Stephens & Foraging - Behavior and Ecology - Chapter 12 docx

12 Community Ecology Burt P. Kotler and Joel S. Brown 12.1 Prologue Two speciesof gerbils,the 24g Allenby’sgerbil andthe 40g greatersand gerbil, live together on sand dunes in the Negev Desert. These species arevery muchalike.They eatmostlyseeds (Baretal. 1984),theyare noc- turnal, they live in burrows, they are caught by the same predators, and they compete intensively with each other (e.g., Mitchell et al. 1990). They invite a central question of community ecology: Whatpromotes the co- existence of close competitors? How do these two species escape com- petitive exclusion? Perhaps the answer has to do with their use of habitats. The two species use the varied substrata of the sand dunes differently. Allenby’s gerbil predominates on sand dunes stabilized by vegetation, while the greater sand gerbil predominates on less stable sand dunes (Rosenzweig and Abramsky 1986). Habitat segregation intensifies at higher population densities (Abramsky and Pinshow 1989; Abramskyet al. 1990,1991). Foraging theorysuggests that habitatselection isbased on thecosts and benefits of habitat use (Fretwell 1972; Rosenzweig 1981). For this to explain species coexistence, each species must have a habitat that it uses and exploits better than its competitor (Brown 1989b). That is, Allenby’s gerbils should use the stabilized sand more because they forage more efficiently there, and greater sand gerbils should forage more efficiently 398 Burt P. Kotler and Joel S. Brown on the looser substratum. Experiments show, however, that Allenby’s gerbils forage more efficiently in both habitats (Brown, Kotler, and Mitchell 1994). Habitat selection resulting from the costs and benefits of foraging evidently does not provide the necessary conditions for the gerbils’ coexistence. So, did foraging theory fail? We think not, and in this chapter, we hope to show how the use of foraging theory helped us discover and test for the mech- anisms underlying the gerbils’ coexistence, and to understand the emergent pattern of habitat selection. 12.2 Introduction Community ecologistswant to understand the mechanismsthat determinethe abundances, numbers, types, and characteristics of species found living in the same place.They studyniches and how organisms thatdiffer fromone another partition those niches. Foraging theory helps us understand how the abilities and liabilities of animals determine where and whenthey can forage profitably and how much theyprofit under different circumstances. Understanding how each species’ fitness changes with the density and frequency of other species will illuminate community ecology. In previous chapters,we have seen that foragers at highdensities select prey opportunistically, and that competition can restrict the numbers of habitats used by individuals of interacting species (often called the compression hy- pothesis; Schoener 1969). Even in these simple cases, foraging matters. Forag- ing both responds to and reveals aspects of intra- and interspecific interactions. In this chapter, we will examine the community consequences of foraging from the perspective of niches and niche partitioning. Much of an animal’s niche involves where it lives and how it feeds. Foraging theory connects the characteristics and behavior of organisms with population dynamics, species coexistence, and community dynamics. It provides the tools for revealing the mechanisms by which species coexist and by which communities are struc- tured through the behaviors of the individuals. Foraging theory provides a window into the evolutionary ecology of communities, from the coadapta- tion of morphologies and behaviors to coevolution and speciation. 12.3 Species Coexistence Two species that occur at the same time in the same place coexist when their population densities are dynamically stable, or at least bounded away from Community Ecology 399 zero. Dynamic stability occurs when a system at equilibrium returns to its equilibrium point following small perturbations (i.e., has a stable equilibrium point). For pairs of interacting species, dynamic stability arises when intraspe- cific interactions are stronger than interspecific ones (e.g., May 1973). Mutual invasibility can also be a condition for species coexistence. Two species can coex- ist if each can increase when rare within a stable or persistent population of the other. Chesson (2000) provides an outstanding review of these mechanisms. Speciescoexistence canbepromoted byresourcepartitioning (whenspecies utilize different food types), frequency-dependent predation (when the rate at which predators kill individuals of different species depends on their relative abundances; Holt 1977; Holt et al. 1994), nonlinear competition combined with resource variability (when the per capita growth rateof a competitorspe- cies increases nonlinearlywith resource availability; Armstrong and McGehee 1980), and storage effects (when temporal variation leads species to be more successful in some seasons or years than in others; Chesson 1990, 2000). These mechanisms can stabilize communities whenever intraspecific interactions are stronger than interspecific ones. They are typically modeled as mass action models in which individuals come together and interact almost like molecules in an ideal gas. Mass action models do not explicitly consider behavior, or if they