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On stochastic Lotka-Volterra predator-prey model

On stochastic Lotka-Volterra predator-prey model


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I am currently working (as a mathematician) on some estimations involving an stochastic predator-prey type model in which some of the coefficients have been perturbed by a Brownian Motion yielding to a system of stochastic differential equations.

The basic formulation of this problem can be found for instance in this article.

We have the following model

egin{equation}label{0} egin{cases} dX(t)=X(t)left(a-bX(t)-frac{sY(t)}{eta+Y(t)} ight)dt X(0)=x>0, dY(t)=Y(t)left(frac{h X(t)}{eta+Y(t)}-c-fY(t) ight)dt Y(0)=y>0. end{cases} end{equation}

where we use Holling II response function.

In the literature (mostly mathematics literature) the authors propose to perturb the birth rate of the preys $amapsto a+dot{B}_1(t)$ and the mortality rate of predators $cmapsto c+dot{B}_2(t)$.

This yields to the following (stochastic) system

egin{equation}label{1} egin{cases} dX(t)=X(t)left(a-bX(t)-frac{sY(t)}{eta+Y(t)} ight)dt+sigma_1 X(t)dB_1(t), X(0)=x>0, dY(t)=Y(t)left(frac{h X(t)}{eta+Y(t)}-c-fY(t) ight)dt+sigma_2 Y(t)dB_2(t), Y(0)=y>0 end{cases} end{equation}

I know nothing about biology but I am concerned about a couple of things regarding this particular formulation:

  1. If we assume that the Brownian motions are perturbing the birth/mortality rates, how can I interpret the fact that this new "perturbed" rates can become negative (due to the effect of the BM). Does it have any sense (in the framework of this particular model) to talk about "negative birth rate" or "negative mortality rate"?
  2. What if the "perturbed" mortality rate goes beyond $100 \%$? Mathematically nothing "wrong" will happen, but what about the interpretation?

Notice that the "noise" and the model as a whole can be interpreted differently (ignoring the fact that we are actually modelling the dynamics of two species) but my main issue is that if I(together with many authors) am stating that we perturb a certain parameter I believe that we must respect the basic assumptions of the model!

I hope everything's clear and I thank you all in advance, any opinion or suggestion will be welcome!


  1. To interpret $a$ and $c$ as a birth and mortality rates is somewhat inaccurate, as $a$ and $c$, as written are density-independent rates that grow or shrink populations exponentially. If $a$ were negative, the prey population would shrink and most people would find a problem like that unexciting unless it was, say, a sink population maintained by immigration from another population. This is true of $c$ being negative, where the predator population would grow independent of the prey population. More accurately, $a$ can be interpreted as the difference between birth, $b$, and deaths, $delta$; i.e., $a = b - delta$. In this case, depending on the kinds of stochasticity being added to the model, negative $a$ is not just normal, but expected. The same is true for $c$. Ultimately, a perturbations like the white noise in this equation is simultaneously perturbing the difference between birth and death rates, which explains the negative difference.
  2. The value of the rates seem to have the same interpretation if they are constant or are perturbed to an arbitrary magnitude.

I hope this helps!


On stochastic Lotka-Volterra predator-prey model - Biology

We investigate spatially inhomogeneous versions of the stochastic Lotka-Volterra model for predator-prey competition and coexistence by means of Monte Carlo simulations on a two-dimensional lattice with periodic boundary conditions. To study boundary effects for this paradigmatic population dynamics system, we employ a simulation domain split into two patches: Upon setting the predation rates at two distinct values, one half of the system resides in an absorbing state where only the prey survives, while the other half attains a stable coexistence state wherein both species remain active. At the domain boundary, we observe a marked enhancement of the predator population density. The predator correlation length displays a minimum at the boundary, before reaching its asymptotic constant value deep in the active region. The frequency of the population oscillations appears only very weakly affected by the existence of two distinct domains, in contrast to their attenuation rate, which assumes its largest value there. We also observe that boundary effects become less prominent as the system is successively divided into subdomains in a checkerboard pattern, with two different reaction rates assigned to neighboring patches. When the domain size becomes reduced to the scale of the correlation length, the mean population densities attain values that are very similar to those in a disordered system with randomly assigned reaction rates drawn from a bimodal distribution.


