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Chapter II-2 (4)
Species interactions
Chapter 4
Relation to the book themes?
System development:
rules for incorporation of species to an
emerging community and elemental
processes that are involved
Scale:
all processes discussed here are local
scale processes but how we see them
may depend on scale
Contents
1. Queen of trees example
2. Direct interactions refreshed
3. Review of indirect interaction types
4. The role of indirect interactions
Major points to remember

Indirect effects come in a greater variety of designs than direct interactions.

Indirect interactions pose a far greater challenge to understanding and predicting effects
of a species on other species than direct, two-species interactions.

Indirect interactions increase in number and relative importance to a community as
species diversity increases; thus they contribute to turning a species assemblage into a
complex interactive system.
As species arrive and settle in the new habitat, they begin to interact. The first plant and
microbial colonizers may, for a short time, have to deal with the physical environment alone but
soon consumers will discover them as food, population growth will lead to competition for
resources, modification of the environment will facilitate success of the new wave of
newcomers. Exploitation, competition, and mutualism become common. Some of these
interactions, particularly the direct ones, are well understood theoretically and supported by
numerous observations from nature. While their examination provides important insights into
how action of one species results in the state of another, whether in space, population size, or
reproductive success, they appear insufficient to understand intricacies of community
construction and persistence. These pair wise interactions between species understate the complexity of indirect interactions that can propagate through chains of three or more species in
complex communities.
Indirect effects describe how the consequences of pair wise direct interactions between
species are transmitted to other species either through behavioral modifications, altered spatial
distributions, or altered abundances in the food web. Behavioral or other phenotypic
modifications such as change of coloration, size, and defensive chemistry are often discussed as
trait mediated effects. Indirect effects are a logical consequence of the fact that interacting
species are embedded in larger food webs. Indirect effects throw a challenge to experimental
ecologists who try to interpret community level experiments, since responses to the additions or
removals of species can result from both direct and indirect effects. However, knowledge of the
potential pathways of indirect interactions can be used to generate testable hypotheses that can
illuminate which indirect interactions probably account for a particular response. Furthermore,
indirect effects of different levels of intensity and different number of participating species may
be the dominant type of interaction – a type that makes a community a community. In fact, it is
difficult to imagine that an interaction
between two species would not have
impact on other species. In this chapter
we will introduce a number of identified
indirect interactions to illustrate the
potential complexity of such interactions
within an ecological system. As before,
we will start with an example.
The Queen of Trees – interactions of a
single tree
To show the rich texture of interactions
in an apparently simple system we will
Fig. 4.1. Sycamore fig, Sycomoros ficus, the locus
of many unusual and important interactions.
use an example. The information below
is extracted from a Public Broadcasting Service series Nature entitled “The Queen of Trees”
filmed by Mark Deeble and Victoria Stone in Kenya (produced by Deeble & Stone
Productions, Thirteen/WNET New York, NHK, Granada International, BBC and ZDF). The
New York Times commented on the documentary: „Sex, drunkenness, treachery, murder.
Not bad for a nature program about a fig tree‟.
Ficus sycomorus is a common large tree that grows in much of Africa where there is
sufficient water (Fig. 4.1). It has an important mutualistic relationship with a fig wasp that is a
Fig. 4.**. Fig wasp life cycle. The cycle begins with a flying wasp crawling into an immature fig
where she brings pollen and lays eggs. The cycle ends with a young fertilized female wasp
leaving a fig via a tunnel and carries pollen to find another tree. Compiled from various
sources.
billion times smaller than its host. Yet, the two are codependent and together provide a basis for
many other species and interactions.
The tree is home for many animals and many more visit and use its products. Numerous
and bizarre insects, lizards, snakes and monkeys are the regulars. Different birds, including a pair
of hornbills, who maintain their chicks inside the tree‟s trunk, nest in the tree or just come to
feed. Yet, all this life depends on the tree‟s reproduction that is tied to the hidden life of the
microscopic fig wasp that lives, mates and dies within the confines of a fig. The wasps lead
unusual lives - the female mates with her brother before she is born. She never feeds, but must
make a single flight across Africa carrying pollen, to ensure the survival of the fig tree and
perpetuate her species. The tree provides home and sustenance for the wasps. Yet, throughout
their short lives, the wasps are under attack from predators and parasites. Once the fig tree has
produced its pollen, it proceeds to producing its fruit. Female wasps carry pollen to other trees.
However, the fig tree must still disperse the seed and dispersing seeds involves attracting animals
to nutritious fruit. This provides the biggest fruit feast on the continent, up to 1000 kilograms a
year. Animals converge on the tree from throughout the bush. Of the millions of figs wasps that
set off with their cargo of pollen and eggs only a few ever find a receptive fig tree. Just before
dying, a female fig wasp pollinates the fig tree flowers. But the flowers are not of the ordinary
kind. They are hidden inside a developing fig. Once wasps enter the fig, they lay their eggs and
the eggs develop entirely inside the fig (Fig. 4.***). The interdependence of the fig and wasp is
the culmination of millions of years of co-evolution between the two partners. Without it, many
other animals would not survive or would face much greater challenges. The fig tree, its fruit,
leaves, wasps and other animals associated with it have an impact on the local environment and
on other animals. The connections are surprising. For example, mature figs may fall into the
High infection success by parasitic wasps prevents fig
wasp males from opening the fig and so all die,
including parasites
Sycamore tree
or leaves
Longhorn
beetle
Bandit wasps
Dragon flies
Cadidae bugs
Birds
Hilda bugs
Ants
Parasitic
wasps
Spiders
Nematodes
Gecko
Mutualism
(farming)
Seed dispersal
Fruit bats
Giraffes
Green pigeon
+100 other
bird species
Preying mantis
People
Pollination
Snakes
Fig wasp
Elephants
Food and shelter
Seed dispersal
Antelopes
Large
predators
Competition
Tiger beetles
Baboons
Bees
Hornbills
Figs
Cicadas
Seed bugs
Vinegar flies
kill seedlings under the tree no
reproduction directly under
the tree
Catfish
Butterflies
Crocodiles
Fig. 4.**). The fig tree and its network of interactions with other organisms and among those organisms.
Arrows indicate direction of action by a network component. Not all interactions are shown (for instance,
insufficient predators are indentified for Cadidae bugs, seed bugs, butterflies,birds, spiders, fruit bats,
preying mantis, and others.
stream where they are eaten by catfish. Catfish in turn move over larger distances and disperse
seeds along stream banks – a preferred habitat for fig seedlings. Furthermore, catfish are food
for crocodiles. Thus, catfish pass the energy and nutrients to the aquatic system, with positive
impacts on a range of organisms from aquatic algae and top predators (see Fig. 4.**).
