Chapter 15 – Dynamics of Consumer Resource Interactions
(302-311, B; 312-315, A; 315-320, C; 321-322, A/B)
Consumers can exert a substantial influence on resource populations. Consider the example of cyclamen mites (pest to
strawberry plants) and their natural predators (also mites). By experimentation, it was found that cyclamen were 25 times
more abundant in the absence of predator mites. Generally the degree to which a resource population may be controlled
by consumers depends on their respective life histories. This influence in not exclusive to animals, in many instances,
invasive plant species have been brought under control by introducing consumer species.
One can consider predator-prey interactions in a relatively simple way: predators eat prey, they increase their own
numbers while decreasing the number of prey; as the number of prey drop, predators can go hungry and reduce in number;
with the reduction in predators, prey can again become abundant. These interactions can be modeled with relatively stable
cycles (1: high prey, low predator, 2: high predator, low prey). The periods of these cycles depend on the reproductive
cycles of specific species (this introduces time delays) and the environment (assuming it follows periodic cycles of
favourable vs. unfavourable conditions, predator-prey cycles are still quite predictable). It is equally important to consider
these interactions do not occurs specifically between two species, other species can consume or be consumed by the
population in question and these interactions may equally be modeled cyclically.
Parasite-host interactions can be modeled in a way similar to predator-prey interactions, but, the cyles are generally
determined by the number of susceptible individuals within a population, depending on inoculated individuals and
individuals with natural antibodies. In nature, parasite-host interactions can mirror predator-prey interactions in the way
they control populations (e.g. tent caterpillar populations peak every two years; the subsequent crash it due to a rise in the
population susceptible to the nuclear polyhedrosis virus).
Modeling predator-prey interactions in laboratories was initially difficult, as the environment is often too simplistic, but
once scientists created more realistic environments, the characteristics oscillations of the two populations could be seen,
e.g. scientist Gause managed to achieve oscillations between predator and prey bacteria with the simple introduction of
glass wool, allowing prey a place to hide; before this, prey were always entirely eradicated by predators.
Predator-prey oscillations can me modeled using Lotka-Volterra equations (continuous growth/decline), which predicts
population size by calculating the rate of change of each relative to the abundance of the other. Equilibrium isocline:
when illustrating a graph of predator-prey change, this line indicates the point of which predators will increase vs. when
prey will increase relative to the abundance of the other. Joint population trajectory: predicts the size of predator and
prey populations by following their individual increases/decreases as a result of each other (pg 313, figure 15.14). Joint
equilibrium point: a combination of predator and prey numbers which allow for a stable population (no oscillations).
Neutral stability: a characteristic of the Lotka-Volterra model offering that the population is either at joint equilibrium or
cycling around it until perturbed. S-I-R model: the simplest model used to model disease transmission while
There are some difficulties associated with Lotka-Volterra in that it doesn’t include some important factors present in
nature. Functional response: relationship between a predator’s rate if food consumption and prey density. Type I
functional response: (unrealistic) offers that predators’ fecundity increases in direct proportion to the density of prey
without including any sort of plateau. Type II functional response: offers that eventually predators reach a state of
satiation, i.e. initially, the number of prey consumes rises as their density does, but then levels with further density
increase. Type III functional response: resembles type II, but also offers that predators may ration the number of prey
they consume when prey density is low. Search image: predators learn to hunt prey more efficiently (finding more
suitable prey) when prey density is high. Switching: predators will begin to consume other, more abundant resources
when specific prey densities are low. Numerical response: includes migration as a factor in prey and predator densities,
i.e. mobile predators may congregate where prey are abundant.
Stability: the achievement of unvarying equilibrium size (i.e. carrying capacity). Destabilizing factors, like time delays,
can cause cycling of equilibrium size; however, stabilizing factors may counter this and offer stability, they are: predator
inefficiency (or enhanced prey escape/defense; results in higher equilibrium levels for both prey and predator); density
dependence exclusive of predator-prey interaction; alternative food sources for predators (allows predators to
maintain themselves at low prey density); refuge for prey at low density (allows prey to maintain themselves at high
predator density); reduced time delay in predator due to changes in prey abundance. In ecological systems which are
simple, these stabilizing factors are often outweighed by time delays, causing cycling.
Alternative stable states: where a population has more than one equilibrium point. Consumer-imposed equilibrium: an
instance where a population is limited/controlled primarily by its predators; generally seen in populations of low density
as competition is low, so the only limiting agent is predation. Resource-imposed equilibrium: an instance where a
population is primarily limited by resources available to it; generally seen in populations at high density (above the
consumer-imposed equilibrium) as consumers can become satiated or limited by their own density (functional response II
and III), so predation is negligible and resources become limited.
As with many models, the environment can have an additional influence over population size and can cause switches
between resource and consumer imposed equilibria.