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2. Stochastic Differential Equation with Markovian Switching

Throughout this paper, unless otherwise specified, we let be a complete probability space with a filtration satisfying the usual conditions (i.e., it is right continuous and contains all -null sets.) Let , , be a right-continuous Markov chain in the probability space tasking values in a finite state space with generator given by

where . Here, is the transition rate from to if , while . We assume that the Markov chain is independent of the Brownian motion. And almost every sample path of is a right-continuous step function with a finite number of simple jumps in any finite subinterval of .

We assume, as a standing hypothesis in following of the paper, that the Markov chain is irreducible. The algebraic interpretation of irreducibility is rank. Under this condition, the Markov chain has a unique stationary distribution which can be determined by solving the following linear equation:


Department of Mathematical Sciences, College of Science, UAE University, Al Ain, 15551, UAE

* Corresponding author: F.A. Rihan ([email protected])

Received April 2020 Revised September 2020 Published November 2020

Fund Project: This work supported by UPAR-Project (Code # G00003440)

Environmental factors and random variation have strong effects on the dynamics of biological and ecological systems. In this paper, we propose a stochastic delay differential model of two-prey, one-predator system with cooperation among prey species against predator. The model has a global positive solution. Sufficient conditions of existence and uniqueness of an ergodic stationary distribution of the positive solution are provided, by constructing suitable Lyapunov functionals. Sufficient conditions for possible extinction of the predator populations are also obtained. The conditions are expressed in terms of a threshold parameter $ _0^s $ that relies on the environmental noise. Illustrative examples and numerical simulations, using Milstein's scheme, are carried out to illustrate the theoretical results. A small scale of noise can promote survival of the species. While relative large noises can lead to possible extinction of the species in such an environment.

References:

J. Alebraheem and Y. A. Hasan, Dynamics of a two predator–one prey system, Computational and Applied Mathematics, 33 (2014), 767-780. doi: 10.1007/s40314-013-0093-8. Google Scholar

A. Bahar and X. Mao, Stochastic delay Lotka-Volterra model, Journal of Mathematical Analysis and Applications, 292 (2004), 364-380. doi: 10.1016/j.jmaa.2003.12.004. Google Scholar

Y. Bai and Y. Li, Stability and Hopf bifurcation for a stage-structured predator–prey model incorporating refuge for prey and additional food for predator, Advances in Difference Equations, 2019 (2019), 1-20. doi: 10.1186/s13662-019-1979-6. Google Scholar

J. Bao and C. Yuan, Stochastic population dynamics driven by Lévy noise, Journal of Mathematical Analysis and Applications, 391 (2012), 363-375. doi: 10.1016/j.jmaa.2012.02.043. Google Scholar

G. A. Bocharov and F. A. Rihan, Numerical modelling in biosciences using delay differential equations, Journal of Computational and Applied Mathematics, 125 (2000), 183-199. doi: 10.1016/S0377-0427(00)00468-4. Google Scholar

E. Buckwar, Introduction to the numerical analysis of stochastic delay differential equations, Journal of Computational and Applied Mathematics, 125 (2000), 297-307. doi: 10.1016/S0377-0427(00)00475-1. Google Scholar

B. Dubey and A. Kumar, Dynamics of prey–predator model with stage structure in prey including maturation and gestation delays, Nonlinear Dynamics, 96 (2019), 2653-2679. doi: 10.1007/s11071-019-04951-5. Google Scholar

M. F. Elettreby, Two-prey one-predator model, Chaos, Solitons & Fractals, 39 (2009), 2018-2027. doi: 10.1016/j.chaos.2007.06.058. Google Scholar

T. C. Gard, Persistence in stochastic food web models, Bulletin of Mathematical Biology, 46 (1984), 357-370. doi: 10.1016/S0092-8240(84)80044-0. Google Scholar

D. J. Higham, An algorithmic introduction to numerical simulation of stochastic differential equations, SIAM Review, 43 (2001), 525-546. doi: 10.1137/S0036144500378302. Google Scholar