Negative feedbacks provide elements of control for some processes. For example, large
concentration of seed bugs near the tree, where most fruits fall, prevents seeds from successful
germination in the immediate proximity of the parent tree. Here, predation prevents competition
between the parent tree and its offspring. Another case of a three way control loop forms
between the fig wasp and its parasitic nematodes. Nematodes infect female and male larvae and
eat them alive, however slowly, such that many manage to reach maturity, free themselves from
the fig, and travel to another tree to deliver pollen, eggs, and nematodes, too. However, when
the nematodes are too successful, they kill or weaken wasps still in the fig. Death of males has
particularly negative consequence as they are needed to burrow a tunnel through the fig outer
tissues. The tunnel is necessary to allow females to exit and fly away. When nematodes kill
most or all males, no tunnel is chewed through and both wasps and nematodes die without
dispersing and producing offspring. This insures that nematodes do not kill all the wasps and
guarantees that there always are pollinated figs and fig seeds to perpetuate the tree. The trees
role is not trivial. By evolving thick fig walls, the tree provides a trap for the enemies of its
partner.
The multitude of interactions illustrated by the species living of, in, and around the fig
tree is great. Fig wasps are consumed by numerous species of birds, spiders, insects including
dragonflies, preying mantis, ants, parasitic wasps, and geckos. Figs are consumed by fruit bats,
catfish, monkeys, giraffes, elephants, green pigeons, and about a 100 other bird species, and
vinegar flies. Figs also provide bees with latex resin and butterflies with sugar. Then, cicadas
suck the sap from the tree and spray sugary liquid that is collected by bees, liked by monkeys,
and, which diverts predation on cicadas by ants. At the same time cicadas fall prey to hornbills,
which in turn directly compete with bees for tree holes. While many species contribute to seed
dispersal by fig consumption, some species eat seeds directly (seed bugs). Because seed
consumption occurs on the ground and concentrates disproportionately on areas where seed lie in
high density, it ultimately controls fig tree reproduction directly under the tree. Thus, predation
on seeds reduces potential competition between seedlings and the parental tree. Other ecological
systems may still reveal many other interactions and their permutations. It is the interactions that
weave species into a community and that affect its assembly and development. As with all
attempts of dealing with the diversity of nature, and the diversity of interactions, it is useful to
identify some general terms that can be applied to known and newly discovered situations.
Ecologists have such a terminology and continue to develop it. Below, we shall try to review
familiar and introduce new concepts to the discussion of species interactions.
Direct interactions
Introductory ecology courses define and explore dynamics and consequences of
predation, competition, and mutualism. These are direct interactions. Sometimes interference
competition and provision are also mentioned and can be depicted as arrows of different colors
and signs (Fig. 4.**). Before we discuss indirect interactions, a brief refresher of the basic
concepts and assumptions that underlie our
understanding of direct interactions will be
useful.
Predation. Predation is one of the
several types of exploitation. Within
Fig. 4.**. Direct interactions
exploitation and depending on the need, ecologists distinguish also herbivory, parasitism,
parasitoism, and disease. Disease does not have a logical justification because it is very similar
to parasitism except that, traditionally, the term is employed when the parasites are very small,
usually microbial and viral, with numerous exceptions. Exploitation has many known
consequences. Predators affect prey age structure, behavior, phenotypic traits, population
dynamics, genetics, spatial distribution, food choices, and many other aspects. When predator
and prey coexist in a system, their numbers are believed to follow cyclic fluctuations whose
regularity and dynamics depend on other species as well as environmental conditions and the
abundance of the players themselves.
Theoretical models of predation have been well developed and their predictions tested in
a variety of situations. They are known as Lotka-Volterra models. Before we look at them, first
recall the population growth model for exponential growth
dN
 rN
dt
or for logistic (density-dependent) growth
K  N 
dN
 rN
dt
K
The basic model of predation is derived from the exponential model of the population
growth and consists of two population growth equations, one for the prey or host and one for the
predator or parasite (Fig. 4.** and 4.**). The prey is assumed to grow without any limitation
due to shortage of resources. Its growth is however affected by losses due to predation. These
losses are calculated in proportion to the number of predators while the predation rate (number of
prey lost to a single predator in a single time
step) is assumed constant. Assumptions for the
dN p
dt
 cpN h N p  d p N p
predator population dynamics are somewhat
different. The predator is assumed to grow as a
Host to predator
conversion rate;
analog of r
Predator
death rate
function of its ability to convert prey to new
predators. This conversion is, indeed, a great
and smart simplification because, in reality, it
stands for the ability to subdue prey, to consume
it in peace, and to process it to create a new
predator. Death rate is considered constant.
Fig. 4.**. Predator growth model. Two new
terms are dp and c, or predator death rate
and host-to-predator conversion rate,
respectively. Thus, the growth of predator
population is a product of how many
predators there are, how many prey, how
well predator captures prey (p), and how
well this capture transforms into new
predators. Deaths are proportional to the
number of predators.
This pair of equations is capable of
producing predator –prey cycles under certain
sets of parameters in spite of the fact that some
important factors are not considered.
Nevertheless, the Lotka-Volterra model offers
a good framework for examining the role of
other species and abiotic conditions on the
dynamics of two species interactions. Such
influences can be incorporated into the model
dN h
 rh N h  pN h N p
dt
Prey per
capita rate of
increase
Predation rate
Number of
predators
Number of
prey (hosts)
Fig. 4.**. Prey or host growth model. Nh is
the number of prey, rh is the intrinsic rate of
growth of prey, p is predation rate and Np is
the number of predators. Thus, the rate of
change in prey population is determined by
density independent growth of prey less the
losses due to predation, with the losses being
a product of predation rate, predator
numbers, and prey numbers.
by making birth rate, predation rate, conversion rate, and death rates dependent on the current
densities of either population, or dependent on the environmental conditions, or dependent on the
population size and activity of a third species. Thus, the Lotka-Volterra equations provide and
excellent spring board for consideration of community complexities. For example, models exist
that include functional responses, time lags in recruitment, self-limitation when the numbers are
high, and a number of other terms that add to the realism of the equations (for some examples
see Case, 2000). While the purpose of this text is not to examine all such fine details of species
interactions, keeping their existence in mind will help, however, to put the addition of species to
a community in a richer perspective.