do, they do not allow behaviors to vary. However, foragers do be- have, and their behavior often varies with population density, resource avail- ability, and environmental conditions. Thus, behavior, especially foraging, can create and shape the stabilizing effects that promote species coexistence. We can introduce foraging behavior into mass action models via functional responses (see chap. 5). Adding foraging decisions to these models generally affects community stability. Functional responses sometimes destabilize com- munities (e.g., Gleeson and Wilson 1986; Fryxell and Lundberg 1994; Krivan 1996), but they can stabilize communities when predators avoid or ignore prey species that are at low population densities. Patch use decisions and con- straints on digestion or handling time can both stabilize communities in this way (Holt 1983; Schmitz, Beckerman, and O’Brien 1997). Here we examine how feeding behaviors shape species interactions and coexistence from the ground up and in greater depth by applying foraging theory. 12.4 Behavioral Indicators and Behavioral Titrations Building community models in which species interactions emerge from the foraging decisions of individuals requires an understanding of how behavior influences fitness. Testing such models requires methods that lead animals to 400 Burt P. Kotler and Joel S. Brown reveal aspects of their fitness through their behavior. Such methods are based on the costs and benefits of foraging when theforager experiences diminishing returns. For example, Kotler and Blaustein (1995) examined microhabitat selection and patch use in the gerbils of the prologue, Allenby’s gerbil and the greater sand gerbil (Gerbillus andersoni allenbyi and G. pyramidum, respectively). They asked how much richer open and dangerous microhabitats had to be for gerbils to value them equally with safer microhabitats under bushes. Kotler and Blaustein conducted their experiment in a large aviary where gerbils could forage on artificial patches (trays filled with seeds mixed into sand) placed in bush and open microhabitats. The gerbils experience diminishing returns while foraging in these trays, so the density of seeds left in a tray after a night of foraging, the giving-up density (GUD; Brown 1988; see box 13.2), reflects the forager’s harvest rate when it leaves the patch. A forager exploits the patch until the harvest rate falls to a value equal to the cost of foraging (see chap. 13). A higher giving-up density signifies higher costs. The experiment used barn owls (Tyto alba) to manipulate the danger level. In response to the owls’ presence, the gerbils showed higher giving-up densi- ties in the open than under bushes, revealing that owls pose a greater threat in the open. Then Kotler and Blaustein added seeds to the open trays until the gerbils were harvesting the same amount of seed from open and bush trays. G. pyramidum needed 4 times and G. a. allenbyi needed 8 times as much initial seed in the open trays to make the open microhabitats of equal value to the bush microhabitats (fig. 12.1). A similar experiment studied guppies (Poecilia reticulata) foraging in the presence of predaceous cichlids (Cichlasoma sp.) and gouramids (Trichogaster leeri) (Abrahams and Dill 1989). The study was based on the idea that foragers should distribute themselves according to an ideal free distribution (see box 10.1). The experiment offeredguppies a choice between two patches differing in danger (one side of the aquarium contained a predator). Most guppies avoided the dangerous side in favor of the safe side, leaving those fish willing to take the risk with higher feeding rates. The resource supply rate in the dangerous habitat was then increased to the level required to equalize the number of guppies on each side. We call studies like these “behavioral titrations” (Kotler and Blaustein 1995). Foraging theory tells us that a forager should perform an activity (feeding, hiding) so long as the marginal benefit it derives from this activity exceeds its marginal cost. A forager should continue with the activity until the marginal benefit falls to equal the marginal cost. When choosing which activities to perform, a forager should allocate more time to activities with Community Ecology 401 Figure 12.1. Behavioral titration. Total amounts of seed harvested from bush versus open microhabitats for (A) Gerbillus andersoni allenbyi and (B) G. pyramidum. Resource trays in the bush microhabitat con- tained a constant amount of seed from night to night, but trays in the open microhabitat varied. Bars of equal height for bush and open habitats indicate that gerbils place the same value on the two microhabi- tats. (After Kotler and Blaustein 1995.) higher net marginal values and reduce time spent on activities with lower net marginal values.Hence, a forager’soptimal allocation of time amongactivities should equilibrate the marginal values of the activities. Behavioral titration experiments provide a window into this equilibration. Researchers can take advantage of the animal’s natural tendency to perform fitness titrations by conducting titrations of their own involving total value, total effort, and so on. In titration experiments, we use a quantifiable dimension of quality, such as food abundance, to measure the fitness value of another, more difficult to quantify dimension, such as predation risk. Titrations carried out in this man- ner form the basis for behavioral indicators that reveal a forager’s perception of costs and benefits. Titrations can be used to test models of species interac- tions that involve foraging behaviors. 