R. Khasminskii, Stochastic Stability of Differential Equations, Springer-Verlag Berlin Heidelberg, 2012. doi: 10.1007/978-3-642-23280-0. Google Scholar

P. E. Kloeden and T. Shardlow, The Milstein scheme for stochastic delay differential equations without using anticipative calculus, Stochastic Analysis and Applications, 30 (2012), 181-202. doi: 10.1080/07362994.2012.628907. Google Scholar

S. Kundu and S. Maitra, Dynamical behaviour of a delayed three species predator–prey model with cooperation among the prey species, Nonlinear Dynamics, 92 (2018), 627-643. doi: 10.1007/s11071-018-4079-3. Google Scholar

D. Li, S. Liu and J. Cui, Threshold dynamics and ergodicity of an SIRS epidemic model with Markovian switching, Journal of Differential Equations, 263 (2017), 8873-8915. doi: 10.1016/j.jde.2017.08.066. Google Scholar

R. S. Liptser, A strong law of large numbers for local martingales, Stochastics, 3 (1980), 217-228. doi: 10.1080/17442508008833146. Google Scholar

Z. Liu and R. Tan, Impulsive harvesting and stocking in a monod–haldane functional response predator–prey system, Chaos, Solitons & Fractals, 34 (2007), 454-464. doi: 10.1016/j.chaos.2006.03.054. Google Scholar

Q. Liu, D. Jiang, T. Hayat and A. Alsaedi, Stationary distribution and extinction of a stochastic predator–prey model with herd behavior, Journal of the Franklin Institute, 355 (2018), 8177-8193. doi: 10.1016/j.jfranklin.2018.09.013. Google Scholar

A. J. Lotka, Elements of Physical Biology, Baltimore: Williams & Wilkins Co., 1925. Google Scholar

X. Mao, Stochastic Differential Equations and Applications, Elsevier, 2008. doi: 10.1533/9780857099402. Google Scholar

X. Mao, G. Marion and E. Renshaw, Environmental Brownian noise suppresses explosions in population dynamics, Stochastic Processes and their Applications, 97 (2002), 95-110. doi: 10.1016/S0304-4149(01)00126-0. Google Scholar

X. Mao, S. Sabanis and E. Renshaw, Asymptotic behaviour of the stochastic Lotka-Volterra model, Journal of Mathematical Analysis and Applications, 287 (2003), 141-156. doi: 10.1016/S0022-247X(03)00539-0. Google Scholar

J. D. Murray, Mathematical Biology, Springer New york, 1993. doi: 10.1007/b98869. Google Scholar

R. Rakkiyappan, A. Chandrasekar, F. A. Rihan and S. Lakshmanan, Exponential state estimation of Markovian jumping genetic regulatory networks with mode-dependent probabilistic time-varying delays, Mathematical Biosciences, 25 (2014), 30-53. doi: 10.1016/j.mbs.2014.02.008. Google Scholar

R Rakkiyappan, G. Velmurugan, F. A. Rihan and and S. Lakshmanan, Stability analysis of memristor-based complex-valued recurrent neural networks with time delays, Complexity, 21 (2015), 14-39. doi: 10.1002/cplx.21618. Google Scholar

F. A. Rihan, S. Lakshmanan, A. H. Hashish, R. Rakkiyappan and E. Ahmed, Fractional-order delayed predator–prey systems with functional response, Nonlinear Dynamics, 80 (2015), 777-789. doi: 10.1007/s11071-015-1905-8. Google Scholar

F. A. Rihan, H. J. Alsakaji and C. Rajivganthi, Stability and Hopf bifurcation of three-species prey-predator system with time delays and Allee effect, Complexity, 2020 (2020), 7306412. doi: 10.1155/2020/7306412. Google Scholar

F. A. Rihan, A. A. Azamov and H. J. Al-Sakaji, An inverse problem for delay differential equations: Parameter estimation, nonlinearity, sensitivity, Applied Mathematics & Information Sciences, 12 (2018), 63-74. doi: 10.18576/amis/120106. Google Scholar

