurcation diagrams of Model (4) where $r=1.5$, $d_1=0.2$, $d_2=0.1$, $K_1=5$, $K_2=3$ and $a_1=0.25$. The left figure (4a) describes describes the number of interior equilibria and their stability when $\rho_1=0.5$ and $\rho_2$ changes from 0 to 0.05. The right figure (4b) describes the number of interior equilibria and their stability when $\rho_1=0.6$ and $\rho_2$ changes from 0 to 1.8. In both figures, blue represents the sink; green represents the saddle; and red represents the source
One and two bifurcation diagrams of Model (4) where $r=1.5$, $d_1=0.2$, $d_2=0.1$, $K_1=5$, $K_2=3$, $a_1=0.25$ and $a_2=0.25$. The left figure (2a) describes how number of interior equilibria changes for different dispersal values $\rho_i, i=1,2$: black regions have three interior equilibria; red regions have two interior equilibria; blue regions have unique interior equilibrium; yellow regions have no interior equilibrium and predator in Patch 2 dies out; white regions have no interior equilibrium and both predator die out. The right figure (2b) describes the number of interior equilibria and their stability when $\rho_1=1$ and $\rho_2$ changes from 0 to 2.5 where $y$-axis is the population size of predator at Patch 1: Blue represents the sink; green represents the saddle; and red represents the source
One and two bifurcation diagrams of Model (4) where $r=1.5$, $d_1=0.2$, $d_2=0.1$, $K_1=5$, $K_2=3$, $a_1=0.35$ and $a_2=0.25$. The left figure (3a) describes how number of interior equilibria changes for different dispersal values $\rho_i, i=1,2$: black regions have three interior equilibria; red regions have two interior equilibria; blue regions have unique interior equilibrium; yellow regions have no interior equilibrium and predator in Patch 2 dies out; white regions have no interior equilibrium and both predator die out. The right figure (3b) describes the number of interior equilibria and their stability when $\rho_1=1$ and $\rho_2$ changes from 0 to 7 where $y$-axis is the population size of predator at Patch 1: Blue represents the sink; green represents the saddle; and red represents the source
The comparison of boundary equilibria between Model (4) and Model (12). LAS refers to the local asymptotical stability, and GAS refers to the global stability
 Scenarios Model (4) whose dispersal is driven by the strength of prey-predator interactions Classical Model (12) whose dispersal is driven by the density of predators $E_{K_10K_20}$ LAS and GAS if $\mu_i>K_i$ for both $i=1,2$. Dispersal has no effects on its stability. GAS if $\mu_i>K_i$ for both $i=1,2$; While LAS if $d_1+d_2+\rho_1+\rho_2>\frac{a_1K_1}{1+K_1}+\frac{a_2K_2}{1+K_2}$ and $\left[ d_1-\frac{a_1K_1}{1+K_1}\right]\left[1-\frac{a_2K_2}{(d_2+\rho_2)(1+K_2)}\right]+\frac{\rho_1}{d_2+\rho_2}\left[ d_2-\frac{a_2K_2}{1+K_2}\right]>0$. Large dispersal may be able to stabilize the equilibrium. $E_{i2}^b$ ($y_i=0$) LAS if $\frac{K_i-1}{2}<\mu_i  Scenarios Model (4) whose dispersal is driven by the strength of prey-predator interactions Classical Model (12) whose dispersal is driven by the density of predators$E_{K_10K_20}$LAS and GAS if$\mu_i>K_i$for both$i=1,2$. Dispersal has no effects on its stability. GAS if$\mu_i>K_i$for both$i=1,2$; While LAS if$d_1+d_2+\rho_1+\rho_2>\frac{a_1K_1}{1+K_1}+\frac{a_2K_2}{1+K_2}$and$\left[ d_1-\frac{a_1K_1}{1+K_1}\right]\left[1-\frac{a_2K_2}{(d_2+\rho_2)(1+K_2)}\right]+\frac{\rho_1}{d_2+\rho_2}\left[ d_2-\frac{a_2K_2}{1+K_2}\right]>0$. Large dispersal may be able to stabilize the equilibrium.$E_{i2}^b$($y_i=0$) LAS if$\frac{K_i-1}{2}<\mu_i
The comparison of prey persistence and extinction between Model (4) and Model (12)