Competition. It will come as little
surprise that we also have Lotka-Volterra
competition equations. For two competing
species we need two equations. Terms and
coefficients for Species 1 are denoted by
subscript 1 and those for Species 2 are
denoted by 2. The competition model relies
on the logistic form of growth as it assumes
that increasing numbers of either Species 1
dN 1 r 1 N 1 ( K 1 - N 1 -  12 N 2 )
=
dt
K1
Carrying capacity of
Species 1
Competition
coefficient
Effect of interspecific
competition on
Species 1
Fig. 4.**. Competitor 1 growth model. Note two
new terms: K and . K denotes the carrying
capacity or the maximum number of individuals
of Species 1 that the environment can support.
 is a competition coefficient that specifies the
effect of one individual of Species 2 on the rate
of population growth of Species 1. The term
12N2 represents the reduction of growth of
Species 1 due to the competition from
individuals of Species 2.
or Species 2, or both, impose limitations to
further growth of populations using shared resource (a condition for competition). Recall that
the logistic growth model has a built-in density dependence. The latter means that as the
population growth, the per capita rate of increase declines. Generally, it is assumed that the main
cause of such decline is exhaustion of resources. The equation for Species 2 looks similar,
except that now the competition coefficient represents the effect of one individual of Species 1
on the rate of population growth of Species 2:
dN 2 r 2 N 2 ( K 2 - N 2 -  21 N 1 )
=
dt
K2
These two equations can
conveniently be converted to graphs that
allow a visual analysis of the competition
between two species. An example of such
analysis is shown in Figure 4.**. Different
carrying capacities and different
competition coefficients will lead to
different arrangement of isoclines and
hence different outcomes of competition.
Mutualism. While predation and
competition are important interactions in
most ecological systems, mutualism
appears to be indispensable ingredient of
life on Earth. Without pollination, coral
reefs, lichens settling on rocks, seed
Fig. 4.**. Graphical interpretation of
competition model. The blue and red lines are
isoclines for Species 1 and 2, respectively.
Consider first the rightmost point. The point
represents N1 and N2 (densities of Species 1
and 2 read on respective axes). Because the
point indicates densities of both species
exceeding their isoclines, both must decline
along the colored vectors (blue for species 1,
red for 2). The derived vector leads to a new
point. By producing a sequence of points that
behave over time according their position with
respect to the isoclines, it is possible to find out
what the outcome of competition will be. As
exercise, try to draw the movement of other
three points shown on the graph.
Box. 4.**. To consider…
Isoclines in competition models – An isocline defines the density of a species above which its
r>1, that is where the population will decline. Specifically, isoclines run along a combination of
densities of both species such that a growth of a species of interest equals 0. The simplest isocline
connects carrying capacity of a species when it lives alone (e.g., K1 on axis of Species 1) and the
density of its competitor on axis of Species 2 that is sufficient to stop growth of Species 1 (the
density of species 2 is expressed in units of Species 1 and takes the form K1/. For derivation of
isoclines see introductory ecology texts.
dispersal, ants defending plants, fungi and vascular plants associations, mycorrhizae (nitrogen
fixing bacteria and plant roots), termite gut microorganisms, and many other obvious or subtle
relations among species, life would not be similar to what we see today. Mutualism differs from
competition and predation in one crucial consequence. This is particularly clear when we think
of obligatory mutualists. Loss of one species makes the survival of its partner impossible. In
contrast, loss of predator or a competitor may change many things, including intra-specific
competition, but allows the continuation of the other species. Only the lost of prey for a
specialized predator may have similar consequences to those seen in mutualism.
Mutualistic relationships may be
modeled in a way very similar to that of
dN 1 r 1 N 1 ( K 1 - N 1   12 N 2 )
=
dt
K1
completion. The only significant difference
Carrying capacity of
Species 1
lies in the sign defining the impact of
Species 2 on Species 1 (Fig. 4.**). In
obligatory mutualism  may be assumed
constant while in facultative (optional)
mutualism  will vary from 0 (mutualistic
relationship is dormant) to some value
greater than zero depending on the
contribution of one species to the success of
the other.
Coefficient of
mutualism
Effect of interspecific mutualism
on Species 1
Fig. 4.**. Mutualist 1 growth model. One of the
two terms K, is the same as in competition
model.  is a mutualism coefficient that
specifies the effect (positive in this case) of one
individual of Species 2 on the rate of population
growth of Species 1. The term 12N2 represents
the improvement of growth of Species 1 due to
the beneficial activity of individuals of Species
2. Sometimes11 is added in front of N1 to
capture intraspecific competition effects.
Mutualism occurs when 11 > 12 (negative
effect of intraspecific competition is greater
than the beneficial effect of another species.
Equation for Species 2 is similar and hence
omitted here.
competition in that it creates greater
interdependence among species. Such
interdependence brings risks to a species
should its partner be in trouble for some
reasons external to the mutualistic
relationship. Consequently, species should
not engage in mutualism haphazardly but
only when it really makes a positive
difference. Figure 4:** captures this idea in
Frequency of competitive interactions
mutualism is different from predation and
Frequency of positive interactions
Earlier, we mentioned that
Mutualistic
defenses
Mutualistic
habitat
improvement
Increasing physical stress
Increasing consumer pressure
Fig. 4.**. Mutualism tends to be most frequent
when species are under stress, whether from
other species (predation, grazing, competition)
or from physical environment (drought,
nutrient scarcity, difficulty in dispersing seeds
or pollen). Bertness and Callaway 1994, TREE
9: 191-193.
a generalized sense (combines expectations and observations). It also makes the point that
mutualism (as well as exploitation and competition) do not function in isolation. In fact, a
simulation study of mutualism between yucca and yucca moth (Bronstein et al. 2003) examined
the effects of two species with antagonistic relationship with yucca. Yucca moths (Tegeticula
alba) adults hide inside yucca flowers during the daytime and fly at night. Between dusk and
midnight females gather pollen from flowers using their maxillary palps. They form balls of
sticky pollen and push them into the receptive tips of yucca pistils. Mating also occurs within
yucca flowers. Larvae grow inside developing yucca fruits. Larvae feed on developing yucca
seeds, consuming a small percentage of the hundreds of seeds within capsules. Here, moths gain
shelter and food and in exchange they provide yucca with an efficient pollination. Of the two
antagonistic relationships studied by Bronstein et al. (2003) one involved flower eating insects
(florivores) and another involved a seed parasite that also pollinates. The researchers were
interested if the antagonistic interactions interfered with the mutualism. They found it did. The
study indicated that those antagonistic species may lead to major fluctuations in the populations
of mutualists and to patchy spatial patterns in their population densities. The lesson from this
study is that mutualistic relationships are vulnerable to biological and, perhaps, abiotic factors.