F. A. Rihan and H. J. Alsakaji, Persistence and extinction for stochastic delay differential model of prey-predator system with hunting cooperation in predators, Advances in Difference Equations, 124 (2020), 1-22. doi: 10.1186/s13662-020-02579-z. Google Scholar

F.A. Rihan, H.J. Alsakaji and C. Rajivganthi, Stochastic SIRC epidemic model with time-delay for COVID-19, Adv. Differ. Equ., 2020 (2020), 1-20. doi: 10.1186/s13662-020-02964-8. Google Scholar

F. A. Rihan, C. Rajivganthi and P. Muthukumar, Fractional stochastic differential equations with Hilfer fractional derivative: Poisson jumps and optimal control, Discrete Dynamics in Nature and Society, 2017 (2017), Art. ID 5394528, 11 pp. doi: 10.1155/2017/5394528. Google Scholar

T. Saha and M. Bandyopadhyay, Dynamical analysis of a delayed ratio-dependent prey–predator model within fluctuating environment, Applied Mathematics and Computation, 196 (2008), 458-478. doi: 10.1016/j.amc.2007.06.017. Google Scholar

G. Tang, S. Tang and R. A. Cheke, Global analysis of a holling type II predator–prey model with a constant prey refuge, Nonlinear Dynamics, 76 (2014), 635-647. doi: 10.1007/s11071-013-1157-4. Google Scholar

D. A. Vasseuri and P. Yodzis, The color of environmental noise, Ecology, 85 (2004), 1146-1152. Google Scholar

V. Volterra, Variazioni e Fluttuazioni Del Numero D'individui in Specie Animali Conviventi, C. Ferrari, 1927. Google Scholar

X. Mao, C. Yuan and J. Zou, Stochastic differential delay equations of population dynamics, Journal of Mathematical Analysis and Applications, 304 (2005), 296-320. doi: 10.1016/j.jmaa.2004.09.027. Google Scholar

X. Zhao and Z. Zeng, Stationary distribution and extinction of a stochastic ratio-dependent predator–prey system with stage structure for the predator, Physica A: Statistical Mechanics and its Applications, 545 (2020), 123310, 17 pp. doi: 10.1016/j.physa.2019.123310. Google Scholar

References:

J. Alebraheem and Y. A. Hasan, Dynamics of a two predator–one prey system, Computational and Applied Mathematics, 33 (2014), 767-780. doi: 10.1007/s40314-013-0093-8. Google Scholar

A. Bahar and X. Mao, Stochastic delay Lotka-Volterra model, Journal of Mathematical Analysis and Applications, 292 (2004), 364-380. doi: 10.1016/j.jmaa.2003.12.004. Google Scholar

Y. Bai and Y. Li, Stability and Hopf bifurcation for a stage-structured predator–prey model incorporating refuge for prey and additional food for predator, Advances in Difference Equations, 2019 (2019), 1-20. doi: 10.1186/s13662-019-1979-6. Google Scholar

J. Bao and C. Yuan, Stochastic population dynamics driven by Lévy noise, Journal of Mathematical Analysis and Applications, 391 (2012), 363-375. doi: 10.1016/j.jmaa.2012.02.043. Google Scholar

G. A. Bocharov and F. A. Rihan, Numerical modelling in biosciences using delay differential equations, Journal of Computational and Applied Mathematics, 125 (2000), 183-199. doi: 10.1016/S0377-0427(00)00468-4. Google Scholar

E. Buckwar, Introduction to the numerical analysis of stochastic delay differential equations, Journal of Computational and Applied Mathematics, 125 (2000), 297-307. doi: 10.1016/S0377-0427(00)00475-1. Google Scholar

B. Dubey and A. Kumar, Dynamics of prey–predator model with stage structure in prey including maturation and gestation delays, Nonlinear Dynamics, 96 (2019), 2653-2679. doi: 10.1007/s11071-019-04951-5. Google Scholar

M. F. Elettreby, Two-prey one-predator model, Chaos, Solitons & Fractals, 39 (2009), 2018-2027. doi: 10.1016/j.chaos.2007.06.058. Google Scholar

