The aim of this post is to guide the reader through plotting trajectories and direction fields for a system of equations in Python. This is useful when investigating the equilibria and stability of the system, and to facilitate in understanding the general behavior of a system under study. I will use a system of predator-prey equations, that my most devoted online readers are already familiar with from my previous posts on identifying equilibria and stability, and on nondimensionalization. Specifically, I’ll be using the Lotka-Volterra set of equations with Holling’s Type II functional response:

where:

x: prey abundance

y: predator abundance

b: prey growth rate

d: predator death rate

c: rate with which consumed prey is converted to predator

a: rate with which prey is killed by a predator per unit of time

K: prey carrying capacity given the prey’s environmental conditions

h: handling time

This system has 3 equilibria: when both species are dead (0,0), when predators are dead and the prey grows to its carrying capacity (K,0) and a non-trivial equilibrium where both species coexist and is generally more interesting, given by:

The following code should produce both trajectories and direction fields for this system of ODEs (python virtuosos please excuse the extensive commenting, I try to comment as much as possible for people new to python):

import numpy as np from matplotlib import pyplot as plt from scipy import integrate # I'm using this style for a pretier plot, but it's not actually necessary plt.style.use('ggplot') """ This is to ignore RuntimeWarning: invalid value encountered in true_divide I know that when my populations are zero there's some division by zero and the resulting error terminates my function, which I want to avoid in this case. """ np.seterr(divide='ignore', invalid='ignore') # These are the parameter values we'll be using a = 0.005 b = 0.5 c = 0.5 d = 0.1 h = 0.1 K = 2000 # Define the system of ODEs # P[0] is prey, P[1] is predator def fish(P, t=0): return ([b*P[0]*(1-P[0]/K) - (a*P[0]*P[1])/(1+a*h*P[0]), c*(a*P[0]*P[1])/(1+a*h*P[0]) - d*P[1] ]) # Define equilibrium point EQ = ([d/(a*(c-d*h)),b*(1+a*h*(d/(a*(c-d*h))))*(1-(d/(a*(c-d*h)))/K)/a]) """ I need to define the possible values my initial points will take as they relate to the equilibrium point. In this case I chose to plot 10 trajectories ranging from 0.1 to 5 """ values = np.linspace(0.1, 5, 10) # I want each trajectory to have a different color vcolors = plt.cm.autumn_r(np.linspace(0.1, 1, len(values))) # Open figure f = plt.figure() """ I need to define a range of time over which to integrate the system of ODEs The values don't really matter in this case because our system doesn't have t on the right hand side of dx/dt and dy/dt, but it is a necessary input for integrate.odeint. """ t = np.linspace(0, 150, 1000) # Plot trajectories by looping through the possible values for v, col in zip(values, vcolors): # Starting point of each trajectory P0 = [E*v for E in EQ] # Integrate system of ODEs to get x and y values P = integrate.odeint(fish, P0, t) # Plot each trajectory plt.plot( P[:,0], P[:,1], # Different line width for different trajectories (optional) lw=0.5*v, # Different color for each trajectory color=col, # Assign starting point to trajectory label label='P0=(%.f, %.f)' % ( P0[0], P0[1]) ) """ To plot the direction fields we first need to define a grid in order to compute the direction at each point """ # Get limits of trajectory plot ymax = plt.ylim(ymin=0)[1] xmax = plt.xlim(xmin=0)[1] # Define number of points nb_points = 20 # Define x and y ranges x = np.linspace(0, xmax, nb_points) y = np.linspace(0, ymax, nb_points) # Create meshgrid X1 , Y1 = np.meshgrid(x,y) # Calculate growth rate at each grid point DX1, DY1 = fish([X1, Y1]) # Direction at each grid point is the hypotenuse of the prey direction and the # predator direction. M = (np.hypot(DX1, DY1)) # This is to avoid any divisions when normalizing M[ M == 0] = 1. # Normalize the length of each arrow (optional) DX1 /= M DY1 /= M plt.title('Trajectories and direction fields') """ This is using the quiver function to plot the field of arrows using DX1 and DY1 for direction and M for speed """ Q = plt.quiver(X1, Y1, DX1, DY1, M, pivot='mid', cmap=plt.cm.plasma) plt.xlabel('Prey abundance') plt.ylabel('Predator abundance') plt.legend(bbox_to_anchor=(1.05, 1.0)) plt.grid() plt.xlim(0, xmax) plt.ylim(0, ymax) plt.show()

This should produce the following plot. All P0s are the initial conditions we defined.



We can also see that this parameter combination produces limit cycles in our system. If we change the parameter values to:

a = 0.005 b = 0.5 c = 0.5 d = 0.1 h = 0.1 K = 200

i.e. reduce the available resources to the prey, our trajectories look like this:

The equilibrium becomes stable, attracting the trajectories to it.

The same can be seen if we increase the predator death rate:

a = 0.005 b = 0.5 c = 0.5 d = 1.5 h = 0.1 K = 2000

The implication of this observation is that an initially stable system, can become unstable given more resources for the prey or less efficient predators. This has been referred to as the Paradox of Enrichment and other predator-prey models have tried to address it (more on this in future posts).

P.S: I would also like to link to this scipy tutorial, that I found very helpful and that contains more plotting tips.