Indirect interactions
Earlier in Fig. 4.** we introduced arrows to represent various interactions among
species. Discussion of indirect interactions will make use of these arrows. The species at the
beginning of an arrow is called the donor and the species receiving its impact is the receiver. In
a symmetric or reciprocal competition or in mutualism both species are donors and receivers at
the same time. From the perspective of community and ecosystem ecology we want to think of
these interactions as building blocks for the whole biological activity. Because, however, such
interactions are embedded in a complex network of activity (just think of the fig tree again), their
models are not realistic and cannot be easily used to understand dynamics and patterns of the
whole system, even if we had sufficient information on all the individual pairs. As a further step,
and perhaps more interesting, we can examine what other modules have been already discovered
or suggested. This will help to paint the picture of the potential relations among species as they
assemble to create and maintain a new community.
Many of those additional modules belong to a class of interactions we call indirect.
Indirect interaction involve a third (or more) species that transmits and transforms the effect onto
the receiver. Whenever more species are added, the number of combinations the arrows in
Figure 4.*** can be arranged in greatly
increases. Nevertheless, some situations are
more common than others and we will
review some typical cases.
Fig. 4.**. Keystone predation. B – a basal
Keystone predation
(prey, host) species, P - predator. Broken line
Keystone predation refers to situations
– indirect effect (Menge 1995).
where a predator plays a major role in the
maintenance of some important features of a
community. The predator may reduce
fluctuations among other species, it may
increase the productivity of the system, or it
may maintain higher species diversity than it
would be possible without its presence. This
last effect may occur when a predator controls
a superior competitor and thus indirectly
protects another species - the inferior
competitor - from being out competed. Robert
Pain has demonstrated such a case
experimentally. By removing star fish,
Pisaster ochraceus, the predator on the Pacific
rocky shores of North America, he showed that
its absence led to the reduction of species
Box. 1.1. To consider…
Regulation of Keystone Predation by
Small Changes in Ocean Temperature
Eric Sanford [email protected]
Key species interactions that are
sensitive to temperature may act as
leverage points or amplifiers through
which small changes in climate could
generate large changes in natural
communities. Field and laboratory
experiments showed that a slight
decrease in water temperature
dramatically reduced the effects of a
keystone predator, the sea star Pisaster
ochraceus, on its principal prey.
Ongoing changes in patterns of cold
water upwelling, associated with El Niño
events and longer term geophysical
changes, may thus have far-reaching
impacts on the composition and diversity
of these rocky intertidal communities.
Science 26 March 1999:
Vol. 283. no. 5410, pp. 2095 - 2097
DOI: 10.1126/science.283.5410.2095
richness from 15 to 8 species of major sedentary invertebrates. He found that this happened
because competitors started excluding each other. The presence of predator prevented such an
outcome under natural conditions by reducing competition and promoting coexistence of
competing species. Further research (see Box 4.**) showed that this effect itself depends on
environmental conditions such as temperature.
Keystone mutualism
The fig wasp and the fig tree are an excellent example. The effects of removal of one of either
one species would spill on many other species indirectly. Certain species - keystone mutualists involved in mutualistic interactions may sometimes assume great importance in the community.
For example, during the dry season in the tropical forest at the Cocha Cashu Biological Station in
Manu National Park of southeastern Peru, only 12 of the approximately 2,000 plant species
support the entire fruit-eating community of mammals and birds. Mutualism here consists of
provision of food in exchange for seed dispersal. During this period, fruit production drops to
less than 5 percent of the peak production, so the food available to the fruit-eating species is
severely restricted. The 12 plant species still producing fruit and nectar meet the food needs of as
much as 80 percent of the entire mammal community and a major fraction of the avian species at
the site. Clearly, the loss of one or more of these "keystone mutualists" could significantly harm
vertebrate frugivore populations.
While the identification of keystone predators may be extremely difficult, keystone
mutualists can generally be identified through non-experimental observations of the resources
that the species use. Once identified, keystone mutualists can be managed to ensure the survival
of the species dependent upon them. By increasing the abundance of the keystone mutualists for
instance, it might be possible to increase populations of dependent species.
Apparent competition
This situation arises whenever a predator
switches between two non-competing prey.
They reciprocal fluctuations give an apparent
picture of competition but are not due to actual
competition but to the fluctuating preferences of
the predator. This happens because many
Fig. 4.**. Apparent competition. B – a basal
(prey, host) species, P - predator. Broken line
– indirect effect (Menge 1995).
predators prefer more abundant prey. It would not be surprising to see squirrel and rabbit
population to vary in a reciprocal manner, which might suggest competition while in fact these
two species do not compete at all as they feed on different foods and require different shelters.
As an example, consider the case of two sessile bivalves or clams (Chama and Mytilus) and
two actively moving gastropods or snails (Tegula and Astraea). The clams occur mostly on reefs
with many nooks and crannies. The gastropods are more abundant in low-relief reefs composed
of rocky cobbles. While the clams use shelters from predators in crevices of the rock they tend
to occupy, snails usually do not seek shelter. This spatial separation does not appear to be due to
competition between the clams and gastropods. The clams and gastropods consume different
kinds of food. The clams filter particles from the water column while the gastropods scrape
algae from the rocks. Competition for space is also unlikely, because clams and gastropods
favor different substrates. Gastropods need to feed on the surface of the rocks, while clams
occupy crevices. Both clams and gastropods are food for many invertebrate predators, including
lobsters, octopi, and whelks. The clams appear much more vulnerable to predators. This is
additionally corroborated by the observation
that both predators and clams are more
abundant on reefs with rich texture and
crevices.
Because the pattern of distribution
suggests that the two categories of species
exclude each other, Russell Schmitt (1987)
performed several experiments to confirm
or exclude competition as the cause of the
patterns. First, he transferred the clams
Chama and Mytilus to the gastropoddominated rocky cobble reefs and observed
Fig. 4.**. Decrease in snail density (solid
circles) at sites receiving alternate prey (clams)
contrasted to unchanged snail densities (open
circles) at control sites without alternate prey.
The decrease is attributed to apparent
competition. (Reprinted from Schmitt, 1987,
with (not yet) permission of the Ecological
Society of America.)
the impact of this transfer on gastropod
mortality and predator abundance. He maintained high density of clams by replacing individuals
lost to predators.
