CHAPTER 15Smith, T. M., & Smith, R. L. (2015). Elements of Ecolo.docx

CHAPTER 15
Smith, T. M., & Smith, R. L. (2015). Elements of Ecology (9th
ed.). Boston, MA: Pearson.
15.1 Parasites Draw Resources from Host Organisms
Parasitism is a type of symbiotic relationship between
organisms of different species. One species—the parasite—
benefits from a prolonged, close association with the other
species—the host—which is harmed. Parasites increase their
fitness by exploiting host organisms for food, habitat, and
dispersal. Although they draw nourishment from the tissues of
the host organism, parasites typically do not kill their hosts as
predators do. However, the host may die from secondary
infection or suffer reduced fitness as a result of stunted growth,
emaciation, modification of behavior, or sterility. In general,
parasites are much smaller than their hosts, are highly
specialized for their mode of life, and reproduce more quickly
and in greater numbers than their hosts.
The definition of parasitism just presented may appear
unambiguous. But as with predation the term parasitism is often
used in a more general sense to describe a much broader range
of interactions (see Section 14.1). Interactions between species
frequently satisfy some, but not all, parts of this definition
because in many cases it is hard to demonstrate that the host is
harmed. In other cases, there may be no apparent specialization
by the parasite or the interaction between the organisms may be
short-lived. For example, because of the episodic nature of their
feeding habits, mosquitoes and hematophagic (blood-feeding)
bats are typically not considered parasitic. Parasitism can also
be used to describe a form of feeding in which one animal
appropriates food gathered by another (the host), which is a
behavior termed cleptoparasitism (literally meaning “parasitism
by theft”). An example is the brood parasitism practiced by
many species of cuckoo (Cuculidae). Many cuckoos use other
bird species as “babysitters”; they deposit their eggs in the nest

of the host species, which raise the cuckoo young as one of their
own (see Chapter 12 opening photograph). In the following
discussion, we use the narrower definition of parasite as given
in the previous paragraph, which includes a wide range of
organisms—viruses, bacteria, protists, fungi, plants, and an
array of invertebrates, among them arthropods. A heavy load of
parasites is termed an infection, and the outcome of an infection
is a disease.
Parasites are distinguished by size. Ecologically, parasites may
be classified as microparasites and macroparasites.
Microparasites include viruses, bacteria, and protists. They are
characterized by small size and a short generation time. They
develop and multiply rapidly within the host and are the class of
parasites that we typically associate with the term disease. The
infection generally lasts a short time relative to the host’s
expected life span. Transmission from host to host is most often
direct, although other species may serve as carriers.
Macroparasites are relatively large. Examples include
flatworms, acanthocephalans, roundworms, flukes, lice, fleas,
ticks, fungi, rusts, and smuts. Macroparasites have a
comparatively long generation time and typically do not
complete an entire life cycle in a single host organism. They
may spread by direct transmission from host to host or by
indirect transmission, involving intermediate hosts and carriers.
Although the term parasite is most often associated with
heterotrophic organisms such as animals, bacteria, and fungi,
more than 4000 species of parasitic plants derive some or all of
their sustenance from another plant. Parasitic plants have a
modified root—the haustorium—that penetrates the host plant
and connects to the vascular tissues (xylem or phloem).
Parasitic plants may be classified as holoparasites or
hemiparasites based on whether they carry out the process of
photosynthesis. Hemiparasites, such as most species of
mistletoe (Figure 15.1), are photosynthetic plants that contain
chlorophyll when mature and obtain water, with its dissolved
nutrients, by connecting to the host xylem. Holoparasites, such

as broomrape and dodder (Figure 15.2), lack chlorophyll and are
thus nonphotosynthetic. These plants function as heterotrophs
that rely totally on the host’s xylem and phloem for carbon,
water, and other essential nutrients.
Parasites are extremely important in interspecific relations. In
contrast with the species interactions of competition and
predation, however, it was not until the late 1960s that
ecologists began to appreciate the role of parasitism in
population dynamics and community structure. Parasites have
dramatic effects when they are introduced to host populations
that have not evolved to possess defenses against them. In such
cases, diseases sweep through and decimate the population.
15.2 Hosts Provide Diverse Habitats for Parasites
Hosts are the habitats of parasites, and the diverse arrays of
parasites that have evolved exploit every conceivable habitat on
and within their hosts. Parasites that live on the host’s skin,
within the protective cover of feathers and hair, are
ectoparasites. Others, known as endoparasites, live within the
host. Some burrow beneath the skin. They live in the
bloodstream, heart, brain, digestive tract, liver, spleen, mucosal
lining of the stomach, spinal cord, nasal tract, lungs, gonads,
bladder, pancreas, eyes, gills of fish, muscle tissue, or other
sites. Parasites of insects live on the legs, on the upper and
lower body surfaces, and even on the mouthparts.
Parasites of plants also divide up the habitat. Some live on the
roots and stems; others penetrate the roots and bark to live in
the woody tissue beneath. Some live at the root collar,
commonly called a crown, where the plants emerge from the
soil. Others live within the leaves, on young leaves, on mature
leaves, or on flowers, pollen, or fruits. A major problem for
parasites, especially parasites of animals, is gaining access to
and escaping from the host. Parasites can enter and exit host
animals through various pathways including the mouth, nasal
passages, skin, rectum, and urogenital system; they travel to
their point of infection through the pulmonary, circulatory, or
digestive systems.

For parasites, host organisms are like islands that eventually
disappear (die). Because the host serves as a habitat enabling
their survival and reproduction, parasites must escape from one
host and locate another, which is something that they cannot do
at will. Endo-macroparasites can escape only during a larval
stage of their development, known as the infective stage, when
they must make contact with the next host. The process of
transmission from one host to another can occur either directly
or indirectly and can involve adaptations by parasites to
virtually all aspects of feeding, social, and mating behaviors in
host species.
15.3 Direct Transmission Can Occur between Host Organisms
Direct transmission occurs when a parasite is transferred from
one host to another without the involvement of an intermediate
organism. The transmission can occur by direct contact with a
carrier, or the parasite can be dispersed from one host to
another through the air, water, or other substrate.
Microparasites are more often transmitted directly, as in the
case of influenza (airborne) and smallpox (direct contact)
viruses and the variety of bacterial and viral parasites
associated with sexually transmitted diseases.
Many important macroparasites of animals and plants also move
from infected to uninfected hosts by direct transmission. Among
internal parasites, the roundworms (Ascaris) live in the
digestive tracts of mammals. Female roundworms lay thousands
of eggs in the host’s gut that are expelled with the feces, where
they are dispersed to the surrounding environment (water, soil,
ground vegetation). If they are swallowed by a host of the
correct species, the eggs hatch in the host’s intestines, and the
larvae bore their way into the blood vessels and come to rest in
the lungs. From there they ascend to the mouth, usually by
causing the host to cough, and are swallowed again to reach the
stomach, where they mature and enter the intestines.
The most important debilitating external parasites of birds and
mammals are spread by direct contact. They include lice, ticks,
fleas, botfly larvae, and mites that cause mange. Many of these

parasites lay their eggs directly on the host; but fleas just lay
their eggs and their larvae hatch in the host’s nests and bedding,
and from there they leap onto nearby hosts.
Some parasitic plants also spread by direct transmission;
notably those classified as holoparasites, such as members of
the broomrape family (Orobanchaceae). Two examples are
squawroot (Conopholis americana), which parasitizes the roots
of oaks (see Figure 15.2), and beechdrops (Epifagus virginiana),
which parasitizes mostly the roots of beech trees. Seeds of these
plants are dispersed locally; upon germination, their roots
extend through the soil and attach to the roots of the host plant.
Some fungal parasites of plants spread through root grafts. For
example, Fomes annosus, an important fungal infection of white
pine (Pinus strobus), spreads rapidly through pure stands of the
tree when roots of one tree grow onto (and become attached to)
the roots of a neighbor.
15.4 Transmission between Hosts Can Involve an Intermediate
Vector
Some parasites are transmitted between hosts by an intermediate
organism, or vector. For example, the black-legged tick (Ixodes
scapularis) functions as an arthropod vector in the transmission
of Lyme disease, which is the major arthropod-borne disease in
the United States. Named for its first noted occurrence at Lyme,
Connecticut, in 1975, the disease is caused by a bacterial
spirochete, Borrelia burgdorferi. It lives in the bloodstream of
vertebrates, from birds and mice to deer and humans. The
spirochete depends on the tick for transmission from one host to
another (see this chapter’s Ecological Issues & Applications).
Malaria parasites infect a wide variety of vertebrate species,
including humans. The four species of protists parasites
(Plasmodium) that cause malaria in humans are transmitted to
the bloodstream by the bite of an infected female mosquito of
the genus Anopheles (Figure 15.3; see this chapter, Ecological
Issues & Applications). Mosquitoes are known to transmit more
than 50 percent of the approximately 102 arboviruses
(a contraction of “arthropod-borne viruses”) that can produce

disease in humans, including dengue and yellow fever.
Insect vectors are also involved in the transmission of parasites
among plants. European and native elm bark beetles (Scolytus
multistriatus and Hylurgopinus rufipes) carry spores of the
fungi Ophiostoma ulmi that spreads the devastating Dutch elm
disease from tree to tree. Mistletoes (Phoradendron spp.) belong
to a group of plant parasites known as hemiparasites (see Figure
15.1) that, although photosynthetic, draw water and nutrients
from their host plant. Transmission of mistletoes between host
plants is linked to seed dispersal. Birds feed on the mistletoe
fruits. The seeds pass through the digestive system unharmed
and are deposited on trees where the birds perch and defecate.
The sticky seeds attach to limbs and send out rootlets that
embrace the limb and enter the sapwood.
15.5 Transmission Can Involve Multiple Hosts and Stages
Previously, we introduced the concept of life cycle—the phases
associated with the development of an organism, typically
divided into juvenile (or prereproductive), reproductive, and
postreproductive phases (Chapter 10). Some species of parasites
cannot complete their entire life cycle in a single host species.
The host species in which the parasite becomes an adult and
reaches maturity is referred to as the definitive host. All others
are intermediate hosts, which harbor some developmental phase.
Parasites may require one, two, or even three intermediate
hosts. Each stage can develop only if the parasite can be
transmitted to the appropriate intermediate host. Thus, the
dynamics of a parasite population are closely tied to the
population dynamics, movement patterns, and interactions of
the various host species.
Many parasites, both plant and animal, use this form of indirect
transmission and spend different stages of the life cycle with
different host species. Figure 15.4 shows the life cycle of the
meningeal worm (Parelaphostrongylus tenuis), which is a
parasite of the white-tailed deer in eastern North America.
Snails or slugs that live in the grass serve as the intermediate
host species for the larval stage of the worm. The deer picks up

the infected snail while grazing. In the deer’s stomach, the
larvae leave the snail, puncture the deer’s stomach wall, enter
the abdominal membranes, and travel via the spinal cord to
reach spaces surrounding the brain. Here, the worms mate and
produce eggs. Eggs and larvae pass through the bloodstream to
the lungs, where the larvae break into air sacs and are coughed
up, swallowed, and passed out with the feces. The snails acquire
the larvae as they come into contact with the deer feces on the
ground. Once within the snail, the larvae continue to develop to
the infective stage.
15.6 Hosts Respond to Parasitic Invasions
Just as the coevolution of predators and prey has resulted in the
adaptation of defense mechanisms by prey species, host species
likewise exhibit a range of adaptations that minimize the impact
of parasites. Some responses are mechanisms that reduce
parasitic invasion. Other defense mechanisms aim to combat
parasitic infection once it has occurred.
Some defensive mechanisms are behavioral, aimed at avoiding
infection. Birds and mammals rid themselves of ectoparasites by
grooming. Among birds, the major form of grooming is
preening, which involves manipulating plumage with the bill
and scratching with the foot. Both activities remove adults and
nymphs of lice from the plumage. Deer seek dense, shaded
places where they can avoid deerflies, which are common to
open areas.
If infection should occur, the first line of defense involves the
inflammatory response. The death or destruction (injury) of host
cells stimulates the secretion of histamines (chemical alarm
signals), which induce increased blood flow to the site and
cause inflammation. This reaction brings in white blood cells
and associated cells that directly attack the infection. Scabs can
form on the skin, reducing points of further entry. Internal
reactions can produce hardened cysts in muscle or skin that
enclose and isolate the parasite. An example is the cysts that
encase the roundworm Trichinella spiralis (Nematoda) in the
muscles of pigs and bears and that cause trichinosis when

ingested by humans in undercooked pork.
Plants respond to bacterial and fungal invasion by forming cysts
in the roots and scabs in the fruits and roots, cutting off fungal
contact with healthy tissue. Plants react to attacks on leaf, stem,
fruit, and seed by gall wasps, bees, and flies by forming
abnormal growth structures unique to the particular gall insect
(Figure 15.5). Gall formation exposes the larvae of some gall
parasites to predation. For example, John Confer and Peter
Paicos of Ithaca College (New York) reported that the
conspicuous, swollen knobs of the goldenrod ball gall (Figure
15.5d) attract the downy woodpecker (Picoides pubescens),
which excavates and eats the larva within the gall.
The second line of defense is the immune response (or immune
system). When a foreign object such as a virus or bacteria—
termed an antigen (a contraction of “antibody-generating”)—
enters the bloodstream, it elicits an immune response. White
cells called lymphocytes (produced by lymph glands) produce
antibodies. The antibodies target the antigens present on the
parasite’s surface or released into the host and help to counter
their effects. These antibodies are energetically expensive to
produce. They also are potentially damaging to the host’s own
tissues. Fortunately, the immune response does not have to kill
the parasite to be effective. It only has to reduce the feeding,
movements, and reproduction of the parasite to a tolerable level.
The immune system is extremely specific, and it has a
remarkable “memory.” It can “remember” antigens it has
encountered in the past and react more quickly and vigorously
to them in subsequent exposures.
The immune response, however, can be breached. Some
parasites vary their antigens more or less continuously. By
doing so, they are able to keep one jump ahead of the host’s
response. The result is a chronic infection of the parasite in the
host. Antibodies specific to an infection normally are composed
of proteins. If the animal suffers from poor nutrition and its
protein deficiency is severe, normal production of antibodies is
inhibited. Depletion of energy reserves breaks down the immune

system and allows viruses or other parasites to become
pathogenic. The ultimate breakdown in the immune system
occurs in humans infected with the human immunodeficiency
virus (HIV)—the causal agent of AIDS—which is transmitted
sexually, through the use of shared needles, or by infected
donor blood. The virus attacks the immune system itself,
exposing the host to a range of infections that prove fatal.
15.7 Parasites Can Affect Host Survival and Reproduction
Although host organisms exhibit a wide variety of defense
mechanisms to prevent, reduce, or combat parasitic infection,
all share the common feature of requiring resources that the
host might otherwise have used for some other function. Given
that organisms have a limited amount of energy, it is not
surprising that parasitic infections function to reduce both
growth and reproduction. Joseph Schall of the University of
Vermont examined the impact of malaria on the western fence
lizard (Sceloporus occidentalis) inhabiting California. Clutch
size (number of eggs produced) is approximately 15 percent
smaller in females infected with malaria compared with
noninfected individuals (Figure 15.6). Reproduction is reduced
because infected females are less able to store fat during the
summer, so they have less energy for egg production the
following spring. Infected males likewise exhibit numerous
reproductive pathologies. Infected males display fewer
courtship and territorial behaviors, have altered sexually
dimorphic coloration, and have smaller testes.
Parasitic infection can reduce the reproductive success of males
by impacting their ability to attract mates. Females of many
species choose mates based on the secondary sex
characteristics, such as bright and ornate plumage of male birds
(see discussion of intrasexual selection in Chapter 10). Full
expression of these characteristics can be limited by parasite
infection, thus reducing the male’s ability to successfully attract
a mate. For example, the bright red color of the male zebra
finch’s beak depends on its level of carotenoid pigments, which
are the naturally occurring chemicals that are responsible for

the red, yellow, and orange coloration patterns in animals as
well as in foods such as carrots. Birds cannot synthesize
carotenoids and must obtain them through the diet. Besides
being colorful pigments, carotenoids stimulate the production of
antibodies and absorb some of the damaging free radicals that
arise during the immune response. In a series of laboratory
experiments, Jonathan Blount and colleagues from the
University of Glasgow (Scotland) found that only those males
with the fewest parasites and diseases can devote sufficient
carotenoids to producing bright red beaks and therefore succeed
in attracting mates and reproducing.
Although most parasites do not kill their host organisms,
increased mortality can result from a variety of indirect
consequences of infection. One interesting example is when the
infection alters the behavior of the host, increasing its
susceptibility to predation. Rabbits infected with the bacterial
disease tularemia (Francisella tularensis), transmitted by the
rabbit tick (Haemaphysalis leporis-palustris), are sluggish and
thus more vulnerable to predation. In another example,
ecologists Kevin Lafferty and Kimo Morris of the University of
California–Santa Barbara observed that killifish (Fundulus
parvipinnis; Figure 15.7a) parasitized by trematodes (flukes)
display abnormal behavior such as surfacing and jerking. In a
comparison of parasitized and unparasitized populations, the
scientists found that the frequency of conspicuous behaviors
displayed by individual fish is related to the intensity of
parasitism (Figure 15.7b). The abnormal behavior of the
infected killifish attracts fish-eating birds. Lafferty and Morris
found that heavily parasitized fish were preyed on more
frequently than unparasitized individuals (Figure 15.7c).
Interestingly, the fish-eating birds represent the trematodes’
definitive host, so that by altering its intermediate host’s
(killifish) behavior, making it more susceptible to predation, the
trematode ensures the completion of its life cycle.
15.8 Parasites May Regulate Host Populations
For parasite and host to coexist under a relationship that is

hardly benign, the host needs to resist invasion by eliminating
the parasites or at least minimizing their effects. In most
circumstances, natural selection has resulted in a level of
immune response in which the allocation of metabolic resources
by the host species minimizes the cost of parasitism yet does
not unduly impair its own growth and reproduction. Conversely,
the parasite gains no advantage if it kills its host. A dead host
means dead parasites. The conventional wisdom about host–
parasite evolution is that virulence is selected against, so that
parasites become less harmful to their hosts and thus persist.
Does natural selection work this way in parasite–host systems?
Natural selection does not necessarily favor peaceful
coexistence of hosts and parasites. To maximize fitness, a
parasite should balance the trade-off between virulence and
other components of fitness such as transmissibility. Natural
selection may yield deadly (high virulence) or benign (low
virulence) parasites depending on the requirements for parasite
reproduction and transmission. For example, the term vertical
transmission is used to describe parasites transmitted directly
from the mother to the offspring during the perinatal period (the
period immediately before or after birth). Typically, parasites
that depend on this mode of transmission cannot be as virulent
as those transmitted through other forms of direct contact
between adult individuals because the recipient (host) must
survive until reproductive maturity to pass on the parasite. The
host’s condition is important to a parasite only as it relates to
the parasite’s reproduction and transmission. If the host species
did not evolve, the parasite might well be able to achieve some
optimal balance of host exploitation. But just as with the
coevolution of predator and prey, host species do evolve (see
discussion of the Red Queen hypothesis in Section 14.9). The
result is an “arms race” between parasite and host.
Parasites can have the effect of decreasing reproduction and
increasing the probability of host mortality, but few studies
have quantified the effect of a parasite on the dynamics of a
particular plant or animal population under natural conditions.

Parasitism can have a debilitating effect on host populations, a
fact that is most evident when parasites invade a population that
has not evolved to possess defenses. In such cases, the spread of
disease may be virtually density independent, reducing
populations, exterminating them locally, or restricting
distribution of the host species. The chestnut blight
(Cryphonectria parasitica), introduced to North America from
Europe, nearly exterminated the American chestnut (Castanea
dentata) and removed it as a major component of the forests of
eastern North America. Dutch elm disease, caused by a fungus
(Ophiostoma ulmi) spread by beetles, has nearly removed the
American elm (Ulmus americana) from North America and the
English elm (Ulmus glabra) from Great Britain. Anthracnose
(Discula destructiva), a fungal disease, is decimating flowering
dogwood (Cornus florida), an important understory tree in the
forests of eastern North America. Rinderpest, a viral disease of
domestic cattle, was introduced to East Africa in the late 19th
century and subsequently decimated herds of African buffalo
(Syncerus caffer) and wildebeest (Connochaetes taurinus).
Avian malaria carried by introduced mosquitoes has eliminated
most native Hawaiian birds below 1000 m (the mosquito cannot
persist above this altitude).
On the other hand, parasites may function as density-dependent
regulators on host populations. Density-dependent regulation of
host populations typically occurs with directly transmitted
endemic (native) parasites that are maintained in the population
by a small reservoir of infected carrier individuals. Outbreaks
of these diseases appear to occur when the host population
density is high; they tend to reduce host populations sharply,
resulting in population cycles of host and parasite similar to
those observed for predator and prey (see Section 14.2).
Examples are distemper in raccoons and rabies in foxes, both of
which are diseases that significantly control their host
populations.
In other cases, the parasite may function as a selective agent of
mortality, infecting only a subset of the population. Distribution

of macroparasites, especially those with indirect transmission,
is highly clumped. Some individuals in the host population
carry a higher load of parasites than others do (Figure 15.8).
These individuals are most likely to succumb to parasite-
induced mortality, suffer reduced reproductive rates, or both.
Such deaths often are caused not directly by the macroparasites,
but indirectly by secondary infection. In a study of
reproduction, survival, and mortality of bighorn sheep (Ovis
canadensis) in south-central Colorado, Thomas Woodard and
colleagues at Colorado State University found that individuals
may be infected with up to seven different species of lungworms
(Nematoda). The highest rates of infection occur in the spring
when lambs are born. Heavy lungworm infections in the lambs
bring about a secondary infection—pneumonia—that kills them.
The researchers found that such infections can sharply reduce
mountain sheep populations by reducing reproductive success.
15.9 Parasitism Can Evolve into a Mutually Beneficial
Relationship
Parasites and their hosts live together in a symbiotic
relationships in which the parasite derives its benefit (habitat
and food resources) at the expense of the host organism. Host
species have evolved a variety of defenses to minimize the
negative impact of the parasite’s presence. In a situation in
which adaptations have countered negative impacts, the
relationship may be termed commensalism, which is a
relationship between two species in which one species benefits
without significantly affecting the other (Section 12.1, Table
12.1). At some stage in host–parasite coevolution, the
relationship may become beneficial to both species. For
example, a host tolerant of parasitic infection may begin to
exploit the relationship. At that point, the relationship is termed
mutualism. There are many examples of “parasitic
relationships” in which there is an apparent benefit to the host
organism. For example, rats infected with the intermediate
stages of the tapeworm Spirometra grow larger than uninfected
rats do because the tapeworm larva produces an analogue of

vertebrate growth hormone. In this example, is the increased
growth beneficial or harmful to the host? Similarly, many
mollusks, when infected with the intermediate stages of
digenetic flukes (Digenea), develop thicker, heavier shells that
could be deemed an advantage. Some of the clearest examples
of evolution from parasites to mutualists involve parasites that
are transmitted vertically from mother to offspring (see
discussion in Section 15.8). Theory predicts that vertically
transmitted parasites are selected to increase host survival and
reproduction because maximization of host reproductive success
benefits both the parasite and host. This prediction has been
supported by studies examining the effects of Wolbachia, a
common group of bacteria that infect the reproductive tissues of
arthropods. Investigations of the effects of Wolbachia on host
fitness in the wasp Nasonia vitripennis have shown that
infection increases host fitness and that infected females
produce more offspring than do uninfected females. Similar
increases in fitness have been reported for natural populations
of fruit flies (Drosophila).
Mutualism is a relationship between members of two species in
which the survival, growth, or reproduction is enhanced for
individuals of both species. Evidence, however, suggests that
often this interaction is more of a reciprocal exploitation than a
cooperative effort between individuals. Many classic examples
of mutualistic associations appear to have evolved from species
interactions that previously reflected host–parasite or predator–
prey interactions. In many cases of apparent mutualism, the
benefits of the interaction for one or both of the participating
species may be dependent on the environment (see Section
12.4). For example, many tree species have the fungal
mycorrhizae associated with their roots (see Section 15.11). The
fungi obtain organic nutrients from the plant via the phloem,
and in nutrient-poor soil the trees seem to benefit by increased
nutrient uptake, particularly phosphate by the fungus. In
nutrient-rich soils, however, the fungi appear to be a net cost
rather than benefit; this seemingly mutualistic association

appears much more like a parasitic invasion by the fungus.
Depending on external conditions, the association switches
between mutualism and parasitism (see further discussion of
example in Section 12.4, Figure 12.9).
15.10 Mutualisms Involve Diverse Species Interactions
Mutualistic relationships involve many diverse interactions that
extend beyond simply acquiring essential resources. Thus, it is
important to consider the different attributes of mutualistic
relationships and how they affect the dynamics of the
populations involved. Mutualisms can be characterized by a
number of variables: the benefits received, the degree of
dependency, the degree of specificity, and the duration of the
intimacy.
Mutualism is defined as an interaction between members of two
species that serves to benefit both parties involved, and the
benefits received can include a wide variety of processes.
Benefits may include provision of essential resources such as
nutrients or shelter (habitat) and may involve protection from
predators, parasites, and herbivores, or they may reduce
competition with a third species. Finally, the benefits may
involve reproduction, such as dispersal of gametes or zygotes.
Mutualisms also vary in how much the species involved in the
mutualistic interaction depend on each other. Obligate
mutualists cannot survive or reproduce without the mutualistic
interaction, whereas facultative mutualists can. In addition, the
degree of specificity of mutualism varies from one interaction
to another, ranging from one-to-one, species-specific
associations (termed specialists) to association with a wide
diversity of mutualistic partners (generalists). The duration of
intimacy in the association also varies among mutualistic
interactions. Some mutualists are symbiotic, whereas others are
free living (nonsymbiotic). In symbiotic mutualism, individuals
coexist and their relationship is more often obligatory; that is,
at least one member of the pair becomes totally dependent on
the other. Some forms of mutualism are so permanent and
obligatory that the distinction between the two interacting

organisms becomes blurred. Reef-forming corals of the tropical
waters provide an example. These corals secrete an external
skeleton composed of calcium carbonate. The individual coral
animals, called polyps, occupy little cups, or corallites, in the
larger skeleton that forms the reef (Figure 15.9). These corals
have single-celled, symbiotic algae in their tissues called
zooxanthellae. Although the coral polyps are carnivores,
feeding on zooplankton suspended in the surrounding water,
they acquire only about 10 percent of their daily energy
requirement from zooplankton. They obtain the remaining 90
percent of their energy from carbon produced by the symbiotic
algae through photosynthesis. Without the algae, these corals
would not be able to survive and flourish in their nutrient-poor
environment (see this chapter, Field Studies: John J.
Stachowicz). In turn, the coral provides the algae with shelter
and mineral nutrients, particularly nitrogen in the form of
nitrogenous wastes.
Lichens are involved in a symbiotic association in which the
fusion of mutualists has made it even more difficult to
distinguish the nature of the individual. Lichens (Figure 15.10)
consist of a fungus and an alga (or in some cases
cyanobacterium) combined within a spongy body called a
thallus. The alga supplies food to both organisms, and the
fungus protects the alga from harmful light intensities, produces
a substance that accelerates photosynthesis in the alga, and
absorbs and retains water and nutrients for both organisms.
There are about 25,000 known species of lichens, each
composed of a unique combination of fungus and alga.
In nonsymbiotic mutualism, the two organisms do not
physically coexist, yet they depend on each other for some
essential function. Although nonsymbiotic mutualisms may be
obligatory, most are not. Rather, they are facultative,
representing a form of mutual facilitation. Pollination in
flowering plants and seed dispersal are examples. These
interactions are generally not confined to two species, but rather
involve a variety of plants, pollinators, and seed dispersers.

In the following sections, we explore the diversity of
mutualistic interactions. The discussion centers on the benefits
derived by mutualists: acquisition of energy and nutrients,
protection and defense, and reproduction and dispersal.
15.11 Mutualisms Are Involved in the Transfer of Nutrients
The digestive system of herbivores is inhabited by a diverse
community of mutualistic organisms that play a crucial role in
the digestion of plant materials. The chambers of a ruminant’s
stomach contain large populations of bacteria and protists that
carry out the process of fermentation (see Section 7.2).
Inhabitants of the rumen are primarily anaerobic, adapted to this
peculiar environment. Ruminants are perhaps the best studied
but are not the only example of the role of mutualism in animal
nutrition. The stomachs of virtually all herbivorous mammals
and some species of birds and lizards rely on the presence of a
complex microbial community to digest cellulose in plant
tissues.
Field Studies John J. StachowiczSection of Evolution and
Ecology, Center for Population Biology, University of
California–Davis
Facilitative, or positive, interactions are encounters between
organisms that benefit at least one of the participants and cause
harm to neither. Such interactions are considered mutualisms, in
which both species derive benefit from the interaction.
Ecologists have long recognized the existence of mutualistic
interactions, but there is still far less research on positive
interactions than on competition and predation. Now, however,
ecologists are beginning to appreciate the ubiquitous nature of
positive interactions and their importance in affecting
populations and in the structuring of communities. The research
of marine ecologist John Stachowicz has been at the center of
this growing appreciation of the importance of facilitation.
Stachowicz works in the shallow-water coastal ecosystems of
the southeastern United States. The large colonial corals and
calcified algae that occupy the warm subtropical waters of this
region provide a habitat for a diverse array of invertebrate and

vertebrate species. In well-lit habitats, corals and calcified
algae (referred to as coralline algae) grow slowly relative to the
fleshy species of seaweed. The persistence of corals appears to
be linked to the high abundance of herbivores that suppress the
growth of the seaweeds, which grow on and over the coral and
coralline algae and eventually cause their death. In contrast, the
relative cover of corals is generally low in habitats such as reef
flats and seagrass beds, where herbivory is less intense.
Stachowicz hypothesized that mutualism plays an influential
role in the distribution of coral species. Although corals are
typically associated with the colorful and diverse coral reef
ecosystems of the tropical and subtropical coastal waters, many
temperate and subarctic habitats support corals, and some
tropical species occur where temperatures drop to 10°C or
below for certain months of the year. One such species is the
coral Oculina arbuscula.
O. arbuscula occurs as far north as the coastal waters of North
Carolina, forming dense aggregations in poorly lit habitats
where seaweeds are rare or absent. In certain areas of the
coastal waters, however, O. arbuscula does co-occur with
seaweeds on natural and artificial reefs. It is the only coral in
this region with a structurally complex branching morphology
that provides shelter for a species-rich epifauna. More than 300
species of invertebrates are known to live among the branches
of Oculina colonies.
How can O. arbuscula persist in the well-lit, shallow-water
systems? In well-lit habitats, corals grow slowly relative to
seaweeds, and the persistence of coral reefs appears to be
tightly linked to high abundance of herbivores that prevent
seaweed from growing on and over the corals. When
herbivorous fish or sea urchins are naturally or experimentally
removed from tropical reefs, seaweed biomass increases
dramatically and corals are smothered. In contrast, on the
temperate reefs of North Carolina, herbivorous fish are less
abundant than in the tropics, and the standing biomass of
seaweed is typically much higher. On these reefs, herbivorous

fish and urchins also alter the species composition of the
seaweed community by selectively removing their preferred
species, but they do not diminish the total seaweed biomass.
The dependence of corals on positive interactions with
herbivores may thus explain why corals are generally
uncommon in temperate latitudes.
Stachowicz suspected the role of a key herbivore in these
temperate reef ecosystems: the herbivorous crab Mithrax
forceps. He hypothesized that the success of O. arbuscula on
temperate reefs derives from its ability to harbor symbiotic,
herbivorous crabs that mediate competition with encroaching
seaweeds. To evaluate the hypothesis, he conducted field
experiments monitoring the fouling (overgrowth by seaweeds)
and growth of corals in the presence and absence of crabs.
Experiments were located at Radio Island Jetty near Beaufort,
North Carolina.
In these experiments, metal stakes were driven into substrate,
and one coral (which had previously been weighed) was
fastened to each stake. A single crab was then placed on a
subset of the corals, and the remainder was left vacant. At the
end of the experiment, all seaweed (and other epiphytic growth)
was removed from the corals, dried, and weighed. After removal
of the seaweeds, the corals were reweighed to measure growth.
To determine if association with O. arbuscula reduced predation
on M. forceps, Stachowicz tethered crabs both with and without
access to coral. He checked each tether after 1 and 24 hours to
see if crabs were still present.
Mutualistic interactions are also involved in the uptake of
nutrients by plants. Nitrogen is an essential constituent of
protein, a building block of all living material. Although
nitrogen is the most abundant constituent of the atmosphere—
approximately 79 percent in its gaseous state—it is unavailable
to most life. It must first be converted into a chemically usable
form. One group of organisms that can use gaseous nitrogen
(N2) is the nitrogen-fixing bacteria of the genus Rhizobium.
These bacteria (called rhizobia) are widely distributed in the

soil, where they can grow and multiply. But in this free-living
state, they do not fix nitrogen. Legumes—a group of plant
species that include clover, beans, and peas—attract the bacteria
through the release of exudates and enzymes from the roots.
Rhizobia enter the root hairs, where they multiply and increase
in size. This invasion and growth results in swollen, infected
root hair cells, which form root nodules (Figure 15.11). Once
infected, rhizobia within the root cells reduce gaseous nitrogen
to ammonia (a process referred to as nitrogen fixation). The
bacteria receive carbon and other resources from the host plant;
in return, the bacteria contribute fixed nitrogen to the plant,
allowing it to function and grow independently of the
availability of mineral (inorganic) nitrogen in the soil (see
Chapter 6, Section 6.11).
Endomycorrhizae have an extremely broad range of hosts; they
have formed associations with more than 70 percent of all plant
species. Mycelia—masses of interwoven fungal filaments in the
soil—infect the tree roots. They penetrate host cells to form a
finely bunched network called an arbuscule (Figure 15.12a).
The mycelia act as extended roots for the plant but do not
change the shape or structure of the roots. They draw in
nitrogen and phosphorus at distances beyond those reached by
the roots and root hairs. Another form, ectomycorrhizae,
produces shortened, thickened roots that look like coral (Figure
15.12b). The threads of the fungi penetrate between the root
cells. Outside the root, they develop into a network that
functions as extended roots. Ectomycorrhizae have a more
restricted range of hosts than do endomycorrhizae. They are
associated with about 10 percent of plant families, and most of
these species are woody.
Together, either ecto- or endomycorrhizae are found associated
with the root systems of the vast majority of terrestrial plant
species and are especially important in nutrient-poor soils. They
aid in the decomposition of dead organic matter and the uptake
of water and nutrients, particularly nitrogen and phosphorus,
from the soil into the root tissue (see Sections 21.7 and 6.11).

