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Predator-Prey Models

Part 2: The Lotka-Volterra Model*

Vito Volterra (1860-1940) was a famous Italian mathematician who retired from a distinguished career in pure mathematics in the early 1920s. His son-in-law, Humberto D'Ancona, was a biologist who studied the populations of various species of fish in the Adriatic Sea. In 1926 D'Ancona completed a statistical study of the numbers of each species sold on the fish markets of three ports: Fiume, Trieste, and Venice. The percentages of predator species (sharks, skates, rays, etc.) in the Fiume catch are shown in the following table:

Percentages of predators in the Fiume fish catch

1914 1915 1916 1917 1918 1919 1920 1921 1922 1923
12 21 22 21 36 27 16 16 15 11

As we did with Canadian furs, we may assume that proportions within the "harvested" population reflect those in the total population. D'Ancona observed that the highest percentages of predators occurred during and just after World War I (as we now call it), when fishing was drastically curtailed. He concluded that the predator-prey balance was at its natural state during the war, and that intense fishing before and after the war disturbed this natural balance -- to the detriment of predators. Having no biological or ecological explanation for this phenomenon, D'Ancona asked Volterra if he could come up with a mathematical model that might explain what was going on. In a matter of months, Volterra developed a series of models for interactions of two or more species. The first and simplest of these models is the subject of this module.

Alfred J. Lotka (1880-1949) was an American mathematical biologist (and later actuary) who formulated many of the same models as Volterra, independently and at about the same time. His primary example of a predator-prey system comprised a plant population and an herbivorous animal dependent on that plant for food.

We repeat our (admittedly simplistic) assumptions from Part 1:

If there were no predators, the second assumption would imply that the prey species grows exponentially, i.e., if x = x(t) is the size of the prey population at time t, then we would have

dx/dt = ax.

But there are predators, which must account for a negative component in the prey growth rate. Suppose we write y = y(t) for the size of the predator population at time t. Here are the crucial assumptions for completing the model:

These assumptions lead to the conclusion that the negative component of the prey growth rate is proportional to the product xy of the population sizes, i.e.,

dx/dt = ax - bxy.

Now we consider the predator population. If there were no food supply, the population would die out at a rate proportional to its size, i.e. we would find

dy/dt = -cy.

(Keep in mind that the "natural growth rate" is a composite of birth and death rates, both presumably proportional to population size. In the absence of food, there is no energy supply to support the birth rate.) But there is a food supply: the prey. And what's bad for hares is good for lynx. That is, the energy to support growth of the predator population is proportional to deaths of prey, so

dy/dt = -cy + pxy.

This discussion leads to the Lotka-Volterra Predator-Prey Model:

dx/dt = ax - bxy,

dy/dt = -cy + pxy,

where a, b, c, and p are positive constants.

The Lotka-Volterra model consists of a system of linked differential equations that cannot be separated from each other and that cannot be solved in closed form. Nevertheless, there are a few things we can learn from their symbolic form.

  1. Show that there is a pair (xs, ys) of nonzero equilibrium populations. That is, if x = xs and y = ys, then neither population ever changes. Express xs and ys in terms of a, b, c, and p.
  2. Explain why dy/dx = (dy/dt) / (dx/dt). (Note: This is a general result in calculus -- not particular to this setting. Give a justification for this result.)
  3. Use the preceding step to write a single differential equation for y as a function of x, with the time variable t eliminated from the problem. The solutions of this differential equation are called trajectories of the system. Separate the variables and integrate to find an equation that defines the trajectories. (Different values of the constant of integration give different trajectories.) What do you learn about trajectories from the solution equation?
  4. Step 2 also allows us to draw a direction field for trajectories. Use the sample values for a, b, c, and p in your worksheet to construct a direction field. What do you learn about trajectories from the direction field?
  5. Add several trajectories to your direction field. Do they agree with what you said about trajectories in the preceding step?
  6. Calculate xs and ys for the sample values for a, b, c, and p in your worksheet. Identify the point (xs, ys) on your direction field. What trajectory passes through this point?
  7. What can you say about slopes along the vertical line x = xs? What does this say about dy/dt at points on that line? What do you conclude about y(t) at those points?
  8. What can you say about slopes along the horizontal line y = ys? What does this say about dx/dt at points on that line? What do you conclude about x(t) at those points?
  9. The lines in the two preceding steps separate the relevant portion of the xy-plane into four "quadrants." Discuss the signs of dx/dt and dy/dt in each of those quadrants, and explain what these signs mean for the predator and prey populations.

Here is a link for a biological perspective on the Lotka-Volterra model that includes discussion of the four quadrants and the lag of predators behind prey.

Additional links are provided in Part 6 for various extensions of the model.

* This material is based in part on section 6.4 of Differential Equations: A Modeling Perspective, by R. L. Borrelli and C. S. Coleman, Wiley, 1996.

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