As it was not possible to perform the reciprocal transfer of gastropods to the high-relief reefs
where Chama and Mytilus usually occurred, Schmitt transplanted clams to areas with high and
low natural densities of snails. This, he anticipated, would allow him to measure possible
interactions between the clams and snails at low predator densities. As expected, when he
transplanted clams to cobble reefs, he recorded increased predator abundance. Snails densities
declined significantly over the 65-day duration of the experiment (Fig. 4.**). Snails had a
similar indirect negative effect on bivalves, with more bivalves being consumed by predators in
areas of high gastropod density (45.1 snails/m2) than in areas of low snail density (4.7 snails/m2).
The results are
consistent with an asymmetric indirect negative effect of bivalves on snails, mediated by the
rapid aggregation of predators in areas with high densities of their preferred prey, the clams.
Tri-trophic interactions
This situation is a specific case of a trophic
cascade. It arises when a predator consumes
a predator of a third species. An increase in
predation on the „transmitter‟ causes the
basal species (the prey at the bottom of the
interaction chain) to benefit. In fact, the triFig. 4.**. Tri-trophic interaction or trophic
cascade. B – a basal (prey, host) species, P –
predator, H –prey, host, or predator. Broken
It is possible to see several such modules
line – indirect effect (Menge 1995).
arranged on top of each other and forming a trophic cascade.
trophic interaction can be seen as a module.
A good illustration of this sort of relationship comes from freshwater lakes. In lakes, the basic
food chain runs from algae to zooplankton to planktivorous fish to piscivorous fish. High
piscivory (predation on fish) pressure from fish such as pike maintains low zooplanktivore (fish
that eat zooplankton such as minnows) abundances. Low zooplanktivore abundances exert weak
predatory effects on zooplankton. As a result, the planktivore zooplankton population density
(e.g., Daphia) increases and often clears water of phytoplankton. However, the predicted
cascading effects seldom appear as decreased phytoplankton abundance (Carpenter et al. 1987).
One reason is that the phytoplankton consists of an array of species that differ in their
vulnerability to grazing by zooplankton, and differences in zooplankton grazing pressure simply
select for complementary communities of algae that differ in grazer resistance. This situation
has been modeled by Mathew Leibold (1989). When zooplankton is abundant, the
phytoplankton is dominated by grazer resistant species. When zooplankton is less abundant, the
phytoplankton is dominated by
competitively superior species that are
vulnerable to grazing. Phytoplankton
remains abundant, but is dominated by
different sets of species. Such a shift is
reminiscent of „keystone predation‟ we have
presented earlier but differs from it in that
Fig. 4.**. Exploitative competition. B – a basal
(prey, host) species, P – predator . Broken line
– indirect effect (Menge 1995).
diversity of species remains the same while
composition changes.
Additional indirect effects may also take place. For example, in presence of numerous
planktivores, zooplankton such as copepods and cladocerans, undertake daily migrations.
During the night they move towards the water surface where they feed on algae while they
migrate to deeper water to hide in the zone of poor visibility from their fish predators.
The best example of a terrestrial trophic cascade comes from a study by Robert Marquis and
Christopher Whelan (1994). They found strong effects of insectivorous birds that were
transmitted through herbivorous insects to white oak trees. Birds were excluded from some trees
by netting (cage treatment), while other trees remained available to the birds (control treatment).
Birds significantly reduced the abundance of herbivorous insects on the oaks. In turn, oaks with
birds and reduced herbivorous insects had less leaf damage from insects and subsequently
attained a higher biomass.
Exploitative competition
This interaction can be direct and indirect, depending on the nature of the resource. If the
resource is space or some other non-consumable resource, the interaction is direct because a third
species is not involved. When, however, a third species or a collection of species such as plant
food is involved, the interaction fits the definition of indirect type because a „transmitter‟ is
present - the distinction comes from the definition of indirect interaction. For example, most
tropical fish reproduce through mass spawning – the eggs and sperm float to the surface where
fertilization takes place. Ocean currents carry the fertilized eggs and then larvae and, after a
period of planktonic life, the larvae settle in a new location. However, they can only succeed if
they are lucky at finding undefended space on the reef and they can out compete many other new
arrivals representing various species. Because they compete with many species, this kind of
competition is often term diffused. In this case it is a diffused direct exploitative competition.
Habitat facilitation
It occurs when activity of one or several
species improves habitat of some other
+
P
P
species by altering the abundance or activity
of a third interactor. The Large Blue lays
its eggs in the buds of thyme - the culinary
herb that grows wild in Europe - in the
tight-bud stage. The butterfly must lay its
B
Fig. 4.**. Habitat facilitation. B – a basal
(prey, host) species, P – predator. Broken line
– indirect effect (Menge 1995).
eggs after the thyme buds have started to
open – otherwise the brood is lost. The eggs hatch after one or two weeks, depending on the
weather; warm weather speeds hatching. The young caterpillars feed on thyme flowers for about
two weeks during late July and early August, then fall to the ground where they are "adopted" by
red ants (Myrmica sabuleti) attracted by a sugary substance secreted from a dorsal gland. The
ants carry the caterpillar back to their nest,
where it then gorges on ant larvae. While
hidden from its predators, the caterpillar
spends 10 months as a predator in the ant
nest, and then pupates there. Until now, the
Large Blue acts as a clever predator that
cons ants into a case of deceptive
mutualism.
After three weeks and after it
changed from caterpillar to chrysalis, the
Fig. 4.**. Large Blue – a European butterfly
whose caterpillars feed initially on plants and
then on ant larvae after seducing ant workers
with liquid containing sugar.
insect emerges from its chrysalis and leaves the red ant nest to find a mate. Usually, red ants will
escort the newly emerged butterfly to the surface, taking it to a low plant or shrub nearby. The
red ants will encircle the butterfly and ward off any predators that attempt to attack the butterfly
as it dries out. After the butterfly is ready to fly away, the ants will retreat back into their nest.
This relationship does not always benefit Large Blue well because M. sabuleti is a
warmth-loving ant that thrives only in short, dry grassland on hot south-facing slopes that are
heavily grazed. For example, 52% of caterpillars survive in ant nests in newly burned patches as
compared to 27% in long-established turf. If the grass grows higher than 3-4 cm and shades the
ground, cooling it, this ant dies out and other species of ant take over - ants that are not interested
in providing free food and lodging for Large Blue caterpillars. Specifically, with black ant
species, caterpillar survival dropped from 15% to less than 2% of the entire
population. Taller grass also crowds out thyme.