15.12 Some Mutualisms Are Defensive
Other mutualistic associations involve defense of the host
organism. A major problem for many livestock producers is the
toxic effects of certain grasses, particularly perennial ryegrass
and tall fescue. These grasses are infected by symbiotic
endophytic fungi that live inside plant tissues. The fungi
(Clavicipitaceae and Ascomycetes) produce alkaloid compounds
in the tissue of the host grasses. The alkaloids, which impart a
bitter taste to the grass, are toxic to grazing mammals,
particularly domestic animals, and to a number of insect
herbivores. In mammals, the alkaloids constrict small blood
vessels in the brain, causing convulsions, tremors, stupor,
gangrene of the extremities, and death. At the same time, these
fungi seem to stimulate plant growth and seed production. This
symbiotic relationship suggests a defensive mutualism between
plant and fungi. The fungi defend the host plant against grazing.
In return, the plant provides food to the fungi in the form of
photosynthates (products of photosynthesis).
A group of Central American ant species (Pseudomyrmex spp.)
that live in the swollen thorns of acacia (Vachellia spp.) trees
provides another example of defensive mutualism. Besides
providing shelter, the plants supply a balanced and almost
complete diet for all stages of ant development. In return, the
ants protect the plants from herbivores. At the least disturbance,
the ants swarm out of their shelters, emitting repulsive odors
and attacking the intruder until it is driven away.
Perhaps one of the best-documented examples of a defensive or
protective mutualistic association is the cleaning mutualism
found in coral reef communities between cleaner shrimp or
cleaner fishes and a large number of fish species. Cleaner fishes
and shrimp obtain food by cleaning ectoparasites and diseased
and dead tissue from the host fish (Figure 15.13a). In so doing,
they benefit the host fish by removing harmful and unwanted
materials.
Cleaning mutualism also occurs in terrestrial environments. The
red-billed oxpecker (Figure 15.13b) of Africa is a bird that

feeds almost exclusively by gleaning ticks and other parasites
from the skin of large mammals such as antelope, buffalo,
rhinoceros, or giraffe (also domestic cattle). It has always been
assumed that these birds significantly reduce the number of
ticks on the host animal, yet a recent study by ecologist Paul
Weeks of Cambridge University brings into question whether
this relationship is indeed mutualistic. In a series of field
experiments, Weeks found that changes in adult tick load of
cattle were unaffected by excluding the birds. In addition,
oxpeckers will peck a vulnerable area (often an ear) and drink
blood when parasites are not available.
15.13 Mutualisms Are Often Necessary for Pollination
The goal of cross-pollination is to transfer pollen from the
anthers of one plant to the stigma of another plant of the same
species (see Figure 12.3). Some plants simply release their
pollen in the wind. This method works well and costs little
when plants grow in large homogeneous stands, such as grasses
and pine trees often do. Wind dispersal can be unreliable,
however, when individuals of the same species are scattered
individually or in patches across a field or forest. In these
circumstances, pollen transfer typically depends on insects,
birds, and bats.
Plants entice certain animals by color, fragrances, and odors,
dusting them with pollen and then rewarding them with a rich
source of food: sugar-rich nectar, protein-rich pollen, and fat-
rich oils (Section 12.3, Figure 12.5). Providing such rewards is
expensive for plants. Nectar and oils are of no value to the plant
except as an attractant for potential pollinators. They represent
energy that the plant might otherwise expend in growth.
Nectivores (animals that feed on nectar) visit plants to exploit a
source of food. While feeding, the nectivores inadvertently pick
up pollen and carry it to the next plant they visit. With few
exceptions, the nectivores are typically generalists that feed on
many different plant species. Because each species flowers
briefly, nectivores depend on a progression of flowering plants
through the season.

Many species of plants, such as blackberries, elderberries,
cherries, and goldenrods, are generalists themselves. They
flower profusely and provide a glut of nectar that attracts a
diversity of pollen-carrying insects, from bees and flies to
beetles. Other plants are more selective, screening their visitors
to ensure some efficiency in pollen transfer. These plants may
have long corollas, allowing access only to insects and
hummingbirds with long tongues and bills and keeping out
small insects that eat nectar but do not carry pollen. Some
plants have closed petals that only large bees can pry open.
Orchids, whose individuals are scattered widely through their
habitats, have evolved a variety of precise mechanisms for
pollen transfer and reception. These mechanisms assure that
pollen is not lost when the insect visits flowers of other species.
15.14 Mutualisms Are Involved in Seed Dispersal
Plants with seeds too heavy to be dispersed by wind depend on
animals to carry them some distance from the parent plant and
deposit them in sites favorable for germination and seedling
establishment. Some seed-dispersing animals on which the
plants depend may be seed predators as well, eating the seeds
for their own nutrition. Plants depending on such animals
produce a tremendous number of seeds during their reproductive
lives. Most of the seeds are consumed, but the sheer number
ensures that a few are dispersed, come to rest on a suitable site,
and germinate (see concept of predator satiation, Section
14.10).
For example, a mutualistic relationship exists between
wingless-seeded pines of western North America (whitebark
pine [Pinus albicaulis], limber pine [Pinus flexilis],
southwestern white pine [Pinus strobiformis], and piñon pine
[Pinus edulis]) and several species of jays (Clark’s nutcracker
[Nucifraga columbiana], piñon jay [Gymnorhinus
cyanocephalus], western scrub jay [Aphelocoma californica],
and Steller’s jay [Cyanocitta stelleri]). In fact, there is a close
correspondence between the ranges of these pines and jays. The
relationship is especially close between Clark’s nutcracker and

the whitebark pine. Research by ecologist Diana Tomback of the
University of Colorado–Denver has revealed that only Clark’s
nutcracker has the morphology and behavior appropriate to
disperse the seeds significant distances away from the parent
tree. A bird can carry in excess of 50 seeds in cheek pouches
and caches them deep enough in the soil of forest and open
fields to reduce their detection and predation by rodents.
Seed dispersal by ants is prevalent among a variety of
herbaceous plants that inhabit the deserts of the southwestern
United States, the shrublands of Australia, and the deciduous
forests of eastern North America. Such plants, called
myrmecochores, have an ant-attracting food body on the seed
coat called an elaiosome (Figure 15.14). Appearing as shiny
tissue on the seed coat, the elaiosome contains certain chemical
compounds essential for the ants. The ants carry seeds to their
nests, where they sever the elaiosome and eat it or feed it to
their larvae. The ants discard the intact seed within abandoned
galleries of the nest. The area around ant nests is richer in
nitrogen and phosphorus than the surrounding soil, providing a
good substrate for seedlings. Further, by removing seeds far
from the parent plant, the ants significantly reduce losses to
seed-eating rodents. Plants may enclose their seeds in a
nutritious fruit attractive to fruit-eating animals—the frugivores
(Figure 15.15). Frugivores are not seed predators. They eat only
the tissue surrounding the seed and, with some exceptions, do
not damage the seed. Most frugivores do not depend exclusively
on fruits, which are only seasonally available and deficient in
proteins.
To use frugivorous animals as agents of dispersal, plants must
attract them at the right time. Cryptic coloration, such as green
unripened fruit among green leaves, and unpalatable texture,
repellent substances, and hard outer coats discourage
consumption of unripe fruit. When seeds mature, fruit-eating
animals are attracted by attractive odors, softened texture,
increasing sugar and oil content, and “flagging” of fruits
with colors.

Most plants have fruits that can be exploited by an array of
animal dispersers. Such plants undergo quantity dispersal; they
scatter a large number of seeds to increase the chance that
various consumers will drop some seeds in a favorable site.
Such a strategy is typical of, but not exclusive to, plants of the
temperate regions, where fruit-eating birds and mammals rarely
specialize in one kind of fruit and do not depend exclusively on
fruit for sustenance. The fruits are usually succulent and rich in
sugars and organic acids. They contain small seeds with hard
seed coats resistant to digestive enzymes, allowing the seeds to
pass through the digestive tract unharmed. Such seeds may not
germinate unless they have been conditioned or scarified by
passage through the digestive tract. Large numbers of small
seeds may be dispersed, but few are deposited on suitable sites.
In tropical forests, 50–75 percent of the tree species produce
fleshy fruits whose seeds are dispersed by animals. Rarely are
these frugivores obligates of the fruits they feed on, although
exceptions include many tropical fruit-eating bats.
15.15 Mutualism Can Influence Population Dynamics
Mutualism is easy to appreciate at the individual level. We
grasp the interaction between an ectomycorrhizal fungus and its
oak or pine host, we count the acorns dispersed by squirrels and
jays, and we measure the cost of dispersal to oaks in terms of
seeds consumed. Mutualism improves the growth and
reproduction of the fungus, the oak, and the seed predators. But
what are the consequences at the population and community
levels?
Mutualism exists at the population level only if the growth rate
of species 1 increases with the increasing density of species 2,
and vice versa (see Quantifying Ecology 15.1). For symbiotic
mutualists where the relationship is obligate, the influence is
straightforward. Remove species 1 and the population of species
2 no longer exists. If ectomycorrhizal spores fail to infect the
rootlets of young pines, the fungi do not develop. If the young
pine invading a nutrient-poor field fails to acquire a
mycorrhizal symbiont, it does not grow well, if at all.

Discerning the role of facultative (nonsymbiotic) mutualisms in
population dynamics can be more difficult. As discussed in
Sections 15.13 and 15.14, mutualistic relationships are common
in plant reproduction, where plant species often depend on
animal species for pollination, seed dispersal, or germination.
Although some relationships between pollinators and certain
flowers are so close that loss of one could result in the
extinction of the other, in most cases the effects are subtler and
require detailed demographic studies to determine the
consequences on species fitness.
Quantifying Ecology 15.1 A Model of Mutualistic Interactions
The simplest model of a mutualistic interaction between two
species is similar to the basic Lotka–Volterra model as
described in Chapter 13 for two competing species. The crucial
difference is that rather than negatively influencing each other’s
growth rate, the two species have positive interactions. The
competition coefficients α and β are replaced by positive
interaction coefficients, reflecting the per capita effect of an
individual of species 1 on species 2 (α12) and the effect of an
individual of species 2 on species 1 (α21).
Species1:dN1dt=r1N1(K1−N1+α21N2K1)Species2:dN2dt=r2N2(
K2−N2+α12N1K2)Species1:dN1dt=r1N1(K1−N1+α21N2K1)Spe
cies2:dN2dt=r2N2(K2−N2+α12N1K2)
All of the terms are analogous to those used in the Lotka–
Volterra equations for interspecific competition, except that
α21N2 and α12N1 are added to the respective population
densities (N1 and N2) rather than subtracted.
This model describes a facultative, rather than obligate,
interaction because the carrying capacities of the two species
are positive, and each species (population) can grow in the
absence of the other. In this model, the presence of the
mutualist offsets the negative effect of the species’ population
on the carrying capacity. In effect, the presence of the one
species increases the carrying capacity of the other.
To illustrate this simple model, we can define values for the
parameters r1, r2, K1, K2, α21, and α12.

r1=3.22,K1=1000,α12=0.5r2=3.22,K2=1000,α21=0.6r1=3.22,K1
=1000,α12=0.5r2=3.22,K2=1000,α21=0.6
As with the Lotka–Volterra model for interspecific competition,
we can calculate the zero isocline for the two mutualistic
species that are represented by the equations presented two
paragraphs above. The zero isocline for species 1 is solved by
defining the values of N1 and N2, where (K1 − N1 + α21N2) is
equal to zero. As with the competition model, because the
equation is a linear function, we can define the line (zero
isocline) by solving for only two points. Likewise, we can solve
for the species 2 isocline. The resulting isoclines are shown in
Figure 1.
Note that, unlike the possible outcomes with the competition
equations, the zero isoclines extend beyond the carrying
capacities of the two species (K1 and K2), reflecting that the
carrying capacity of each species is effectively increased by the
presence of the mutualist (other species, see Figure 14.2). If we
use the equations to project the density of the two populations
through time (Figure 2), each species attains a higher density in
the presence of the other species than when they occur alone (in
the absence of the mutualist).
1. On the graph displaying the zero isoclines shown in Figure
1, plot the four points listed and indicate the direction of change
for the two populations.
(N1,N2)=500,500(N1,N2)=3500,3000(N1,N2)=3000,1000(N1,N
2)=1000,3000(N1,N2)=500,500(N1,N2)=3500,3000(N1,N2)=300
0,1000(N1,N2)=1000,3000
2. What outcome do the isoclines indicate for the interaction
between these two species?
When the mutualistic interaction is diffuse, involving a number
of species—as is often the case with pollination systems (see
discussion of pollination networks in Section 12.5) and seed
dispersal by frugivores—the influence of specific species–
species interactions is difficult to determine. In other situations,
the mutualistic relationship between two species may be
mediated or facilitated by a third species, much the same as for

vector organisms and intermediate hosts in parasite–host
interactions. Mutualistic relationships among conifers,
mycorrhizae, and voles in the forests of the Pacific Northwest
as described by ecologist Chris Maser of the University of
Puget Sound (Washington) and his colleagues are one such
example (Figure 15.16). To acquire nutrients from the soil, the
conifers depend on mycorrhizal fungi associated with the root
system. In return, the mycorrhizae depend on the conifers for
energy in the form of carbon (see Section 15.10). The
mycorrhizae also have a mutualistic relationship with voles that
feed on the fungi and disperse the spores, which then infect the
root systems of other conifer trees.
Perhaps the greatest limitation in evaluating the role of
mutualism in population dynamics is that many—if not most—
mutualistic relationships arise from indirect interaction in
which the affected species never come into contact. Mutualistic
species influence each other’s fitness or population growth rate
indirectly through a third species or by altering the local
environment (habitat modification)—topics we will revisit later
(Chapter 17). Mutualism may well be as significant as either
competition or predation in its effect on population dynamics
and community structure.
Ecological Issues & Applications Land-use Changes Are
Resulting in an Expansion of Infectious Diseases Impacting
Human Health
The cutting and clearing of forests to allow for the expansion of
agriculture and urbanization has long been associated with
declining plant and animal populations and the reduction of
biological diversity resulting from habitat loss (see Chapters 9
and 12, Ecological Issues & Applications); however, recent
research is showing that these land-use changes are directly
impacting human health because they facilitate the expansion of
infectious diseases. In many regions of the world, forest
clearing has altered the abundance or dispersal of pathogens—
parasites causing disease in the host organisms—by influencing
the abundance and distribution of animal species that function

as their hosts and vectors. One of the best-documented cases of
forest clearing impacting the transmission of an infectious
disease involves Lyme disease, which is an infectious disease
that has been dramatically increasing in the number of reported
cases in North America (see Section 15.4). New estimates
indicate that Lyme disease is 10 times more common than
previous national counts indicated, with approximately 300,000
people, primarily in the Northeast, contracting the disease each
year.
Lyme disease is caused by the bacterial parasite Borrelia
burgdorferi, which, in eastern and central North America, is
transmitted by the bite of an infected blacklegged tick (Ixodes
scapularis). The ticks have a four-stage life cycle: egg, larvae,
nymph, and adult (Figure 15.17). Larval ticks hatch uninfected;
however, they feed on blood, and if they feed on an organism
infected by the Borrelia burgdorferi bacteria, they too can
become infected and later transmit the bacteria to people.
Whether a larval tick will acquire an infection and subsequently
molt into an infected nymph depends largely on the species of
host on which it feeds. The larval ticks may feed on a wide
variety of host species that carry the bacterial parasite,
including birds, reptiles, and mammals. However, not all host
species are equally likely to transmit the infection to the
feeding tick. One species with high rates of transmission to
larval ticks that feed on its blood is the white-footed mouse
(Peromyscus leucopus), which infects between 40 and 90
percent of feeding tick larvae. It is at this point in the story that
human activity comes into play.
Human activities in the northeastern United States have resulted
in the fragmentation of what was once a predominantly forested
landscape. Fragmentation involves both a reduction in the total
forested area as well as a reduction in the average size of
remaining forest patches (see Chapter 19). One key consequence
of the fragmentation of previously continuous forest is a
reduction in species diversity (Section 19.4). However, certain
species thrive in highly fragmented landscapes. One such

organism is the white-footed mouse, the small mammal species
with high transmission rates of the bacterial parasite B.
burgdorferi to their primary vector of transmission to humans,
larval blacklegged ticks. White-footed mice reach unusually
high densities in small forest fragments, which is most likely a
result of decreased abundance of both predators and
competitors. Could forest fragmentation and associated
increases in the populations of white-footed mice in the
Northeast be responsible for the increased transmission of Lyme
disease in this region? To address this question, Brian Allen of
Rutgers University and colleagues Felicia Keesing and Richard
Ostfeld undertook a study to examine the impact of forest
clearing and fragmentation in southeastern New York State on
the potential for transmission of Lyme disease. The researchers
hypothesized that small forest patches (<2 hectares [ha]) have a
higher density of infected nymphal blacklegged ticks than larger
patches (2–8 ha). To test this hypothesis Allen and his
colleagues sampled tick density and B. burgdorferi infection
prevalence in forest patches, ranging in size from 0.7 to 7.6 ha.
The researchers found both an exponential decline in the density
of nymphal ticks, as well as a significant decline in the nymphal
infection prevalence with increasing size of forest patches
(Figure 15.18). The consequence was a dramatic increase in the
density of infected nymphs, and therefore in Lyme disease risk,
with decreasing size of forest patches. Forest clearing and
fragmentation clearly lead to a potential increase in the
transmission of Lyme disease.
An additional factor resulting from forest clearing and
fragmentation in the region is an increase in the population of
white-tailed deer, the primary host species for the adult ticks.
Adult ticks feed on white-tailed deer, after which the female
tick drops her eggs to the ground for the cycle to begin once
again. Together, the increases in white-footed mice and white-
tailed deer population in the Northeast that have resulted from
alterations of the landscape have dramatically increased the
populations of ticks, and the transmission rate of the bacterial

pathogen that causes Lyme disease.
Forest clearing has had a similar impact on the rise of vector-
borne infectious disease in the tropical regions. Deforestation in
the Amazon rainforest has been linked to an increase in the
prevalence of malaria. Malaria is a recurring infection produced
in humans by protists parasites transmitted by the bite of an
infected female mosquito of the genus Anopheles (Section
15.4). Forty percent of the world’s population is currently at
risk for malaria, and more than two million people are killed
each year by this disease. Of all the forest species that transmit
diseases to humans, mosquitoes are among the most sensitive to
environmental changes resulting from deforestation. Their
survival, population density, and geographic distribution are
dramatically influenced by small changes in environmental
conditions, such as temperature, humidity, and the availability
of suitable breeding sites. The main vectors of malaria in the
Amazon, Anopheles darlingi mosquitoes, seek out larval habitat
in partially sunlit areas, with clear water of neutral pH and
aquatic plant growth. A. darlingi prefers to lay its eggs in water
surrounded by short vegetation, so the abundance of this
mosquito species has been enhanced by forest clearing in the
Amazon region.
To examine the impact of tropical rainforest clearing on
malaria, Amy Vittor of Stanford University and colleagues
conducted a year-long study focused on a region of the Peruvian
Amazon to examine the influence of forest clearing on the
abundance of A. darlingi, and the rates at which they fed on
humans in areas with varying degrees of forest clearing. The
researchers found that the likelihood of ?nding A. darlingi
larvae doubled in breeding sites with <20 percent forest
compared with sites with 20–60 percent forest, and the
likelihood increased sevenfold when compared with sites with
>60 percent forest (Figure 15.19). As a result, deforested sites
had a biting rate that was approximately 300 times higher than
the rate of areas that were predominantly forested. Their results
indicate that A. darlingi is both more abundant and displays

significantly increased human-biting activity in areas that have
undergone deforestation.
A similar pattern was observed by Sarah Olson of the University
of Wisconsin and colleagues who examined the role of forest
clearing on the transmission of malaria in the Amazon Basin of
Brazil. The researchers found that after adjusting for
population, access to health care and district size, a 4.3 percent
increase in deforestation between 1997 and 2000 was associated
with a 48 percent increase in malaria risk.
The impacts of forest clearing and changing land-use patterns
are not limited to the enhancement of pathogen populations and
their vectors. Land-use change and expansion of human
populations into forest areas is resulting in the exposure of
humans and domestic animal populations to pathogens not
previously encountered but that naturally occur in wildlife. The
result has been the emergence of new and often deadly parasites
and associated diseases. There is also potential for changes in
the distribution of pathogens and their vectors as a result of
changing climate conditions (see Chapter 2, Ecological Issues &
Applications), a subject we will address later in Chapter 27.
Summary
Characteristics of Parasites 15.1
Parasitism is a symbiotic relationship between individuals of
two species in which one benefits from the association,
wherease the other is harmed. Parasitic infection can result in
disease. Microparasites include viruses, bacteria, and protozoa.
They are small, have a short generation time, multiply rapidly
in the host, tend to produce immunity, and spread by direct
transmission. They are usually associated with dense
populations of hosts. Macroparasites are relatively large and
include parasitic worms, lice, ticks, fleas, rusts, smuts, fungi,
and other forms. They have a comparatively long generation
time, rarely multiply directly in the host, persist with continual
reinfection, and spread through both direct and indirect
transmission.
Parasite–Host Relationships 15.2

Parasites exploit every conceivable habitat in host organisms.
Many are specialized to live at certain sites, such as in plant
roots or an animal’s liver. Parasites must (1) gain entrance to
and (2) escape from the host. Their life cycle revolves about
these two problems.
Direct Transmission 15.3
Transmission for many species of parasites occurs directly from
one host to another. It occurs either through direct physical
contact or through the air, water, or another substrate.
Indirect Transmission 15.4
Some parasites are transmitted between hosts by means of other
organisms, called vectors. These carriers become intermediate
hosts of some developmental or infective stage of the parasite.
Intermediate Hosts 15.5
Other species of parasites require more than one type of host.
Indirect transmission takes them from definitive to intermediate
to definitive host. Indirect transmission often depends on the
feeding habits of the host organisms.
Response to Infection 15.6
Hosts respond to parasitic infections through behavioral
changes, inflammatory responses at the site of infection, and
subsequent activation of their immune systems.
Influence on Mortality and Reproduction 15.7
A heavy parasitic load can decrease reproduction of the host
organism. Although most parasites do not kill their hosts,
mortality can result from secondary factors. Consequently,
parasites can reduce fecundity and increase mortality rates of
the host population.
Population Response 15.8
Under certain conditions, parasitism can regulate a host
population. When introduced to a population that has not
developed defense mechanisms, parasites can spread quickly,
leading to high rates of mortality and in some cases to virtual
extinction of the host species.
Predation to Mutualism 15.9
Mutualism is a positive reciprocal relationship between two

species that may have evolved from predator–prey or host–
parasite relationships. Where adaptations have countered the
negative impacts of predators or parasites, the relationship is
termed commensalism. Where the interaction is beneficial to
both species, the interaction is termed mutualism.
Mutualistic Relationships 15.10
Mutualistic relationships involve diverse interactions.
Mutualisms can be characterized by a wide number of variables
relating to the benefits received, degree of dependency of the
interaction, degree of specificity, and duration and intimacy of
the association.
Nutrient Uptake 15.11
Symbiotic mutualisms are involved in the uptake of nutrients in
both plants and animals. The chambers of a ruminant’s stomach
contain large populations of bacteria and protozoa that carry out
the process of fermentation. Some plant species have a
mutualistic association with nitrogen-fixing bacteria that infect
and form nodules on their roots. The plants provide the bacteria
with carbon, and the bacteria provide nitrogen to the plant.
Fungi form mycorrhizal associations with the root systems of
plants, assisting in the uptake of nutrients. In return, they derive
energy in the form of carbon from the host plant.
Mutualisms Involving Defense 15.12
Other mutualistic associations are associated with defense of
the host organism.
Pollination 15.13
Nonsymbiotic mutualisms are involved in the pollination of
many species of flowering plants. While extracting nectar from
the flowers, the pollinator collects and exchanges pollen with
other plants of the same species. To conserve pollen, some
plants have morphological structures that permit only certain
animals to reach the nectar.
Seed Dispersal 15.14
Mutualism is also involved in seed dispersal. Some seed-
dispersing animals that the plant depends on may be seed
predators as well, eating the seeds for their own nutrition.

Plants depending on such animals must produce a tremendous
number of seeds to ensure that a few are dispersed, come to rest
on a suitable site, and germinate. Alternatively, plants may
enclose their seeds in a nutritious fruit attractive to frugivores
(fruit-eating animals). Frugivores are not seed predators. They
eat only the tissue surrounding the seed and, with some
exceptions, do not damage the seed.
Population Dynamics 15.15
Mutualistic relationships, both direct and indirect, may
influence population dynamics in ways that we are just
beginning to appreciate and understand.
Deforestation and Disease Ecological Issues & Applications
Land-use changes associated with human activities have led to
an increase in the transmission of infectious diseases. In many
regions of the world, forest clearing has altered the abundance
or dispersal of pathogens by influencing the abundance and
distribution of animal species that function as their hosts and
vectors.

Counseling Competencies – The Application of Ethical
Guidelines and Laws to Record Keeping
Program Transcript
NARRATOR: In this video program, Doctors Tiffany Rush-
Wilson, Matthew
Buckley, Jason Patton, and Stacee Reicherzer discuss the
importance of record
keeping of counseling sessions.
TIFFANY RUSH-WILSON: How has record keeping been
integrated into your
practice? How do you know what to record and what not to
record? What if you're
not sure, how do you make that decision?
JASON PATTON: The primary thing that I want to get down is
if I had to transfer
this case, if this person had to be admitted to some hospital or
needed some
other kind of level of care, I want whomever is taking on this
client's case to have
a good idea of what we've done together, to have a good idea of
my
conceptualization of this client's stuff. And it shouldn't be put

in a way that is in
any way belittling of the client's experience.
It shouldn't be-- my notes are not necessarily in incredibly
clinical terms, although
I have to use diagnoses. But I do a lot of contextual stuff to my
notes. But
everything that I put in my notes is always about client stated or
my client noted.
Because it's not that I'm making a clinical interpretation of what
this client came
in as depressed. He told me he was depressed. Those are
important things that
I'd like to get down in my notes because I don't want someone
to misinterpret
what we did together as something other than what it was.
TIFFANY RUSH-WILSON: You mentioned something quite
interesting. You
mentioned writing your notes for potentially another clinician
reading in the future.
Who is the audience when you write the note? It's not always
another clinician. It
might be the legal system. It might be, I don't know who else it
might be, but it
could be someone other than another clinician. So do you write
your notes with
the idea that someone will read them in mind?
JASON PATTON: Most of my notes don't tend to use overly
clinical language, I
don't think. And that tends to be because I want this client to be
able to get a
copy of their notes, if they want a copy of their notes, if they
want their medical
records. And they need to be able to see what I've written. If

they so choose, they
need to know what I thought about it.
And this is not a secret for me. And I don't know that every
clinician takes it that
way. But in particular, it's that I want whomever this client
wants these notes to
be released to I want them to be able to interpret it in some way.
It may have a
diagnosis that's specific to the DSM, but you should be able to
reference that with
the DSM.
© 2016 Laureate Education, Inc. 1

MATTHEW BUCKLEY: I think an important aspect of this
conversation is really
what your professional experience dictates or how that's
informed you. Because
oftentimes counselors who work in agency settings have a very
definite protocol
of what needs to go into a client record. If you're working in
private practice,
which I have done most of my professional life, I write my
notes to remind me
and to keep a continuity of the work that I'm doing and not for
any particular other
audience including my clients.
It's to help me remember the important details, important
aspects of what
occurred in the session and how it's adding to the treatment
plan. I would feel
awkward and I would also feel like it would border on unethical
practice of me if I
wrote my notes with fear that some lawyer or some judge was
going to be
reading this, and so I needed to have some complete and
accurate running
record of what my client and I did in session. And I hear what
you're saying about
the example that you used, about the notes from this particular
clinician helped
her avoid some litigation.

But I think that it's important to be responsible, I really
appreciate what you said
just said, Jason, about being able to use the kind of terminology
that you use that
helps inform you. But if we write our clinical notes with the
fear that we're going to
be sued, it's going to greatly influence what we do in our
records and it actually
might end up hurting our clients.
TIFFANY RUSH-WILSON: That's very interesting. , We were
actively taught after
this huge case, it was a really big case it was even covered on a
national news
magazine to write our notes with the legal system in mind by
our practices
attorney. So I think it probably is best to instruct our students to
check in with the
rules and dictates and norms of the places where they're
working, in order to
write their notes, whatever types of notes they are being asked
to keep.
MATTHEW BUCKLEY: And I think that's a really important
point that I certainly
don't want to promote the idea that students shouldn't be
responsible in how they
keep their clinical notes. And this is a great example of how the
profession
evolves. Because we really do live in a litigious society.
And so in order to protect ourselves, which ultimately protects
our ability to
practice, continue to practice responsibly, we have to be aware
of what we need
to include. And so I think it's a good idea to have attorney

friends and colleagues
that we can consult with on a regular basis around these types
of issues.
TIFFANY RUSH-WILSON: I think it's imperative. As a private
practitioner, we
always have to have an attorney connected to the practice
because we don't
have a big agency to help guide us. So we need to have
independent
relationships with attorneys.