T. C. Gard, Persistence in stochastic food web models, Bulletin of Mathematical Biology, 46 (1984), 357-370. doi: 10.1016/S0092-8240(84)80044-0. Google Scholar

D. J. Higham, An algorithmic introduction to numerical simulation of stochastic differential equations, SIAM Review, 43 (2001), 525-546. doi: 10.1137/S0036144500378302. Google Scholar

R. Khasminskii, Stochastic Stability of Differential Equations, Springer-Verlag Berlin Heidelberg, 2012. doi: 10.1007/978-3-642-23280-0. Google Scholar

P. E. Kloeden and T. Shardlow, The Milstein scheme for stochastic delay differential equations without using anticipative calculus, Stochastic Analysis and Applications, 30 (2012), 181-202. doi: 10.1080/07362994.2012.628907. Google Scholar

S. Kundu and S. Maitra, Dynamical behaviour of a delayed three species predator–prey model with cooperation among the prey species, Nonlinear Dynamics, 92 (2018), 627-643. doi: 10.1007/s11071-018-4079-3. Google Scholar

D. Li, S. Liu and J. Cui, Threshold dynamics and ergodicity of an SIRS epidemic model with Markovian switching, Journal of Differential Equations, 263 (2017), 8873-8915. doi: 10.1016/j.jde.2017.08.066. Google Scholar

R. S. Liptser, A strong law of large numbers for local martingales, Stochastics, 3 (1980), 217-228. doi: 10.1080/17442508008833146. Google Scholar

Z. Liu and R. Tan, Impulsive harvesting and stocking in a monod–haldane functional response predator–prey system, Chaos, Solitons & Fractals, 34 (2007), 454-464. doi: 10.1016/j.chaos.2006.03.054. Google Scholar

Q. Liu, D. Jiang, T. Hayat and A. Alsaedi, Stationary distribution and extinction of a stochastic predator–prey model with herd behavior, Journal of the Franklin Institute, 355 (2018), 8177-8193. doi: 10.1016/j.jfranklin.2018.09.013. Google Scholar

A. J. Lotka, Elements of Physical Biology, Baltimore: Williams & Wilkins Co., 1925. Google Scholar

X. Mao, Stochastic Differential Equations and Applications, Elsevier, 2008. doi: 10.1533/9780857099402. Google Scholar

X. Mao, G. Marion and E. Renshaw, Environmental Brownian noise suppresses explosions in population dynamics, Stochastic Processes and their Applications, 97 (2002), 95-110. doi: 10.1016/S0304-4149(01)00126-0. Google Scholar

X. Mao, S. Sabanis and E. Renshaw, Asymptotic behaviour of the stochastic Lotka-Volterra model, Journal of Mathematical Analysis and Applications, 287 (2003), 141-156. doi: 10.1016/S0022-247X(03)00539-0. Google Scholar

J. D. Murray, Mathematical Biology, Springer New york, 1993. doi: 10.1007/b98869. Google Scholar

R. Rakkiyappan, A. Chandrasekar, F. A. Rihan and S. Lakshmanan, Exponential state estimation of Markovian jumping genetic regulatory networks with mode-dependent probabilistic time-varying delays, Mathematical Biosciences, 25 (2014), 30-53. doi: 10.1016/j.mbs.2014.02.008. Google Scholar

R Rakkiyappan, G. Velmurugan, F. A. Rihan and and S. Lakshmanan, Stability analysis of memristor-based complex-valued recurrent neural networks with time delays, Complexity, 21 (2015), 14-39. doi: 10.1002/cplx.21618. Google Scholar

F. A. Rihan, S. Lakshmanan, A. H. Hashish, R. Rakkiyappan and E. Ahmed, Fractional-order delayed predator–prey systems with functional response, Nonlinear Dynamics, 80 (2015), 777-789. doi: 10.1007/s11071-015-1905-8. Google Scholar

F. A. Rihan, H. J. Alsakaji and C. Rajivganthi, Stability and Hopf bifurcation of three-species prey-predator system with time delays and Allee effect, Complexity, 2020 (2020), 7306412. doi: 10.1155/2020/7306412. Google Scholar