402 Burt P. Kotler and Joel S. Brown 12.5 Behaviorally Mediated Indirect Effects Tadpoles of two species of frogs, bullfrogs (Rana catesbeiana) and green frogs (R. clamitans), live together with the predatory dragonfly larva Anax junius in Michigan ponds. Werner and Anholt (1996) studied this system experimen- tally, manipulating the presence of caged Anax larvae while simultaneously manipulating the densities and size classes of tadpoles. The caged Anax larvae could not, of course, eat the tadpoles, but their presence did change the tad- poles’ behavior: in general, the tadpoles moved more slowly, which affected their feeding, mortality, and growth rates. Some of the effects were surpris- ing. The growth rates of green frog and small bullfrog tadpoles were reduced, but those of large bullfrog tadpoles were enhanced, and more large bullfrogs completed metamorphosis in the presence of Anax! This happened because while large and small bullfrogs compete strongly, Anax has a greater effect on small bullfrogs. So, from the large bullfrogs’ point of view, the presence of Anax reduced competition from small tadpoles, allowing the large bullfrog tadpoles to feed and grow faster. In the terminology of community ecology, Anax had a behavioral indirect effect on large bullfrog tadpoles via their interaction with small bullfrog tad- poles. In our example, the effect of Anax on the behavior of small bullfrogs shaped the way in which small bullfrogs competed with large bullfrogs. Stu- dents of indirect effects typically focus on effects mediated through changes in population densities and population growth rates, but one can consider other traits, including activity times, foraging speeds, and individual growth rates. When changes in behavior cause an indirect effect (e.g., as in our example with Anax and Rana), we call it a behaviorally mediated indirect effect (Miller and Kerfoot 1987; Werner 1992). Indirect effects can cause what community ecologists call trophic cascades, in which a predator reduces the density or foraging activity of its herbivore prey, which in turn allows greater numbers of plants to grow (see chap. 13). Indirect effects can result in higher-order interactions wherein the intensity of the per capita effects of one species on another is altered by the presence of a third (Kotler and Holt 1989). In our example, the Anax scare the small bullfrog tadpoles, which move less, eat less, and grow more slowly. Because the small tadpoles eat less, each one has less of a negative effect on both its competitors and its periphyton food. Reduced feeding by small tadpoles allows for greater periphyton density. The effect of predators on the tadpoles thus “cascades” down to lower trophic levels. To see how behaviorally mediated indirect effects can affect community structure and coexistence, consider an environment with two equally productive Community Ecology 403 habitat types. One habitat provides more protection from potential predators. Two species that share a common predator and a common resource live in this hypothetical environment. The two species compete for the limited resource, but one is more vulnerable to predation than the other. In the absence of the predator, we expect the two species to compete intensely in both habitats, depleting all the available resources. We expect coexistence only if the two species differ in their resource-harvesting abilities in the two different habitats or in their relative energetic costs of foraging in the two habitats. Otherwise, the most efficient forager will win. With the predator present, things change. Now, one habitat offers safety but little food, and the other offers more food that comes at a cost (recall our discussion of behavioral titrations in section 12.4). As the foragers balance the costs and benefits of each habitat and adjust their activities and habitat use accordingly,competition intensifiesinthe safehabitat,but weakensin the dan- gerous habitat. The predator indirectly affects the competitive interaction be- tween the two prey species by influencing their behavior, so we have a behav- iorally mediated indirect effect. In addition, we have a higher-order interaction because the predator’s presence reduces the per capita effect of one competitor on the growth rate of the other. The presence of the predator and its effects on the habitat choices of the prey promote species coexistence, provided that the better competitor is more affected by the predator. Werner and Anholt (1993) modeled key aspects of the tadpole-Anax system. They sought to understand how the individual decisions of foragers combined to create the observed behaviors that led to the indirect effects. They had their model foragers select swimming speed and proportion of time spent active so as to minimize the ratio of mortality risk to harvest rate. In- creasing these parameters increased risk of predation and rates of resource de- pletion. Hence, these decisions permit the forager to determine its mortality risk, harvest rate,and individual growth rate. In general, both competition for resources and predation risk lead to slower optimal foraging speeds, lower ac- tivity