INTERSPECIES INTERACTIONS


     A community consists of all the species of plants and
animals they inhabit the same area.  Given the sheer
magnitude of species number, it is not possible to analyze
the complex interactions among all the species in a
particular community.  Community ecologists have therefore
concentrated on different types of interspecies interactions
and analyzed them in terms of two (or sometimes three)
interacting species rather than attempt to disentangle the
actual interactions among species in a single community.
These types of interactions are summarized below in terms of
the positive (+) and the negative (-) effects of two species
on one another.  The absence of an effect is indicated by 0.

        Summary of Interspecies Interaction Types

     Type of             Effect on          Effect on
     Interaction         Species 1          Species 2
__________________________________________________________


Neutralism               0                   0

Commensalism             +                   0

Amensalism               -                   0

Protocooperation         +                   +
(nonobligatory)

Mutualism                +                   +
(obligatory)

Predation                + (predator)        - (prey)

Parasitism               + (parasite)        - (host)

Competition              -                   -

___________________________________________________________


     The designation positive and negative is based on the
effect one species has on the population growth rate of the
other species.  Only two types of interaction have been
extensively studied from the point of view of population
dynamics and these will be discussed below.

                Interspecific Competition

     The inhibitory effect each of two competing species has
on the population growth of the other has been modeled by
extending the logistic equation so that each individual of
one species is included in the density of the other species.
The model requires two growth equations: one for species 1
and one for species 2

Growth of species 1:

          dN1/dt = r1N1 (K1 - N1 - aN2/K1)


Growth of species 2:

          dN2/dt = r2N2 (K2 - N2 - bN1/K2)


     The terms of this model are the same as for the
logistic model with the addition of a and b, the
coefficients of competition.  These coefficients translate
the effect of an individual of one species in terms of the
other species.  For example, if a = 0.5, then one individual
of species 2 has one-half the effect on the population
growth of species 1 as has one individual of species 1.
Consequently, two individuals of species 2 have the same
effect as one individual of species 1 in depressing the
growth rate of species 1.

     The assumptions underlying this competition model are
the same as those underlying the logistic model with one
addition: the coefficients of competition (a and b) are
constants and don't vary with density.

     The outcome of competition is the elimination of one
species by the other.  Both species inhibit the growth of
the other species for a while, but eventually one species
finally wins by increasing at the expense of the other.  The
winning species is finally limited by its own K value.  The
losing species becomes extinct (N = 0).

           The competitive exclusion principle

     This result has been called the competitive exclusion
principle and exclusion has been documented in both
laboratory and field experiments.  The competitive exclusion
principle (also know as Gause's axiom after the Russian
ecologist who tested the model in the lab) can be stated as
follows: No two species can indefinitely occupy the same
ecological niche because the inevitable result is that one
species will eliminate the other.  Occupying the same
ecological niche means that both species use the same
limited environmental resources in the same way.
                 The concept of niche

     The term "niche" has been the object of much confusion.
It is a functional attribute of a species and not a spatial
unit.  Hence, niche, which defines a species' role in the
community, is not the same as the species' habitat which
defines its location in a community, i.e., whether it lives
under logs on the forest floor or on leaves in trees.  It
has often been stated that the habitat of a species is its
address while its niche is its profession.  This distinction
is useful in stressing the functional nature of niche.

     In 1957 G. Evelyn Hutchinson attempted to clarify the
meaning of niche by stressing that it described the response
of a species to the entire range of environmental forces
which impact on the species.  To define a species' niche one
would have to plot the range of tolerance, i.e., survival
ability, of the species for each environmental factor to
which the species was exposed.  The range of tolerance is a
unit of physiological space.

     Suppose we were to plot on one axis of a graph the
tolerance range of a species to humidity and on the other
its tolerance range to temperature.  The area where these
two ranges overlap would be an area of physiological space,
i.e., that species' response to a combination of temperature
and humidity.  If one were to plot a third variable, then
the area of physiological space would become a volume of
physiological space.  The addition of a fourth variable
would produce a hypervolume of physiological space.
Hutchinson called his concept the species' fundamental niche
which he defined as an n-dimensional hypervolume of
physiological space.  The actual response of a species to
the real environmental variables which it encounters in
nature is called it realized niche.

     Hutchinson's model has conceptual value but no
practical force for measuring a niche.  The reason is that
the factors are too numerous to graph and not all can be
conveniently ordered in a linear fashion (which is essential
for constructing such a graph).  In practice, a niche is
measured by selecting one or two variables and comparing two
or more different species.  Usually, this is enough to
demonstrate resource partitioning among the species which
prevents them from entering competition.  Such resource
partitioning allows coexistence of closely related species
which are potential competitors.

                Predator-prey Interaction

     Predators can act to control prey populations by
increasing their mortality rate, but, on the other hand, the
prey population can control the number of predators since
prey constitute the food of the predator.

     A mathematical model, analogous to the ones presented
above for intraspecific and interspecific competition, which
explains predator-prey interactions as a density-dependent
interaction has not been experimentally verified.  Because
this model has proven inadequate, we need not discuss its
details other than mention its prediction: an oscillatory
pattern of first prey and then predator abundance.  The
model, however, is too oversimplified and so coexistence
between predators and prey in laboratory populations can
only be maintained if spatial complexity is introduced to
provide a refuge for the prey and alternate prey species are
available to predators when their primary prey species is in
low abundance.  Without these modifications the result of
laboratory experiments is always the same: the predator
eliminates the prey species and then dies from starvation.

     Despite the complexity of the predator-prey
relationship which makes modeling very difficult, there is
sufficient field and laboratory evidence to support the
claim that predators can influence the size of the prey
population.  When predators are removed, as has been the
case in western North America with the extinction of wolves,
coyotes and cougars, prey populations (deer) increase
dramatically up to and beyond their K limit with the result
that massive starvation ensues.  The only way deer
populations can be managed so that they are not always on
the verge of extinction due to overexploitation of their
food supply is by human intervention with humans acting as
predators.

     In approaching the problem of population control
mechanisms ecologists have identified several factors:
weather, which influences population growth in a
density-independent manner, and the density-dependent
factors of intraspecific competition, interspecific
competition and predation.  No one factor controls any
single species population: they all interact but at various
intensities at different times.  This conclusion may sound
very unsatisifying as a general conclusion, but it does
reflect the complexity of nature and provides population
ecologists clues to solving the riddle of population control
in any given species.