F. A. Rihan, A. A. Azamov and H. J. Al-Sakaji, An inverse problem for delay differential equations: Parameter estimation, nonlinearity, sensitivity, Applied Mathematics & Information Sciences, 12 (2018), 63-74. doi: 10.18576/amis/120106. Google Scholar

F. A. Rihan and H. J. Alsakaji, Persistence and extinction for stochastic delay differential model of prey-predator system with hunting cooperation in predators, Advances in Difference Equations, 124 (2020), 1-22. doi: 10.1186/s13662-020-02579-z. Google Scholar

F.A. Rihan, H.J. Alsakaji and C. Rajivganthi, Stochastic SIRC epidemic model with time-delay for COVID-19, Adv. Differ. Equ., 2020 (2020), 1-20. doi: 10.1186/s13662-020-02964-8. Google Scholar

F. A. Rihan, C. Rajivganthi and P. Muthukumar, Fractional stochastic differential equations with Hilfer fractional derivative: Poisson jumps and optimal control, Discrete Dynamics in Nature and Society, 2017 (2017), Art. ID 5394528, 11 pp. doi: 10.1155/2017/5394528. Google Scholar

T. Saha and M. Bandyopadhyay, Dynamical analysis of a delayed ratio-dependent prey–predator model within fluctuating environment, Applied Mathematics and Computation, 196 (2008), 458-478. doi: 10.1016/j.amc.2007.06.017. Google Scholar

G. Tang, S. Tang and R. A. Cheke, Global analysis of a holling type II predator–prey model with a constant prey refuge, Nonlinear Dynamics, 76 (2014), 635-647. doi: 10.1007/s11071-013-1157-4. Google Scholar

D. A. Vasseuri and P. Yodzis, The color of environmental noise, Ecology, 85 (2004), 1146-1152. Google Scholar

V. Volterra, Variazioni e Fluttuazioni Del Numero D'individui in Specie Animali Conviventi, C. Ferrari, 1927. Google Scholar

X. Mao, C. Yuan and J. Zou, Stochastic differential delay equations of population dynamics, Journal of Mathematical Analysis and Applications, 304 (2005), 296-320. doi: 10.1016/j.jmaa.2004.09.027. Google Scholar

X. Zhao and Z. Zeng, Stationary distribution and extinction of a stochastic ratio-dependent predator–prey system with stage structure for the predator, Physica A: Statistical Mechanics and its Applications, 545 (2020), 123310, 17 pp. doi: 10.1016/j.physa.2019.123310. Google Scholar

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Parameters Description
$ r_1 $ , $ r_2 $ intrinsic growth rate for x and y
$ k_1 $ , $ k_2 $ carrying capacity for x and y
$ alpha_1 $ , $ alpha_2 $ rate of predation of preys x and y
$ eta $ rate of cooperation of preys x and y against predator z
$ delta $ Predator death rate
$ alpha_3 $ rate of intra-species competition within the predators
$ a_1 $ , $ a_2 $ transformation rate of predator to preys $ x $ and $ y $ .
Parameters Description
$ r_1 $ , $ r_2 $ intrinsic growth rate for x and y
$ k_1 $ , $ k_2 $ carrying capacity for x and y
$ alpha_1 $ , $ alpha_2 $ rate of predation of preys x and y
$ eta $ rate of cooperation of preys x and y against predator z
$ delta $ Predator death rate
$ alpha_3 $ rate of intra-species competition within the predators
$ a_1 $ , $ a_2 $ transformation rate of predator to preys $ x $ and $ y $ .