Events in Britain brought changes of fortune to Large Blue. One was the
Large Blue is a
predator of ants,
with which it has a
complex
relationship
increased use of chemical fertilizers that promote vigorous grass growth, which
smother off small wild flowers such as thyme. Then, sheep were pulled off the land by a change
in livestock farming. For a few years, rabbits spread and kept the grass short in habitats favored
by the butterfly, but in the 1950s myxomatosis (a viral disease of rabbits) was introduced and
eliminated them. Pastures also were previously burned over, which kept the grass short, but this
is no longer done. This sequence of changes exposed the second layer of relationships: grazers
(P) change the composition and size of meadows (B) such that it favors red ants which in turn
benefit Large Blue (P).
Habitat facilitation appears to be very common in nature whenever a species has major
impact on some other species or abiotic habitat, there will almost always be
other species that benefit from the change. Elephants thin gallery forests to
Habitat facilitation
is very common
and important
create habitat for grasses and grassland animals, ants living in symbiosis with
Acacias prune seedlings of other plants around to improve Acacia access to
nutrients, water, and light, or beavers cut trees and open riparian habitat for a variety of plants
that could not live in the shaded environment.
Indeed, beavers also create aquatic habitat for
pond and stream organisms.
P
-
Apparent predation
When predation on one species results in a
decline of another, a potential but not actual
prey, we can think of apparent predation.
B
B
Fig. 4.**. Apparent predation. B – a basal
(prey, host) species, P – predator. Broken
line – indirect effect (Menge 1995).
This situation is clearly illustrated by whelk,
a large marine snail, that preys on barnacles.
Whelk has the ability to penetrate barnacle
plates and eat them. Barnacles, being
sedentary on hard substrates such as rocks,
Fig. 4.**. Apparent predation. Barnacles, whelk
(right inset) and Littorina sp.(left inset).
logs, and ships often form dense beds of
organisms and provide shelter for much smaller Littorina snails that live in the same
environment. The predation of whelk on barnacles leads reduced protective habitat and, in
consequence, to decline of Littorina. In this situation we see a habitat modification by one
species that produces a negative effect. This is in contrast to the effect observed in the case of
habitat facilitation.
Indirect mutualism
It arises when two species benefit each other
by acting on other species. Interactions
between two predators on two competitors
in freshwater ponds constitute a case of
indirect mutualism. A salamander larva is a
Fig. 4.**. Indirect mutualism.
voracious predator of copepod crustaceans.
These copepods are more efficient in
consuming algae than cladocerans such as
Daphnia. In natural ponds, Ambystoma
and Chaoborus are almost always found
together, and Chaoborus usually does not
occur in ponds without Ambystoma.
Dodson (1970) explained this pattern as a
consequence of Ambystoma maintaining
the feeding niche provided by small-bodied
prey consumed by Chaoborus.
Presumably, the large-bodied zooplankton
that predominate in ponds without
Fig. 4.**. Indirect mutualism. Salamanders
reduce copepods (upper left)- competitors of
cladocera (upper right) – a shift that benefits
phantom midges (Chaoborus, lower photo).
Ambystoma are inappropriate prey for Chaoborus.
Comparing ponds with and without salamanders, Ambystoma, Dodson (1970) discovered
that when salamanders reduce copepod population, cladocerans increase in numbers and benefit
their own predator – phantom midge larva (transparent hunter). Indirectly, salamander benefits
phantom midge. Conversely, reduction of Daphnia improves food supply for copepods and thus
food for salamanders.
However, there is a twist. Dethier
and Duggins (1984) suggest that the
conditions favoring indirect mutualism or
commensalism as opposed to competition
for prey are predictable if one knows
whether the predators are specialized in
Fig. 4.**. Indirect commensalism.
their prey items or not. If the consumers
are generalists that feed on both kinds of resources, the consumers will simply compete. If the
consumers are sufficiently specialized so that each requires different sets of competing resources,
as is the case in the case of the salamander and phantom midge, then a positive indirect effect
will result. Whether the effect is reciprocal (a mutualism) or asymmetric (a commensalism)
depends on the extent to which the resource species compete in an asymmetric fashion.
Asymmetric competition among the resource species should favor an indirect commensalism,
whereas symmetric competition among the resources should lead to a more mutualistic
interaction among highly specialized consumers.
Indirect commensalism (joint feeding)
For this interaction to take place we need prey that are competitors such that the superior
prey excludes inferior in absence of predation from the generalist. There must also be
another predator that specializes on the inferior competitor. Parrot fish feed on coral and
algae. By doing so, they expose coral skeleton that is a necessary substrate for tunicates to
colonize and thrive. Tunicates will not settle unless parrot fish clear some space. Tunicates
are however food for another reef predator, the surgeon fish. Parrots then provides indirectly
food for surgeon fish, even though it may
also feed on tunicates. A similar situation
has been observed in the rocky intertidal
system. Here, Katharina, a chiton that
consumes larger competitively dominant
algae, positively affects the abundance of
limpets. This happens because limpets,
graze on small diatoms that are
competitively excluded by larger algae.
The limpets have no reciprocal indirect
Fig. 4.**. Indirect commensalism. Parrot fish
(on the left) creates space for tunicates (purple
barrels) to settle. Surgeon fish is the primary
beneficiary.
effect on Katharina, which is what makes this interaction an indirect commensalism. If they
did, we could classify this interaction as a case of mutualism.
Picture and process may depend on scale
Yet, as illuminating and as
useful in organizing the different
situations as the above examples can be,
things inevitably may be more
complicated in nature. Let us revisit
one of the many relationships of the fig
tree. Recall that there are nematodes
that parasitize the wasp. If we try to
apply our graphical convention, we will
Fig. 4.**. Interpretation of direct and indirect
interactions depends on scale. Relationships
among the same three species of the fig tree
ecosystem can be represented differently depending
whether small or large scale (time, space) is
considered.
soon realize that fitting such interactions into a straight jacket of formal arrows is not easy (Fig.
4.**). At small time scale, that of one tree or one wasp generation, fig tree indirectly benefits the
nematode by providing its partner, the wasp, as food for the parasite. At a larger scale though,
the tree competes with nematode for the wasp. The tree needs the wasp as a partner while the
nematode might greatly reduce the number of emerging female wasps. Yet, the tree has a
weapon, whether evolved directly for this purpose or not, it limits success of nematodes to the
level that they cannot interfere in a detrimental way with the wasp life cycle and thus tree
reproduction.
Furthermore, all the specific models presented above included a minimum number of
hypothetical species needed for the particular interaction to occur. However, it is easy to
imagine, and many situations in nature exemplify it, that more than on transmitter may be
involved. It is also possible to imagine that transmitters themselves might interact or that there
are alternative transmitters in the same system. In such situation, indirect interaction would be
much more diffused and much more difficult to detect unambiguously.