MATTHEW BUCKLEY: And with each other too, being able to
consult with one
another.
STACEE REICHERZER: My perspective is a little bit different
than Matt's
because I think I want to write my notes in a way that with an
understanding that
these could be read by a lot of different people at any time. And
so for me,
transparency means that I write things in the clearest way
possible. And so
whether I'm using a SOAP formula or a DAP formula, it's
important to be
formulaic in how I do this.
Because this could be information that at some point I'm trying
to send on to a
psychiatrist or to somebody else based on whatever's needed in
the client
record. Or it maybe something that the client sees, as Jason
pointed, out it could
be something that's part of the legal system later on. So I'm
really very focused
on making sure that in that even though
I'm in private practice, It's likely that I'll be the only person
who's going to ever
read this. This could be read by a lot of people far down the
road, based on
whatever happens in the person's experience. And I really care
about that. And I

try to maintain that focus in all the work that I'm doing with my
clients.
TIFFANY RUSH-WILSON: I think this is really an important
discussion. Before
we move on, I want to say that our students have struggled a
little bit with
understanding what to include in the record. Sometimes they'll
believe that a
particular detail is too important or too personal to include in
the record. So I want
them to understand and be able to reflect on what to include and
what not to
include in.
MATTHEW BUCKLEY: I think what Jason said about what the
client said, I don't
think it needs to be necessarily a verbatim record, but there may
be some key
phrases that a client uses that would be very useful to illustrate
something.
Depending on some formulas, there there's usually a kind of a
clinical impression
section where the counselor will, where I will share what my
clinical impressions
are of what's going on, and then my plan and what I plan to do
and follow up
with.
Also I like to include consults that I may have had with others
about something
that's going on, so that it's part of the record that I acted
responsibly and I
consulted with another professional. So I think those are
important elements.
Obviously diagnostic impressions are part of that. If a client

makes a suicidal
gesture, that's something that you want to note and that you've
done some follow
up with.
JASON PATTON: Not to get off point with that, but it is it's
important for me to tell
a client that I'm going to be making notes about their session,
that there will be a

record of our work together. And that's part of our disclosure
statement. That's
part of how we inform them and they consent to that.
I often say, what kinds of things do you want people to know
about you? There
may be things that, they're like I'm telling you this in
confidence, and it's has no
legal implications. It's something I want you just to think about
right at this
moment. And he may want to tell me that. It may not be
something that I need to
remember in a future session. But I do want the client to know
that there will be a
record of whatever we're talking about. And we should talk
about that before we
actually get there.
MATTHEW BUCKLEY: I think it's important how that's
communicated to clients
because what I would not want to communicate to a client is
that I'm making a
record and anybody could read this. Because what would happen
is that would
actually, I think, prove detrimental. It make kind of close them
down. And
particularly with some clients who have had these types of
experiences, where
they've been with someone who has maybe tendencies to feel a
little paranoid
about things.
That would not be something that would go over very well with

them. So I think
that it's important to communicate that this is part of my
professional practice,
that there may be some instances where others may need to
know what happens
here. But I want you to know that those would be instances that
I would
communicate with you around when.
TIFFANY RUSH-WILSON: That leads me to the last point I
wanted to make.
Group and family therapy, I work with eating disorders and
primarily I work with
the person who has the eating disorder, not with their family,
sometimes in a
group setting, but not typically with the family. How do you
distinguish the
differences between group and family therapy? Who is the
client? Who has
access to the record?
Can anybody in the family read the record? Can anybody in a
group read the
record? How do we make distinctions between those two
processes and the
relationships we see within those two settings?
JASON PATTON: Well I think one similarity that should be
noted probably even
before breaking them down is that we can't guarantee the
confidentiality that the
group members or family members have. So if something is
shared in the room
with others present, that same level of confidentiality that we
have as counselors
is not necessarily going to be upheld, although I have full

expectation that they
will do so. And I ask them to do so.
I can't guarantee that. And that's something I always want to set
out in the very
beginning. Now for me, the biggest difference between group
and family therapy
is that in a family system there are already alliances set up.
There are already

mom and daughter kind of team up against so-and-so, or mom
and son do so. Or
it's experienced that that's the case.
So there are already things happening before you're in the room.
With group,
hopefully that's not the case. And so you want to name those
things with early on
to say, I want to come in and talk to each other. And we may be
naming that
there's a dynamic already in place. And we may be talking about
it. And we want
to hopefully, name it if it comes up in the group as well, but
they shouldn't already
be in place.
TIFFANY RUSH-WILSON: Can they all have access to the
record? Can anybody
in the group or anybody in the family have access?
JASON PATTON: Well say, for instance in a group, a group
member doesn't
have access to another group member's notes at all. And if I'm
making notes
about a particular group member, that's the only group members
name that I
make in that group member's notes. In a family, the family may
be the group that
I'm seeing. I'm seeing this family as a whole, where with the
group that's not the
case.
STACEE REICHERZER: I'm a believer when working with a
couple or a family
that there's value to maintaining separate files. And this is

particularly one of the
things that comes up in couples work for example. When people
go through
divorces, there's all kinds of weird stuff that they try to do.
And so, if I'm working with a couple, Darren and Terry, In
Darren's file, I'm going
to just simply say T or partner or something like that. Darren
reported that partner
was not responding to his blah, blah, blah, whatever that might
be. But I don't
want Terry's name to be prominently featured throughout the
file. And there's a
lot of reasons for that.
Because with confidentiality, Darren has a right to his record
and he can sign the
release and have me send over files to his doctor, things like
that. But Terry has
her own rights to release. And so Darren can't just sign
paperwork that I can
send all of the stuff over and there's Terry stuff in there because
then I've just
broken Terry's confidentiality.
And so it's very important with couples work that there are
separate files kept,
and that we're just making a note, using T, using whatever it is,
so that there's
not another person's identifying information in client one's file.
That's a major
issue for print for protection and to avoid litigation.
MATTHEW BUCKLEY: Regarding the group, the keeping of
group notes, I think
it's an important practice that I do when I do group notes, is I'll

write what a
particular individual has said in a particular group. But I won't
necessarily write
how others responded or those types of things. Because the
focus is on what
work that person is doing.

Now if there's a particular interaction that's relevant to that, I
certainly don't want
to name anyone else in that particular record. But I think part of
what you're
asking about also is just the differences and the similarities with
groups and
families. And I like really what Jason said about families
already having those
established dynamics and groups not.
As people come together in a group they're sort of getting to
know each other
and they don't really have those family relationships well
established. But what
I've noticed in groups that's also interesting is that people in
groups will play out
their family dynamics very, very, very strongly. And I really
liked something that
Irwin Yalom said.
And that is that people don't remain indifferent to each other in
a group for very
long. As they begin to get in, as they begin to interact, those
family of origin
issues will come up and really make themselves manifest in a
very dynamic way.
And so that's where I think that groups and families are similar
is that we're
always sort of acting out our family experiences in those types
of relationships.
TIFFANY RUSH-WILSON: Can I go back to one thing you said
before about
groups? If you have a group of eight people, are you keeping
eight records for
that group or are you keeping a one record on the entire group?

MATTHEW BUCKLEY: My particular practice is that I keep a
record on the
group. And I don't particularly do-- I've done a lot of group
work, but I don't
particularly do a lot of group work now where I would keep
separate records. I
inform myself, I keep a good sense of what's going on, so that if
there is a need
somewhere along the line that someone subpoenas a record that
I can go back
and I can take a look.
And then I can go ahead and create a treatment summary for a
particular group
member. But I would not keep eight separate files. Now that
may not necessarily
be best practice, but that's something that I do. And I think it's
important to check,
to consult with others, other professionals to see how they do it.
To consult with
an attorney. And this is where it gets into how individual
counselors keep their
records.
JASON PATTON: It's going to really depend on your setting. I
know that in
agency work in my past when I have done group work, it was
required that we
have a specific file for each person. If a person is also doing
individual, that their
group be separate from their individual therapy as well. But my
general way of
doing it was a personal preference.

There is an overall group content that's not specific to the
person. And then
there's a specific node about what happened with the specific
client. And
although it is a lengthy process, it is one that met the needs of
this particular
agency.
STACEE REICHERZER: And see my background, because I
came to it from an

agency administrator's perspective, I was a clinical director and
worked in this
agency setting for a long time. So I never divorced myself from
the need and
logic of keeping eight separate files. And so my notes were
obviously not as
lengthy as they would be in individual, simply because-- for a
lot of reasons,
obviously. Couldn't be doing this all night long.
But also for the fact that within an hour and a half group
session, there was going
to be a whole lot less content and a lot less process that's
necessary. So it may
be just two or three short sentences that describe the situation
very, very briefly
and summarize it within group. But I wanted the eight separate
files because I
wanted to also maintain that these were eight individuals. And I
didn't want things
getting overlooked.
That way also I've had a very good perspective, each week. If
Sharon is never
talking in group, I was really noting Sharon was silent again
last week. And that
she seems to only do this when Terry brings up x, y, z. So
there's important
things to being able to do that.
But I also again, from an administration perspective tend to
look at things from
the possibility that these could be subpoenaed. These are things
that I might
need to pull together rapidly. And I want the flexibility to do
that and to respond to

whatever it may be happening in a moment's notice.
TIFFANY RUSH-WILSON: So it's almost a protective thing.
It's a formulaic
approach that allows you to see patterns and also to have a
consistent way to
present the notes if ever they need to be seen by another party.
MATTHEW BUCKLEY: Well I think if you're talking about
best practice, that's
best practice. That's best practice.
STACEE REICHERZER: And for me, it's not ever that I feel
you know paranoid
or weird or oh my god, who's going to read these notes? It's that
I always
understand that I'm a professional. I'm in the profession of
working with people,
helping them move through their experiences, and I'm also a
business woman
who has the responsibility to protect the public and to protect
her professional
practice. And those are things that are always going to be all
going on for me at
once. So you know it keeps me focused and it keeps my work
with clients, it
keeps an emphasis and a structure around it that I think is
necessary.

TIFFANY RUSH-WILSON: So these notes help protect the
public, the
professional, and the profession itself.
STACEE REICHERZER: Very well said. Yes.
MATTHEW BUCKLEY: Exactly. You said the word focus. And
I think that's a
really important distinction is that I'll just be honest, there are a
lot of things to
keep track of early on. And as a new professional, it's going to
seem like you're
bombarded with new information all the time. I like sometimes
to just have a
good record that I can go back to and look at. And so probably
in the beginning, I
took more notes than maybe I do now.
And it was more about, I only have so much space in my head.
And I can only
put so much in there. Sometimes I just need to have it in front

of me.
MATTHEW BUCKLEY: And it becomes a challenge if you're in
a situation where
you're seeing clients back to back. Because you have maybe 10
minutes that you
can pull together those important points and put it together. It's
not like you want
to be spending a half hour writing a note on each session. It
would just be
horrible to do that. Because you would be having 12 hour days.
So you want to
be able to write in a way that gets the details that you need, gets
the context, and
you're able to record that and move on.
Because what you said, Jason, is really true. There's only so
much space you
have on your head until you're moving on to the next thing. And
that's where I
think that this is a really important skill to be able to develop a
style of note taking
that's going to be able to do that, that's going to meet that need.
STACEE REICHERZER: And you will become more efficient
with time.
CHAPTER 14
14.1 Predation Takes a Variety of Forms
The broad definition of predation as the consumption of one
living organism (the prey) by another (the predator) excludes

scavengers and decomposers. Nevertheless, this definition
results in the potential classification of a wide variety of
organisms as predators. The simplest classification of predators
is represented by the categories of heterotrophic organisms
presented previously, which are based on their use of plant and
animal tissues as sources of food: carnivores (carnivory—
consumption of animal tissue), herbivores (herbivory—
consumption of plant or algal tissue), and omnivores
(omnivory—consumption of both plant and animal tissues); see
Chapter 7. Predation, however, is more than a transfer of
energy. It is a direct and often complex interaction of two or
more species: the eater and the eaten. As a source of mortality,
the predator population has the potential to reduce, or even
regulate, the growth of prey populations. In turn, as an essential
resource, the availability of prey may function to regulate the
predator population. For these reasons, ecologists recognize a
functional classification, which provides a more appropriate
framework for understanding the interconnected dynamics of
predator and prey populations and which is based on the
specific interactions between predator and prey.
In this functional classification of predators, we reserve the
term predator, or true predator, for species that kill their prey
more or less immediately upon capture. These predators
typically consume multiple prey organisms and continue to
function as agents of mortality on prey populations throughout
their lifetimes. In contrast, most herbivores (grazers and
browsers) consume only part of an individual plant. Although
this activity may harm the plant, it usually does not result in
mortality. Seed predators and planktivores (aquatic herbivores
that feed on phytoplankton) are exceptions; these herbivores
function as true predators. Like herbivores, parasites feed on the
prey organism (the host) while it is still alive and although
harmful, their feeding activity is generally not lethal in the
short term. However, the association between parasites and their
host organisms has an intimacy that is not seen in true predators
and herbivores because many parasites live on or in their host

organisms for some portion of their life cycle. The last category
in this functional classification, the parasitoids, consists of a
group of insects classified based on the egg-laying behavior of
adult females and the development pattern of their larvae. The
parasitoid attacks the prey (host) indirectly by laying its eggs
on the host’s body. When the eggs hatch, the larvae feed on the
host, slowly killing it. As with parasites, parasitoids are
intimately associated with a single host organism, and they do
not cause the immediate death of the host.
In this chapter we will use the preceding functional
classifications, focusing our attention on the two categories of
true predators and herbivores. (From this point forward, the
term predator is used in reference to the category of true
predator). We will discuss the interactions of parasites and
parasitoids and their hosts later, focusing on the intimate
relationship between parasite and host that extends beyond the
feeding relationship between predator and prey (Chapter 15).
We will begin by exploring the connection between the hunter
and the hunted, developing a mathematical model to define the
link between the populations of predator and prey. The model is
based on the same approach of quantifying the per capita effects
of species interactions on rates of birth and death within the
respective populations that we introduced previously (Chapter
13, Section 13.2). We will then examine the wide variety of
subjects and questions that emerge from this simple mathematic
abstraction of predator–prey interactions.
14.2 Mathematical Model Describes the Interaction of Predator
and Prey Populations
In the 1920s, Alfred Lotka and Vittora Volterra turned their
attention from competition (see Section 13.2) to the effects of
predation on population growth. Independently, they proposed
mathematical statements to express the relationship between
predator and prey populations. They provided one equation for
the prey population and another for the predator population.
The population growth equation for the prey population consists
of two components: the exponential model of population growth

(dN/dt = rN; see Chapter 9) and a term that represents mortality
of prey from predation. Mortality resulting from predation is
expressed as the per capita rate at which predators consume
prey (number of prey consumed per predator per unit time). The
per capita consumption rate by predators is assumed to increase
linearly with the size of the prey population (Figure 14.1a) and
can therefore be represented as cNprey, where c represents the
capture efficiency of the predator, defined by the slope of the
relationship shown in Figure 14.1a. (Note that the greater the
value of c, the greater the number of prey captured and
consumed for a given prey population size, which means that
the predator is more efficient at capturing prey.) The total rate
of predation (total number of prey captured per unit time) is the
product of the per capita rate of consumption (cNprey) and the
number of predators (Npred), or (cNprey)Npred. This value
represents a source of mortality for the prey population and
must be subtracted from the rate of population increase
represented by the exponential model of growth. The resulting
equation representing the rate of change in the prey population
(dNprey/dt) is:
dNprey/dt=rNprey−(cNprey)NpreddNprey/dt=rNprey−(cNprey)
Npred
The equation for the predator population likewise consists of
two components: one representing birth and the other death of
predators. The predator mortality rate is assumed to be a
constant proportion of the predator population and is therefore
represented as dNpred, where d is the per capita death rate (this
value is equivalent to the per capita death rate in the
exponential model of population growth developed in Chapter
9). The per capita birthrate is assumed to be a function of the
amount of food consumed by the predator, the per capita rate of
consumption (cNprey), and increases linearly with the per
capita rate at which prey are consumed (Figure 14.1b). The per
capita birthrate is therefore the product of b, the efficiency with
which food is converted into population growth (reproduction),
which is defined by the slope of the relationship shown in

Figure 14.1b, and the rate of predation (cNprey), or b(cNprey).
The total birthrate for the predator population is then the
product of the per capita birthrate, b(cNprey), and the number
of predators, Npred: b(cNprey)Npred. The resulting equation
representing the rate of change in the predator population is:
dNpred/dt=b(cNprey)Npred−dNpreddNpred/dt=b(cNprey)Npred
−dNpred
The Lotka–Volterra equations for predator and prey population
growth therefore explicitly link the two populations, each
functioning as a density-dependent regulator on the other.
Predators regulate the growth of the prey population by
functioning as a source of density-dependent mortality. The
prey population functions as a source of density-dependent
regulation on the birthrate of the predator population. To
understand how these two populations interact, we can use the
same graphical approach used to examine the outcomes of
interspecific competition (Chapter 13, Section 13.2).
In the absence of predators (or at low predator density), the
prey population grows exponentially (dNprey/dt = rNprey). As
the predator population increases, prey mortality increases until
eventually the mortality rate resulting from predation,
(cNprey)Npred, is equal to the inherent growth rate of the prey
population, rNprey, and the net population growth for the prey
species is zero (dNprey/dt = 0). We can solve for the size of the
predator population (Npred) at which this occurs:
cNpreyNpred=rNpreycNpred=rNpred=rccNpreyNpred=rNprey
cNpred=r Npred=rc
Simply put, the growth rate of the prey population is zero when
the number of predators is equal to the per capita growth rate of
the prey population (r) divided by the efficiency of predation
(c).
This value therefore defines the zero-growth isocline for the
prey population (Figure 14.2a). As with the construction of the
zero-growth isoclines in the analysis of the Lotka–Volterra
competition equations (see Section 13.2, Figure 13.1), the two
axes of the graph represent the two interacting populations. The

x-axis represents the size of the prey population (Nprey), and
the y-axis represents the predator population (Npred). The prey
zero-growth isocline is independent of the prey population size
(Nprey) and is represented by a line parallel to the x-axis at a
point along the y-axis represented by the value Npred = r/c. For
values of Npred below the zero-growth isocline, mortality
resulting from predation, (cNprey)Npred, is less than the
inherent growth rate of the prey population (rNprey), so
population growth is positive and the prey population increases,
as represented by the green horizontal arrow pointing to the
right. If the predator population exceeds this value, mortality
resulting from predation, (cNprey)Npred, is greater than the
inherent growth rate of the prey population (rNprey) and the
growth rate of the prey becomes negative. The corresponding
decline in the size of the prey population is represented by the
green arrow pointing to the left.
Likewise, we can define the zero-growth isocline for the
predator population by examining the influence of prey
population size on the growth rate of the predator population.
The growth rate of the predator population is zero
(dNpred/dt = 0) when the rate of predator increase (resulting
from the consumption of prey) is equal to the rate of mortality:
b(cNprey)Npred=dNpredbcNprey=dNprey=dbcb(cNprey)Npred=
dNpred bcNprey=d Nprey=dbc
The growth rate of the predator population is zero when the size
of the prey population (Nprey) equals the per capita mortality
rate of the predator (d) divided by the product of the efficiency
of predation (c) and the ability of predators to convert the prey
consumed into offspring (b). Note that these are the two factors
that determine the per capita predator birthrate for a given prey
population (Nprey). As with the prey population, we can now
use this value to define the zero-growth isocline for the predator
population (Figure 14.2b). The predator zero-growth isocline is
independent of the predator population size (Npred) and is
represented by a line parallel to the y-axis at a point along the
x-axis (represented by the value Nprey = d/bc). For values of

Nprey to the left of the zero-growth isocline (toward the origin)
the rate of birth in the predator population, b(cNprey)Npred, is
less than the rate of mortality, dNpred, and the growth rate of
the predator population is negative. The corresponding decline
in population size is represented by the red arrow pointing
downward. For values of Nprey to the right of the predator
zero-growth isocline, the population birthrate is greater than the
mortality rate and the population growth rate is positive. The
increase in population size is represented by the vertical red
arrow pointing up.
As we did in the graphical analysis of competitive interactions
(see Section 13.3, Figure 13.2), the two zero-growth isoclines
representing the predator and prey populations can be combined
to examine changes in the growth rates of two interacting
populations for any combination of population sizes (Figure
14.2c). When plotted on the same set of axes, the zero-growth
isoclines for the predator and prey populations divide the graph
into four regions. In the lower right-hand region, the combined
values of Nprey and Npred are below the prey zero-isocline
(green dashed line), so the prey population increases, as
represented by the green arrow pointing to the right. Likewise,
the combined values lie above the zero-growth isocline for the
predator population so the predator population increases, as
represented by the red arrow pointing upward. The next value of
(Nprey, Npred) will therefore be within the region defined by
the green and red arrows represented by the black arrow. The
combined dynamics indicated by the black arrow point toward
the upper right region of the graph. For the upper right-hand
region, combined values of Nprey and Npred are above the prey
isocline, so the prey population declines as indicated by the
green horizontal arrow pointing left. The combined values are
to the right of the predator isocline, so the predator population
increases as indicated by the vertical red arrow pointing up. The
black arrow indicating the combined dynamics points toward the
upper left-hand region of the graph. In the upper left-hand
region of the graph, the combined values of Nprey and Npred

are above the prey isocline and to the left of the predator
isocline so both populations decline. In this case, the combined
dynamics (black arrow) point toward the origin. In the last
region of the graph, the lower left, the combined values of
Nprey and Npred are below the prey isocline and to the left of
the predator isocline. In this case, the prey population increases
and the predator population declines. The combined dynamics
point in the direction of the lower left-hand region of the graph,
completing a circular, or cyclical, pattern, where the combined
dynamics of the predator and prey populations move in a
counterclockwise pattern through the four regions defined by
the population isoclines.
14.3 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.
14.4 Model Suggests Mutual Population Regulation
The Lotka–Volterra model of predator–prey interactions
assumes a mutual regulation of predator and prey populations.
In the equations presented previously, the link between the
growth of predator and prey populations is described by a single
term relating to the consumption of prey: (cNprey)Npred. For
the prey population, this term represents the regulation of
population growth through mortality. In the predator population,
it represents the regulation of population growth through
reproduction. Regulation of the predator population growth is a
direct result of two distinct responses by the predator to
changes in prey population. First, predator population growth
depends on the per capita rate at which prey are captured
(cNprey). The relationship shown in Figure 14.1a implies that
the greater the number of prey, the more the predator eats. The
relationship between the per capita rate of consumption and the
number of prey is referred to as the predator’s functional
response. Second, this increased consumption of prey results in
an increase in predator reproduction [b(cNprey)], referred to as
the predator’s numerical response.

This model of predator–prey interaction has been widely
criticized for overemphasizing the mutual regulation of predator
and prey populations. The continuing appeal of these equations
to population ecologists, however, lies in the straightforward
mathematical descriptions and in the oscillatory behavior that
seems to occur in predator–prey systems. Perhaps the greatest
value of this model is in stimulating a more critical look at
predator–prey interactions in natural communities, including the
conditions influencing the control of prey populations by
predators. A variety of factors have emerged from laboratory
and field studies, including the availability of cover (refuges)
for the prey (as in the experiments discussed in Section 14.3),
the increasing difficulty of locating prey as it becomes scarcer,
choice among multiple prey species, and evolutionary changes
in predator and prey characteristics (coevolution). In the
following sections, we examine each of these topics and
consider how they influence predator–prey interactions.
14.5 Functional Responses Relate Prey Consumed to Prey
Density
The English entomologist M. E. Solomon introduced the idea of
functional response in 1949. A decade later, the ecologist C. S.
Holling explored the concept in more detail, developing a
simple classification based on three general types of functional
response (Figure 14.6). The functional response is the
relationship between the per capita predation rate (number of
prey consumed per predator per unit time, Ne) and prey
population size (Nprey) shown in Figure 14.1a. How a
predator’s rate of consumption responds to changes in the prey
population is a key factor influencing the predator’s ability to
regulate the prey population.
In developing the predatory prey equations in Section 14.2, we
defined the per capita rate of predation as cNprey, where c is
the “efficiency” of predation, and Nprey is the size of the prey
population. This is what Holling refers to as a Type I functional
response. In the Type I functional response, the number of prey
captured per unit time by a predator (per capita rate of

predation, Ne) increases linearly with increasing number of prey
(Nprey; Figure 14.6a). The rate of prey mortality as a result of
predation (proportion of prey population captured per predator
per unit time) for the Type I response is constant, equal to the
efficiency of predation (c), as in Figure 14.6b.
The Type I functional response is characteristic of passive
predators, such as filter feeders that extract prey from a
constant volume of water that washes over their filtering
apparatus. A range of aquatic organisms, from zooplankton
(Figure 14.7a) to blue whales, exhibit this feeding bahavior.
Filter feeders capture prey that flow through and over their
filtering system, so for a given rate of water flow over their
feeding apparatus, the rate of prey capture will be a direct
function of the density of prey per volume of water.
The Type I functional response is limited in its description of
the response of predators to prey abundance for two reasons.
First, it assumes that predators never become satiated, that is,
the per capita rate of consumption increases continuously with
increasing prey abundance. In reality, predators will become
satiated (“full”) and stop feeding. Even for filter feeders, there
will be a maximum amount of prey that can be captured
(filtered) per unit time above that it can no longer increase
regardless of the increase in prey density (see Figure 14.7a).
Secondly, even in the absence of satiation, predators will be
limited by the handling time, that is, the time needed to chase,
capture, and consume each prey item. By incorporating the
constraint of handling time, the response of the per capita rate
of predation (Ne) to increasing prey abundance (Nprey) now
exhibits what Holling refers to as a Type II functional response.
In the Type II functional response, the per capita rate of
predation (Ne) increases in a decelerating fashion, reaching a
maximum rate at some high prey population size (see Figure
14.6a). The reason that the value of Ne approaches an asymptote
is related to the predator’s time budget (Figure 14.8; for a
mathematical derivation of the Type II functional response, see
Quantifying Ecology 14.1).

We can think of the total amount of time that a predator spends
feeding as T. This time consists of two components: time spent
searching for prey, Ts, and time spent handling the prey once it
has been encountered, Th. The total time spent feeding is then:
T = Ts + Th. Now as prey abundance (Nprey) increases, the
number of prey captured (Ne) during the time period T increases
(because it is easier to find a prey item as the prey become more
abundant); however, the handling time (Th) also increases
(because it has captured more prey to handle), decreasing the
time available for further searching (Ts). Handling time (Th)
will place an upper limit on the number of prey a predator can
capture and consume in a given time (T). At high prey density,
the search time approaches zero and the predator is effectively
spending all of its time handling prey (Th approaches T). The
result is a declining mortality rate of prey with increasing prey
density (see Figure 14.6b). The Type II functional response is
the most commonly reported for predators (see Figure 14.7b).
Holling also described a Type III functional response,
illustrated in Figures 14.6a and 14.7c. At high prey density, this
functional response is similar to Type II, and the explanation
for the asymptote is the same. However, the rate at which prey
are consumed is low at first, increasing in an S-shaped
(sigmoid) fashion as the rate of predation approaches the
maximum value. In the Type III functional response, mortality
rate of the prey population is negligible at low prey abundance,
but as the prey population increases (as indicated by the upward
sweep of the curve), the mortality rate of the population
increases in a density-dependent fashion (Figure 14.6b).
However, the regulating effect of predators is limited to the
interval of prey density where mortality increases. If prey
density exceeds the upper limit of this interval, then mortality
resulting from predation starts to decline.
Quantifying Ecology 14.1 Type II Functional Response
The Type I functional response suggests a form of predation in
which all of the time allocated to feeding is spent searching
(Ts). In general, however, the time available for searching is

shorter than the total time associated with consuming the Ne
prey because time is required to “handle” the prey item.
Handling includes chasing, killing, eating, and digesting. (Type
I functional response assumes no handling time below the
maximum rate of ingestion.) If we define th as the time required
by a predator to handle an individual prey item, then the time
spent handling Ne prey will be the product Neth. The total time
(T) spent searching and handling the prey is now:
Relationship between the density of prey population (x-axis)
and the per capita rate of prey consumed (y-axis) for the model
of predator functional response presented above that includes
both search (Ts) and handling (Th = Neth) time (T = Ts + Th).
At low prey density, the number of prey consumed is low, as is
handling time. As prey density increases, the number of prey
consumed increases; a greater proportion of the total foraging
time (T) is spent handling prey, reducing time available for
searching. As the handling time approaches the total time spent
foraging, the per capita rate of prey consumed approaches an
asymptote. The resulting curve is referred to as a Type II
functional response.
T=Ts+(Neth)T=Ts+(Neth)
By rearranging the preceding equation, we can define the search
time as:
Ts=T−NethTs=T−Neth
For a given total foraging time (T), search time now varies,
decreasing with increasing allocation of time to handling.
We can now expand the original equation describing the type I
functional response [Ne – (cNprey)Ts] by substituting the
equation for Ts just presented. This includes the additional time
constraint of handling the Ne prey items:
Ne=c(T−Neth)NpreyNe=c(T−Neth)Nprey
Note that Ne, the number of prey consumed during the time
period T, appears on both sides of the equation, so to solve for
Ne, we must rearrange the equation.
Ne=c(NpreyT−NpreyNeth)Ne=c(NpreyT−NpreyNeth)
Move c inside the brackets, giving:

Ne=cNpreyT−NecNpreythNe=cNpreyT−NecNpreyth
Add NecNpreythNecNpreyth to both sides of the equation,
giving:
Ne+NecNpreyth=cNpreyTNe+NecNpreyth=cNpreyT
Rearrange the left-hand side of the equation, giving:
Ne(1+cNpreyth)=cNpreyTNe(1+cNpreyth)=cNpreyT
Divide both sides of the equation by
(1+cNpreyth),(1+cNpreyth), giving:
Ne=cNpreyT(1+cNpreyth)Ne=cNpreyT(1+cNpreyth)
We can now plot the relationship between Ne and Nprey for a
given set of values for c, T, and th. (Recall that the values of c,
T, and th are constants.)
Several factors may result in a Type III response. Availability
of cover (refuge) that allows prey to escape predators may be an
important factor. If the habitat provides only a limited number
of hiding places, it will protect most of the prey population at
low density, but the susceptibility of individuals will increase
as the population grows.
Another reason for the sigmoidal shape of the Type III
functional response curve may be the predator’s search image,
an idea first proposed by the animal behaviorist L. Tinbergen.
When a new prey species appears in the area, its risk of
becoming selected as food by a predator is low. The predator
has not yet acquired a search image—a way to recognize that
species as a potential food item. Once the predator has captured
an individual, it may identify the species as a desirable prey.
The predator then has an easier time locating others of the same
kind. The more adept the predator becomes at securing a
particular prey item, the more intensely it concentrates on it. In
time, the number of this particular prey species becomes so
reduced or its population becomes so dispersed that encounters
between it and the predator lessen. The search image for that
prey item begins to wane, and the predator may turn its
attention to another prey species.
A third factor that can result in a Type III functional response is
the relative abundance of different, alternative prey species.

Although a predator may have a strong preference for a certain
prey, in most cases it can turn to another, more abundant prey
species that provides more profitable hunting. If rodents, for
example, are more abundant than rabbits and quail, foxes and
hawks will concentrate on rodents.
Ecologists call the act of turning to more abundant, alternate
prey switching (Figure 14.9a). In switching, the predator feeds
heavily on the more abundant species and pays little attention to
the less abundant species. As the relative abundance of the
second prey species increases, the predator turns its attention to
that species.
The point in prey abundance when a predator switches depends
considerably on the predator’s food preference. A predator may
hunt longer and harder for a palatable species before turning to
a more abundant, less palatable alternate prey. Conversely, the
predator may turn from the less desirable species at a much
higher level of abundance than it would from a more palatable
species.
In a series of laboratory experiments, Roger Hughes and M. I.
Croy of the University of Wales (Great Britain) examined prey
switching in 15-spined stickleback (Spinachia spinachia)
feeding on two prey species: amphipod (Gammarus locusta) and
brine shrimp (Aremia spp.). In all experiments, fish showed the
sigmoid response to changing relative abundances of prey,
typical of switching (Figure 14.9b). The researchers found that
a combination of changing attack efficiency and search image
formation contributed to the observed pattern of prey switching.
Although simplistic, the model of functional response developed
by Holling has been a valuable tool. It allows ecologists to
explore how various behaviors—exhibited by both the predator
and prey species—influence predation rate and subsequently
predator and prey population dynamics. Because the model
explicitly addresses the principle of time budget in the process
of predation, this framework has been expanded to examine
questions relating to the efficiency of foraging, a topic we will
return to in Section 14.7.