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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

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

Malay Banerjee, Nayana Mukherjee, Vitaly Volpert. Prey-predator model with nonlocal and global consumption in the prey dynamics. Discrete & Continuous Dynamical Systems - S, 2020, 13 (8) : 2109-2120. doi: 10.3934/dcdss.2020180

Gianni Gilioli, Sara Pasquali, Fabrizio Ruggeri. Nonlinear functional response parameter estimation in a stochastic predator-prey model. Mathematical Biosciences & Engineering, 2012, 9 (1) : 75-96. doi: 10.3934/mbe.2012.9.75

Aniello Buonocore, Luigia Caputo, Enrica Pirozzi, Amelia G. Nobile. A non-autonomous stochastic predator-prey model. Mathematical Biosciences & Engineering, 2014, 11 (2) : 167-188. doi: 10.3934/mbe.2014.11.167

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

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

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

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

Rui Xu, M.A.J. Chaplain, F.A. Davidson. Periodic solutions of a discrete nonautonomous Lotka-Volterra predator-prey model with time delays. Discrete & Continuous Dynamical Systems - B, 2004, 4 (3) : 823-831. doi: 10.3934/dcdsb.2004.4.823

Guanqi Liu, Yuwen Wang. Stochastic spatiotemporal diffusive predator-prey systems. Communications on Pure & Applied Analysis, 2018, 17 (1) : 67-84. doi: 10.3934/cpaa.2018005


Enzymatic reactions

Enzymes are molecules that mediate chemical reactions. A certain set of reactants can be converted into a certain set of products with some energy expenditure. The enzyme reduces the energy threshold for the reaction to occur. The simplest enzymatic reaction involves the conversion of a substrate $S$ into a product $P$. Enzymes $E$ react with the substrate to generate an intermediate product $I$. The intermediate product $I$ then liberates the enzyme $E$ to yield the final product $P$. A simple model of this process is given by the following set of reactions

Reaction $R_1$ models the combination of a substrate molecule $S$ and an enzyme $E$ to yield an intermediate specimen $I$. The latter may disassociate from the enzyme yielding either the original substrate $S$ (Reaction $R_2$) or the the final product $P$ (Reaction $R_3$). Hazards can be determined using the ideas in previous examples. It is ready to realize that hazards can be written as $h_1(S,E,I,P)=c_1 S E$, $h_2(S,E,I,P)=c_2 I$ and $h_3(S,E,I,P)=c_3 I$. Deterministic simulation of enzymatic reactions is in the file ssas_enzyme_deterministic. Stochastic simulation of enzymatic reactions is in ssas_enzyme_stochastic.m.


Introduction

In the past, unexpected large quotas in animal and fish catches had been occasionally reported. The phenomenon was attributed to the predators in those communities 1,2,3,4 . This predator and prey relationship is probably the most studied ecological dynamics in recent history. Theoretical studies of this system began when Alfred Lotka and Vito Volterra independently developed the well-known predator-prey model in the 1920s. Lotka developed the model to study autocatalytic chemical reactions and Volterra extended it to explain the fish catches in the Adriatic Sea. Since the earliest developments of the basic Lotka-Volterra system (LV system) 5,6,7,8,9,10 , many mathematical variations of predator-prey systems have been developed to explain unexpected changes and temporal fluctuations in the dynamics of animal populations. This system is considered to explain the dynamics of natural populations of snowshoe hare (Lepus americanus, the prey) and Canadian lynx (Lynx canadensis, the predator) that were estimated from the yearly changes in the collected number of furs 11 . In addition, unlike in natural communities, it has been shown that long-term coexistence of predators and prey is possible in a laboratory using predatory and prey mites 12 . Thus, the LV system has become the classical mathematical model for explaining the predator-prey interactions in natural communities 5,6,7,8,9,10,11 .

Mathematical properties of the classical LV system show either cyclic oscillation or divergent extinction of one species 13 . In any closed LV system, it is also important to note that the predators will eventually die out with the extinction of the preys. This means that the persistent predator-prey systems, without additional stabilizing mechanisms, should exhibit cyclic oscillations. However, stable coexistence in wild predator-prey systems has been observed 10 . Nonetheless, the case of the lynx-hare interaction 8 seems extremely unique natural predator-prey system. We often find stable coexistence of both prey and predator populations in the wild such as that of spider wasps (Pompilidae family, the predator) and spiders (the prey) 10 . These observations imply that there should be an additional stabilizing mechanism in natural predator-prey systems. For example, strong intraspecific competition in both predator and prey yields stable coexistence 14 . However, we have no evidence of such strong intraspecific competition in the wild. Thus, these observations of the most natural systems contradict with the original solution of the classical LV system.