Box. 4.**. To consider…
Model estimating lions’ food intake under various scenarios of prey density and prey and
predator grouping behavior.
How this is done?
In order to find out how much lions eat, we need to consider how much area they can cover
searching for prey, how long it will take them to stalk, capture, eat, and digest the prey, and
how many prey are available in area investigated by lions. Thus, if lions hunt alone, they are
expected to capture and eat the quantity of wildebeest according to
( )
(1)
(
)
where a is the area of effective search per unit of time, h1 is the expected time to attack and
subdue each prey item, h2 is the expected time to consume and digest each prey item, N is
wildebeest density per km2, and ( ) is prey intake per lion per day (in kilograms). Equation
1 is the example of type II functional response explained in detail in most introductory ecology
texts. Essentially, it says that lions should be eating more wildebeest if they check more area,
waste less time on killing and eating, and wildebeest are more numerous. However, when lions
hunt in groups, which is the most common situation, the time to stalk, kill, and eat prey
(handling time) becomes shorter and the amount of food each lions takes changes according to
(
)
(2)
(
)
where G is the number of lions in a group. If you analyze this equation, you will see that
denominator becomes larger, which means lions take less food in groups. This is because
group hunting does not significantly increase the chance of finding prey.
But what happens when wildebeests aggregate?
This question can be answered by changing the encounter rate (measured by aN in equation
(2) to the new term acNb. The two coefficients in this term, c and b, are borrowed from a
function that relates number of wildebeest groups per km2 to the number of wildebeests per km2
(density). It is reasonable to think that more groups will form when more wildebeests are
present. However, the relationship is curvilinear and needs to be ‘straightened up’ by
employing natural logs. Only then c and b can be found. By inserting the new encounter rate
into Equation 1 we produce
( )
(
(3)
)
Finally, we can combine Equations 2 and 3 to calculate how many kilograms of wildebeest
each lion will eat when both the lions and wildebeest interact as groups
(
)
(
)
(4)
Issues like this may go beyond the choice of analytical scale. Processes that occur at
different scales may also produce apparently puzzling patterns. Classical analysis of predatorprey interactions between lions and wildebeests suggest great fluctuations of both populations,
with a possible collapse of the whole system. However, Fryxell and colleagues (2007) found
that several factors ranging from climate patterns to group behavior act jointly to smooth out
population variability that would otherwise be caused by predation. In Serengeti (Tanzania)
wildebeests migrate in response to seasonal migration of rains and thus suitable grasses. Lions
live if large prides in order to defend territories where they hunt. Wildebeests may pass through
those territories at the time of migration but their availability is unpredictable from the lion
perspective. The model developed by the Fryxell‟s group (Box 4.**) shows that lions would
benefit greatly from solitary hunting, especially should wildebeest forage individually (Fig.
4.**). However, daily food acquisition turns
Rather, they give priority to long-term
stability of food delivery and reproduction
via maintenance and defense of well marked
hunting territories. To maintain those
territories lions need to cooperate and
forming permanent groups (prides) fulfills
that need. Thus, need to migrate (wildebeest)
and maintain territory (lions) jointly reduce
the effectiveness of lions as hunters but
ensure a large and fairly stable wildebeest
population. Wildebeest aggregative behavior
Kg of prey per lion/day
out to be a secondary consideration for lions.
0.7
a
0.6
b
0.5
c
0.4
0.3
d
0.2
0.1
0
100
200
300
400
500
Prey density (individuals per km2)
Fig. 4.**. Predicted predation success by
Serengeti lions on wildebeests depending on
tendency to form groups: a – both predators
and prey are solitary; b – only predators act in
groups; c – only prey are gregarious; d – both
predators and prey are gregarious. Note that
lions would enjoy the greatest rewards if both,
lions and wildebeests were solitary. However,
both tend to form groups in a natural setting,
which reduces wildebeest losses to lions. Based
on Equation 4 from Box 4.**)
provides additional protection against predation. Thus, temporal, spatial, and organizational
scales affect fundamentally the dynamics we observe. Things might turn out differently in parts
of Africa where wildebeests do not engage in major seasonal migrations.
Overall picture
Total direct links
how common various indirect interactions
Links/S ()
numerous communities in order to assess
Links ()
Menge (1995) has compared data from
are (Fig. 4.**). Panel A shows that total
number of all known direct interactions
S
not surprising as such a growth of the
number of links is simply expected by
chance – more species can have more
Indirect only
interactions. The number of links per
Indirect effects/S ()
number of species, S, increases. This is
Indirect effects ()
(links) among species increases as the
S
species grows also but appears to slow
Fig. 4.**. Interactive links as a function of
down. This may indicate that species stop
richness, S, of a community. Each point is a
engaging in additional interactions even as
community. Open circles stand for mean
the number of potential partners, prey
number of links per species. Black circles are
items, or enemies increases. In other
all links found in a community. Curves were
words, direct links decline relative to their
fitted by eye to help visualize trends. A – direct
potential number. A different picture
links; B – indirect links.
emerges when one looks at the indirect
interactions (panel B). Here, the links show no
tendency to decline on a per species bases. The
more species in a community, the more indirect
links they appear to engage in. A message from
this points to the consequences of biodiversity:
Box. 4.**. To consider…
Methodological issue (related to scale)
–it is possible that the number of direct
links per species grows in proportion to
S but, as S becomes large, each
interaction is weaker or less frequent
and hence more difficult to record. The
trend might thus be a consequence of
researcher’s inability to detect some
interactions
an increase in species nubmer leads to an
increase in links among them but, in particular, indirect links. Such links form and integrated and
complex network of interactions. This network can be analized in small steps by using
elementary interactions types discussed earlier but its behavior as a whole is indeed much harder
to understand.
Elementary interactions are of great interest for conceptual and theoretical reasons. In
nature however things may be much more complicated (cf. sycamore fig interaction diagram) as
any single species may have numerous direct and indirect interactions that involve a number of
steps. Sometimes, or perhaps often, such interactions can be mediated by physical processes,
with community wide consequences. For example, studies of coral bleaching (loss of mutualistic
zooxantellae) have shown that reef building corals are not affected directly by increased nutrients
that are most often associated with eutrophication (high nitrogen and phosphorus availability)
(e.g., Smith et al. 2006). Instead they find that dissolved organic carbon (DOC) will cause coral
mortality as a result of increased microbial activity and subsequent smothering and hypoxia.