14.6 Predators Respond Numerically to Changing Prey Density
As the density of prey increases, the predator population growth
rate is expected to respond positively. A numerical response of
predators can occur through reproduction by predators (as
suggested by the conversion factor b in the Lotka–Volterra
equation for predators) or through the movement of predators
into areas of high prey density (immigration). The latter is
referred to as an aggregative response (Figure 14.10). The
tendency of predators to aggregate in areas of high prey density
can be a crucial feature in determining a predator population’s
ability to regulate prey density. Aggregative response is
important because most predator populations grow slowly in
comparison to those of their prey.
Marc Salamolard of the Center for Biological Studies (French
National Center for Scientific Research) and colleagues provide
an example of how these two components of numerical response
(immigration and increased reproduction) can combine to
influence the response of a predator population to changes in
prey abundance. Salamolard quantified the functional and
numerical responses of Montagu’s harrier (Circus pygargus), a
migratory raptor, to variations in abundance of its main prey,
the common vole (Microtus arvalis). The researchers monitored
variations in the vole population over a 15-year period and the
response of the harrier population to this variable food supply.
This predatory bird species exhibits a Type II functional
response; the per capita rate of predation increases with
increasing prey density up to some maximum (see Figure
14.11a). The researchers were able to provide a number of
measures relating to the bird’s numerical response. The
breeding density of birds increases with increasing prey. This
increase in predator density is a result of an increase in the
number of nesting pairs occupying the area and represents an
aggregative response density (Figure 14.11b). In addition, the
mean brood size of nesting pairs (mean number of chicks at
fledging) also increased (Figure 14.11c). The net result is an
increase in the growth rate of the predator population in

response to an increase in the abundance of prey (vole
population).
The work of Włodzimierz Je̜ drzejewski and colleagues at the
Mammal Research Institute of the Polish Academy of Sciences
provides an example where the numerical response of the
predator population is dominated by reproductive effort.
Je̜ drzejewski examined the response of a weasel (Mustela
nivalis) population to the density of two rodents, the bank vole
(Clethrionomys glareolus) and the yellow-necked mouse
(Apodemus flavicollis), in Białowieża National Park in eastern
Poland in the early 1990s. During that time, the rodents
experienced a two-year irruption in population size brought
about by a heavy crop of oak, hornbeam, and maple seeds. The
abundance of food stimulated the rodents to breed throughout
the winter. The long-term average population density was 28–74
animals per hectare. During the irruption, the rodent population
reached nearly 300 per hectare and then declined precipitously
to 8 per hectare (Figure 14.12).
The weasel population followed the fortunes of the rodent
population. At normal rodent densities, the winter weasel
density ranged from 5–27 per km2 declining by early spring to
0–19. Following reproduction, the midsummer density rose to
42–47 weasels per km2. Because reproduction usually requires a
certain minimal time (related to gestation period), a lag
typically exists between an increase of a prey population and a
numerical response by a predator population. No time lag,
however, exists between increased rodent reproduction and
weasel reproductive response. Weasels breed in the spring, and
with an abundance of food they may have two litters or one
larger litter. Young males and females breed during their first
year of life. During the irruption, the number of weasels grew to
102 per km2 and during the crash the number declined to 8 per
km2. The increase and decline in weasels was directly related to
changes in the rates of birth and death in response to the spring
rodent density.
The work of Mark O’Donoghue and colleagues at the University

of British Columbia (Canada) provides an example of a
numerical response of a predator population in which there is a
distinct lag between the prey and predator populations. The
researchers monitored populations of Canadian lynx (Lynx
canadensis) and their primary prey, the snowshoe hare (Lepus
americanus) at a site in the southwest Yukon Territory, Canada,
between 1986 and 1995. During this time, the lynx population
increased 7.5-fold in response to a dramatic increase in the
number of snowshoe hares (Figure 14.13a). The abundance of
lynx lagged behind the increase in the hare population, reaching
its maximum a year later than the peak in numbers of snowshoe
hares. The increase in the lynx population eventually led to a
decline in the hare population. The decline in the number of
lynx was associated with lower reproductive output and high
emigration rates. Few to no kits (offspring) were produced by
lynx after the second winter of declining numbers of hares.
High emigration rates were characteristic of lynx during the
cyclic peak and decline, and low survival was observed late in
the decline. The delayed numerical response (lag) results in a
cyclic pattern when the population of lynx is plotted as a
function of size of the prey population (Figure 14.13b), as was
observed in the analysis of the Lotka–Volterra model in Section
14.2 (see Figure 14.2c).
14.7 Foraging Involves Decisions about the Allocation of Time
and Energy
Thus far, we have discussed the activities of predators almost
exclusively in terms of foraging. But all organisms are required
to undertake a wide variety of activities associated with
survival, growth, and reproduction. Time spent foraging must be
balanced against other time constraints such as defense,
avoiding predators, searching for mates, or caring for young.
This trade-off between conflicting demands has led ecologists
to develop an area of research known as optimal foraging
theory. At the center of optimal foraging theory is the
hypothesis that natural selection favors “efficient” foragers, that
is, individuals that maximize energy or nutrient intake per unit

of effort. Efficient foraging involves an array of decisions: what
food to eat, where and how long to search, and how to search.
Optimal foraging theory approaches these decisions in terms of
costs and benefits. Costs can be measured in terms of the time
and energy expended in the act of foraging, and benefits should
be measured in terms of fitness. However, it is extremely
difficult to quantify the consequences of a specific behavioral
choice on the probability of survival and reproduction. As a
result, benefits are typically measured in terms of energy or
nutrient gain, which is assumed to correlate with individual
fitness.
One of the most active areas of research in optimal foraging
theory has focused on the composition of animal diets—the
process of choosing what to eat from among a variety of
choices. We can approach this question using the framework of
time allocation developed in the simple model of function
response in Section 14.5, where the total time spent foraging
(T) can be partitioned into two categories of activity: searching
(Ts) and handling (Th). Here we will define the search time for
a single prey (per capita search time) as ts, and the handling
time for a single captured prey as th (capital letters refer to
total search and handling time during a given period of hunting
or feeding, T).
For simplicity, consider a predator hunting in a habitat that
contains just two kinds of prey: P1 and P2. Assume that the two
prey types yield E1 and E2 units of net energy gain (benefits),
and they require th1th1 and th2th2 seconds to handle (costs).
Profitability of the two prey types is defined as the net energy
gained per unit handling time: E1/th1E1/th1 and E2/th2E2/th2 .
Now suppose that P1 is more profitable than
P2: E1/th1> E2/th2P2: E1/th1> E2/th2 . Optimal foraging theory
predicts that P1 would be the preferred prey type because it has
a greater profitability.
This same approach can be applied to a variety of prey items
within a habitat. Behavioral ecologist Nicholas B. Davies of the
University of Cambridge examined the feeding behavior of the

pied wagtail (Motacilla alba) in a pasture near Oxford, England.
The birds fed on various dung flies and beetles attracted to
cattle droppings. Potential prey types were of various sizes:
small, medium, and large flies and beetles. The wagtails showed
a decided preference for medium-sized prey (Figure 14.14a).
The size of the prey selected corresponded to the prey the birds
could handle most profitably (E/th; Figure 14.14b). The birds
virtually ignored smaller prey. Although easy to handle (low
value of th), small prey did not return sufficient energy (E), and
large prey required too much time and effort to handle relative
to the energy gained.
The simple model of optimal foraging presented here provides a
means for evaluating which of two or more potential prey types
is most profitable based on the net energy gain per unit of
handling time. As presented, however, it also implies that the
predator always chooses the most profitable prey item. Is there
ever a situation in which the predator would choose to eat the
alternative, less profitable prey? To answer this question, we
turn our attention to the second component of time involved in
foraging, search time (ts).
Quantifying Ecology 14.2 A Simple Model of Optimal Foraging
Faced with a variety of potential food choices, predators make
decisions regarding which types of food to eat and where and
how long to search for food. But how are these decisions made?
Do predators function opportunistically, pursuing prey as they
are encountered, or do they make choices and pass by potential
prey of lesser quality (energy content) while continuing the
search for more preferred food types? If the objective is to
maximize energy intake (energy gain per unit time), a predator
should forage in a way that maximizes benefits (energy gained
from consuming prey) relative to costs (energy expended). This
concept of maximizing energy intake is the basis of models of
optimal foraging.
Any food item has a benefit (energy content) and a cost (in
terms of time and energy involved in search and acquisition).
The benefit–cost relationship determines how much profit a

particular food item represents. The profitability of a prey item
is the ratio of its energy content (E) to the time required for
handling the item (th), or E/th.
Let us assume that a predator has two possible choices of prey,
P1 and P2. The two prey types have energy contents of E1 and
E2 (units of kilojoules [kJ]) and take th1th1 and th2th2 seconds
to handle. The searching time for the two prey types are ts1ts1
and ts2ts2 in seconds. We will define P1 as the most profitable
prey type (greater value of E/th).
As the predator searches for P1, it encounters an individual of
P2. Should the predator capture and eat P2 or continue to search
for another individual of P1? Which decision—capture P2 or
continue to search—would be the more profitable and maximize
the predator’s energy intake? This is the basic question posed
by optimal foraging theory, and the solution depends on the
search time for P1.
The profitability of capturing and eating P2 is E2/th2E2/th2 and
the profitability of continuing the search, capturing, and eating
another individual of P1 is E1/(th1+ts1)E1/(th1+ts1) . Notice
that the decision to ignore P2 and continue the search carries
the additional cost of the average search time for P1, ts1ts1 .
Therefore, the optimal solution, the decision that will yield the
greater profit, is based on the following conditions:
If:
E2/th2>E1/(th1+ts1)E2/th2>E1/(th1+ts1)
then capture and eat P2.
If:
E2/th2<E1/(th1+ts1)E2/th2<E1/(th1+ts1)
then ignore P2 and continue to search for P1.
Therefore, if the search time for P1 is short, the predator will be
better off continuing the search; if the search time is long, the
most profitable decision is to capture and consume P2.
The benefit–cost trade-off for the optimal choice in prey
selection is best understood through an actual example. David
Irons and colleagues at Oregon State University examined the
foraging behavior of glaucous-winged gulls (Larus glaucescens)

that forage in the rock intertidal habitats of the Aleutian
Islands, Alaska. Data on the abundance of three prey types
(urchins, chitons, and mussels) in three intertidal zones (A, B,
and C) are presented in the table. Mean densities of the three
prey types in numbers per m2 are given for the three zones.
Average energy content (E), handling time (th), and search time
(ts) for each of the three prey types are also listed in the table.
In feeding preference experiments, where search and handling
time were not a consideration, chitons were the preferred prey
type and the obvious choice for maximizing energy intake.
However, the average abundance of urchins across the three
zones is greater than that of chitons. As a gull happens upon an
urchin while hunting for chitons, should it capture and eat the
urchin or continue to search for its preferred food? Under
conditions of optimal foraging, the decision depends on the
conditions outlined previously. The profit gained by capturing
and consuming the urchin is E/th = (7.45 kJ/8.3 s), or 0.898. In
contrast, the profit gained by ignoring the urchin and searching,
capturing, and consuming another chiton is E/(th + ts) = [24.52
kJ/(3.1 s + 37.9 s)] or 0.598. Because the profit gained by
consuming the urchin is greater than the profit gained by
ignoring it and continuing the search for chitons, it would make
sense for the gull to capture and eat the urchin.
What about a gull foraging in intertidal zone A that happens
upon a mussel? The profit gained by capturing and eating the
mussel is (1.42/2.9), or 0.490, and the profit gained by
continuing the search for a chiton remains [24.52
kJ/(3.1 s + 37.9 s)] or 0.598. In this case, the gull would be
better off ignoring the mussel and continuing the search for
chitons.
We now know what the gulls “should do” under the hypothesis
of optimal foraging. But do they in fact forage optimally as
defined by this simple model of benefits and costs? If gulls are
purely opportunistic, their selection of prey in each of the three
zones would be in proportion to their relative abundances. Irons
and colleagues, however, found that the relative preferences for

urchins and chitons were in fact related to their profitability
(E/th); mussels, however, were selected less frequently than
predicted by their relative value of E.
1. How would reducing the energy content of chitons by half (to
12.26 kJ) influence the decision whether the gull should capture
and eat the mussel or continue searching for a chiton in the
example presented?
2. Because the gulls do not have the benefit of the optimal
foraging model in deciding whether to select a prey item, how
might natural selection result in the evolution of optimal
foraging behavior?
Alternate View
Prey Type
Density Zone A
Density Zone B
Density Zone C
Energy (kJ/individual)
Handling Time (s)
Search Time (s)
Urchins
0.0
3.9
23.0
7.45
8.3
35.8
Chitons
0.1
10.3
5.6
24.52
3.1
37.9
Mussels
852.3
1.7

0.6
1.42
2.9
18.9
Suppose that while searching for P1, the predator encounters an
individual of P2. Should it eat it or continue searching for
another individual of P1? The optimal choice will depend on the
search time for P1, defined as ts1ts1 . The profitability of
consuming the individual of P2 is E2/th2E2/th2 ; the alternative
choice of continuing to search, capture, and consume an
individual of P1 is E1/(th1+ts1)E1/(th1+ts1) , which now
includes the additional time cost of searching for another
individual of P1 (ts1ts1 ). If
E2/th2>E1/(th1+ts1)E2/th2>E1/(th1+ts1) , then according to
optimal foraging theory, the predator would eat the individual
of P2. If this condition does not hold true, then the predator
would continue searching for P1. Testing this hypothesis
requires the researcher to quantify the energy value and search
and handling times of the various potential prey items. An
example of this simple model of optimal prey choice is
presented in Quantifying Ecology 14.2.
A wealth of studies examines the hypothesis of optimal prey
choice in a wide variety of species and habitats, and patterns of
prey selection generally follow the rules of efficient foraging.
But the theory as presented here fails to consider the variety of
other competing activities influencing a predator’s time budget
and the factors other than energy content that may influence
prey selection. One reason that a predator consumes a varied
diet is that its nutritional requirements may not be met by eating
a single prey species (see Chapter 7).
14.8 Risk of Predation Can Influence Foraging Behavior
Most predators are also prey to other predatory species and
therefore face the risk of predation while involved in their
routine activities, such as foraging. Habitats and foraging areas
vary in their foraging profitability and their risk of predation. In
deciding whether to feed, the forager must balance its potential

energy gains against the risk of being eaten. If predators are
about, then it may be to the forager’s advantage not to visit a
most profitable, but predator-prone, area and to remain in a less
profitable but more secure part of the habitat. Many studies
report how the presence of predators affects foraging behavior.
In one such study, Jukka Suhonen of the University of
Jyväskylä (Finland) examined the influence of predation risk on
the use of foraging sites by willow tits (Parus montanus) and
crested tits (Parus cristatus) in the coniferous forests of central
Finland. During the winter months, flocks of these two bird
species forage in spruce, pine, and birch trees. The major threat
to their survival is the Eurasian pygmy owl (Glaucidium
passerinum). The owl is a diurnal ambush, or sit-and-wait
hunter, that pounces downward on its prey. Its major food is
voles, and when vole populations are high, usually every three
to five years, the predatory threat to these small passerine birds
declines. When vole populations are low, however, the small
birds become the owl’s primary food. During these periods, the
willow and crested tits forsake their preferred foraging sites on
the outer branches and open parts of the trees, restricting their
foraging activity to the denser inner parts of spruce trees that
provide cover and to the tops of the more open pine and leafless
birch trees.
14.9 Coevolution Can Occur between Predator and Prey
By acting as agents of mortality, predators exert a selective
pressure on prey species (see Chapter 12, Section 12.3). That is,
any characteristic that enables individual prey to avoid being
detected and captured by a predator increases its fitness.
Natural selection functions to produce “smarter,” more evasive
prey (fans of the Road Runner cartoons should already
understand this concept). However, failure to capture prey
results in reduced reproduction and increased mortality of
predators. Therefore, natural selection also produces “smarter,”
more skilled predators. As characteristics that enable them to
avoid being caught evolve in prey species, more effective means
of capturing prey evolve in predators. To survive as a species,

the prey must present a moving target that the predator can
never catch. This view of the coevolution between predator and
prey led the evolutionary biologist Leigh Van Valen to propose
the Red Queen hypothesis. In Lewis Carroll’s Through the
Looking Glass,andWhat Alice Found There, there is a scene in
the Garden of Living Flowers in which everything is
continuously moving. Alice is surprised to see that no matter
how fast she moves, the world around her remains motionless—
to which the Red Queen responds, “Now, here, you see, it takes
all the running you can do, to keep in the same place.” So it is
with prey species. To avoid extinction at the hands of predators,
prey must evolve means of avoiding capture; they must keep
moving just to stay where they are.
14.10 Animal Prey Have Evolved Defenses against Predators
Animal species have evolved a wide range of characteristics to
avoid being detected, selected, and captured by predators. These
characteristics are collectively referred to as predator defenses.
Chemical defense is widespread among many groups of animals.
Some species of fish release alarm pheromones (chemical
signals) that, when detected, induce flight reactions in members
of the same and related species. Arthropods, amphibians, and
snakes employ odorous secretions to repel predators. For
example, when disturbed, the stinkbug (Cosmopepla bimaculata)
discharges a volatile secretion from a pair of glands located on
its back (Figure 14.15a). The stinkbug can control the amount
of fluid released and can reabsorb the fluid into the gland. In a
series of controlled experiments, Bryan Krall and colleagues at
Illinois State University have found that the secretion deters
feeding by both avian and reptile predators.
Many arthropods possess toxic substances, which they acquire
by consuming plants and then store in their own bodies. Other
arthropods and venomous snakes, frogs, and toads synthesize
their own poisons.
Prey species have evolved numerous other defense mechanisms.
Some animals possess cryptic coloration, which includes colors
and patterns that allow prey to blend into the background of

their normal environment (Figure 14.15b). Such protective
coloration is common among fish, reptiles, and many ground-
nesting birds. Object resemblance is common among insects.
For example, walking sticks (Phasmatidae) resemble twigs
(Figure 14.15c), and katydids (Pseudophyllinae) resemble
leaves. Some animals possess eyespot markings, which
intimidate potential predators, attract the predators’ attention
away from the animal, or delude them into attacking a less
vulnerable part of the body. Associated with cryptic coloration
is flashing coloration. Certain butterflies, grasshoppers, birds,
and ungulates, such as the white-tailed deer, display extremely
visible color patches when disturbed and put to flight. The
flashing coloration may distract and disorient predators; in the
case of the white-tailed deer, it may serve as a signal to promote
group cohesion when confronted by a predator (Figure 14.15d).
When the animal comes to rest, the bright or white colors
vanish, and the animal disappears into its surroundings.
Animals that are toxic to predators or use other chemical
defenses often possess warning coloration, or aposematism, that
is, bold colors with patterns that may serve as warning to
would-be predators. The black-and-white stripes of the skunk,
the bright orange of the monarch butterfly, and the yellow-and-
black coloration of many bees and wasps and some snakes may
serve notice of danger to their predators (Figures 14.15e and
14.15f). All their predators, however, must have an unpleasant
experience with the prey before they learn to associate the color
pattern with unpalatability or pain.
Some animals living in the same habitats with inedible species
sometimes evolve a coloration that resembles or mimics the
warning coloration of the toxic species. This type of mimicry is
called Batesian mimicry after the English naturalist H. E. Bates,
who described it when observing tropical butterflies. The
mimic, an edible species, resembles the inedible species, called
the model. Once the predator has learned to avoid the model, it
avoids the mimic also. In this way, natural selection reinforces
the characteristic of the mimic species that resembles that of the

model species.
Most discussions of Batesian mimicry concern butterflies, but
mimicry is not restricted to Lepidoptera and other invertebrates.
Mimicry has also evolved in snakes with venomous models and
nonvenomous mimics (Figure 14.16). For example, in eastern
North America, the scarlet king snake (Lampropeltis
triangulum) mimics the eastern coral snake (Micrurus fulvius)
and in southwestern North America, the mountain kingsnake
(Lampropeltis pyromelana) mimics the western coral snake
(Micruroides euryxanthus). Mimicry is not limited to color
patterns. Some species of nonvenomous snakes are acoustic
mimics of rattlesnakes. The fox snake (Elaphe vulpina) and the
pine snake of eastern North America, the bull snake of the Great
Plains, and the gopher snake of the Pacific States, all subspecies
of Pituophis melanoleucus, rapidly vibrate their tails in leafy
litter to produce a rattle-like sound.
Another type of mimicry is called Müllerian, after the 19th-
century German zoologist Fritz Müller. With Müllerian
mimicry, many unpalatable or venomous species share a similar
color pattern. Müllerian mimicry is effective because the
predator must only be exposed to one of the species before
learning to stay away from all other species with the same
warning color patterns. The black-and-yellow striped bodies of
social wasps, solitary digger wasps, and caterpillars of the
cinnabar moths warn predators that the organism is inedible
(Figure 14.17). All are unrelated species with a shared color
pattern that functions to keep predators away.
Some animals employ protective armor for defense. Clams,
armadillos, turtles, and many beetles all withdraw into their
armor coats or shells when danger approaches. Porcupines,
echidnas, and hedgehogs possess quills (modified hairs) that
discourage predators.
Still other animals use behavioral defenses, which include a
wide range of behaviors by prey species aimed at avoiding
detection, fleeing, and warning others of the presence of
predators. Animals may change their foraging behavior in

response to the presence of predators, as in the example of the
willow and crested tits (see Section 14.8). Some species give an
alarm call when a predator is sighted. Because high-pitched
alarm calls are not species specific, they are recognized by a
wide range of nearby animals. Alarm calls often bring in
numbers of potential prey that mob the predator. Other
behavioral defenses include distraction displays, which are most
common among birds. These defenses direct the predator’s
attention away from the nest or young.
For some prey, living in groups is the simplest form of defense.
Predators are less likely to attack a concentrated group of
individuals. By maintaining tight, cohesive groups, prey make it
difficult for any predator to obtain a victim (Figure 14.18).
Sudden, explosive group flight can confuse a predator, leaving
it unable to decide which individual to follow.
A subtler form of defense is the timing of reproduction so that
most of the offspring are produced in a short period. Prey are
thus so abundant that the predator can take only a fraction of
them, allowing a percentage of the young to escape and grow to
a less-vulnerable size. This phenomenon is known as predator
satiation. Periodic cicadas (Magicicada spp.) emerge as adults
once every 13 years in the southern portion of their range in
North America and once every 17 years in the northern portion
of their range, living the remainder of the period as nymphs
underground. Though these cicadas emerge only once every 13
or 17 years, a local population emerges somewhere within their
range virtually every year. When emergence occurs, the local
density of cicadas can number in the millions of individuals per
hectare. Ecologist Kathy Williams of San Diego State
University and her colleagues tested the effectiveness of
predator satiation during the emergence of periodic cicadas in
northwest Arkansas. Williams found that the first cicadas
emerging in early May were eaten by birds, but avian predators
quickly became satiated. Birds consumed 15–40 percent of the
cicada population at low cicada densities but only a small
proportion as cicada densities increased (Figure 14.19).

Williams’s results demonstrated that, indeed, the synchronized,
explosive emergences of periodic cicadas are an example of
predator satiation.
The predator defenses just discussed fall into two broad classes:
permanent and induced. Permanent, or constitutive defenses, are
fixed features of the organism, such as object resemblance and
warning coloration. In contrast, defenses that are brought about,
or induced, by the presence or action of predators are referred
to as induced defenses. Behavioral defenses are an example of
induced defenses, as are chemical defenses such as alarm
pheromones that, when detected, induce flight reactions.
Induced defenses can also include shifts in physiology or
morphology, representing a form of phenotypic plasticity (see
this chapter, Field Studies: Rick A. Relyea).
14.11 Predators Have Evolved Efficient Hunting Tactics
As prey have evolved ways of avoiding predators, predators
have evolved better ways of hunting. Predators use three
general methods of hunting: ambush, stalking, and pursuit.
Ambush hunting means lying in wait for prey to come along.
This method is typical of some frogs, alligators, crocodiles,
lizards, and certain insects. Although ambush hunting has a low
frequency of success, it requires minimal energy. Stalking,
typical of herons and some cats, is a deliberate form of hunting
with a quick attack. The predator’s search time may be great,
but pursuit time is minimal. Pursuit hunting, typical of many
hawks, lions, wolves, and insectivorous bats, involves minimal
search time because the predator usually knows the location of
the prey, but pursuit time is usually great. Stalkers spend more
time and energy encountering prey. Pursuers spend more time
capturing and handling prey.
Predators, like their prey, may use cryptic coloration to blend
into the background or break up their outlines (Figure 14.20).
Predators use deception by resembling the prey. Robber flies
(Laphria spp.) mimic bumblebees, their prey (Figure 14.21).
The female of certain species of fireflies imitates the mating
flashes of other species to attract males of those species, which

she promptly kills and eats. Predators may also employ
chemical poisons, as do venomous snakes, scorpions, and
spiders. They may form a group to attack large prey, as lions
and wolves do.
14.12 Herbivores Prey on Autotrophs
Although the term predator is typically associated with animals
that feed on other animals, herbivory is a form of predation in
which animals prey on autotrophs (plants and algae). Herbivory
is a special type of predation because herbivores typically do
not kill the individuals they feed on. Because the ultimate
source of food energy for all heterotrophs is carbon fixed by
plants in the process of photosynthesis (see Chapter 6),
autotroph–herbivore interactions represent a key feature of all
communities.
If you measure the amount of biomass actually eaten by
herbivores, it may be small—perhaps 6–10 percent of total plant
biomass present in a forest community or as much as 30–50
percent in grassland communities (see Chapter 20, Section
20.12). In years of major insect outbreaks, however, or in the
presence of an overabundance of large herbivores, consumption
is considerably higher (Figure 14.22). Consumption, however, is
not necessarily the best measure of the impact of herbivory
within a community. Grazing on plants can have a subtler
impact on both plants and herbivores.
The removal of plant tissue—leaf, bark, stems, roots, and sap—
affects a plant’s ability to survive, even though the plant may
not be killed outright. Loss of foliage and subsequent loss of
roots will decrease plant biomass, reduce the vigor of the plant,
place it at a competitive disadvantage with surrounding
vegetation, and lower its reproductive effort. The effect is
especially strong in the juvenile stage, when the plant is most
vulnerable and least competitive with surrounding vegetation.
A plant may be able to compensate for the loss of leaves with
the increase of photosynthesis in the remaining leaves.
However, it may be adversely affected by the loss of nutrients,
depending on the age of the tissues removed. Young leaves are

dependent structures—importers and consumers of nutrients
drawn from reserves in roots and other plant tissues. Grazing
herbivores, both vertebrate and invertebrate, often concentrate
on younger leaves and shoots because they are lower in
structural carbon compounds such as lignins, which are difficult
to digest and provide little if any energy (see Section 21.4). By
selectively feeding on younger tissues, grazers remove
considerable quantities of nutrients from the plant.
Field Studies Rick A. RelyeaDepartment of Biological Sciences,
University of Pittsburgh
Ecologists have long appreciated the influence of predation on
natural selection. Predators select prey based on their sizes and
shapes, thereby acting as a form of natural selection that alters
the range of phenotypes within the population. In doing so,
predators alter the genetic composition of the population (gene
pool), which determines the range of phenotypes in future
generations. Through this process, many of the mechanisms of
predator avoidance discussed in Section 14.10 are selected for
in prey populations. In recent years, however, ecologists have
discovered that predators can have a much broader influence on
the characteristics of prey species through nonlethal effects. For
example, presence of a predator can change the behavior of
prey, causing them to reduce activity (or hide) to avoid being
detected. This change in behavior can reduce foraging activity.
In turn, changes in the rate of food intake can influence prey
growth and development, resulting in shifts in their morphology
(size and shape of body). This shift in the phenotype of
individual prey, induced by the presence and activity of
predators, is termed induction and represents a form of
phenotypic plasticity (see Section 5.4).
The discovery that predators can influence the characteristics
(phenotype) of prey species through natural selection and
induction presents a much more complex picture of the role of
predation in evolution. Although ecologists are beginning to
understand how natural selection and induction function
separately, little is known about how these two processes

interact to determine the observed range of phenotypes within a
prey population. Thanks to the work of ecologist Rick Relyea,
however, this picture is becoming much clearer.
Relyea’s research is conducted in wading pools that are
constructed to serve as experimental ponds. In one series of
experiments, Relyea explored the nature of induced changes in
behavior and morphology in prey (gray tree frog tadpoles, Hyla
versicolor) by introducing caged predators (dragonfly larvae,
Anax longipes) into the experimental ponds (Figure 1). The
tadpoles can detect waterborne chemicals produced by the
predators, allowing Relyea to simulate the threat of predation to
induce changes in the tadpoles while preventing actual
predation. By comparing the characteristics of tadpoles in
control ponds (no predator present) and in ponds with caged
predators, he was able to examine the responses induced by the
presence of predators.
Results of the experiments reveal that induction by predatory
chemical cues altered the tadpoles’ behavior. They became less
active in the presence of predators (Figure 2). Reduced activity
makes prey less likely to encounter predators and improves their
probability of survival. The predators’ presence also induced a
shift in the morphology of tadpoles—a form of phenotypic
plasticity. Tadpoles raised in the experimental ponds in which
predators were present have a greater tail depth and shorter
overall body length than do individuals raised in the absence of
predators (control ponds; Figure 3). Interestingly, previous
studies showed that tadpoles with deeper tails and shorter
bodies escape dragonfly predators better than tadpoles with the
opposite morphology. Therefore, the induced morphological
responses that were observed in Relyea’s experiments are
adaptive; they are a form of phenotypic plasticity that functions
to increase the survival of individual tadpoles. To assess the
heritability of traits and trait plasticities, Relyea conducted
artificial crosses of adults, reared their progeny in predator and
no-predator environments, and then quantified tadpole behavior
(activity), morphology (body and tail shape), and life history