In this paper, we explore the convergent solutions in predator-prey systems by modifying the classical LV system. Because most predator-prey systems in the wild are not isolated, we consider the effects of fixed (or random) number of immigrants at regular intervals on the predator and prey populations. By adding few immigrants, the LV systems with type I, II, and III functional responses exhibit asymptotic stability. Similarly, adding few immigrants to the predator population stabilizes the modified LV systems where both predator and prey coexist. In the latter case, the LV system may be interpreted as a host-parasite system, because the parasites are more likely to become immigrants. We then briefly discuss the implications of the modified LV system on the predator-prey systems found in the wild.


On stochastic Lotka-Volterra predator-prey model - Biology

p-ISSN: 2325-0046 e-ISSN: 2325-0054

Utility Functions and Lotka-Volterra Model: A Possible Connection in Predator-Prey Game

Institute of Radiology, Faculty of Medicine and Surgery, Second University of Naples, Naples, Italy

Correspondence to: Nicola Serra, Institute of Radiology, Faculty of Medicine and Surgery, Second University of Naples, Naples, Italy.

Email:

Copyright © 2014 Scientific & Academic Publishing. All Rights Reserved.

The present paper deals a preliminary study on possible connection between the Lotka-Volterra model and predator-prey utility functions. Therefore a generalization of the utility functions to predator-prey population is defined, considering that the utility functions depend by parameters as the strategies adopted, physical efficiency of the predator versus the prey, environmental conditions, prey prudence, etc.

Keywords: Prey – predator interaction, Lotka-Volterra model, Utility function, Game theory


The Lotka-Volterra predator-prey model with foraging-predation risk trade-offs

This article studies the effects of adaptive changes in predator and/or prey activities on the Lotka-Volterra predator-prey population dynamics. The model assumes the classical foraging-predation risk trade-offs: increased activity increases population growth rate, but it also increases mortality rate. The model considers three scenarios: prey only are adaptive, predators only are adaptive, and both species are adaptive. Under all these scenarios, the neutral stability of the classical Lotka-Volterra model is partially lost because the amplitude of maximum oscillation in species numbers is bounded, and the bound is independent of the initial population numbers. Moreover, if both prey and predators behave adaptively, the neutral stability can be completely lost, and a globally stable equilibrium would appear. This is because prey and/or predator switching leads to a piecewise constant prey (predator) isocline with a vertical (horizontal) part that limits the amplitude of oscillations in prey and predator numbers, exactly as suggested by Rosenzweig and MacArthur in their seminal work on graphical stability analysis of predator-prey systems. Prey and predator activities in a long-term run are calculated explicitly. This article shows that predictions based on short-term behavioral experiments may not correspond to long-term predictions when population dynamics are considered.


Stability in distribution of a three-species stochastic cascade predator-prey system with time delays

Meng Liu, Meng Fan, Stability in distribution of a three-species stochastic cascade predator-prey system with time delays, IMA Journal of Applied Mathematics, Volume 82, Issue 2, April 2017, Pages 396–423, https://doi.org/10.1093/imamat/hxw057

Stability in distribution of solutions (SDS) is important but challenging in stochastic population models with delays since the traditional methods are difficult to apply. This article focuses on a three-species stochastic delay prey-mesopredator-superpredator system and explores its SDS by a new approach. The new approach avoids the difficulties of some existing methods and can also be applied to investigate the SDS of other stochastic delay population models. The study reveals that the complete dynamic scenarios of SDS are characterized by three parameters |$eta_1>eta_2>eta_3$|⁠ : if |$eta_1<1$|⁠ , then all the populations are extinct if |$eta_1>1>eta_2>eta_3$|⁠ , then the prey converges weakly to a unique ergodic invariant distribution (UEID) while both the mesopredator and the superpredator are extinct if |$eta_1>eta_2>1>eta_3$|⁠ , then both the prey and the mesopredator converge weakly to a UEID while the superpredator are extinct if |$eta_3>1$|⁠ , then the distributions of prey-mesopredator-superpredator converge weakly to a UEID.


Watch the video: Mathematical Biology. 14: Predator Prey Model (February 2023).