 Scenarios Model (4) whose dispersal is driven by the strength of prey-predator interactions Classical Model (12) whose dispersal is driven by the density of predators Persistence of prey Always persist, dispersal of predator has no effects One or both prey persist if conditions 4. in Theorem (4.1) holds. Small dispersal of predator in Patch $i$ and large dispersal of predator in Patch $j$ can help the persistence of prey in Patch $i$. Extinction of prey Never extinct $x_i$ extinct if $\frac{K_j-1}{2}<\widehat{\mu}_j  Scenarios Model (4) whose dispersal is driven by the strength of prey-predator interactions Classical Model (12) whose dispersal is driven by the density of predators Persistence of prey Always persist, dispersal of predator has no effects One or both prey persist if conditions 4. in Theorem (4.1) holds. Small dispersal of predator in Patch$i$and large dispersal of predator in Patch$j$can help the persistence of prey in Patch$i$. Extinction of prey Never extinct$x_i$extinct if$\frac{K_j-1}{2}<\widehat{\mu}_j
The comparison of predator persistence and extinction between Model (4) and Model (12)
 Scenarios Model (4) whose dispersal is driven by the strength of prey-predator interactions Classical Model (12) whose dispersal is driven by the density of predators Persistence of predator Predator at Patch $j$ is persistent if Conditions in Theorem 3.6 holds. Small dispersal of predator in Patch $j$ can help the persistence of predator in that patch. Dispersal is able to promote the persistence of predator when predator goes extinct in the single patch model. Predators in both patches have the same persistence conditions. They persist if $0<{\mu}_iK_i$ or $\mu_i<0$ for $i=1,2$
 Scenarios Model (4) whose dispersal is driven by the strength of prey-predator interactions Classical Model (12) whose dispersal is driven by the density of predators Persistence of predator Predator at Patch $j$ is persistent if Conditions in Theorem 3.6 holds. Small dispersal of predator in Patch $j$ can help the persistence of predator in that patch. Dispersal is able to promote the persistence of predator when predator goes extinct in the single patch model. Predators in both patches have the same persistence conditions. They persist if $0<{\mu}_iK_i$ or $\mu_i<0$ for $i=1,2$
 [1] 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 [2] Razvan C. Fetecau, Beril Zhang. Self-organization on Riemannian manifolds. Journal of Geometric Mechanics, 2019, 11 (3) : 397-426. doi: 10.3934/jgm.2019020 [3] 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 [4] 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 [5] Yaying Dong, Shanbing Li, Yanling Li. Effects of dispersal for a predator-prey model in a heterogeneous environment. Communications on Pure & Applied Analysis, 2019, 18 (5) : 2511-2528. doi: 10.3934/cpaa.2019114 [6] 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 [7] 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 [8] Sampurna Sengupta, Pritha Das, Debasis Mukherjee. Stochastic non-autonomous Holling type-Ⅲ prey-predator model with predator's intra-specific competition. Discrete & Continuous Dynamical Systems - B, 2018, 23 (8) : 3275-3296. doi: 10.3934/dcdsb.2018244 [9] 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 [10] 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 [11] 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 [12] 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 [13] 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 [14] 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 [15] Yongli Cai, Malay Banerjee, Yun Kang, Weiming Wang. Spatiotemporal complexity in a predator--prey model with weak Allee effects. Mathematical Biosciences & Engineering, 2014, 11 (6) : 1247-1274. doi: 10.3934/mbe.2014.11.1247 [16] Yang Lu, Xia Wang, Shengqiang Liu. A non-autonomous predator-prey model with infected prey. Discrete & Continuous Dynamical Systems - B, 2018, 23 (9) : 3817-3836. doi: 10.3934/dcdsb.2018082 [17] 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 [18] 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 [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] 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

2018 Impact Factor: 1.313