(mass and development). Results of the study found that
predator-induced traits were heritable, however, the magnitude
of heritability varied across traits and environments.
Interestingly, several traits had significant heritability for
plasticity, suggesting a potential for selection to act on
phenotypic plasticity per se. Relyea’s experiments clearly show
that predators can induce changes in prey phenotype and that
the induced changes are heritable and result from natural
selection.
The experiments discussed here focus on only one life stage in
the development of the tree frog: the larval (tadpole) stage. But
how might these changes in morphology early in development
affect traits later in life? As the tadpoles metamorphose into
adult frogs, they have drastically different morphologies and
occupy different habitats. To answer this question, Relyea
conducted an experiment to examine how differences in the
morphology of wood frog tadpoles (Rana sylvatica), induced by
the presence of predators, subsequently affected the morphology
of the adult frog later in development.
As in previous experiments, tadpoles reared with caged
predators developed relatively deeper tail fins and had shorter
bodies, lower mass, and longer developmental times than did
tadpoles reared without predators. Adult frogs that emerged
from the tadpoles exposed to predators (and exhibiting these
induced changes during the larval stage) exhibited no
differences in mass but developed relatively large hindlimbs
and forelimbs and narrower bodies as compared to individuals
emerging from environments where predators were absent
(Figure 4). These results clearly show that predator-induced
shifts in traits early in development can subsequently alter traits
later in development.
Plants respond to defoliation with a flush of new growth that
drains nutrients from reserves that otherwise would go to
growth and reproduction. For example, Anurag Agrawal of the
University of Toronto found that herbivory by longhorn beetles
(Tetraopes spp.) reduced fruit production and mass of milkweed

plants (Asclepias spp.) by as much as 20–30 percent.
If defoliation of trees is complete (Figure 14.22a), as often
happens during an outbreak of gypsy moths (Lymantria dispar)
or fall cankerworms (Alsophila pometaria), leaves that regrow
in their place are often quite different in form. The leaves are
often smaller, and the total canopy (area of leaves) may be
reduced by as much as 30–60 percent. In addition, the plant uses
stored reserves to maintain living tissue until new leaves form,
reducing reserves that it will require later. Regrown twigs and
tissues are often immature at the onset of cold weather,
reducing their ability to tolerate winter temperatures. Such
weakened trees are more vulnerable to insects and disease. In
contrast to deciduous tree species, defoliation kills coniferous
species.
Browsing animals such as deer, rabbits, and mice selectively
feed on the soft, nutrient-rich growing tips (apical meristems)
of woody plants, often killing the plants or changing their
growth form. Burrowing insects, like the bark beetles, bore
through the bark and construct egg galleries in the phloem–
cambium tissues. In addition to phloem damage caused by larval
and adult feeding, some bark beetle species carry and introduce
a blue stain fungus to a tree that colonizes sapwood and disrupts
water flow to the tree crown, hastening tree death.
Some herbivores, such as aphids, do not consume tissue directly
but tap plant juices instead, especially in new growth and young
leaves. Sap-sucking insects can decrease growth rates and
biomass of woody plants by as much as 25 percent.
Grasses have their meristems, the source of new growth, close
to the ground. As a result, grazers first eat the older tissue and
leave intact the younger tissue with its higher nutrient
concentration. Therefore, grasses are generally tolerant of
grazing, and up to a point, most benefit from it. The
photosynthetic rate of leaves declines with leaf age. Grazing
stimulates production by removing older tissue functioning at a
lower rate of photosynthesis, increasing the light availability to
underlying young leaves. Some grasses can maintain their vigor

only under the pressure of grazing, even though defoliation
reduces sexual reproduction. Not all grasses, however, tolerate
grazing. Species with vulnerable meristems or storage organs
can be quickly eradicated under heavy grazing.
14.13 Plants Have Evolved Characteristics that Deter
Herbivores
Most plants are sessile; they cannot move. Thus, avoiding
predation requires adaptations that discourage being selected by
herbivores. The array of characteristics used by plants to deter
herbivores includes both structural and other defenses.
Structural defenses, such as hairy leaves, thorns, and spines,
can discourage feeding (Figure 14.23), thereby reducing the
amount of tissues removed by herbivores.
For herbivores, often the quality rather than the quantity of food
is the constraint on food supply. Because of the complex
digestive process needed to break down plant cellulose and
convert plant tissue into animal flesh, high-quality forage rich
in nitrogen is necessary (see Chapter 7, Section 7.2). If the
nutrient content of the plants is not sufficient, herbivores can
starve to death on a full stomach. Low-quality foods are tough,
woody, fibrous, and indigestible. High-quality foods are young,
soft, and green or they are storage organs such as roots, tubers,
and seeds. Most plant tissues are relatively low in quality, and
herbivores that have to live on such resources suffer high
mortality or reproductive failure.
Plants contain various chemicals that are not involved in the
basic metabolism of plant cells. Many of these chemicals,
referred to as secondary compounds, either reduce the ability of
herbivores to digest plant tissues or deter herbivores from
feeding. Although these chemicals represent an amazing array
of compounds, they can be divided into three major classes
based on their chemical structure: nitrogen-based compounds,
terpenoids, and phenolics. Nitrogen-based compounds include
alkaloids such as morphine, atropine, nicotine, and cyanide.
Terpenoids (also called isoprenoids) include a variety of
essential oils, latex, and plant resins (many spices and

fragrances contain terpenoids). Phenolics are a general class of
aromatic compounds (i.e., contain the benzene ring) including
the tannins and lignins.
Some secondary compounds are produced by the plant in large
quantities and are referred to as quantitative inhibitors. For
example, tannins and resins may constitute up to 60 percent of
the dry weight of a leaf. In the vacuoles of their leaves, oaks
and other species contain tannins that bind with proteins and
inhibit their digestion by herbivores. Between 5–35 percent of
the carbon contained in the leaves of terrestrial plants occurs in
the form of lignins—complex, carbon-based molecules that are
impossible for herbivores to digest, making the nitrogen and
other essential nutrients bound in these compounds unavailable
to the herbivore. These types of compounds reduce digestibility
and thus potential energy gain from food (see Section 7.2).
Other secondary compounds that function as defenses against
herbivory are present in small to minute quantities and are
referred to as qualitative inhibitors. These compounds are toxic,
often causing herbivores to avoid their consumption. This
category of compounds includes cyanogenic compounds
(cyanide) and alkaloids such as nicotine, caffeine, cocaine,
morphine, and mescaline that interfere with specific metabolic
pathways of physiological processes. Many of these compounds,
such as pyrethrin, have become important sources of pesticides.
Although the qualitative inhibitors function to protect against
most herbivores, some specialized herbivores have developed
ways of breaching these chemical defenses. Some insects can
absorb or metabolically detoxify the chemical substances. They
even store the plant poisons to use them in their own defense, as
the larvae of monarch butterflies do, or in the production of
pheromones (chemical signals). Some beetles and certain
caterpillars sever veins in leaves before feeding, stopping the
flow of chemical defenses.
Some plant defenses are constitutive, such as structural defenses
or quantitative inhibitors (tannins, resins, or lignins) that
provide built-in physical or biological barriers against the

attacker. Others are active, induced by the attacking herbivore.
These induced responses can be local (occur at the site of the
attack) or can extend systematically throughout the plant. Often,
these two types of defenses are used in combination. For
example, when attacked by bark beetles carrying an infectious
fungus in their mouthparts, conifer trees release large amounts
of resin (constitutive, quantitative defense) from the attack sites
that flows out onto the attackers, entombing the beetles.
Meanwhile, the tree mobilizes induced defenses against the
pathogenic fungus that the intruder has deposited at the wound
site.
In another kind of plant–insect interaction, some plants appear
to “call for help,” attracting the predators of their predators.
Parasitic and predatory arthropods often prevent plants from
being severely damaged by killing herbivores as they feed on
the plants. Recent studies show that a variety of plant species,
when injured by herbivores, emit chemical signals to guide
natural enemies to the herbivores. It is unlikely that the
herbivore-damaged plants initiate the production of chemicals
solely to attract predators. The signaling role probably evolved
secondarily from plant responses that produce toxins and
deterrents against herbivores. For example, in a series of
controlled laboratory studies, Ted Turlings and James
Tumlinson, researchers at the Agricultural Research Service of
the U.S. Department of Agriculture, found that corn seedlings
under attack by caterpillars release several volatile terpenoid
compounds that function to attract parasitoid wasps (Cotesia
marginiventris) that then attack the caterpillars. Experiment
results showed that the induced emission of volatiles is not
limited to the site of damage but occurs throughout the plant.
The systematic release of volatiles by injured corn seedlings
results in a significant increase in visitation by the parasitoid
wasp.
Various hypotheses have been put forward to explain why
different types of defenses that help in the avoidance of
herbivores have evolved in plants. A feature common to all of

these hypotheses is the trade-off between the costs and benefits
of defense. The cost of defense in diverting energy and nutrients
from other needs must be offset by the benefits of avoiding
predation.
14.14 Plants, Herbivores, and Carnivores Interact
In our discussion thus far, we have considered herbivory on
plants and carnivory on animals as two separate topics, linked
only by the common theme of predation. However, they are
linked in another important way. Plants are consumed by
herbivores, which in turn are consumed by carnivores.
Therefore, we cannot really understand an herbivore–carnivore
system without understanding plants and their herbivores, nor
can we understand plant–herbivore relations without
understanding predator–herbivore relationships. All three—
plants, herbivores, and carnivores—are interrelated. Ecologists
are beginning to understand these three-way relationships.
A classic case (Figure 14.24) is the three-level interaction of
plants, the snowshoe hare (Lepus americanus), and its
predators—lynx (Felis lynx), coyote (Canis latrans), and horned
owl (Bubo virginianus). The snowshoe hare inhabits the high-
latitude forests of North America. In winter, it feeds on the
buds of conifers and the twigs of aspen, alder, and willow,
which are termed browse. Browse consists mainly of smaller
stems and young growth rich in nutrients. The hare–vegetation
interaction becomes critical when the amount of essential
browse falls below that needed to support the population over
winter (approximately 300 grams [g] per individual per day).
Excessive browsing when the hare population is high reduces
future woody growth, bringing on a food shortage.
The shortage and poor quality of food lead to malnutrition,
parasite infections, and chronic stress. Those conditions and
low winter temperatures weaken the hares, reducing
reproduction and making them extremely vulnerable to
predation. Intense predation causes a rapid decline in the
number of hares. Now facing their own food shortage, the
predators fail to reproduce, and populations decline.

Meanwhile, upon being released from the pressures of browsing
by hares, plant growth rebounds. As time passes, with the
growing abundance of winter food as well as the decline in
predatory pressure, the hare population starts to recover and
begins another cycle. Thus, an interaction between predators
and food supply (plants) produces the hare cycle and, in turn,
the hare cycle affects the population dynamics of its predators
(see Figure 14.13).
14.15 Predators Influence Prey Dynamics through Lethal and
Nonlethal Effects
The ability of predators to suppress prey populations has been
well documented. Predators can suppress prey populations
through consumption; that is, they reduce prey population
growth by killing and eating individuals. Besides causing
mortality, however, predators can cause changes in prey
characteristics by inducing defense responses in prey
morphology, physiology, or behavior (see this chapter, Field
Studies: Rick A. Relyea). Predator-induced defensive responses
can help prey avoid being consumed, but such responses often
come at a cost. Prey individuals may lose feeding opportunities
by avoiding preferred but risk-prone habitats, as in the example
of foraging by willow and crested tits presented in Section
14.8. Reduced activity by prey in the presence of predators can
reduce prey foraging time and food intake, subsequently
delaying growth and development. A convincing demonstration
of the long-term costs of anti-predator behavior comes from
studies of aquatic insects such as mayflies (Baetis tricaudatus),
which do not feed during their adult life stages. Mayflies are
ideal study subjects because their adult fitness depends on the
energy reserves they develop during the larval stage. Thus, it
has been possible to show that a marked reduction in feeding
activity by mayfly larvae in the presence of predators leads to
slower growth and development, which ultimately translates
into smaller adults that produce fewer eggs (Figure 14.29).
Interpreting Ecological Data
1. Q1. Based on the results of the experimental study presented

in Figure 14.29, how does the reduced activity of larval
mayflies in the presence of predators influence the time
required for larvae to develop into adult mayflies?
2. Q2. How does the presence of predators and associated
reduction in activity during the larval stage influence the fitness
of adult mayflies? Explain the variables you used to draw your
conclusions about adult fitness.
Predator-induced defensive responses can potentially influence
many aspects of prey population regulation and dynamics, given
the negative reproductive consequences of anti-predator
behavior. Translating behavior decisions to population-level
consequences, however, can be difficult. But research by Eric
Nelson and colleagues at the University of California–Davis has
clearly demonstrated an example of reduction in prey population
growth resulting from predator-induced changes in prey
behavior. Nelson and colleagues studied the interactions
between herbivorous and predatory insects in fields of alfalfa
(Medicago sativa). Pea aphids (Acyrthosiphon pisum) feed by
inserting their mouthparts into alfalfa phloem tissue, and they
reproduce parthenogenetically (asexual reproduction through
the development of an unfertilized ovum) at rates of 4 to 10
offspring per day. A suite of natural enemies attacks the aphids,
including damsel bugs (Nabis spp.). The aphids respond to the
presence of foraging predators by interrupting feeding and
walking away from the predator or dropping off the plant. The
costs suffered by the aphids because of their defensive behavior
may include increased mortality or reduced reproduction.
Damsel bugs feed by piercing aphids with a long proboscis and
ingesting the body contents. Damsel bugs, therefore, influence
prey in two ways: first by consuming aphids and second by
disturbing their feeding behavior. In a series of controlled
experiments, Nelson was able to distinguish between the effects
of these two influences by surgically removing the mouthparts
(proboscises) of some damsel bugs, therefore making them
unable to kill and feed on aphids. By exposing aphids to these
damsel bugs, the researchers were able to test the predators’

ability to suppress aphid population growth through behavioral
mechanisms only. Normal predators that were able to consume
and disturb the aphids caused the greatest reduction in aphid
population growth; however, nonconsumptive predators also
strongly reduced aphid population growth (Figure 14.26).
These field experiments clearly demonstrated that predators
reduce population growth partly through predator-induced
changes in prey behavior and partly through direct mortality
(consuming prey individuals).
An array of specific behavioral, morphological, and
physiological adaptations influence the relationship between a
predator and its prey, making it difficult to generalize about the
influence of predation on prey populations. Nonetheless, many
laboratory and field studies offer convincing evidence that
predators can significantly alter prey abundance. Whereas the
influence of competition on community structure is somewhat
obscure, the influence of predation is more demonstrable.
Because all heterotrophs derive their energy and nutrients from
consuming other organisms, the influence of predation can be
more readily noticed throughout a community. As we shall see
later in our discussion, the direct influence of predation on the
population density of prey species can have the additional
impact of influencing the interactions among prey species,
particularly competitive relationships (Chapter 17).
Ecological Issues & Applications Sustainable Harvest of
Natural Populations Requires Being a “Smart Predator”
Although the advent of agriculture some 10000 years ago
reduced human dependence on natural populations of plants and
animals as a food source, more than 80 percent of the world’s
commercial catches of fish and shellfish is from the harvest of
naturally occurring populations in the oceans (71 percent) and
inland freshwaters (10 percent). When humans exploit natural
fish populations as a food resource, they are effectively
functioning as predators. So what effect is predation by humans
having on natural fish populations? Unfortunately, in most cases
it is a story of overexploitation and population decline. The cod

fishery of the North Atlantic provides a case in point.
For 500 hundred years the waters of the Atlantic Coast from
Newfoundland to Massachusetts supported one of the greatest
fisheries in the world. The English explorer John Cabot in 1497
discovered and marveled at the abundance of cod off the
Newfoundland Coast. Upon returning to Britain, he told of seas
“swarming with fish that could be taken not only with nets but
with baskets weighted down with stone.” Some cod were five to
six feet long and weighed up to 200 pounds. Cabot’s news
created a frenzy of exploitative fishery. Portuguese, Spanish,
English, and French fishermen sailed to Newfoundland, and by
1542 the French sailed no fewer than 60 ships, each making two
trips a year. In the 1600s, England took control of
Newfoundland and its waters and established numerous coastal
posts where English merchants salted and dried cod before
shipping it to England. So abundant were the fish that the
English thought nothing could seriously affect this seemingly
inexhaustible resource.
Catches remained rather stable until after World War II, when
the demand for fish increased dramatically and led to intensified
fishing efforts. Large factory trawlers that could harvest and
process the catch at sea replaced smaller fishing vessels.
Equipped with sonar and satellite navigation, fishing fleets
could locate spawning schools. They could engulf schools with
huge purse nets and sweep the ocean floor clean of fish and all
associated marine life. In the 1950s, annual average catch off
the coast of Newfoundland was 300,000 metric tons (MT) of
cod, but by the 1960s the catch had almost tripled (Figure
14.27). In 15 years from the mid-1950s through the 1960s, 200
factory ships off Newfoundland took as many northern cod as
were caught over the prior 250-year span since Cabot’s arrival.
The cod fishery could not endure such intense exploitation. By
1978 the catch had declined to less than a quarter of the harvest
just a decade before. To protect their commercial interests in
the fishery, the Canadian and U.S. governments excluded all
foreign fisheries in a zone extending 200 miles. But instead of

capitalizing on this opportunity to allow the fish populations to
recover, the Canadian government provided the industry with
subsidies to build huge factory trawlers. After a brief surge in
catches during the 1980s, in 1992 the North Atlantic Canadian
cod fishery collapsed (see Figure 14.31).
The story of the North Atlantic cod fishery is an example of the
rate of predation exceeding the ability of the prey population to
recover; and unlike natural predator–prey systems, there is no
negative feedback on the predator population. (Despite the
economic consequences of the collapse of the fishery, humans
do not exhibit a numerical response to declining fish
populations). Unfortunately, the story of the North Atlantic cod
fishery is not unique (Figure 14.28). Often following the
collapse of one fishery, the industry shifts to another species,
and the pattern of overexploitation repeats itself. Over the past
decades, however, there has been a growing effort toward the
active scientific management of fisheries resources to ensure
their continuance. The goal of fisheries science is to provide for
the long-term sustainable harvesting of fish populations based
on the concept of sustainable yield. The amount of resources
(fish) harvested per unit of time is called the yield.Sustainable
yield is the yield that allows for populations to recover to their
pre-harvest levels. The population of fish will be reduced by a
given harvest, but under sustainable management, the yield
should not exceed the ability of natural population growth
(reproduction) to replace the individuals harvested, allowing the
level of harvest (yield) to be sustained through time.
A central concept of sustainable harvest in fisheries
management is the logistic model of population growth
(Chapter 11, see Section 11.1). Under conditions of the logistic
model, growth rate (overall numbers of new organisms produced
per year) is low when the population is small (Figure 14.28). It
is also low when a population nears its carrying capacity (K)
because of density-dependent processes such as competition for
limited resources. Intermediate-sized populations have the
greatest growth capacity and ability to produce the most

harvestable fish per year. The key insight of this model is that
fisheries can optimize harvest of a particular species by keeping
the population at an intermediate level and harvesting the
species at a rate equal to its annual growth rate (Figure 14.29).
This strategy is called the maximum sustainable yield.
In effect, the concept of sustainable yield is an attempt at being
a “smart predator.” The objective is to maintain the prey
population at a density where the production of new individuals
just offsets the mortality represented by harvest. The higher the
rate of population increase, the higher will be the rate of harvest
that produces the maximum sustainable yield. Species
characterized by a high rate of population growth often lose
much of their production to a high density-independent
mortality, influenced by variation in the physical environment
such as temperature (see Section 11.13). The management
objective for these species is to reduce “waste” by taking all
individuals that otherwise would be lost to natural mortality.
Such species are difficult to manage, however, because
populations can be depleted if annual patterns of reproduction
are interrupted as a result of environmental conditions. An
example is the Pacific sardine (Sardinops sagax). Exploitation
of the Pacific sardine population in the 1940s and 1950s shifted
the age structure of the population to younger age classes.
Before exploitation, reproduction was distributed among the
first five age classes (years). In the exploited population, this
pattern of reproduction shifted, and close to 80 percent of
reproduction was associated with the first two age classes. Two
consecutive years of environmentally induced reproductive
failure (a result of natural climate variations associated with El
Niño–Southern Oscillation [ENSO]; see Chapter 2) caused a
population collapse the species never recovered from.
Sustainable yield requires a detailed understanding of the
population dynamics of the fish species. Recall that the intrinsic
rate of population growth, r, is a function of the age-specific
birthrate and mortality rate (Chapter 9). Unfortunately, the
usual approach to maximum sustained yield more often than not

fails to consider adequately the sex ratio, size and age class
structure, size and age-dependent rates of mortality and
reproduction, and environmental uncertainties—all data that is
difficult to obtain. Adding to the problem is the common-
property nature of the resource; because it belongs to no one, it
belongs to everyone to use as each of us sees fit.
Perhaps the greatest problem with sustainable harvest models is
that they fail to incorporate the most important component of
population exploitation: economics. Once commercial
exploitation begins, the pressure is on to increase it to maintain
the underlying economic investment. Attempts to reduce the
rate of exploitation meet strong opposition. People argue that
reduction will mean unemployment and industrial bankruptcy—
that, in fact, the harvest effort should increase. This argument is
shortsighted. An overused resource will fail, and the livelihoods
it supports will collapse, because in the long run the resource
will be depleted. The presence of abandoned fish processing
plants and rusting fishing fleets support this view. With
conservative, sustainable exploitation, the resource can be
maintained.
Summary
Forms of Predation 14.1
Predation is defined generally as the consumption of all or part
of one living organism by another. Forms of predation include
carnivory, parasitoidism, cannibalism, and herbivory.
Model of Predation 14.2
A mathematical model that links the two populations through
the processes of birth and death can describe interactions
between predator and prey. Predation represents a source of
mortality for the prey population, whereas the reproduction of
the predator population is linked to the consumption of prey.
Population Cycles 14.3
The models of predator–prey interactions predict oscillations of
predator and prey populations, with the predator population
lagging behind that of the prey population.
Mutual Population Regulation 14.4

The results of the models assume mutual regulation of predator
and prey populations. The growth rate of the prey population is
influenced by the per capita consumption of prey by the
predator population. The relationship between the per capita
rate of consumption and the number of prey is referred to as the
predator’s functional response. This increased consumption of
prey results in an increase in predator reproduction referred to
as the predator’s numerical response.
Functional Response 14.5
There are three types of functional responses. In Type I, the
number of prey affected increases linearly. In Type II, the
number of prey affected increases at a decreasing rate toward a
maximum value. The Type II response is a function of
allocation of feeding time by predators between the activities of
searching for prey and handling prey (chasing, capturing,
killing, consuming, etc.). In Type III, the number of prey
consumed increases sigmoidally as the density of prey
increases.
Numerical Response 14.6
A numerical response is the increase of predators with an
increased food supply. Numerical response may involve an
aggregative response: the influx of predators to a food-rich
area. More important, a numerical response involves a change in
the growth rate of a predator population through changes in
fecundity.
Optimal Foraging 14.7
Central to the study of predation is the concept of optimal
foraging. This approach to understanding the foraging behavior
of animals assumes that natural selection favors “efficient”
foragers, that is, individuals that maximize their energy or
nutrient intake per unit of effort. Decisions are based on the
relative profitability of alternative prey types, defined as the
energy gained per unit of handling time. An optimal diet
includes the most efficient size of prey for handling and net
energy return.
Foraging Behavior and Risk of Predation 14.8

Most predators are also prey to other predatory species and thus
face the risk of predation while involved in their routine
activities, such as foraging. If predators are about, it may be to
the forager’s advantage not to visit a most profitable but
predator-prone area and to remain in a less profitable but more
secure part of the habitat.
Coevolution of Predator and Prey 14.9
Prey species evolve characteristics to avoid being caught by
predators. Predators have evolved their own strategies for
overcoming these prey defenses. This process represents a
coevolution of predator and prey in which each functions as an
agent of natural selection on the other.
Predator Defenses 14.10
Chemical defense in animals usually takes the form of
distasteful or toxic secretions that repel, warn, or inhibit would-
be attackers. Cryptic coloration and behavioral patterns enable
prey to escape detection. Warning coloration declares that the
prey is distasteful or disagreeable. Some palatable species
mimic unpalatable species for protection. Armor and aggressive
use of toxins defend some prey. Alarms and distraction displays
help others. Another form of defense is predator satiation
wherein prey species produce many young at once so that
predators can take only a fraction of them. Predator defenses
can be classified as permanent or induced.
Predator Evolution 14.11
Predators have evolved different methods of hunting that
include ambush, stalking, and pursuit. Predators also employ
cryptic coloration for hiding and aggressive mimicry for
imitating the appearance of prey.
Herbivory 14.12
Herbivory is a form of predation. The amount of plant or algal
biomass actually eaten by herbivores varies between
communities. Plants respond to defoliation with a flush of new
growth, which draws down nutrient reserves. Such drawdown
can weaken plants, especially woody ones, making them more
vulnerable to insects and disease. Moderate grazing may

stimulate leaf growth in grasses up to a point. By removing
older leaves less active in photosynthesis, grazing stimulates
the growth of new leaves.
Herbivore Defenses 14.13
Plants affect herbivores by denying them palatable or digestible
food or by producing toxic substances that interfere with growth
and reproduction. Certain specialized herbivores are able to
breach the chemical defenses. They detoxify the secretions,
block their flow, or sequester them in their own tissues as a
defense against predators. Defenses can be either permanent
(constitutive) or induced by damage inflicted by herbivores.
Vegetation–Herbivore–Carnivore Systems 14.14
Plant–herbivore and herbivore–carnivore systems are closely
related. An example of a three-level feeding interaction is the
cycle of vegetation, hares, and their predators. Malnourished
hares fall quickly to predators. Recovery of hares follows
recovery of plants and decline in predators.
Lethal and Nonlethal Influences 14.15
Besides influencing prey population directly through mortality,
predators can cause changes in prey characteristics by inducing
defense responses in prey morphology, physiology, or behavior.
Reduced activity by prey in the presence of predators can
reduce foraging time and food intake, subsequently delaying
growth and development. The net result can be a reduction in
the growth rate of the prey population.
Fisheries Management Ecological Issues & Applications
The harvesting of natural fish populations often leads to
overexploitation and population decline. Management practices
based on sustainable yield attempt to limit harvests to levels at
which natural recruitment (reproduction) offsets mortality
resulting from fishing activities.
CHAPTER 13

13.1 Interspecific Competition Involves Two or More Species
A relationship that affects the populations of two or more
species adversely (– –) is interspecific competition. In
interspecific competition, as in intraspecific competition,
individuals seek a common resource in short supply (see
Chapter 11). But in interspecific competition, the individuals
are of two or more species. Both kinds of competition may take
place simultaneously. In the deciduous forest of eastern North
America, for example, gray squirrels compete among themselves
for acorns during a year when oak trees produce fewer acorns.
At the same time, white-footed mice, white-tailed deer, wild
turkey, and blue jays vie for the same resource. Because of
competition, one or more of these species may broaden the base
of their foraging efforts. Populations of these species may be
forced to turn away from acorns to food that is less in demand.
Like intraspecific competition, interspecific competition takes
two forms: exploitation and interference (see Section 11.3). As
an alternative to this simple dichotomous classification of
competitive interactions, Thomas Schoener of the University of
California–Davis proposed that six types of interactions are
sufficient to account for most instances of interspecific
competition: (1) consumption, (2) preemption, (3) overgrowth,
(4) chemical interaction, (5) territorial, and (6) encounter.
Consumption competition occurs when individuals of one
species inhibit individuals of another by consuming a shared
resource, such as the competition among various animal species
for acorns. Preemptive competition occurs primarily among
sessile organisms, such as barnacles, in which the occupation by
one individual precludes establishment (occupation) by others.
Overgrowth competition occurs when one organism literally
grows over another (with or without physical contact),
inhibiting access to some essential resource. An example of this
interaction is when a taller plant shades those individuals
below, reducing available light (as discussed in Chapter 4,
Section 4.2). In chemical interactions, chemical growth
inhibitors or toxins released by an individual inhibit or kill

other species. Allelopathy in plants, in which chemicals
produced by some plants inhibit germination and establishment
of other species, is an example of this type of species
interaction. Territorial competition results from the behavioral
exclusion of others from a specific space that is defended as a
territory (see Section 11.10). Encounter competition results
when nonterritorial meetings between individuals negatively
affect one or both of the participant species. Various species of
scavengers fighting over the carcass of a dead animal provide
an example of this type of interaction.
13.2 The Combined Dynamics of Two Competing Populations
Can Be Examined Using the Lotka–Volterra Model
In the early 20th century, two mathematicians—the American
Alfred Lotka and the Italian Vittora Volterra—independently
arrived at mathematical expressions to describe the relationship
between two species using the same resource (consumption
competition). Both men began with the logistic equation for
population growth that we developed previously in Chapter 11 :
Species1:dN1/dt=r1N1(1−N1/K1)Species2:dN2/dt=r2N2(1−N2/
K2)Species1:dN1/dt=r1N1(1−N1/K1)Species2:dN2/dt=r2N2(1−
N2/K2)
Next, they both modified the logistic equation for each species
by adding to it a term to account for the competitive effect of
one species on the population growth of the other. For species
1, this term is αN2, where N2 is the population size of species
2, and α is the competition coefficient that quantifies the per
capita effect of species 2 on species 1. Similarly, for species 2,
the term is βN1, where β is the per capita competition
coefficient that quantifies the per capita effect of species 1 on
species 2. The competition coefficients can be thought of as
factors for converting an individual of one species into the
equivalent number of individuals of the competing species,
based on their shared use of the resources that define the
carrying capacities (see Chapter 12, Section 12.2 and Figure
12.3, and Quantifying Ecology 12.1). In resource use, an
individual of species 1 is equal to β individuals of species 2.

Likewise, an individual of species 2 is equivalent to α
individuals of species 1. These terms (α and β), in effect,
convert the population size of the one species into the
equivalent number of individuals of the other. For example,
assume species 1 and species 2 are both grazing herbivores that
feed on the exact same food resources (grasses and other
herbaceous plants). If individuals of species 2 have, on average,
twice the body mass as individuals of species 1 and consume
food resources at twice the rate, with respect to the food
resources, an individual of species 2 is equivalent to two
individuals of species 1 (that is, α = 2.0). Likewise, consuming
food resources at only half the rate as species 2, an individual
of species 1 is equivalent to one-half an individual of species 2
(that is, β = 0.5).
Now we have a pair of equations that consider both intraspecific
and interspecific competition.
Species1:dN1/dt=r1N1(1−(N1+αN2)/K1)Species2:dN2/dt=r2N2(
1−(N2+βN1)/K2) (1) (2)Species1:dN1/dt=r1N1(1−(N1+αN2)/
K1)Species2:dN2/dt=r2N2(1−(N2+βN1)/K2) (1) (2)
As you can see, in the absence of interspecific competition—
either α or N2 = 0 in Equation (1) and β or N1 = 0 in Equation
(2)— the population of each species grows logistically to
equilibrium at K, the respective carrying capacity. In the
presence of competition, however, the picture changes.
For example, the carrying capacity for species 1 is K1, and as
N1 approaches K1, the population growth (dN1/dt) approaches
zero. However, species 2 is also vying for the limited resource
that determines K1, so we must consider the impact of species
2. Because α is the per capita effect of species 2 on species 1,
the total effect of species 2 on species 1 is αN2, and as the
combined population N1 + αN2 approaches K1, the growth rate
of species 1 approaches zero as well. The greater the population
size of the competing species (N2), the greater the reduction in
the growth rate of species 1 is (see discussion in Section 12.2
and Figure 12.3).
The simplest way to examine the possible outcomes of

competition using the Lotka–Volterra equations presented is a
graphical approach in which we first define the zero-growth
isocline for each of the two competing species. The zero-growth
isocline represents the combined values of population size for
species 1 (N1) and species 2 (N2) at which the population
growth rate of the respective species is zero (dN/dt = 0). This
occurs when the combined population sizes are equal to the
carrying capacity of that species (see Figure 12.3). We can
begin by defining the zero-growth isocline for species 1
(Figure 13.1a). The two axes in the graph shown in Figure
13.1a define the population size of species 1 (x-axis, N1) and
species 2 (y-axis, N2). We must now solve for the combined
values of N1 and N2 at which the growth rate of species 1 is
equal to zero (dN1/dt = 0). This occurs when: (1 –
(N1 + αN2)/K1) = 0 or K1 = N1 + αN2 (see Equation 1). In
effect, we are determining the combined values of N1 and N2
that equal the carrying capacity of species 1 (K1). This task is
made simple because K1 = N1 + αN2 represents a line and all
that is necessary to draw the line is to solve for two points. The
two simplest solutions are to solve for the two intercepts (where
the line intersects the two axes). The x-intercept occurs when
N2 = 0, giving us a value of N1 = K1. The y-intercept occurs
when N1 = 0, giving us a value of αN2 = K1, or N2 = K1/a.
Given these two points (values for N1, N2), we can draw the
line defining the zero isocline for species 1 (Figure 13.1a). For
any combined value of N1, N2 along this line, N1 + αN2 = K1
and dN1/dt = 0. For combinations of (N1, N2) that fall below
the line (toward the origin: 0, 0), N1 + αN2 < K1 and the
population of species 1 can continue to grow. An increase in the
population of species 1 is represented by a green horizontal
arrow pointing to the right. The arrow is horizontal because the
x-axis represents the population of species 1. For combinations
of N1 and N2 that fall above the line, N1 + αN2 > K1, the
population growth rate is negative (as represented by the green
horizontal line pointing to the left), and the population size
declines until it reaches the line.

