Predator–Prey Interaction Results in Population Cycles
The graphical analysis of the combined dynamics of the predator (Npred) and prey (Nprey) populations using the zero-growth isoclines presented in Figure 14.2c reveal a cyclical pattern that represents the changes in the two populations through time (Figure 14.3a). If we plot the changes in the predator and prey populations as a function of time, we see that the two populations rise and fall in oscillations (Figure 14.3b) with the predator population lagging behind the prey population. The oscillation occurs because as the predator population increases, it consumes more and more prey until the prey population begins to decline. The declining prey population no longer supports the large predator population. The predators now face a food shortage, and many of them starve or fail to reproduce. The predator population declines sharply to a point where the reproduction of prey more than balances its losses through predation. The prey population increases, eventually followed by an increase in the population of predators. The cycle may continue indefinitely. The prey is never quite destroyed; the predator never completely dies out.
How realistic are the predictions of the Lotka–Volterra model of predator–prey interactions? Do predator–prey cycles actually occur, or are they just a mathematical artifact of this simple model? The Russian biologist G. F. Gause was the first to empirically test the predictions of the predator–prey models in a set of laboratory experiments conducted in the mid-1930s. Gause raised protozoans Paramecium caudatum (prey) and Didinium nasutum (predator) together in a growth medium of oats. In these initial experiments, Didinium always exterminated the Paramecium population and then went extinct as a result of starvation (Figure 14.4a). To add more complexity to the experimental design, Gause added sediment to the oat medium. The sediment functioned as a refuge for the prey, allowing the Paramecium to avoid predation. In this experiment the predator population went extinct, after which the prey hiding in the sediment emerged and increased in population (Figure 14.4b). Finally, in a third set of experiments in which Gause introduced immigration into the experimental design (every third day he introduced one new predator and prey individual to the populations), the populations produced the oscillations predicted by the model (Figure 14.4c). Gause concluded that the oscillations in predator–prey populations are not a property of the predator–prey interactions suggested by the model but result from the ability of populations to be “supplemented” through immigration.
In the mid-1950s, the entomologist Carl Huffaker (University of California–Berkley) completed a set of experiments focused on the biological control of insect populations (controlling insect populations through the introduction of predators). Huffaker questioned the conclusions drawn by Gause in his experiments. He thought that the problem was the simplicity of the experiment design used by Gause. Huffaker sought to develop a large and complex enough laboratory experiment in which the predator–prey system would not be self-exterminating. He chose as the prey the six-spotted mite, Eotetranychus sexmaculatus, which feeds on oranges and another mite, Typhlodromus occidentalis, as predator. When the predator was introduced to a single orange infested by the prey, it completely eliminated the prey population and then died of starvation, just as Gause had observed in his experiments. However, by introducing increased complexity into his experimental design (rectangular tray of oranges, addition of barriers, partially covered oranges that functioned as refuges for prey, etc.) he was finally able to produce oscillations in predator–prey populations (Figure 14.5).
These early experiments show that predator–prey cycles can result from the direct link between predator and prey populations as suggested by the Lotka–Volterra equations (Section 14.2), but only by introducing environmental heterogeneity—which is a factor not explicitly considered in the model. As we shall see as our discussion progresses, environmental heterogeneity is a key feature of the natural environment that influences species interactions and community structure. However, these laboratory experiments do confirm that predators can have a significant effect on prey populations, and likewise, prey populations can function to control the dynamics of predators.