levels, and slower growth rates. These effects in the context of interact- ing competitors yield indirect effects like those observed with the tadpoles. Experiments by Peacor and Werner (1997) showed that the behaviorally mediated indirect effect predicted by theory and observed in the simple tadpole-Anax food web applies to more complex food webs, too. Peacor and Werner placed the same numbers of green frog and small bullfrog tadpoles in each of several experimental ponds. They then varied the densities of large bullfrog tadpoles and two classes of odonate predators (free-ranging Tramea lacerata; caged Anax junius and Anax longipens). Caged Anax led green frogs and large bullfrogs to reduce their activities. This treatment gave rise to three 404 Burt P. Kotler and Joel S. Brown behaviorally mediated indirect effects, due mostly to the nonlethal effects of the Anax: 1. Large bullfrogs increased the movement of the smaller tadpoles (via interference and reduced resource levels), increasing Tramea predation on green frogs and small bullfrogs (an indirect effect spanning three trophic links). 2. Caged Anax reduced green frog activity, decreasing Tramea predation on green frogs. 3. Caged Anax increased the competitive advantage of small bullfrogs over green frogs, because green frogs responded more strongly to predation risk and thus spent less time active and grew more slowly (another indirect effect spanning three trophic links). This example demonstrates how behavioral responses to predators can alter competitive interactions and even interactions among predators (see Schmitz 1998 and Wootton 1992 for similar studies with different taxa). 12.6 Habitat Selection The world is heterogeneous. Resource density, cover from predators, forag- ing substratum, and types and numbers of competitors and predators are just some of the things that can vary in space or time. Specializations that increase a forager’s ability to exploit particular conditions often come at the expense of decreasing its ability to exploit others. Consequently, selection can favor the ability of a forager to direct its activity to situations where it profits most. This coadaptation of ability and behavior can affect species interactions and community structure. For example, habitat selection can reduce competition if two species select different habitats. In fact, the strengths of species interac- tions emerge from the optimal behaviors of the interacting individuals. Box 12.1 explains two graphical tools (isodars and isolegs) that reveal properties of habitat selection as well as communityorganization based on habitatselection. The following examples apply these tools. In the Rocky Mountains of southern Alberta, pine chipmunks (Tamias amoenus) coexist with deer mice (Peromyscus maniculatus) and red-backed voles (Clethrionomys gapperi ) across a range of conditions differing in aspect and plant community, from xeric open meadow to mesic fir forest. Chipmunks are diurnal, forest-dwelling ground squirrels that larder-hoard seeds and nuts. Deer mice are nocturnal caching omnivores that climb well, while red-backed voles are terrestrial herbivores that are active day and night and eat seeds and BOX 12.1 Isolegs and Isodars The ideal free distribution (IFD) of Fretwell and Lucas (1969) provides the basis for understanding how individuals should distribute themselves among habitats in response to habitat quality and population density. The IFD is described in box 10.1. Isodars (Morris 1988) and isolegs (Rosen- zweig 1981) link the habitat choices of individuals with the dynamics of populations and communities. Isodars The ideal free distribution assumes that foragers can change habitats with- out cost. Individuals choose the habitat that offers the highest fitness, and individuals can enter a habitat on an equal basis with those already there. Furthermore, the ideal free distribution assumes that fitness (per capita population growth rate) in a habitat declines with the habitat’s population density (fig. 12.1.1). For example, the relationship between density and fitness may be linear:  1 N A  dN A dt  = r A − b A N A , (12.1.1) where N A equals population density in habitat A, r A equals maximum per capita population growth rate in habitat A, and b A is the strength of density dependence in habitat A. Consider two habitats, A and B. If habitat A offers higher fitness at low population density, then all individuals should choose habitat A at low density. As density in A increases, fitness decreases for each individual there. Eventually, fitness in habitat A drops to the point at which fitness in a crowded habitat A equals fitness in an unoccupied habitat B. At that point, individuals should be indifferent to habitat choice because both habitats offer equal returns.Aspopulation density growsfurther,individuals should distribute themselves such that fitnesses across the two habitats are equal:  1 N A  dN A dt  =  1 N B  dN B dt  , (12.1.2) which is equivalent to A A − b A N A = A B − b B N B . (Box 12.1 continued) Figure 12.1.1. Ideal free distribution. The graphs show how per capita fitness declines in each of two habitats with each habitat’s population density. At low population sizes, all individuals crowd into the preferred habitat A, as it provides a higher fitness reward than habitat B (shown by the upper solid circle emanating from the highest horizontal lines). At a critical population size in habitat A (shown by the solid squares), unoccupied habitat B offers the same reward as habitat A. At this critical density, individuals should be indifferent to the choice between habitat A and habitat B. At total population sizes above this critical density, individuals should spread themselves between habitats A and B such that expected fitnesses are the same for A and B, as shown by the solid circles emanating from the lowest horizontal equal fitness lines. (A) Habitat A has twice the productivity of habitat B. (B) Habitat B offers resources that are twice as easy to encounter as those in habitat A. (After Brown 1998b.) [...]