We can take this same approach and define the zero isocline for
species 2 (Figure 13.1b). The x-intercept is N2 = 0 and N1 =
K2/β, and the y-intercept is N2 = K2 and N1 = 0. As with the
zero-growth isocline for species 1, for combinations of N1 and
N2 that fall below the line, N2 + βN1 < K2 and the population
of species 2 can continue to grow. The yellow vertical arrow
pointing up represents an increase in the population of species
2. The arrow is vertical because the x-axis represents the
population of species 2. For combinations of (N1, N2) that fall
above the line, N2 + βN1 > K2, the population growth rate is
negative (yellow vertical arrow pointing down), and the
population size declines until it reaches the line (see Figure
13.1b). We can now combine the two zero-growth isoclines onto
a single graph and examine the combined population dynamics
of the two species for different values of N1 and N2.
13.3 There Are Four Possible Outcomes of Interspecific
Competition
To interpret the combined dynamics of the two competing
species, their isoclines must be drawn on the same x–y graph.
Although there are an infinite number of isoclines that can be
constructed by using different values of K1, K2, α, and β, there
are only four qualitatively different ways in which to plot the
isoclines. These four possible outcomes are shown in Figure
13.2. In the first case (Figure 13.2a), the isocline of species 1 is
parallel to, and lies completely above, the isocline of species 2.
In this case, the isoclines define three areas of the graph. In the
lower left-hand area of the graph (point A), the combined values
of N1 and N2 are below the zero-growth isoclines for both
species, and the populations of both species can increase. The
green horizontal arrow representing species 1 points right,
indicating an increase in the population of species 1, whereas
the orange vertical arrow representing species 2 points up,
indicating an increase in the population of species 2. The next
point representing the combined values of N1 and N2 must
therefore lie somewhere between the two arrows and is
represented by the black arrow pointing away from the origin.

In the upper right-hand corner of the graph, the combined
values of N1 and N2 are above the zero-growth isoclines for
both species. In this case, the populations of both species
decline (black arrow points toward the origin).
In the interior region between the two isoclines, the dynamics of
the two populations diverge. Here (at point C) the combined
values of N1 and N2 are below the isocline for species 1, so its
population increases in size, and the green horizontal arrow
points to the right. However, this region is above the isocline
for species 2, so its population is declining, and the yellow
vertical arrow is pointing down. The black arrow now points
down and toward the right, which takes the populations toward
the carrying capacity of species 1 (K1). Note that this occurs
regardless of where the initial point (N1, N2) lies within this
region. If the isocline of species 1 lies above the isocline for
species 2, species 1 is the more competitive species and species
2 is driven to extinction (N2 = 0).
In the second case (Figure 13.2b), the situation is reversed. The
zero-growth isocline for species 2 lies above the isocline for
species 1, and therefore species 2 “wins” leading to the
extinction of species 1 (N1 = 0). Note that in the interior region
(between the isoclines), the combined values of N1 and N2 are
now below the isocline for species 2 allowing its population to
grow (yellow vertical arrow pointing up), whereas it is above
the isocline for species 1, causing its population to decline
(green horizontal arrow pointing to the left). The result is a
movement of the populations toward the upper left (see black
arrow), the carrying capacity of species 2 (K2).
In the remaining two cases (Figures 13.2c and 13.2d), the
isoclines of the two species cross, dividing the graph into four
regions, but the outcomes of competition for the two cases are
quite different. As with the previous two cases, we determine
the outcomes by plotting the arrows, indicating changes in the
two populations within each of the regions. However, the point
where the two isoclines cross represents an equilibrium point, a
combined value of N1 and N2 for which the growth of both

species 1 and species 2 is zero. At this point, the combined
population sizes of the two species are equal to the carrying
capacities of both species (N1 + αN2 = K1 and N2 + βN1 = K2).
The third case is presented in Figure 13.2c. The region closest
to the origin (point A) is below the isocline of both species, and
therefore the growth of both populations is positive and the
arrows point outward. The upper right-hand region (point B) is
above the isoclines for both species, so both populations decline
and the arrows point inward toward the axes and origin. In the
bottom right-hand region of the graph (point C), we are above
the isocline for species 1, but below the isocline for species 2.
In this region, the population of species 1 declines (green
horizontal arrow points to left), whereas the population of
species 2 increases (yellow vertical arrow points up). As a
result, the combined dynamics (black arrow) point toward the
center of the graph where the two isoclines intersect. The upper
left-hand region of the graph (point D) is above the isocline for
species 2 but below the isocline for species 1. In this region, the
population of species 2 declines, and the population of species 1
increases. Again, the combined dynamics (black arrow) point
toward the center of the graph where the two isoclines intersect.
The fact that the arrows in all four regions of the graph point to
where the two isoclines intersect indicates that this point
(combined values of N1 and N2) represents a “stable
equilibrium.” The equilibrium is stable when no matter what the
combined values of N1 and N2 are, both populations move
toward the equilibrium value.
In the fourth case (Figure 13.2d), the isoclines cross, but in a
different manner than in the previous case (Figure 13.2c).
Again, both populations increase in the region of the graph
closest to the origin (point A). Likewise, both populations
decline in the upper right-hand region (point B). However, the
dynamics differ in the remaining two regions of the graph. In
the lower right-hand region, the combined values of N1 and N2
(point C) are below the isocline for species 1 but above the
isocline for species 2. In this region, the population of species 1

decreases, whereas the population of species 2 continues to
grow. The combined dynamics (black arrow) move away from
the equilibrium point where the two isoclines intersect (point E)
and toward the carrying capacity of species 1 (K1 on x-axis). In
the upper left-hand region of the graph, the combined values of
N1 and N2 (point D) are below the isocline for species 2 but
above the isocline for species 1. In this region of the graph, the
combined dynamics (black arrow) move away from the
equilibrium point where the two isoclines intersect (point E)
and toward the carrying capacity of species 2 (K2 on y-axis).
This case represents an “unstable equilibrium.” If the combined
values of N1 and N2 are displaced from the equilibrium (point
E), the populations move into one of the two regions of the
graph that will eventually lead to one species excluding the
other (driving it to extinction: N = 0). Which of the two species
will “win” is difficult to predict and depends on the initial
population values (N1 and N2) and the growth rates of the
populations (r1 and r2
13.4 Laboratory Experiments Support the Lotka–Volterra Model
The theoretical Lotka–Volterra equations stimulated studies of
competition in the laboratory, where under controlled
conditions an outcome is more easily determined than in the
field. One of the first to study the Lotka–Volterra competition
model experimentally was the Russian biologist G. F. Gause. In
a series of experiments published in the mid-1930s, he
examined competition between two species of
Paramecium,Paramecium aurelia and Paramecium caudatum. P.
aurelia has a higher rate of population growth than P. caudatum
and can tolerate a higher population density. When Gause
introduced both species to one tube containing a fixed amount
of bacterial food, P. caudatum died out (Figure 13.3). In another
experiment, Gause reared the species that was competitively
displaced in the previous experiment, P. caudatum, with another
species, Paramecium bursaria. These two species coexisted
because P. caudatum fed on bacteria suspended in solution,
whereas P. bursaria confined its feeding to bacteria at the

bottom of the tubes. Each species used food unavailable to the
other.
In the 1940s and 1950s, Thomas Park at the University of
Chicago conducted several classic competition experiments with
laboratory populations of flour beetles. He found that the
outcome of competition between Tribolium castaneum and
Tribolium confusum depended on environmental temperature,
humidity, and fluctuations in the total number of eggs, larvae,
pupae, and adults. Often, the outcome of competition was not
determined until many generations had passed.
In a much later study, ecologist David Tilman of the University
of Minnesota grew laboratory populations of two species of
diatoms, Asterionella formosa and Synedra ulna. Both species
require silica for the formation of cell walls. The researchers
monitored population growth and decline as well as the level of
silica in the water. When grown alone in a liquid medium to
which silica was continually added, both species kept silica at a
low level because they used it to form cell walls. However,
when grown together, the use of silica by S. ulna reduced the
concentration to a level below that necessary for A. formosa to
survive and reproduce (Figure 13.4). By reducing resource
availability, S. ulna drove A. formosa to extinction.
13.5 Studies Support the Competitive Exclusion Principle
In three of the four situations predicted by the Lotka–Volterra
equations, one species drives the other to extinction. The results
of the laboratory studies just presented tend to support the
mathematical models. These and other observations have led to
the concept called the competitive exclusion principle, which
states that “complete competitors” cannot coexist. Complete
competitors are two species (non-interbreeding populations)
that live in the same place and have exactly the same ecological
requirements (see concept of fundamental niche in Chapter 12,
Section 12.6). Under this set of conditions, if population A
increases the least bit faster than population B, then A will
eventually outcompete B, leading to its local extinction.
Competitive exclusion, then, invokes more than competition for

a limited resource. The competitive exclusion principle involves
assumptions about the species involved as well as the
environment in which they exist. First, this principle assumes
that the competitors have exactly the same resource
requirements. Second, it assumes that environmental conditions
remain constant. Such conditions rarely exist. The idea of
competitive exclusion, however, has stimulated a more critical
look at competitive relationships in natural situations. How
similar can two species be and still coexist? What ecological
conditions are necessary for coexistence of species that share a
common resource base? The resulting research has identified a
wide variety of factors affecting the outcome of interspecific
competition, including environmental factors that directly
influence a species’ survival, growth, and reproduction but are
not consumable resources (such as temperature or pH), spatial
and temporal variations in resource availability, competition for
multiple limiting resources, and resource partitioning. In the
following sections, we examine each topic and consider how it
functions to influence the nature of competition.
13.6 Competition Is Influenced by Nonresource Factors
Interspecific competition involves individuals of two or more
species vying for the same limited resource. However, features
of the environment other than resources also directly influence
the growth and reproduction of species (see Chapters 6 and 7)
and therefore can influence the outcome of competitive
interactions. For example, environmental factors such as
temperature, soil or water pH, relative humidity, and salinity
directly influence physiological processes related to growth and
reproduction, but they are not consumable resources that species
compete over.
For example, in a series of field and laboratory experiments,
Yoshinori Taniguchi and colleagues at the University of
Wyoming examined the influence of water temperature on the
relative competitive ability of three fish species that show
longitudinal replacement in Rocky Mountain streams. Brook
trout (Salvelinus fontinalis) are most abundant at high

elevations, brown trout (Salmo trutta) at middle elevations, and
creek chub (Semotilus atromaculatus) at lower elevations.
Previous studies have shown that interference competition for
foraging sites is an important factor influencing the relative
success of individuals at sites where the species co-occur.
Based on the distribution of these three species along elevation
gradients in the Rocky Mountain streams and differences in
physiological performance with respect to temperature, the
researchers hypothesized that the brook trout would be
competitively superior at cold water temperatures, brown trout
at moderate water temperatures, and creek chub would be
competitively superior at warmer water temperatures. To test
this hypothesis, Taniguchi and his colleagues used experimental
streams to examine competitive interactions at seven different
water temperatures: 3, 6, 10, 22, 22, 24, and 26°C.
Prior to each test, fish were thermally acclimated by increasing
or decreasing the temperature by 1°C per day until the test
temperature was reached (see Section 7.9 for discussion of
thermal acclimation). For each test, individuals of each species
were matched for size (<10%) and placed in the experimental
stream together. Aggressive interactions and food intake were
monitored. Competitive superiority was based on which species
consumed the most food items because food intake is considered
a limiting factor for these drift-feeding, stream fishes.
Patterns of food consumption clearly show changes in the
relative competitive abilities of the three fish species across the
gradient of water temperatures (Figure 13.5). At 3°C, brook
trout exhibited the highest rate of food consumption, although
differences between the two trout species were minimal below
20°C, and both trout species consumed significantly more food
than creek chub. However, as temperature increased, food
consumption by creek chub increased. At 24°C, food intake by
brook trout dropped to zero, whereas intake rate of brown trout
still exceeded that of creek chub. At 26°C, the rate of food
intake reversed for the two species and food intake by creek
chub exceeded that of brown trout. In an additional series of

experiments, the researchers were able to establish that the
observed patterns of food intake during the competition trials
were a result of differences in competitive ability and no
changes in appetite because of water temperature.
The transition in competitive ability from 24 to 26°C in the
laboratory experiments are in agreement with the transition in
dominance from trout species to creek chub at a similar
temperature range in the field. The results of Taniguchi and his
colleagues provide a clear example of temperature mediation of
competitive interactions. The relative competitive abilities of
the three fish species for limiting food resources are directly
influenced by abiotic conditions, that is, water temperature.
A similar case of competitive ability being influenced by
nonresource factors is illustrated in the work of Susan Warner
of Pennsylvania State University. Warner and her colleagues
examined the effect of water pH (acidity) on interspecific
competition between two species of tadpoles (Hyla gratiosa and
Hyla femoralis). The two species overlap broadly in their
geographic distribution, yet differ in their responses to water
acidity. The researchers conducted experiments using two levels
of water pH (4.5 and 6.0) and varying levels of population
densities to examine the interactions of pH and population
density on both intra- and interspecific competition. The results
of the experiments indicated that interspecific interactions were
minimal at low water pH (4.5); however, at higher water pH
(6.0), interspecific competition from H. fermoralis caused
decreased survival and an increased larval period for H.
gratiosa. The latter resulted in decreased size at metamorphosis
for H. gratiosa individuals.
13.7 Temporal Variation in the Environment Influences
Competitive Interactions
When one species is more efficient at exploiting a shared,
limiting resource, it may be able to exclude the other species
(see Section 13.2). However, when environmental conditions
vary through time, the competitive advantages may also change.
As a result, no one species reaches sufficient density to displace

its competitors. In this manner, environmental variation allows
competitors to coexist whereas under constant conditions, one
would exclude the other.
The work of Peter Dye of the South African Forestry Research
Institute provides an example of shifting competitive ability
resulting from temporal variation in resource availability in the
grasslands of southern Africa. He examined annual variations in
the relative abundance of grass species occupying a savanna
community in southwest Zimbabwe. From 1971 to 1981, the
dominant grass species shifted from Urochloa mosambicensis to
Heteropogon contortus (Figure 13.6a). This observed shift in
dominance was a result of yearly variations in rainfall (Figure
13.6b). Rainfall during the 1971–1972 and 1972–1973 rainy
seasons was much lower than average. U. mosambicensis can
maintain higher rates of survival and growth under dry
conditions than can H. contortus, making it a better competitor
under conditions of low rainfall. With the return to higher
rainfall during the remainder of the decade, H. contortus
became the dominant grass species. Annual rainfall in this
semiarid region of southern Africa is highly variable, and
fluctuations in species composition such as those shown in
Figure 13.6 are a common feature of the community.
Peter Adler (Utah State University) and colleagues observed a
similar pattern for a prairie grassland site at Hays, Kansas, in
the Great Plains region of North America. Adler and colleagues
examined the role of interannual climate variability on the
relative abundance of prairie grasses over a period of 30 years
(1937–1968). The researchers found that year-to-year variations
in climate correlated with interannual variations in species
performance. The year-to-year variations in the relative
competitive abilities of the species functioned to buffer species
from competitive exclusion.
Besides shifting the relative competitive abilities of species,
variation in climate can function as a density-independent
limitation on population growth (see Section 11.13). Periods of
drought or extreme temperatures may depress populations below

carrying capacity. If these events are frequent enough relative
to the time required for the population to recover (approach
carrying capacity), resources may be sufficiently abundant
during the intervening periods to reduce or even eliminate
competition.
13.8 Competition Occurs for Multiple Resources
In many cases, competition between species involves multiple
resources and competition for one resource often influences an
organism’s ability to access other resources. One such example
is the practice of interspecific territoriality, where competition
for space influences access to food and nesting sites (see
Section 11.10).
A wide variety of bird species in the temperate and tropical
regions exhibit interspecific territoriality. Most often, this
practice involves the defense of territories against closely
related species, such as the gray (Empidonax wrightii) and
dusky (Empidonax oberholseri) flycatchers of the western
United States. Some bird species, however, defend their
territories against a much broader range of potential
competitors. For example, the acorn woodpecker (Melanerpes
formicivorus) defends territories against jays and squirrels as
well as other species of woodpeckers. Strong interspecific
territorial disputes likewise occur among brightly colored coral
reef fish.
Competition among plants provides many examples of how
competition for one resource can influence an individual’s
ability to exploit other essential resources, leading to a
combined effect on growth and survival. R. H. Groves and J. D.
Williams examined competition between populations of
subterranean clover (Trifolium subterraneum) and skeletonweed
(Chondrilla juncea) in a series of greenhouse experiments.
Plants were grown both in monocultures (single populations)
and in mixtures (two populations combined). The investigators
used a unique experimental design to determine the independent
effects of competition for aboveground (light) and belowground
(water and nutrients) resources (see Section 11.11). In the

monocultures, plants were grown in pots, allowing for the
canopies (leaves) and roots to intermingle. In the two-species
mixtures (Figure 13.7), three different approaches were used:
(1) plants of both species were grown in the same pot, allowing
their canopies and roots to intermingle, (2) plants of both
species were grown in the same pot allowing their roots to
overlap, but with their canopies separated, (3) the plant species
were grown in separate pots with their canopies intermingled,
but not allowing the roots to overlap.
Clover was not significantly affected by the presence of
skeletonweed; however, the skeletonweed was affected in all
three treatments where the two populations were grown
together. When the roots were allowed to intermingle, the
biomass (dry weight of the plant population) of skeletonweed
was reduced by 35 percent compared to the biomass of the
species when grown as a monoculture. The biomass was reduced
by 53 percent when the canopies were intermingled. When both
the canopies and roots were intermingled, the biomass was
reduced by 69 percent, indicating an interaction in the
competition for aboveground and belowground resources.
Clover plants were the superior competitors for both
aboveground and belowground resources, resulting in a
combined effect of competition for these two resources (see
Sections 11.11 and 18.4). This type of interaction has been seen
in a variety of laboratory and field experiments. The species
with the faster growth rate grows taller than the slower-growing
species, reducing its available light, growth, and demand for
belowground resources. The result is increased access to
resources and further growth by the superior competitor.
In a series of field studies, James Cahill of the University of
Alberta (Canada) examined the interactions between
competition for above- and belowground resources in an old
field grassland community in Pennsylvania. With an
experimental design in the field similar to that used by Groves
and Williams in the greenhouse, Cahill grew individual plants
with varying degrees of interaction with the roots of

neighboring plants through the use of root exclusion tubes made
of PVC pipe. He planted the target plant inside an exclusion
tube that was placed vertically into the soil to separate roots of
the target plant from the roots of other individuals in the
population that naturally surround it. He controlled the degree
of belowground competition by drilling varying numbers of
holes in the PVC pipe that allowed access to the soil volume
from neighboring plants (see Section 11.11 and Figure 11.20 for
further description of method). Cahill varied the level of
aboveground competition by tying back the aboveground
neighboring vegetation. In total, he created 16 combinations of
varying intensities of above- and belowground interaction with
neighboring plants. This experimental design allowed Cahill to
compare the response of individuals exposed to varying
combinations of above- and belowground competition to control
plants isolated from neighbors. The results of his experiments
show a clear pattern of interaction between above- and
belowground competition. In general, increased competition for
belowground resources functions to reduce growth rates and
plant stature, the result of which is reduced competitive ability
for light (aboveground resource).
13.9 Relative Competitive Abilities Change along
Environmental Gradients
As environmental conditions change, so do the relative
competitive abilities of species. Shifts in competitive ability
can result either from changes in the carrying capacities of
species (values of K; see Quantifying Ecology 13.1) related to a
changing resource base or from changes in the physical
environment that interact with resource availability.
Many laboratory and field studies have examined the outcomes
of competition among plant species across experimental
gradients of resource availability. Mike Austin and colleagues
at the Commonwealth Scientific and Industrial Research
Organization (CSIRO) research laboratory in Canberra,
Australia, have conducted several greenhouse studies to explore
the changing nature of interspecific competition among plant

species across experimental gradients of nutrient availability. In
one such experiment, the researchers examined the response of
six species of thistle along a gradient of nutrient availability
(application of nutrient solution). Plants were grown both in
monoculture (single species) and mixture (all six species) under
11 different nutrient treatments, ranging from 1/64 to 16 times
the recommended concentration of standard greenhouse nutrient
solution. After 14 weeks, the plants were harvested, and their
dry weights were determined. Responses of the six species
along the nutrient gradient for monoculture and mixture
experiments are shown in Figure 13.8.
1. Q1. Which of the three species of thistle included in the
graph had the highest biomass production under the 1/64
nutrient treatment? What does this imply about this species’
competitive ability under low nutrient availability relative to
other thistle species?
2. Q2. Using relative biomass production at each treatment level
as an indicator of competitive ability, which thistle species is
the superior competitor under the standard concentration of
nutrient solution (1.0)?
3. Q3. At which nutrient level is the relative biomass of the
three species most similar (smallest difference in the biomass of
the three species)?
Two important results emerged from the experiment. First, when
grown in mixture, the response of each species along the
resource gradient differed from the pattern observed when
grown in isolation—interspecific competition directly
influenced the patterns of growth for each species. Second, the
relative competitive abilities of the species changed along the
nutrient gradient. This result was easily seen when the response
of each species in the mixed-species experiments was expressed
on a relative basis. The relative response of each species across
the gradient was calculated by dividing the biomass (dry
weight) value for each species at each nutrient level by the
value of the species that achieved the highest biomass at that

level. The relative performance of each species at each nutrient
level then ranged from 0 to 1.0. Relative responses of the three
dominant thistle species along the nutrient gradient are shown
in Figure 13.9. Note that Carthamus lanatus was the superior
competitor under low nutrient concentrations, Carduus
pycnocephalus at intermediate values, and Silybum marianum at
the highest nutrient concentrations.
In a series of field experiments, Richard Flynn and colleagues at
the University of KwaZulu-Natal (South Africa) examined
trade-offs in competitive ability among five perennial C4 grass
species at different levels of soil fertility and disturbance. Soil
fertility treatments were established through the application of
different levels of fertilizer, whereas varying levels of clipping
were used to simulate disturbance resulting from grazing by
herbivores. Individuals of the five grass species were grown in
both monoculture and mixtures at each treatment level. The
results of their experiments show a pattern of changing relative
competitive abilities of the species along the gradients of soil
fertility and disturbance (Figure 13.10). Moreover, in some of
the results there were clear interactions between soil fertility
and disturbance on competitive outcomes.
Field studies designed to examine the influence of interspecific
competition across an environmental gradient often reveal that
multiple environmental factors interact to influence the
response of organisms across the landscape. In New England
salt marshes, the boundary between frequently flooded low
marsh habitats and less frequently flooded high marsh habitats
is characterized by striking plant zonation in which
monocultures of the cordgrass Spartina alterniflora (smooth
cordgrass) dominate low marsh habitats, whereas the high marsh
habitat is generally dominated by Spartina patens (Figure
13.11a). The gradient from high to low marsh is characterized
by changes in nutrient availability as well as increasing
physical stress relating to waterlogging, salinity, and oxygen
availability in the soil and sediments. In a series of field
experiments, ecologist Mark Bertness of Brown University

found that S. patens individuals transplanted into the low marsh
zone (dominated by S. alterniflora) were severely stunted with
or without S. alterniflora neighbors, that is, with or without
competition (Figure 13.11b). In contrast, S. alterniflora
transplants grew vigorously in the high marsh (zone dominated
by S. patens) when neighbors were removed (without
competition), but were excluded from the high marsh when S.
patens was present, that is, with competition (Figure 13.11c).
Bertness also observed that S. alterniflora rapidly invaded the
high marsh habitats in the absence of S. patens. He concluded
that S. alterniflora dominates the physically stressful low marsh
habitats because of its ability to persist in anoxic (low oxygen)
soils, but it is competitively excluded from the high marsh by S.
patens.S. patens is limited to high marsh habitats as a result of
its inability to tolerate the harsh physical conditions in
frequently flooded low marsh habitats.
Chipmunks furnish a striking example of the interaction of
competition and tolerance to physical stress in determining
species distribution along an environmental gradient. In this
case, physiological tolerance, aggressive behavior, and
restriction to habitats in which one organism has competitive
advantage all play a part. On the eastern slope of the Sierra
Nevada live four species of chipmunks: the alpine chipmunk
(Tamias alpinus), the lodgepole chipmunk (Tamias speciosus),
the yellow-pine chipmunk (Tamias amoenus), and the least
chipmunk (Tamias minimus). Each of these species has strongly
overlapping food requirements, and each species occupies a
different altitudinal zone (Figure 13.12).
The line of contact between chipmunks is determined partly by
interspecific aggression. Aggressive behavior by the dominant
yellow-pine chipmunk determines the upper range of the least
chipmunk. Although the least chipmunk can occupy a full range
of habitats from sagebrush desert to alpine fields, it is restricted
in the Sierra Nevada to sagebrush habitat. Physiologically, it is
more capable of handling heat stress than the others, enabling it
to inhabit extremely hot, dry sagebrush. In a series of field

experiments, ecologist Mark Chappell of Stanford University
found that when the yellow-pine chipmunk is removed from its
habitat, the least chipmunk moves into vacated open pine
woods. However, if the least chipmunk is removed from the
sagebrush habitat, the yellow-pine chipmunk does not invade
the habitat. The aggressive behavior of the lodgepole chipmunk
in turn determines the upper limit of the yellow-pine chipmunk.
The lodgepole chipmunk is restricted to shaded forest habitat
because it is vulnerable to heat stress. Most aggressive of the
four, the lodgepole chipmunk also may limit the downslope
range of the alpine chipmunk. Thus, the range of each chipmunk
is determined both by aggressive exclusion and by its ability to
survive and reproduce in a habitat hostile to the other species.
Quantifying Ecology 13.1 Competition under Changing
Environmental Conditions: Application of the Lotka–Volterra
Model
Under any set of environmental conditions, the outcome of
interspecific competition reflects the relative abilities of the
species involved to gain access and acquire the essential
resources required for survival, growth, and reproduction. As
we have seen in the analysis of interspecific competition using
the Lotka–Volterra equations, two factors interact to influence
the outcome of competition—the competition coefficients (α
and β), and the carrying capacities of the species involved (K1
and K2). The competition coefficients represent the per capita
effect of an individual of one species on the other. These values
will be a function of both the overlap in diets and the rates of
resource uptake of the two species. These values, therefore,
reflect characteristics of the species. In contrast, the carrying
capacities are a function of the resource base (availability) for
each species in the prevailing environment. Changes in
environmental conditions that influence resource availability,
therefore, influence the relative carrying capacities of the
species and can directly influence the nature of competition.
Consider, for example, two species (species 1 and 2) that draw
on the same limiting food resource: seeds. The diets of the two

species are shown in Figure 1a. Note that the overlap in diet of
the two species is symmetric. If the rate of food intake (seeds
eaten per unit time) is the same, we can assume that the
competition coefficients are the same. For this example, let us
assume a value of 0.5 for both α and β.
Now let’s assume that the size distribution of seeds and their
abundance vary as a function of environmental conditions. For
example, in Figure 1b the average seed size increases from
environment A to B and C. As the size distribution of seeds
changes, so will the carrying capacity (K) for each species. Now
assume that the carrying capacities of the two species vary as
shown in the following table.
13.10 Interspecific Competition Influences the Niche of a
Species
Previously, we defined the ecological niche of a species as the
range of physical and chemical conditions under which it can
persist (survive and reproduce) and the array of essential
resources it uses and drew the distinction between the concepts
of fundamental and realized niche (Chapter 12, Section 12.6).
The fundamental niche is the ecological niche in the absence of
interactions with other species, whereas the realized niche is the
portion of the fundamental niche that a species actually exploits
as a result of interactions with other species. As preceding
examples have illustrated, competition may force species to
restrict their use of space, range of foods, or other resource-
oriented activities. As a result, species do not always occupy
that part of their fundamental niche where conditions yield the
highest growth rate, reproductive rate, or fitness. The work of
Jessica Gurevitch of the University of New York–Stony Brook
illustrates this point well. Gurevitch examined the role of
interspecific competition on the local distribution of Stipa
neomexicana, a C3 perennial grass found in the semiarid
grassland communities of southeastern Arizona. Stipa is found
only on the dry ridge crests where grass cover is low, rather
than in moister, low-lying areas below the ridge crests where
grass cover is greater. In a series of experiments, Gurevitch

removed neighboring plants from individual Stipa plants in
ridge-crest, midslope, and lower-slope habitats. She compared
the survival, growth, and reproduction of these plants with
control individuals (whose neighboring plants were not
removed). Her results clearly show that Stipa has a higher
growth rate, produces more flowers per plant, and has higher
rates of seedling survival in midslope and lower-slope habitats
(Figure 13.13). But its population density in these habitats is
limited by competition with more successful grass species.
Thus, Stipa distribution (or realized niche) is limited to
suboptimal habitats because of interspecific competition.
1. Q1. How does the influence of interspecific competition on
seedling survival of Stipa differ between the ridge-crest and
lower-slope habitats?
2. Q2. Experiment results show that Stipa individuals can
effectively grow at the lower slope even under conditions of
interspecific competition, as indicated by values of mean basal
area in part (b). Based on the results in Figure 13.13, what
part(s) of the Stipa life cycle are most heavily influenced by
interspecies competition, and how would these limitations affect
distribution of the species on the landscape?
Much of the evidence for competition comes from studies, such
as the one just presented, demonstrating the contraction of a
fundamental niche in the presence of a competitor. Conversely,
when a species’ niche expands in response to the removal of a
competitor, the result is termed competitive release.
Competitive release may occur when a species invades an island
that is free of potential competitors, moves into habitats it never
occupied on a mainland, and becomes more abundant. Such
expansion may also follow when a competing species is
removed from a community, allowing remaining species to
move into microhabitats they previously could not occupy. Such
was the case with the distribution of cattails along the gradient
of water depth discussed previously, where in the absence of
competition from Typha latifoli, the distribution of Typha

angustifolia expanded to areas above the shoreline (expressed as
negative values of water depth; see Figure 12.13).
An example of competitive release in a lake ecosystem is
presented by Daniel Bolnick and colleagues at the University of
Texas. Bolnick and his colleagues tested for short-term changes
in the feeding niche of the three-spine stickleback (Gasterosteus
aculeatus) after experimentally manipulating the presence or
absence of two interspecific competitors: juvenile cut-throat
trout (Oncorhynchus clarki) and prickly sculpin (Cottus asper).
Direct examination of stomach contents of sculpin and trout
reveals overlap with stickleback diets. Sculpin are exclusively
benthic feeders, whereas juvenile trout feed at the surface and
in the water column. In contrast, stickleback feed in both
microhabitats. The experiment consisted of 20 experimental
enclosures (made of netting) in Blackwater Lake on northern
Vancouver Island, British Columbia. Five replicate blocks of
four enclosures each were distributed along the shoreline of the
lake. Sticklebacks collected from similar habitats nearby were
placed in the enclosures. The enclosures in each of the blocks
were assigned to one of four treatments: (1) competition with
sculpin and trout present, (2) release from sculpin with trout
present, (3) release from trout with sculpin present, and (4) total
release with no competitors. The experimental treatments were
left undisturbed for 15 days, after which all sticklebacks were
removed, and the researchers identified (to the lowest feasible
taxonomic level) and counted prey in the stomach of each
stickleback. The diversity of prey species in the diet of the
sticklebacks in each treatment was used as a measure of niche
breadth. Results of the experiment reveal no significant change
in the niche breadth (diversity of prey consumed) for the
stickleback population when released from competition from
sculpin. When released from competition from juvenile cut-
throat trout, however, the researchers observed a significant
expansion of niche breadth for the stickleback population
(Figure 13.14).
13.11 Coexistence of Species Often Involves Partitioning

