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);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

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  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 the competition  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;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 populationand can therefore be represented as cNprey, where c represents the capture efficiency of the predator, defined by the slope of the relationship shown in (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, 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, .

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  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 ( 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 . 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 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  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 tor–prey cycles can result from the direct link between predator and prey populations as suggested by the Lotka–Volterra equations, 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  .

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 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 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

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

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 cT, and th. (Recall that the values of cT, 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 rea, 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. 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 . 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

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 a. 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

The work of Włodzimierz Je̜drzejewski 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. Je̜drzejewski 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

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. 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 populatio in the analysis of the Lotka–Volterra model in 

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 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/thFigure  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 TypeDensity Zone ADensity Zone BDensity Zone CEnergy (kJ/individual)Handling Time (s)Search Time (s)
Urchins0.03.923.07.458.335.8
Chitons0.110.35.624.523.137.9
Mussels852.31.70.61.422.918.9

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

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, and What 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. Relyea Department 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

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

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