This mechanism may explain how shifts (commonly called phase-shifts) from coral to algal
dominance occur. It could be that by setting up a positive feedback loop whereby increases in
algae (caused by overfishing, nutrient
Direct
interactions
enrichment, bleaching, etc.) fuel microbial
Predator-prey,
competition, mutualism
activity and cause local coral mortality; this
opens up more space for algal colonization
Structure
of indirect
Importance
Multiple
mechanisms
Three or more
species, transmitter
Common, increase
with richness
Contributory
processes act at
several scales
and thus more exudation and microbial
activity and so on until a phase shift has
Types
occurred .
Many combinations
possible; only some have
names
Fig. 4.**. Graphical summary of concepts
appearing in this chapter.
Self-test questions
1. Are you able to explain the difference between the competition equation and
the mutualism equation?
2. Do direct links among species increase per species when community richness
increases?
3. Can you give a hypothetical example of habitat facilitation using indirect
interactions convention?
4. Under what circumstances mutualism fails?
Suggested readings/viewings
The Queen of Trees video from PBS.
Points to think about when considering the video



Are the links shown in the video a complete representation of the complexity of
interactions involving the fig tree?
Would removal or loss of some of the interacting species cause a cascade of
undesirable consequences, that is negative consequences for the fig tree or some of its
major users? Can you think of some possibilities?
What are the different means of seed dispersal a fig tree uses?
Indirect effects in marine rocky intertidal interaction webs: patterns and
importance. B.A. Menge. Ecological Monographs, (1995) 65: 21-74.
Points to think about when reading the paper:

Which types of interactions may be most common, direct or indirect?

Would indirect effects be easy to detect?

Is rocky intertidal habitat unusual – speculate.
Tri-Trophic Interactions – some additional examples
Robert Paine (1980) coined the term trophic cascade to describe how the top-down
effects of predators could influence the abundances of species in lower trophic levels.
Others have focused on indirect effects that propagate from the bottom up through
multiple trophic levels, called tri-trophic effects when the interaction involves three
trophic levels (Price et al. 1980). Regardless of the direction of transmission, once an
effect proceeds beyond the adjacent trophic level, it becomes indirect. Hairston et al.
(1960) and Fretwell (1977) clearly invoke the trophic cascade phenomenon in their
writings about population regulation, although they did not call the process by this
particular name. Evidence for top-down trophic cascades is surprisingly scarce, and
comes primarily from aquatic systems (Strong 1992). There is at least one convincing
terrestrial example (Marquis and Whelan 1994). It has been suggested that the scarcity
of trophic cascades in terrestrial systems represents a real difference in the structure of
terrestrial and aquatic food webs. Strong (1992) suggests that aquatic food webs may
tend to be more linear than terrestrial ones. Trophic cascades might be likely to
develop in linear food chains, in which effects of one trophic level are readily passed on
to other levels. In contrast, in reticulate food webs, distinctions between trophic levels
are blurred and effects of one species are likely to diffuse over many adjacent species.
Studies of stream communities provide some of the best examples of trophic cascades.
Power et al. (1985) showed that a top predator, in this case largemouth bass, had strong
indirect effects that cascaded down through the food web to influence the abundance of
benthic algae in prairie streams. The system is best caricatured as a simple three-level
food chain, running from algae (mostly Spirogyra) to herbivorous minnows
(Campostoma sp.) to bass (Micropterus sp.). The prairie streams typically experience
periods of low water flow, during which the streams become series of isolated pools
connected by shallow riffles. At such times, two categories of pools become obvious:
bass pools with bass and luxuriant algae, and minnow pools with abundant herbivorous
minnows but without bass or much algae. The pattern suggests a cascading effect of
bass, which prey on minnows and could thereby promote algal growth by eliminating
an important herbivore.
To test this idea, Power et al. selected three pools for observation and experimental
manipulations. The manipulations consisted of the addition of bass to a minnow pool
and the removal of bass from a bass pool, which was then divided in half. One half of
the pool received minnows; the other half remained minnow free. A third minnow pool
remained unmanipulated as a control. The response of interest was the height of
filamentous algae in the pools over time. After bass removal, Campostoma greatly
reduced algal abundance to low, heavily grazed levels similar to those observed in a
natural control pool with abundant minnows. Addition of bass to a minnow pool
resulted in a rapid increase in algal abundance, whereas algae remained scarce in the
control pool without bass. These results are consistent with a cascading indirect effect
of bass transmitted through minnows to the algae. The actual mechanism involved
appears to be largely a behavioral avoidance of bass by minnows. Minnows leave
pools with bass, and, when confined with bass, limit their foraging to shallow water
where the risk of bass predation is least.
Similar kinds of trophic cascades may occur in lakes (Carpenter et al. 1985; McQueen
et al. 1989) and have been proposed as a possible way to control nuisance blooms of
algae in eutrophic waters. Trophic cascades are less dramatic in lakes than in prairie
streams, and the influence of top predators generally fails to propagate all the way
down to the algae. In lakes, the basic food chain (ignoring the microbial loop) runs
from algae to zooplankton to planktivorous fish to piscivorous fish. Where strong
trophic cascades occur, lakes with abundant piscivorous fish should have less algae
than lakes in which planktivorous fish form the top trophic level, since zooplankton
should be more abundant and should reduce phytoplankton to lower levels. However,
the predicted cascading effects seldom appear as decreased phytoplankton abundance
(Carpenter et al. 1987). One reason is that the phytoplankton consists of an array of
species that differ in their vulnerability to grazing by zooplankton, and differences in
zooplankton grazing pressure simply select for complementary communities of algae
that differ in grazer resistance. This situation has been modeled by Mathew Leibold
(1989). When zooplankton are abundant, the phytoplankton is dominated by grazer
resistant species. When zooplankton are less abundant, the phytoplankton is dominated
by competitively superior species that are vulnerable to grazing. Phytoplankton
remains abundant, but is dominated by different sets of species. Consequently, the
prospects for manipulating fish populations to control the abundance of nuisance algae
seem limited.
The best example of a terrestrial trophic cascade comes from a study by Robert
Marquis and Christopher Whelan (1994). They found strong effects of insectivorous
birds that were transmitted through herbivorous insects to white oak trees. Birds were
excluded from some trees by netting (cage treatment), while other trees remained
available to the birds (control treatment). Birds significantly reduced
the abundance of herbivorous insects on the oaks. In turn, oaks with birds and reduced
herbivorous insects had less leaf damage from insects and subsequently attained a
higher biomass. The effect of birds on insects was further corroborated by including an
insect removal treatment (spray treatment) consisting of applications of a spray
insecticide combined with the hand removal of remaining insects.