... species, 1 and 2, that share habitats A and B The two species can either show a shared preference for the same, best habitat (say, A), or they can do better in different habitats (say, species 1 does best in A and species 2 does best in B) and show distinct (Box 12. 1 continued) Figure 12. 1.3 Isolegs and isoclines (A) The isolegs and isoclines for distinct-preference, twospecies, density-dependent habitat... ecology (see box 12. 1) Abramsky, Rosenzweig, and colleagues manipulated the densities of gerbils in 1 ha field enclosures where two gerbil species, Allenby’s gerbil and the greater sand gerbil, could choose between stabilized and semi-stabilized sand dunes within a mosaic of habitats (Abramsky et al 1990, 1991; Rosenzweig and Abramsky 1997) The results supported the shared preference model (see fig 12. 1.3B)... If two species, 1 and 2, share habitats A and B, then we can rewrite equation (12. 1.3) as follows: N1A + αN2A = [C + β(N1B + βN2B )], where α = b11A /b12A and gives the average competitive effect of one individual of species 2 on species 1 in habitat A; C = (A1A − A1B )b1A and gives the quantitative differences between the two habitats; and β = (b12B /b12A ) and gives the average competitive effect... values, and handling times all affect rates of energy gain and determine diet and patch use decisions (see chap 1) The species compete through their effects on resource density The foraging aptitudes of individuals and the foraging choices that they make determine their effects on the resources and their energy gains What are the conditions for species coexistence that emerge from the species’ optimal behaviors?... distribution and characteristics of the resources and the foraging behavior of the consumers determine the position and shape of the depletion vectors For substitutable resources occurring in the Community Ecology same habitat, the slope of the depletion vector is determined by the consumer’s rates of encountering the two resources, a1 and a2 , and the abundances of the two resources, R1 and R2 The... habitats, and microhabitats they utilize, their vigilance, and so on The foragers’ abilities, in the context of the environment, determine where and when they can forage profitably Foraging theory allows us to quantify their behaviors, measure their costs and benefits of foraging, and test possible mechanisms of coexistence This approach makes it possible to identify salient features of the environment and. .. stressful habitat type Foraging costs increase with habitat stress Foragers Community Ecology with a stress-intolerant strategy have very low foraging costs in benign habitats, but their foraging costs increase rapidly with habitat stress Foragers with a stress-tolerant strategy have relatively high foraging costs in benign habitats (relative to the stress-intolerant strategy), but their foraging costs increase... of habitat selection, the cost of temporally partitioning the night, and the cost of apprehensive foraging under predation risk (Abramsky et al 2001, 2002a, 2002b) 415 416 Burt P Kotler and Joel S Brown Figure 12. 3 The density-dependent habitat selection isolegs for Gerbillus andersoni allenbyi (lines) and G pyramidum (light curve) and the isocline of G a allenbyi (heavy curve) drawn in a state space... regions of optimal behavior In region I, both species prefer semi-stabilized sand dunes; in region II, G pyramidum still prefers the semi-stabilized habitats, but G a allenbyi opportunistically exploits both the semi-stabilized and stabilized habitats; in region III, G a allenbyi exhibits apparent preference for the stabilized habitats, and G pyramidum continues to prefer the semi-stabilized habitats;... more efficiently in all other forest types Squirrels and chipmunks coexist via a combination of habitat selection in time and in space Different forest types and different seasons provide the necessary environmental heterogeneity, and differences in body size, torpor strategies, and arboreal abilities provide the necessary trade-offs Studying the foraging behaviors of two or more coexisting species often . in A and species 2 does best in B) and show distinct (Box 12. 1 continued) Figure 12. 1.3. Isolegs and isoclines (A) The isolegs and isoclines for distinct-preference, two- species, density-dependent. Forag- ing both responds to and reveals aspects of intra- and interspecific interactions. In this chapter, we will examine the community consequences of foraging from the perspective of niches and. foragers do be- have, and their behavior often varies with population density, resource avail- ability, and environmental conditions. Thus, behavior, especially foraging, can create and shape the stabilizing