Available Resources
All terrestrial plants require light, water, and essential nutrients
such as nitrogen and phosphorus. Consequently, competition
between various co-occurring species is common. The same is
true for the variety of insect-feeding bird species inhabiting the
canopy of a forest, large mammalian herbivores feeding on
grasslands, and predatory fish species that make the coral reef
their home. How is it that these diverse arrays of potential
competitors can coexist in the same community? The
competitive exclusion principle introduced in Section 13.5
suggests that if two species have identical resource
requirements, then one species will eventually displace the
other. But how different do two species have to be in their use
of resources before competitive exclusion does not occur (or
conversely, how similar can two species be in their resource
requirements and still coexist)?
We have seen that the coexistence of competitors is associated
with some degree of “niche differentiation”—differences in the
range of resources used or environmental tolerances—in the
species’ fundamental niches. Observations of similar species
sharing the same habitat suggest that they coexist by
partitioning available resources. Animals use different kinds
and sizes of food, feed at different times, or forage in different
areas. Plants require different proportions of nutrients or have
different tolerances for light and shade. Each species exploits a
portion of the resources unavailable to others, resulting in
differences among co-occurring species that would not be
expected purely as a result of chance.
Field studies provide many reports of apparent resource
partitioning. One example involves three species of annual
plants growing together on prairie soil abandoned one year after
plowing. Each plant exploits a different part of the soil resource
(Figure 13.15). Bristly foxtail (Setaria faberii) has a fibrous,
shallow root system that draws on a variable supply of moisture.
It recovers rapidly from drought, takes up water rapidly after a
rain, and carries on a high rate of photosynthesis even when

partially wilted. Indian mallow (Abutilon theophrasti) has a
sparse, branched taproot extending to intermediate depths,
where moisture is adequate during the early part of the growing
season but is less available later on. The plant is able to carry
on photosynthesis at low water availability (Section 6.10). The
third species, smartweed (Polygonum pensylvanicum), has a
taproot that is moderately branched in the upper soil layer and
develops mostly below the rooting zone of other species, where
it has a continuous supply of moisture.
Apparent resource partitioning is also common among related
animal species that share the same habitat and draw on a similar
resource base. Tamar Dayan, at Tel Aviv University, examined
possible resource partitioning in a group of coexisting species
of wild cats inhabiting the Middle East. Dayan and colleagues
examined differences among species in the size of canine teeth,
which are crucial to wild cats in capturing and killing their
prey. For these cats, there is a general relationship between the
size of canine and the prey species selected. Dayan found clear
evidence of systematic differences in the size of the canine
teeth, not only between male and female individuals within each
of the species (sexual dimorphism) but also among the three
coexisting cat species (Figure 13.16; see also Chapter 10). The
pattern observed suggests an exceptional regularity in the
spacing of species along the axis defined by the average size of
canine teeth (x-axis in Figure 13.16). Dayan and colleagues
hypothesize that intraspecific and interspecific competition for
food has resulted in natural selection favoring the observed
differences, thereby reducing the overlap in the types and sizes
of prey that are taken.
The patterns of resource partitioning discussed previously are a
direct result of differences among co-occurring species in
specific physiological, morphological, or behavioral adaptations
that allow individuals access to essential resources while at the
same time function to reduce competition (see Chapter 5).
Because the adaptations function to reduce competition, they
are often regarded as a product of coevolutionary forces (see

Chapter 12, Sections 12.3 and Section 12.6 for discussion and
example of coevolution driven by competition). Although
patterns of resource partitioning observed in nature are
consistent with the hypothesis of phenotypic divergence arising
from coevolution between competing species, it is difficult to
prove that competition functioned as the agent of natural
selection that resulted in the observed differences in resource
use (observed differences in fundamental niches of the species).
Differences among species may relate to adaptation for the
ability to exploit a certain environment or range of resources
independent of competition. Differences among species have
evolved over a long period of time, and we have limited or no
information about resources and potential competitors that may
have influenced natural selection. This issue led Joseph
Connell, an ecologist at the University of California–Santa
Barbara, to refer to the hypothesis of resource portioning as a
product of coevolution between competing species as the
“ghosts of competition past.” Unable to directly observe the
role of past competition on the evolution of characteristics,
some of the strongest evidence supporting the role of
“competition past” comes from studies examining differences in
the characteristics of subpopulations of a species that face
different competitive environments. A good example is the work
of Peter Grant and Rosemary Grant, of Princeton University,
involving two Darwin’s finches of the Galápagos Islands. The
Grants studied the medium ground finch (Geospiza fortis) and
the small ground finch (Geospiza fuliginosa), both of which
feed on an overlapping array of seed sizes—for further
discussion and illustrations, see Section 5.9. On the large island
of Santa Cruz, where the two species of finch coexist, the
distribution of beak sizes (phenotypes) of the two species does
not overlap. Average beak size is significantly larger for G.
fortis than for the smaller G. fuliginosa (Figure 13.17a). On the
adjacent—and much smaller—islands of Los Hermanos and
Daphne Major, the two species do not coexist, and the
distributions of beak sizes for the two species are distinctively

different from the patterns observed on Santa Cruz. The medium
ground finch is allopatric (lives separately) on the island of
Daphne Major, and the small ground finch is allopatric on Los
Hermanos. Populations of each species on these two islands
possess intermediate and overlapping distributions of beak sizes
(Figures 13.17b and 13.17c). These patterns suggest that on
islands where the two species coexist, competition for food
results in natural selection favoring medium ground finch
individuals with a large beak size that can effectively exploit
larger seeds while also favoring small ground finch individuals
that feed on smaller seeds. The outcome of this competition was
a shift in feeding niches. When the shift involves features of the
species’ morphology, behavior, or physiology, it is referred to
as character displacement.
The preceding example suggests that the competing species on
the island of Santa Cruz exhibit character displacement as a
result of coevolutionary forces—that is, divergence in
phenotypic traits relating to the exploitation of a shared and
limited resource. However, until recently, the process of
character displacement had never been documented by direct
observational data. The first direct evidence of character
displacement is provided by the work of Peter and Rosemary
Grant on the population of G. fortis inhabiting the small island
of Daphne Major.
Before 1982, G. fortis (medium ground finch) was the only
species of ground finch inhabiting the island of Daphne Major.
The situation changed in 1982 when a new competitor species
emigrated from the larger adjacent islands—the large ground
finch, Geospiza magnirostris (see Section 5.9 and Figure 5.20).
G. magnirostris is a potential competitor on the island as a
result of diet overlap with G. fortis.G. magnirostris feeds
primarily on seeds of the herbaceous forb, Jamaican feverplant
(Tribulus cistoides). The seeds are contained within a hard seed
coat and exposed when a finch cracks or tears away the woody
outer coating. Large-beaked members of the G. fortis population
also feed on these seeds; in fact, during the 1976–1977 drought,

the survival of the population depended on this seed resource
(see Section 5.6 for a discussion of natural selection in this
population).
Initially, the population of G. magnirostris on Daphne Major
was too small in relation to the food supply to have anything
but a small competitive effect on G. fortis. From 1982 to 2003,
however, the population increased. Then little rain fell on the
island during 2003 and 2004, and populations of both finch
species declined dramatically as a result of declining food
resources. During this period, G. magnirostris depleted the
supply of large seeds from the Jamaican feverplant, causing the
G. fortis population to depend on the smaller seed resources on
the island. The result of this shift in resource availability
because of competition from G. magnirostris was that during
2004 and 2005, G. fortis experienced strong directional
selection against individuals with large beaks. The resulting
decrease in the average beak size of the G. fortis population
provides a clear example of the coevolutionary process of
character displacement.
13.12 Competition Is a Complex Interaction Involving Biotic
and Abiotic Factors
Demonstrating interspecific competition in laboratory “bottles”
or the greenhouse is one thing; demonstrating competition under
natural conditions in the field is another. In the field,
researchers (1) have little control over the environment,
(2) have difficulty knowing whether the populations are at or
below carrying capacity, and (3) lack full knowledge of the life
history requirements or the subtle differences between the
species.
In the previous sections, we reviewed an array of studies
examining the role of competition in the field. Perhaps the most
common are removal experiments, in which one of the potential
competitors is removed and the response of the remaining
species is monitored. These experiments might appear
straightforward, yielding clear evidence of competitive
influences. But removing individuals may have direct and

indirect effects on the environment that are not intended or
understood by the investigators and that can influence the
response of the remaining species. For example, removing
(neighboring) plants from a location may increase light reaching
the soil surface, soil temperatures, and evaporation. The result
may be reduced soil moisture and increased rates of
decomposition, influencing the abundance of belowground
resources. These sometimes “hidden treatment effects” can
hinder the interpretation of experimental results.
As we have seen in previous sections, competition is a complex
interaction that seldom involves the interaction between two
species for a single limiting resource. Interaction between
species involves a variety of environmental factors that directly
influence survival, growth, and reproduction; these factors vary
in both time and space. The outcome of competition between
two species for a specific resource under one set of
environmental conditions (temperature, salinity, pH, etc.) may
differ markedly from the outcome under a different set of
environmental conditions. As we shall see in the following
chapters, competition is only one of many interactions occurring
between species—interactions that ultimately influence
population dynamics and community structure.
Ecological Issues & Applications Is Range Expansion of Coyote
a Result of Competitive Release from Wolves?
Before European settlement, two species of wild dog (genus
Canis) were among the most abundant large carnivores
occupying the North American continent. The gray wolf, Canis
lupus, once ranged from the Atlantic to the Pacific coast and
from Alaska to northern Mexico (Figure 13.18). It occurred in
virtually all North American habitats (grasslands, eastern
deciduous forest, northern conifer forest, southwest desert,
etc.). In contrast, the coyote (Canis latrans) had a much more
restricted distribution to the prairie grassland and desert
habitats of the Great Plains and desert region of the southwest
and Mexico (Figure 13.19). Since European settlement of the
continent, however, the fate of these two species has taken

different paths.
As early as 1630, the Massachusetts Bay Colony paid an
average month’s salary for any wolf that was killed. Bounties
like this continued until the last wolf in the Northeast was
killed around 1897. The fate of the wolf population in other
areas of its range was similar. Settlers moving westward
depleted the populations of bison, deer, elk, and moose on
which the wolves preyed. Wolves then turned to attacking sheep
and cattle, and to protect livestock, ranchers and government
agencies began an eradication campaign. Bounty programs
initiated in the 19th century continued as late as 1965. Wolves
were trapped, shot, dug from their dens, and hunted with dogs.
Poisoned animal carcasses were left out for wolves, a practice
that also killed eagles, ravens, foxes, bears, and other animals
that fed on the tainted carrion. By the time wolves were
protected by the Endangered Species Act of 1973, only a few
hundred remained in extreme northeastern Minnesota and a
small number on Isle Royale, Michigan.
In contrast to the gray wolf, the coyote did not originally occur
in eastern North America, and with the westward expansion of
settlement into the Great Plains, the coyote was perceived as
less of a threat to farmers and ranchers. By the turn of the 20th
century, it began to take advantage of newly open habitat that
agriculture and logging had created, and its distribution
expanded eastward. There were two main waves of colonization,
northern and southern (Figure 13.19). The northern wave
occurred first—coyote were reported in Michigan in about 1900,
in southern Ontario by 1919, and in northern New York in the
late 1930s. Most of the southeast was not colonized until the
1960s. Whereas the gray wolf population has been virtually
eliminated in the continental United States, the range of the
coyote has expanded to cover most of the areas once occupied
by wolves, and coyote now occupy virtually every habitat in
eastern North America (compare Figures 13.18 and 13.19) from
forests, wooded areas, grassland, and agricultural land to
suburban areas.

The concurrent expansion of the coyote with the decline of the
wolf population in North America has caused ecologists to
question whether the two occurrences are linked in some way.
In North American ecosystems where gray wolves occur,
interactions with other large carnivores are common, with
competition being most intense with species having a similar
ecology. Interference competition (see Section 13.1) occurs
between the wolves and coyotes, with wolves limiting coyote
access to resources by direct aggression. Field studies in
regions where wolves and coyotes overlap indicate that coyotes
are excluded from wolf territories and that wolves will go out of
their way to kill coyotes. One of the leading hypotheses put
forward to explain the dramatic range expansion of the coyote is
that the eradication of the gray wolf from its former range may
have reduced the competitive pressures limiting coyotes to their
former range: range expansion is a result of “competitive
release” (see Section 13.10). Now as a result of recent
conservation efforts, ecologists are able to test this hypothesis
directly.
Thanks to conservation efforts, the gray wolf is beginning to
make a comeback. The wolf’s comeback within the United
States is as a result of its listing under the Endangered Species
Act, which provided protection from unregulated killing and
resulted in increased scientific research, along with
reintroduction and management programs. As of 2013 about
2200 wolves live in Minnesota, 8 on Lake Superior’s Isle
Royale, about 650 in Michigan’s Upper Peninsula, and at least
800 in Wisconsin. In the northern Rocky Mountains, the U.S.
Fish and Wildlife Service reintroduced gray wolves into
Yellowstone National Park and U.S. Forest Service lands in
central Idaho in 1995 and 1996. The reintroduction was
successful, and as of 2013 there were at least 1650 wolves in
the northern Rocky Mountains of Montana, Idaho, and
Wyoming. These reintroductions of wolves into areas now
occupied by coyotes have enabled ecologists to directly examine
the role of competition on the populations of the two carnivores

and test the hypothesis that the range expansion of the coyote in
the United States is in part the result of competitive release
from wolves.
Kim Berger and Eric Gese of Utah State University used data
collected on wolf and coyote distribution and abundance to test
the hypothesis that interference competition with wolves limits
the distribution and abundance of coyotes in two regions of the
Northern Rocky Mountains in which wolves have been recently
reintroduced. From August 2001 to August 2004, the two
researchers gathered data on cause-specific mortality and
survival rates of coyotes captured at wolf-free and wolf-
abundant sites in Grand Teton National Park (GTNP), and data
on population densities of both species at three study areas
across the Greater Yellowstone Ecosystem (GYE), to determine
whether competition with wolves is sufficient to reduce coyote
densities in these areas.
Berger and Gese found that although coyotes were the
numerically dominant predator, across the GYE, densities
varied spatially and temporally as a function of wolf abundance.
Mean coyote densities were 33 percent lower at wolf-abundant
sites in GTNP, and densities declined 39 percent in Yellowstone
National Park following wolf reintroduction. A strong negative
relationship between coyote and wolf densities (Figure 13.20),
both within and across study sites, supports the hypothesis that
competition with wolves limits coyote populations. Overall
mortality of coyotes resulting from wolf predation was low but
differed significantly for resident and transient individuals.
Resident coyotes were members of packs that defended well-
defined territories, whereas transients were associated with
larger areas that encompassed the home ranges of several
resident packs but were not associated with a particular pack or
territory. Wolves were responsible for 56 percent of transient
coyote deaths. In addition, dispersal rates of transient coyotes
captured at wolf-abundant sites were 117 percent higher than
for transients captured in wolf-free areas.
The work by Jerod Merkle and colleagues at the Yellowstone

Wolf Project (Yellowstone Center for Resources, Yellowstone
National Park) provides a detailed picture of the nature of
competitive interactions between wolves and coyotes in areas
where wolves have been reintroduced. In a series of field
studies, the researchers examined interference competition
between gray wolves and coyotes in Yellowstone National Park
using radio-collared wolves (Figure 13.21). Merkle and
colleagues documented 337 wolf–coyote interactions from 1995
to 2007. The majority (75 percent) of interactions occurred at
the sites of wolf-killed ungulate carcasses (elk, buffalo, moose,
mule deer, etc.) with coyotes attempting to scavenge. Wolves
initiated the majority of encounters (85 percent), generally
outnumbered coyotes (39 percent), and dominated (91 percent)
most interactions. Wolves typically (79 percent) chased coyotes
without physical contact; however, 7 percent of encounters
resulted in a coyote death. Interactions decreased over time,
suggesting coyote adaptation or a decline in coyote density. The
results clearly show that wolves dominate interactions with
coyotes.
Although data are limited to the few regions in which wolf
populations have been successfully introduced, when combined
with the results of studies of wolf–coyote interactions and
population studies for regions of North America where these
two species naturally co-occur (regions of Minnesota and
Canada), a consistent picture emerges that the dramatic range
expansion of coyote over the past century is as a result, at least
in part, of the decline of wolf populations throughout most of
its former range.
Summary
Interspecific Competition 13.1
In interspecific competition, individuals of two or more species
share a resource in short supply, thus reducing the fitness of
both. As with intraspecific competition, competition between
species can involve either exploitation or interference. Six types
of interactions account for most instances of interspecific
competition: (1) consumption, (2) preemption, (3) overgrowth,

(4) chemical interaction, (5) territorial, and (6) encounter.
Competition Model 13.2–13.3
The Lotka–Volterra equations describe four possible outcomes
of interspecific competition. species 1 may outcompete species
2; species 2 may outcompete species 1. Both of these outcomes
represent competitive exclusion. The other two outcomes
involve coexistence. One is unstable equilibrium, in which the
species that was most abundant at the outset usually
outcompetes the other. A final possible outcome is stable
equilibrium, in which two species coexist but at a lower
population level than if each existed without the other.
Experimental Tests 13.4
Laboratory experiments with species interactions support the
Lotka–Volterra model.
Competitive Exclusion 13.5
Experiment results led to the formulation of the competitive
exclusion principle—two species with exactly the same
ecological requirements cannot coexist. This principle has
stimulated critical examinations of competitive relationships
outside the laboratory, especially of how species coexist and
how resources are partitioned.
Nonresource Factors 13.6
Environmental factors such as temperature, soil or water pH,
relative humidity, and salinity directly influence physiological
processes related to growth and reproduction but are not
consumable resources that species compete over. By
differentially influencing species within a community, these
nonresource factors can influence the outcome of competition.
Environmental Variability 13.7
Environmental variability may give each species a temporary
advantage. It allows competitors to coexist, whereas under
constant conditions one would exclude the other.
Multiple Factors 13.8
In many cases, competition between species involves multiple
resources. Competition for one resource often influences an
organism’s ability to access other resources.

Environmental Gradients 13.9
As environmental conditions change, so may the relative
competitive ability of species. Shifts in competitive ability can
result either from changes in the carrying capacities related to a
changing resource base or from changes in the physical
environment that interact with resource availability. Natural
environmental gradients often involve the covariation of
multiple factors—both resource and nonresource factors—such
as salinity, temperature, and water depth.
Niche 13.10
A species’ fundamental niche compresses or shifts when
competition restricts the species’ type of food or habitat. In
some cases, the realized niche may not provide optimal
conditions for the species. In the absence of competition, the
species may experience competitive release, and its niche may
expand.
Resource Partitioning 13.11
Many species that share the same habitat coexist by partitioning
available resources. When each species exploits a portion of the
resources unavailable to others, competition is reduced.
Resource partitioning is often viewed as a product of the
coevolution of characteristics that function to reduce
competition. Interspecific competition can reduce the fitness of
individuals. If certain phenotypes within the population
function to reduce competition with individuals of other
species, those individuals will encounter less competition and
increased fitness. The result is a shift in the distribution of
phenotypes (characteristics) within the competing population(s).
When the shift involves features of the species’ morphology,
behavior, or physiology, it is referred to as character
displacement.
Complexity of Competition 13.12
Competition is a complex interaction that seldom involves the
interaction between two species for a single limiting resource.
Competition involves a variety of environmental factors that
directly influence survival, growth, and reproduction—factors

that vary in both time and space.
Wolves and Coyotes Ecological Issues & Applications
The decline of gray wolf populations throughout much of North
America have been paralleled by a dramatic expansion in the
range of coyotes. Evidence from areas in which wolves have
been reintroduced suggests that the expansion of coyotes was in
part a result of competitive release from wolf populations over
the past century.
CHAPTER 12
12.1 Species Interactions Can Be Classified Based on Their
Reciprocal Effects
If we designate the positive effect of one species on another as
+, a detrimental effect as −, and no effect as 0, we can use this
qualitative description of the different ways in which
populations of two species interact to develop a classification of
possible interactions between two co-occurring species (Table
12.1). When neither of the two populations affects the other, the
relationship is (00), or neutral. If the two populations mutually
benefit, the interaction is (++), or positive, and the relationship
is called mutualism (Chapter 15). When one species maintains
or provides a condition that is necessary for the welfare of
another but does not affect its own well-being, the relationship
(+0) is called commensalism. For example, the trunk or limb of
a tree provides the substrate on which an epiphytic orchid grows
(Figure 12.1). The arrangement benefits the orchid, which gets
nutrients from the air and moisture from aerial roots, whereas
the tree is unaffected.
When the relationship is detrimental to the populations of both
species (−−), the interaction is termed competition (Chapter 13).
In some situations, the interaction is (−0). One species reduces
or adversely affects the population of another, but the affected
species has no influence in return. This relationship is

amensalism. It is considered by many ecologists as a form of
asymmetric competition, such as when taller plant species shade
species of smaller stature.
Relationships in which one species benefits at the expense of
the other (+−) include predation, parasitism, and parasitoidism
(see Chapter 14 for more information on predation and Chapter
15 for more information on parasitism and parasitoidism).
Predation is the process of one organism feeding on another,
typically killing the prey. Predation always has a negative effect
on the individual prey. In parasitism, one organism feeds on the
other but rarely kills it outright. The parasite and host live
together for some time. The host typically survives, although its
fitness is reduced. Parasitoidism, like predation, kills the host
eventually. Parasitoids, which include certain wasps and flies,
lay eggs in or on the body of the host. When the eggs hatch, the
larvae feed on it. By the time the larvae reach the pupal stage,
the host has succumbed.
12.2 Species Interactions Influence Population Dynamics
The varieties of species interactions outlined in the previous
section typically involve the interaction of individual
organisms. A predator captures a prey or a bacterium infects a
host organism. Yet through their beneficial or detrimental
effects on the individuals involved, these interactions influence
the collective properties of birth and death at the population
level, and in doing so, influence the dynamics of the respective
populations. For example, by capturing and killing individual
prey, predators function as agents of mortality. We might
therefore expect that as the number of predators (Npredator) in
an area increases, the number of prey captured and killed will
likewise increase. If we assume the simplest case of a linear
relationship, we can represent the influence of changes in the
predator population (Npredator) on the death rate of the prey
population (dprey) as shown in Figure 12.2a. As the number of
predators in the population (Npredator) increases, the
probability of an individual in the prey population (Nprey)
being captured and killed increases. Subsequently, the death

rate of the prey population increases. The net effect is a decline
in the growth rate of the prey population. Note the similarity in
the functional relationship presented in Figure 12.2a with the
example of density-dependent population control presented
earlier (Chapter 11, Figure 11.1). Previously, we examined how
an increase in population size can function as a negative
feedback on population growth by increasing the mortality rate
or decreasing the birthrate (density-dependent population
regulation; Section 11.2 and Figure 11.4). The relationship
shown in Figure 12.2a expands the concept of density-
dependent population regulation to include the interaction
between species. As the population of predators increases, there
is a subsequent decline in the population of prey as a direct
result of the prey’s increased rate of mortality.
A similar approach can be taken to evaluate the positive effects
of species interactions. In the example of predation, whereas the
net effect of predation on the prey is negative, the predator
benefits from the capture and consumption of prey. Prey
provides basic food resources to the predator and directly
influences its ability to survive and reproduce. If we assume
that the ability of a predator to capture and kill prey increases
as the number of potential prey increase (Nprey), and that the
reproductive fitness of a predator is directly related to its
consumption of prey, then we would expect the birthrate of the
predator population (bpredator) to increase as the size of the
prey population increases (Figure 12.2b). The result is a direct
link between the availability of prey (size of the prey
population, Nprey) and the growth rate of the predator
population (dNpredator/dt).
In Chapter 11, we developed a logistic model of population
growth. It is a model of intraspecific competition and density-
dependent population regulation using the concept of carrying
capacity, K. The carrying capacity represents the maximum
sustainable population size that can be supported by the
available resources. The carrying capacity functions to regulate
population growth in that as the population size approaches K,

the population growth rate approaches zero (dN/dt = 0).
When individuals of two different species share a common
limiting resource that defines the carrying capacity, there is
potential for competition between individuals of the two species
(interspecific competition). For example, let’s define a
population of a grazing antelope inhabiting a grassland as N1,
and the carrying capacity of the grassland to support that
population as K1 (the subscript 1 refers to species 1). The
logistic model of population growth (see Section 11.1) would
then be:
dN1/dt = r1N1(1 − N1/K1)dN1/dt = r1N1(1 − N1/K1)
Now let’s assume that a second species of antelope inhabits the
same grassland, and to simplify the example, we assume that
individuals of the second species—whose population we define
as N2—have the same body size and exactly the same rate of
food consumption (grazing of grass) as do individuals of the
first species. As a result, when we evaluate the role of density-
dependent regulation on the population of species 1 (N1), we
must now also consider the number of individuals of species 2
(N2) because individuals of both species feed on the grass that
defines the carrying capacity of species 1 (K1). The new
logistic model for species 1, will be:
dN1/dt = r1N1(1 − (N1 + N2)/K1)dN1/dt = r1N1(1 − (N1 + N2)/
K1)
For example, if the carrying capacity of the grassland for
species 1 is 1000 individuals (K1 = 1000)—because species 2
draws on the exact same resource in exactly the same manner—
the combined carrying capacity of the grassland is also 1000. If
there are 250 individuals of species 2 (N2 = 250) living on the
grassland, it effectively reduces the carrying capacity for
species 1 from 1000 to 750 (Figure 12.3a). The population
growth rate of species 1 now depends on the population sizes of
both species 1 and 2 relative to the carrying capacity (Figure
12.3b). Although we have defined the two antelope species as
being identical in their use of the limiting resource that defines
the carrying capacity, this is not always the case. In reality, it is

necessary to evaluate the overlap in resource use and quantify
the equivalency of one species to another (see Quantifying
Ecology 12.1).
In all cases in which individuals of two species interact, the
nature of the interaction can be classified qualitatively as
neutral, positive, or negative, and the influence of the specific
interaction can be evaluated in terms of its impact on the
survival or reproduction of individuals within the populations.
In the discussion that follows, we develop quantitative models
to examine how the diversity of species interactions outlined in
Table 12.1 influence the combined population dynamics of the
species involved (Chapters 13, 14, and 15). In all cases, these
models involve quantifying the per capita effect of interacting
individuals on the birthrates and death rates of the respective
populations.
Quantifying Ecology 12.1 Incorporating Competitive
Interactions in Models of Population Growth
When individuals of two different species (represented as
populations N1 and N2) share a common limiting resource that
defines the carrying capacity for each population (K1 and K2),
there is potential for competition between individuals of the two
species (interspecific competition). Thus, the population density
of both species must be considered when evaluating the role of
density-dependent regulation on each population. In Section
12.2, we gave the example of two species of antelope that share
the common limiting food resource of grass. We assumed that
individuals of the two species were identical in their food
selection and the rate at which they feed, therefore, with respect
to the carrying capacity of the grassland, individuals of the two
species are equivalent to each other; that is, in resource
consumption one individual of species 1 is equivalent to one
individual of species 2. As a result, when evaluating the growth
rate of species 1 using the logistic model of population growth,
it is necessary to include the population sizes of both species
relative to the carrying capacity (see Figure 12.4):
dN1/dt = r1N1(1 − (N1 + N2)/K1)dN1/dt = r1N1(1 − (N1 + N2)/

K1)
However, two species, even closely related species, are unlikely
to be identical in their use of resources. So it is necessary to
define a conversion factor that can equate individuals of species
2 to individuals of species 1 as related to the consumption of
the shared limited resource. This is accomplished by using a
competition coefficient, defined as a, that quantifies individuals
of species 2 in terms of individuals of species 1 as related to the
consumption of the shared resource. Using the example of two
antelope species, let us now assume that both species still feed
on the same resource (grass), however, individuals of species 2
have on average only half the body mass of individuals of
species 1 and therefore consume grass at only half the rate of
species 1. Now an individual of species 2 is only equivalent to
one-half an individual of species 1 with respect to the use of
resources. In this case, a = 0.5, and we can rewrite the logistic
model for species 1 shown previously as:
dN1/dt = r1N1(1 − (N1 + αN2)/K1)dN1/dt = r1N1(1 − (N1 + αN
2)/K1)
Because in Section 12.2 we defined the carrying capacity of the
grassland for species 1 as K1 = 1000, we can substitute the
values of a and K1 in the preceding equation:
dN1/dt = r1N1(1 − (N1 + 0.5N2)/1000)dN1/dt = r1N1(1 − (N1 +
0.5N2)/1000)
Now the growth rate of species 1 (dN1/dt) approaches zero as
the combined populations of species 1 and 2, represented as N1
+ 0.5N2, approach a value of 1000 (the value of K1).
We have considered how to incorporate the effects of
competition from species 2 into the population dynamics of
species 1 using the competition coefficient a, but what about the
effects of species 1 on species2? The competition for food
resources (grass) will also function to reduce the availability of
resources to species 2. We can take the same approach and
define a conversion factor that can equate individuals of species
1 to individuals of species 2, defined as b. Because individuals
of species 1 consume twice as much resource (grass) as

individuals of species 2, it follows that an individual of species
1 is equivalent to 2 individuals of species 2; that is, b = 2.0. It
also follows that if individuals of species 2 require only half the
food resources as individuals of species 1, then the carrying
capacity of the grassland for species 2 should be twice that for
species 1; that is, K2 = 2000. The logistic growth equation for
species 2 is now:
dN2/dt = r2N2(1 − (N2 + βN1)/K2)dN2/dt = r2N2(1 − (N2 + βN
1)/K2)
or, substituting the values for b and K2
dN2/dt = r2N2(1 − (N2 + 2.0N1)/2000)dN2/dt = r2N2(1 − (N2 +
2.0N1)/2000)
We now have a set of equations that can be used to calculate the
growth of the two competing species that considers their
interaction for the limiting food resource. We explore this
approach in more detail in the following chapter (Chapter 13).
In the example of the two hypothetical antelope species
presented previously, the estimation of the competition
coefficients (a and b) appear simple and straightforward. Both
species are identical in their diet and differ only in the rate at
which they consume the resource (which is defined as a simple
function of their relative body masses). In reality, even closely
related species drawing on a common resource (such as grazing
herbivores) differ in their selection (preferring one group of
grasses of herbaceous plants over another), foraging behavior,
timing of foraging, and other factors that influence the nature of
their relative competitive effects on each other. As such,
quantifying species interactions, such as resource competition,
can be a difficult task, as we shall see in the following chapter
(Chapter 13, Interspecific Competition).
12.3 Species Interactions Can Function as Agents of Natural
Selection
For a number of reasons, the interaction between two species
will not influence all individuals within the respective
populations equally. First, interactions among species involve a
diverse array of physiological processes and behavioral

activities that are influenced by phenotypic characteristics
(physiological, morphological, and behavioral characteristics of
the individuals). Secondly, these phenotypic characteristics vary
among individuals within the populations (see Chapter 5).
Therefore, the variations among individuals within the
populations will result in differences in the nature and degree of
interactions that occur. For example, imagine a species of seed-
eating bird that feeds on the seeds of a single plant species.
Individuals of the plant species exhibit a wide degree of
variation in the size of seeds that they produce. Some
individuals produce smaller seeds, whereas others produce
larger seeds (Figure 12.4a), and seed size is a heritable
characteristic (genetically determined). Seed size is important
to the birds because the larger the seed, the thicker the seed
coat, and the more difficult it is for a bird to crush the seed with
its bill. If the seed coat is not broken, the seed passes through
the digestive system undigested and provides no food value to
the bird. As a result, birds actively select smaller seeds in their
diet (Figure 12.4c). In doing so, the birds are decreasing the
reproductive success of individual plants that produce small
seeds while increasing the relative fitness of those individuals
that produce larger seeds. The net effect is a shift in the
distribution of phenotypes in the plant population to individuals
that produce larger, harder seeds (Figure 12.4d). In this
situation, the bird population (and pattern of seed predation) is
functioning as an agent of natural selection, increasing the
relative fitness of one phenotype over another (see Section
5.6). Over time, the result represents a directional change in the
genetic structure of the population (gene frequencies), that is,
the process of evolution (Chapter 5).
In this example, the predator functions as an agent of natural
selection, decreasing the reproduction for certain phenotypes
(small seed-producing individuals) within the plant population
and increasing the relative fitness of other phenotypes (large
seed-producing individuals). But the shifting distribution of
phenotypes within the plant population and the resulting change

in the distribution of food resources will in turn have a potential
influence on the predator population (Figure 12.4b). The
directional selection for increased seed size within the plant
population decreases the relative abundance of smaller seeds,
effectively decreasing the availability of food resources for
birds with smaller bill sizes. If the birds with smaller bills are
unable to crack the larger seeds, these individuals will
experience a decreased probability of survival and reproduction,
which increases the relative fitness of individuals with larger
bill size. The shift in the distribution of phenotypes in the plant
population, itself a function of selective pressures imposed by
the bird population, now functions as an agent of natural
selection in the predator (bird) population. The result is a shift
in the distribution of phenotypes and associated gene
frequencies within the bird population toward larger bill size
(Figure 12.4e). This process in which two species undergo
reciprocal evolutionary change through natural selection is
called coevolution.
Unlike adaptation to the physical environment, adaptation in
response to the interaction with another species can produce
reciprocal evolutionary responses that either thwart (counter)
these adaptive changes, as in the previous example, or in
mutually beneficial interactions, magnify (reinforce) their
effect. An example of the latter can be found in the relationship
between flowering plants and their animal pollinators. Many
species of flowering plants require the transfer of pollen from
one individual to another for successful fertilization and
reproduction (outcrossing; Figure 12.5). In some plant species,
this is accomplished through passive transport by the wind, but
many plants depend on animals to transport pollen between
flowers. By attracting animals, such as insects or birds, to the
flower, pollen is spread. When the animal comes into contact
with the flower, pollen is deposited on its body, which is then
transferred to another individual as the animal travels from
flower to flower. This process requires the plant species to
possess some mechanism to attract the animal to the flower.

