logo

Food Chain System in Dynamics 📂Dynamics

Food Chain System in Dynamics

Model 1

R˙=R(1RK)xcycCRR+R0C˙=xcC(ycRR+R01)xpypPCC+C0P˙=xpP(ypCC+C01) \begin{align*} \dot{R} =& R \left( 1 - {\frac{ R }{ K }} \right) - {\frac{ x_{c} y_{c} C R }{ R + R_{0} }} \\ \dot{C} =& x_{c} C \left( {\frac{ y_{c} R }{ R + R_{0} }} - 1 \right) - {\frac{ x_{p} y_{p} P C }{ C + C_{0} }} \\ \dot{P} =& x_{p} P \left( {\frac{ y_{p} C }{ C + C_{0} }} - 1 \right) \end{align*}

Variables

  • R(t)R(t): The density of resource at time tt.
  • C(t)C(t): The density of consumer at time tt.
  • P(t)P(t): The density of predator at time tt.

Parameters

  • K=0.94K = 0.94: The carrying capacity of the resource.
  • xc=0.4x_{c} = 0.4, yc=1.7y_{c} = 1.7, R0=0.16129R_{0} = 0.16129: Values related to the consumers preying on the resource.
  • yp=5.0y_{p} = 5.0, xp=0.08x_{p} = 0.08, C0=0.5C_{0} = 0.5: Values related to the predators preying on the consumers.

Description

The introduced 33-dimensional food-chain system follows a logistic growth model for resources, and can be viewed as a coupled dynamic system of two Lotka-Volterra predator-prey models: the resource-consumer and the consumer-predator.

Typically, systems composed of such simple structures cannot be complex, but with the inclusion of a Holling Type II functional response between RCPR-C-P, it becomes a chaotic system. Indeed, the bifurcation diagrams drawn by varying K,yc,ypK, y_{c}, y_{p} are as follows.

alt text

From a research standpoint, understanding such systems could add value to a paper. Although there are other examples of 33-dimensional chaotic systems, such as the Lorenz attractor, it is too famous and overused to hold appeal as a benchmark. Moreover, as of now, it may seem less complex since it can be expressed by polynomial functions within 22 dimensions. In contrast, the food-chain system inherently carries intuitive significance in ecology, is chaotic, and includes rational functions in its formulation, making it relatively complex 2.


  1. Zhai, Z. M., Moradi, M., Glaz, B., Haile, M., & Lai, Y. C. (2024). Machine-learning parameter tracking with partial state observation. Physical Review Research, 6(1), 013196. https://doi.org/10.1103/PhysRevResearch.6.013196 ↩︎

  2. Moradi, M., Panahi, S., Bollt, E. M., & Lai, Y. C. (2024). Data-driven model discovery with Kolmogorov-Arnold networks. arXiv preprint arXiv:2409.15167. https://doi.org/10.48550/arXiv.2409.15167 ↩︎