A wide variety of characteristics has evolved in flowering
plants that function to entice animals through either signal or
reward. Signals can involve brightly colored flowers or scents.
The most common reward to potential pollinators is nectar, a
sugar-rich liquid produced by plants, which serves no purpose
for the individual plant other than to attract potential
pollinators. Nectar is produced in glands called nectaries, which
are most often located at the base of the floral tube (see Figure
12.5).
The relationship between nectar-producing flowers and nectar-
feeding birds provides an excellent example of the
magnification of reciprocal evolutionary responses—
coevolution—resulting from a mutually beneficial interaction.
The elongated bill of hummingbirds distinguishes them from
other birds and is uniquely adapted to the extraction of nectar
(Figure 12.6). Their extremely long tongues are indispensable in
gaining nectar from long tubular flowers. Let us assume a
species of hummingbird feeds on a variety of flowering plants
within a tropical forest but prefers the flowers of one plant
species in particular because it produces larger quantities of
nectar. Thus, the reward to the hummingbird for visiting this
species is greater than that of other plant species in the forest.
Now assume that flower size (an inherited characteristic) varies
among individuals within the plant population and that an
increase in nectar production is associated with elongation of
the floral tube (larger flower size). Individual plants with larger
flowers and greater nectar production would have an increased
visitation rate by hummingbirds. If this increase in visitation
rate results in an increase in pollination and reproduction, the
net effect is an increase in the relative fitness of individuals
that produce larger flowers, shifting the distribution of
phenotypes within the plant population. The larger flower size
and longer floral tube, however, make it more difficult to gain
access to the nectar. Individual hummingbirds with longer bills
are more efficient at gaining access, and bill size varies among
individuals within the population. With increased access to

nectar resources, the relative fitness of longer-billed individuals
increases at the expense of individuals with shorter bills. In
addition, any gene mutation that results in increasing bill length
with be selected for because it will increase the fitness of the
individual and its offspring (assuming that they exhibit the
phenotype). The genetic changes that are occurring in each
population are reinforced and magnified by the mutually
beneficial interaction between the two species. The plant
characteristic of nectar production is reinforced and magnified
by natural selection in the form of improved pollination success
by the plant and reproductive success by the hummingbird. In
turn, the increased flower size and associated nectar production
functions as a further agent of natural selection in the bird
population, resulting in an increase in average bill size (length).
One consequence of this type of coevolutionary process is
specialization, wherein changes in phenotypic characteristics of
the species involved function to limit the ability of the species
to carry out the same or similar interactions with other species.
For example, the increase in bill size in the hummingbird
population will function to limit its ability to efficiently forage
on plant species that produce smaller flowers, restricting its
feeding to the subset of flowering plants within the tropical
forest that produces large flowers with long floral tubes (see
Figure 12.6). In the extreme case, the interaction can become
obligate, where the degree of specialization in phenotypic
characteristics results in the two species being dependent on
each other for survival and successful reproduction. We will
examine the evolution of obligate species interactions in detail
later (Chapter 15).
Unlike the case of mutually beneficial interactions in which
natural selection functions to magnify the intensity of the
interaction, interactions that are mutually negative to the
species involved can lead to the divergence in phenotypic
characteristics that function to reduce the intensity of
interaction. Such is the case when the interaction involves
competition for essential resources. Consider the case wherein

two species of seed-eating birds co-occur on an island. The two
populations differ in average body and bill size, yet the two
populations overlap extensively in the range of these phenotypic
characteristics (Figure 12.7a) and subsequently in the range of
seed sizes on which they forage (Figure 12.7b). The selection of
seeds by individual birds is related to body and bill size.
Smaller individuals are limited to feeding on the smaller, softer
seeds, whereas only larger individuals are capable of cracking
the larger, harder seeds. Although larger birds are able to feed
on smaller seeds, it is energetically inefficient; therefore, their
foraging is restricted to relatively larger seeds (see Section 5.8
for an example).
Seed resources on the island are limited relative to the
populations of the two species, hence, competition is often
intense for the intermediate-sized seeds for which both species
forage. If competition for intermediate-sized seeds functions to
reduce the fitness of individual birds that depend on these
resources, the result would be reduced survival and reproductive
rates for larger individuals of the smaller species and smaller
individuals of the larger species (Figure 12.7c). This result
represents a divergence in the average body and bill size for the
two populations that functions to reduce the potential for
competition between the two species (Figure 12.7d).
12.4 The Nature of Species Interactions Can Vary across
Geographic Landscapes
We have examined how natural selection can result in genetic
differentiation, that is, genetic differences among local
populations. Species with wide geographic distributions
generally encounter a broader range of physical environmental
conditions than species whose distribution is more restricted.
The variation in physical environmental conditions often gives
rise to a corresponding variation in phenotypic characteristics.
As a result, significant genetic differences can occur among
local populations inhabiting different regions (see Section 5.8
for examples). In a similar manner, species with wide
geographic distributions are more likely to encounter a broader

range of biotic interactions. For example, a bird species such as
the warbling vireo (Vireo gilvus) that has an extensive
geographic range in North America, extending from northern
Canada to Texas and from coast to coast, is more likely to
encounter a greater diversity of potential competitors, predators,
and pathogens than will the cerulean warbler (Dendroica
cerula), whose distribution is restricted to a smaller geographic
region of the eastern United States (see Figure 17.2 for
distribution maps). Changes in the nature of biotic interactions
across a species geographic range can result in different
selective pressures and adaptations to the local biotic
environment. Ultimately, differences in the types of species
interactions encountered by different local populations can
result in genetic differentiation and the evolution of local
ecotypes similar to those that arise from geographic variations
in the physical environment (see Section 5.8 for examples of
the latter). The work of Edmund Brodie Jr. of Utah State
University presents an excellent example.
Brodie and colleagues examined geographic variation among
western North American populations of the garter snake
(Thamnophis sirtalis) in their resistance to the neurotoxin
tetrodotoxin (TTX). The neurotoxin TTX is contained in the
skin of newts of the genus Taricha on which the garter snakes
feed (Figure 12.8a). These newts are lethal to a wide range of
potential predators, yet to garter snakes having the TTX-
resistant phenotype, the neurotoxin is not fatal. Both the
toxicity of newts (TTX concentration in their skin) and the TTX
resistance of garter snakes vary geographically (Figure 12.8b).
Previous studies have established that TTX resistance in the
garter snake is highly heritable (passed from parents to
offspring), so if TTX resistance in snakes has co-evolved in
response to toxicity of the newt populations on which they feed,
it is possible that levels of TTX resistance exhibited by local
populations of garter snakes will vary as a function of the
toxicity of newts on which they feed. The strength of selection
for resistance would vary as a function of differences in

selective pressure (the toxicity of the newts).
To test this hypothesis, the researchers examined TTX
resistance in more than 2900 garter snakes from 40 local
populations throughout western North America, as well as the
toxicity of newts at each of the locations. The researchers found
that the level of TTX resistance in local populations varies with
the presence of toxic newts. Where newts are absent or nontoxic
(as is the case on Vancouver Island, British Columbia), snakes
are minimally resistant to TTX. In contrast, levels of TTX
resistance increased more than a thousand-fold with increasing
toxicity of newts (see Figure 12.8b). Brodie and his colleagues
found that for local populations, the level of resistance to TTX
varies as a direct function of the levels of TTX in the newt
population on which they prey (Figure 12.8c). The resistance
and toxicity levels match almost perfectly over a wide
geographic range, reflecting the changing nature of natural
selection across the landscape.
In some cases, even the qualitative nature of some species
interactions can be altered when the background environment is
changed. For example, mycorrhizal fungi are associated with a
wide variety of plant species (see Chapter 15, Section 15.11).
The fungi infect the plant root system and act as an extension of
the root system. The fungi aid the plant in the uptake of
nutrients and water, and in return, the plant provides the fungi
with a source of carbon. In environments in which soil nutrients
are low, this relationship is extremely beneficial to the plant
because the plant’s nutrient uptake and growth increase. (Figure
12.9a). Under these conditions, the interaction between plant
and fungi is mutually beneficial. In environments in which soil
nutrients are abundant, however, plants are able to meet nutrient
demand through direct uptake of nutrients through their root
system. Under these conditions, the fungi are of little if any
benefit to the plant; however, the fungi continue to represent an
energetic cost to the plant, reducing its overall net carbon
balance and growth (Figure 12.9b). Across the landscape, the
interaction between plant and fungi changes from mutually

beneficial (++) to parasitic (+−) with increasing soil nutrient
availability.
1. Q1. Given the preceding figure, is there a net benefit to the
plant of having an association with mycorrhizal fungi under
conditions of low soil nutrients?
2. Q2. At which point along the gradient of soil nutrient
concentration is the net benefit to the plant equal to zero
(costs = benefits)?
12.5 Species Interactions Can Be Diffuse
The examples of species interactions that we have discussed
thus far focus on the direct interaction between two species.
However, most interactions (e.g., predator–prey, competitors,
mutually beneficial) are not exclusive nor involve only two
species. Rather, they involve a number of species that form
diffuse associations. For example, most terrestrial communities
are inhabited by an array of insect, small mammal, reptile, and
bird species that feed on seeds. As a result, there is a potential
for competition to occur among any number of species that draw
on this limited food resource. Similarly, there are numerous
examples of highly specific mutually beneficial interactions
between two species (see Figure 12.6); however, most mutually
beneficial interactions are somewhat diffuse. In plant-pollinator
interactions, most plants are pollinated by multiple animal
species, and each animal species pollinates multiple plant
species. For example, honey bees (Apis melifera) are known to
visit the flowers of hundreds of plant species, and white
mangrove (Laguncularia racemosa) is visited by more than 50
different insect species. Species of plants and pollinators form
pollination networks, and the resulting selective forces that
reinforce the mutually beneficial interactions are likewise
diffuse (Figure 12.10). This process in which a network of
species undergoes reciprocal evolutionary change through
natural selection is referred to as diffuse coevolution.
In diffuse coevolution, groups of species interact with other
groups of species, leading to natural selection and evolutionary

changes that cannot be identified as examples of specific,
pairwise coevolution between two species. For example, the
evolution of resistance to the neurotoxin TTX by garter snakes
presented in the previous section (see Figure 12.8) is in
response to TTX concentrations in the skin of newts of the
genus Taricha on which they prey. This genus consists of three
species and four subspecies of western newts, so the evolution
of resistance by snake populations is not in response to its
interaction with a single species but rather a group of closely
related species that all produce the neurotoxin and on which
they feed. Likewise, the evolution of toxicity by members of the
genus Taricha provides a defense mechanism to avoid predation
by an array of vertebrate predators, not just a single species of
predator.
In the chapters that follow, we will explore an array of
examples of co-evolution. Some represent highly specialized co-
adaptations between two species in which the interaction has
become obligate (essential to the survival of the two species
involved), whereas others represent the result of generalized
relationships between groups of species—diffuse relationships
between competitors, predator and prey, or mutualists.
12.6 Species Interactions Influence the Species’ Niche
The diversity of species inhabiting our planet reflect different
evolutionary solutions to the same basic processes of
assimilation and reproduction, and that the characteristics that
distinguish each species often reflect adaptations (products of
natural selection) that allow individuals of that species to
survive, grow, and reproduce under a particular set of
environmental conditions (see Part Two). As such, each species
may be described in terms of the range of physical and chemical
conditions under which it persists (survives and reproduces) and
the array of essential resources it uses. This characterization of
a species is referred to as its ecological niche.
The concept of the ecological niche was originally developed
independently by ecologists Joseph Grinnell (1917, 1924) and
Charles Elton (1927), who proposed slightly different meanings

for the term. Grinnell’s definition centered on the concept of
habitat (see Section 7.14, Figure 7.25) and the limitations
imposed by the physical environment (as discussed in Chapters
6 and 7), whereas Elton emphasized the role of the species in
the context of the community (species interactions). The
limnologist G. Evelyn Hutchinson (1957) later expanded the
concept of the niche by proposing the idea of the niche as a
multidimensional space called a hypervolume, in which each
axis (dimension) is defined by a variable relating to the specific
resource need or environmental factor that is essential for a
species’ survival and successful reproduction. We can begin to
visualize this concept of a multidimensional niche by modeling
a three-dimensional one—a niche defined by three resources or
environmental variables: temperature, salinity, and pH (Figure
12.11). For each axis there is a range of values (conditions) that
permit a species to survive and reproduce (or in Hutchinson’s
own words, “for the population to persist indefinitely”). For
example, in Chapters 6 and 7 we presented numerous examples
of the response of plant (Figures 5.19– 5.22) and animal
(Figures 7.14 and 7.18) species to variation in environmental
temperature. Each of these figures represents a description of
the species’ niche for the single dimension (variable) of
environmental temperature. Likewise, the distribution of seed
sizes used by the three species of Darwin’s ground finch
inhabiting the Galapagos Islands presented in Figure 5.20
represents a description of the species’ niches for the single
dimension of food resource size.
Hutchinson referred to this hypervolume that defines the
environmental conditions under which a species can survive and
reproduce as the fundamental niche. The fundamental niche,
sometimes referred to as the physiological niche, provides a
description of the set of environmental conditions under which a
species can persist. As we have discussed in the previous
sections, however, a population’s response to the environment
may be modified by interactions with other species. Hutchinson
recognized that interactions such as competition may restrict the

environment in which a species can persist and referred to the
portion of the fundamental niche that a species actually exploits
as a result of interactions with other species as the realized
niche (Figure 12.12).
An illustration of the difference between a species’ fundamental
and realized niche is provided in the work of J. B. Grace and R.
G. Wetzel of the University of Michigan. Two species of cattail
(Typha) occur along the shorelines of ponds in Michigan. One
species, Typha latifolia (wide-leaved cattail), dominates in the
shallower water, whereas Typha angustifolia (narrow-leaved
cattail) occupies the deeper water farther from shore. When
these two species grew along the water depth gradient in the
absence of the other species, a comparison of the results with
their natural distributions revealed how competition influences
their realized niche (Figure 12.13). Both species can survive in
shallow waters, but only the narrow-leaved cattail, T.
angustifolia, can grow in water deeper than 80 centimeters (cm).
When the two species grow together along the same gradient of
water depth, their distributions, or realized niches, change.
Even though T. angustifolia can grow in shallow waters (0–20
cm depth) and above the shoreline (−20 to 0 cm depth), in the
presence of T. latifolia it is limited to depths of 20 cm or
deeper. Individuals of T. latifolia outcompete individuals of T.
angustifolia for the resources of nutrients, light, and space,
limiting the distribution of T. angustifolia to the deeper waters.
Note that the maximum abundance of T. angustifolia occurs in
the deeper waters, where T. latifolia is not able to survive.
As originally proposed, the concept of realized niche focused on
how the fundamental niche of a species is restricted as a result
of negative interactions with other species. Competition can
function to restrict the range of resources or environmental
conditions used by a species, as in the example of the
distribution of T. angustifolia along the gradient of water depth
presented in the previous example. In other cases, the presence
of predators or pathogens may restrict the range of behaviors
exhibited by a potential prey species, the resources it uses, or

ultimately the habitats in which it can persist (see Chapter 14,
Section 14.8 for an example of changes in foraging behavior
under the risk of predation). As such, the realized niche of a
species was seen as a subset of the broader, more inclusive
range of conditions and resources that the species could use in
the absence of interactions with other species. In more modern
times, however, ecologists have come to appreciate the
importance of positive interactions, particularly mutually
beneficial interactions, and how this class of interactions can
modify the species’ fundamental niche. By either directly or
indirectly enhancing the probabilities of survival and
reproduction of individuals in the participating populations,
interactions that are either beneficial to one species and neutral
to the other (commensalism), or mutually beneficial to both
(mutualism), can function to expand the range of environmental
conditions or resources under which a species can persist. In
this case, the realized niche of the species is greater (more
expansive) than that of its fundamental niche. For example,
nitrogen-fixing Rhizobium bacteria associated with the root
systems of certain plant species provide a direct source of
mineral nitrogen to the plant, enabling it to persist in soils that
have low mineral nitrogen content (see Section 15.11 for a
detailed discussion of this mutualistic interaction). In the
absence of interaction with the bacteria, the plants are restricted
to a narrower range of soils that have higher availability of
mineral nitrogen.
Although the realized niche is by definition a product of species
interactions, over evolutionary time, biotic interactions can play
a critical role in defining a species’ fundamental niche. The
previous discussion of species’ adaptation to the environment
focused almost exclusively on the role of the physical and
chemical environments as agents of natural selection (see Part
Two). We now have seen, however, that species interactions
also function as agents of natural selection, and phenotypic
characteristics often reflect adaptations to these selective
pressures. As such, over evolutionary timescales, species

interactions can have a major role in determining the range of
physical and chemical conditions under which species can
persist (survive and reproduce) and the array of essential
resources they use, that is, the species’ ecological niches.
12.7 Species Interactions Can Drive Adaptive Radiation
Adaptive radiation is the process by which one species gives
rise to multiple species that exploit different features of the
environment, such as food resources or habitats (see Section
5.9, Figure 5.22). Different features of the environment exert
the selective pressures that push populations in various
directions (phenotypic divergence); reproductive isolation, the
necessary condition for speciation to occur, is often a by-
product of the changes in morphology, behavior, or habitat
preferences that are the actual objects of selection. Likewise,
variations among local populations in biotic interactions can
result in phenotypic divergence and therefore have the potential
to function as mechanisms of adaptive radiation. Resource
competition is often inferred as a primary factor driving
phenotypic divergence. For example, species of the globeflower
fly Chiastocheta present a unique case of adaptive radiation as a
result of resource competition. At least six sister species of the
genus Chiastocheta lay their eggs (oviposition) on the fruits of
the globeflower, Trollius europaeus (Figure 12.14); however,
the different species of globeflower flies differ in the timing of
their egg laying. One species lays its eggs in 1-day-old flowers,
whereas all the other species sequentially deposit their eggs
throughout the flower life span. In a series of field experiments,
Laurence Despres and Mehdi Cherif of Université Joseph
Fourier (Grenoble, France) found evidence that supports the
hypothesis that the evolutionary divergence of species of
Chiastocheta was a result of disruptive selection on the timing
of egg laying (reproduction). The researchers established that
intense intraspecific competition occurs within each of the
species, but differences in the timing of egg laying and larval
development functions to minimize competition among species
(the concept of resource partitioning will be examined in

Chapter 13).
Although numerous studies have illustrated the role of
competitive interactions in adaptive radiation, the importance of
other interactions, such as mutualism or predation, remain
largely unexplored. The research of Patrik Nosil and Bernard
Crespi of Simon Fraser University (British Columbia, Canada),
however, has shown that adaptive radiation can result from
divergent adaptations to avoid predators. Nosil and Crespi’s
research focused on two ecotypes (populations of the same
species adapted to their local environments) of the stick insect
Timema cristinae (see Section 5.8 and Chapter 5, Field Studies:
Hopi Hoekstra for discussion of ecotypes). Timema walking
sticks are wingless insects inhabiting southwestern North
America. Individuals feed and mate on the host plants on which
they reside. The two distinct ecotypes of Timema are adapted to
feeding on different host plants, Ceanothus and Adenostoma.
The two host plants differ strikingly in foliage form, with
Ceanothus plants being relatively large and tree-like with broad
leaves and Adenostoma plants being small and shrub-like with
thin, needle-like leaves (Figure 12.15).
The two Timema ecotypes differ in 11 quantitative traits (see
Figure 12.15), comprising aspects of color, color pattern, body
size, and body shape. These differences between the two
ecotypes appears to be a result of divergent selection. The
different traits exhibited by each of the ecotypes appear to
provide crypsis (avoidance of observation) from avian predators
on the respective host-plant species. Field experiments were
conducted to determine how differences in phenotypic traits
influenced the survival rates of the two ecotypes on the two
plant species. Each of the two Timema ecotypes was placed on
each of the two host-plant species. The results of the experiment
clearly indicated that the direction and magnitude of divergence
in traits represent adaptations that function to reduce rates of
predation on Timema on their respective host-plant species. The
ecotypes of T. cristinae, like the example of the limnetic and
benthic ecotypes of sticklebacks examined in Chapter 5, can be

considered to represent an early stage of adaptive radiation
because studies indicate that reproductive isolation is not
complete (see Section 5.6, Figure 5.15).
Ecological Issues & Application Urbanization Has Negatively
Impacted Most Species while Favoring a Few
As we will see in the chapters that follow, species interactions
are ubiquitous in nature and play a fundamental role in the
structuring of ecological communities. Perhaps no other
interaction, however, has as great an impact on the diverse array
of plants and animals that inhabit our planet as their interaction
with the human species.
As we first presented in Chapter 9 (Ecological Issues &
Applications), the primary cause of population declines and
recent species extinctions is habitat loss as a result of human
activities—namely, changing land-use patterns. There are two
major land-use changes that are responsible for habitat loss in
terrestrial environments: expanding agriculture and
urbanization.
According to the Food and Agricultural Organization (FAO)
United Nations’ statistics, at present some 11 percent (1.5
billion hectares) of the globe’s land surface (13.4 billion ha) is
used in crop production (arable land and land under permanent
crops), and even more land (3.2 to 3.6 billion ha) is used to
raise livestock. Together, agricultural lands account for almost
40 percent of Earth’s land surface. The negative impacts of the
expansion of agriculture to meet the needs of the growing
human population have been central to the discussion of the
decline of biological diversity on our planet, a topic we will
examine in more detail in Chapter 26. The increasing
urbanization of the human population over the past century
(Figure 12.16), however, has led to the emergence of a new
field of ecology—urban ecology—to study the ecology of
organisms in the context of the urban environment.
Ecology has historically focused on “pristine” natural
environments; however, by as early as the 1970s, many
ecologists began turning their attention toward ecological

interactions taking place in urban environments. What has
emerged is a picture of species interactions dominated by
humans, which negatively impacts most species and benefits
only a few.
Estimates of urban land area vary widely from 0.5 to slightly
more than 2.0 percent of the world’s land, depending on the
criteria used to define urban development. Historically, cities
have been compact areas with high population densities that
grew slowly in their physical extent. Today, however, urban
areas are expanding twice as fast as their populations.
According to the United States Census Bureau, about 30 percent
of the U.S. population currently lives in cities, whereas another
50 percent lives in the suburbs. More than 5 percent of the total
surface area of the United States is covered by urban and other
developed areas; this is more than the land covered by the
combined totals of national and state parks.
The expansion of urbanization produces some of the greatest
local extinction rates and frequently eliminates the large
majority of native species. Eyal Shochat of Arizona State
University’s Global Institute of Sustainability and colleagues
used data from Phoenix, Arizona, and Baltimore, Maryland, to
contrast the distribution of species in these two urban areas as
compared to the surrounding natural ecosystems. Their findings
show a general pattern of decline in the number of species in
urban environments as compared to both surrounding
agricultural and natural ecosystems (Figure 12.17).
Species vary in their ability to adapt to the often drastic
physical changes along the gradient from rural to urban habitat.
Moving from the rural landscape of natural ecosystems and
cultivated lands into the suburban landscape, one moves through
a heterogeneous mixture of residential areas, commercial
centers, and the managed vegetation of parks and cemeteries.
The main cause for the loss of species in these suburban
environments is habitat alteration. Yet in contrast to the decline
in the number of species, both suburban areas and urban centers
are usually characterized by higher population densities of

resident species as compared to adjacent natural lands. For
example, in a study of population of northern cardinals
(Cardinalis cardinalis) in the metropolitan area of Columbus,
Ohio, and surrounding forested landscape of central Ohio,
Lionel Leston and Amanda Rodewald of Ohio State University
found that birds were four times more abundant in urban than
rural forests. Their research showed that food abundance was as
much as four times greater in the urban habitat as compared to
the forests of the surrounding region because exotic vegetation,
refuse, and bird feeders may all provide food sources for birds
in these urban environments.
Some mammals, such as raccoons (Procyonlotor), skunks
(Mephitismephitis), and rabbits (Sylvilagus spp.) have also
benefited from the spread of the suburban landscape, finding
shelter beneath sheds and porches, and an abundance of food—
for raccoons, garbage; for skunks, insects and larvae on lawns
and in gardens; and for rabbits, an abundance of high quality
food plants in gardens and flowerbeds. Larger species, rapidly
adapting to human presence, are moving into the suburban
landscape and dramatically increasing in number. White-tailed
deer (Odocoileusvirginiaus), carriers of Lyme disease, find an
abundance of forage on grass, shrubs, and gardens. Resident
Canada geese (Brantacanadensis), attracted to large open areas
of grass—including golf courses and parks—create both a
nuisance and health problems. In recent years, coyotes
(Canislatrans), attracted by garbage and small prey including
rodents and pets (cats and small dogs), are becoming more
common in suburban areas. Even black bears (Ursusamericanus)
are attracted to backyard bird feeders and dumpsters in
suburban areas adjacent to forested, rural landscapes.
In addition to increased abundance and predictability of food
resources, recent research indicates that a reduction in predator
populations in urban environments favors resident species.
Evidence has been gathered that supports the idea that urban
environments are safer for some species than are rural habitats.
Both birds and squirrels in urban environments benefit from

reduced nest predation and are able to spend a greater
proportion of their time foraging compared with individuals in
the surrounding natural ecosystems, indicating that the urban
habitat is less risky than the surrounding rural habitats.
Species adapted to habitats along the suburban gradient drop out
as they come to urban centers where habitat changes sharply.
Vegetation is limited to scattered parks, some tree-lined streets,
and vacant lots. Species that benefit from the habitat provided
by these core urban centers are often referred to as “urban
exploiters.” Among plants, urban exploiters tend to be ruderal
species (see discussion of plant life history classification in
Section 10.13) that can tolerate high levels of disturbance.
Examples include wind-dispersed weeds (grasses and annuals)
that colonize abandoned lots and properties, and plants that can
grow in and around pavement.
Bird species that thrive in urban habitats are often adapted to
nesting in environments that are similar to the cityscape. For
example, species that use cliff-like rocky areas, such as the rock
dove (pigeons, Columba livia) and peregrine falcon (Falco
peregrinus), are “pre-adapted” to using the barren concrete
edifices of urban buildings, whereas cavity-nesting species,
such as the house sparrow (Passer domesticus), house finch
(Haemorhous mexicanus), and European starling (Sturnus
vulgaris) are able to inhabit human dwellings.
Mammalian urban exploiters consist of species that are able to
find shelter in human dwellings and exploit the rich food source
provided by refuse, such as the house mouse (Mus musculus),
the black rat (Rattus rattus), and brown rat (Norway rat: Rattus
norvegicus).
Urban environments typically have more in common with other
cities than with adjacent natural ecosystems, so species that
flourish in urban habitats are often not native to the region.
Rather, these species tend to disperse from city to city, typically
with assistance—either intentionally or unintentionally—from
humans (see Chapter 8, Ecological Issues & Applications).
Species such as rock doves, starlings, house sparrows, Norway

rats, and the house mouse are found in all cities in Europe and
North America. As a result, many studies have found that the
number (and proportion) of non-native species tends to increase
as you move from rural habitats toward urban centers. In
general, the proportion of species that is non-native goes from
less than a few percent in rural areas to more than 50 percent at
the urban core.
This combination of negative interactions with the majority of
native species—while enhancing a small subset of often non-
native species, which we have manipulated to serve our needs,
facilitated through dispersal, or created urban environments in
which their populations flourish—is resulting in what urban and
conservation ecologists refer to as biotic homogenization, which
is the gradual replacement of regionally distinct ecological
communities with cosmopolitan communities that reflect the
increasing global activity of humans.
Summary
Classification 12.1
By designating the positive effect of one species on another as
+, a detrimental effect as −, and no effect as 0, we can develop
a classification of possible interactions between two co-
occurring species: (00) neutral; (0+) commensalism; (++)
mutualism; (0−) amensalism; (−−) competition; (+−) predation,
parasitism, or parasitoidism.
Population Dynamics 12.2
Species interactions typically involve the interaction of
individual organisms within the respective populations. By
influencing individuals’ probabilities of survival or
reproduction, interactions influence the collective properties of
birth and death at the population level, and in doing so,
influence the dynamics of the respective populations.
Natural Selection 12.3
Phenotypic variations among individuals within the populations
can result in differences in the nature and degree of interactions
that occur. These phenotypic differences may influence the
relative fitness of individuals within the populations in the

degree of interaction, resulting in the process of natural
selection. The process in which two species undergo reciprocal
evolutionary change through natural selection is called
coevolution. Mutually beneficial interactions typically serve to
reinforce the phenotypic changes that result from the species
interaction, and mutually detrimental interactions typically
result in phenotypic changes that function to reduce the
intensity of interaction.
Geographic Variation 12.4
Species with wide geographic distributions are more likely to
encounter a broader range of biotic interactions. Changes in the
nature of biotic interactions across a species’ geographic range
can result in different selective pressures and adaptations to the
local biotic environment. Ultimately, differences in the types of
species interactions encountered by different local populations
can result in genetic differentiation and the evolution of local
ecotypes.
Diffuse Interactions 12.5
Most interactions are not exclusive involving only two species
but rather involve a number of species that form diffuse
associations.
Niche 12.6
The range of physical and chemical conditions under which a
species can persist and the array of essential resources it uses
define its ecological niche. The ecological niche of a species in
the absence of interactions with other species is referred to as
the fundamental niche. The species’ realized niche is its
ecological niche as modified by its interactions with other
species within the community. Species interactions can function
to either restrict or expand the fundamental niche of a species
dependent on whether the interaction is detrimental or
beneficial.
Adaptive Radiation 12.7
Variations among local populations in biotic interactions can
result in phenotypic divergence and therefore have the potential
to function as mechanisms of adaptive radiation, if the

divergence in phenotypic characteristics results in reproductive
isolation.
Urban Ecology Ecological Issues & Applications
Urban ecology is the study of the ecology of organisms in the
context of the urban environment. Increased urbanization has
led to a decline in habitat and loss of many native species,
while providing habitat for other, often non-native species.
Required Resources
Text
· Chapter 12: Species Interactions, Population Dynamics, and
Natural Selection pp 243 - 260
· Chapter13: Interspecific Competition pp 262-282
· Chapter 14: Predation pp 285-311
· Chapter15: Parasitism and Mutualism pp 314-333
Multimedia
Western University. (2011, November 28). Western researchers
find fear itself affects predator-prey relationship (Links to an
external site.)Links to an external site. [Video File]. Retrieved
from https://www.youtube.com/watch?v=42efTOfJlhw
· Researchers from Western University have found that fear of
predation is powerful enough to affect wildlife populations even
when predators are prevented from directly killing any prey.
The video has important applications for wildlife management
and is used in the week four discussion.
Accessibility Statement
(Links to an external site.)Links to an external site.Privacy
Policy (Links to an external site.)Links to an external site.
LASI Bee Research & Outreach. (2013, October 14).
Quantifying variation among garden plants in attractiveness to
bees and other insects (Links to an external site.)Links to an
external site.[Video File]. Retrieved from
https://www.youtube.com/watch?v=4u2LeTPGo9w

· This video presents a research project that determined which
garden plants were most attractive to bees and other insect
pollinators. The objective is to create floral communities that
will promote and sustain healthy pollinator populations. The
video is used in the week four discussion.
Huy Channel. (2015, August 15). Predator/prey interactions,
camouflage, mimicry & warning coloration (Links to an
external site.)Links to an external site. [Video File]. Retrieved
from https://www.youtube.com/watch?v=Y9Ll5P6qeNU
· This video introduces viewers to the selective pressures
brought on by predator-prey interactions and provides several
examples of species adaptations that were prompted through
predator-prey interactions. The video will provide contact depth
by linking predator-prey interactions with species adaptations.
The video will aid in completing the week four discussion and
the week five final paper.

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CHAPTER 15Smith, T. M., & Smith, R. L. (2015). Elements of Ecolo.docx