Stephens & Foraging - Behavior and Ecology - Chapter 12 docx

In previous chapters, we have seen that foragers at high densities select preyopportunistically, and that competition can restrict the numbers of habitatsused by individuals of interacti

Community Ecology

Burt P Kotler and Joel S Brown

12.1 Prologue

Two species of gerbils, the 24 g Allenby’s gerbil and the 40 g greater sand

gerbil, live together on sand dunes in the Negev Desert These species

are very much alike They eat mostly seeds (Bar et al 1984), they are

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: What promotes 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; Abramsky et al 1990, 1991)

Foraging theory suggests that habitat selection is based on the costs 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

on the looser substratum Experiments show, however, that Allenby’s gerbilsforage more efficiently in both habitats (Brown, Kotler, and Mitchell 1994).Habitat selection resulting from the costs and benefits of foraging evidentlydoes not provide the necessary conditions for the gerbils’ coexistence.

So, did foraging theory fail? We think not, and in this chapter, we hope toshow how the use of foraging theory helped us discover and test for the mech-anisms underlying the gerbils’ coexistence, and to understand the emergentpattern of habitat selection

12.2 Introduction

Community ecologists want to understand the mechanisms that determine theabundances, numbers, types, and characteristics of species found living in thesame place They study niches and how organisms that differ from one anotherpartition those niches Foraging theory helps us understand how the abilitiesand liabilities of animals determine where and when they can forage profitablyand how much they profit under different circumstances Understanding howeach species’ fitness changes with the density and frequency of other specieswill illuminate community ecology

In previous chapters, we have seen that foragers at high densities select preyopportunistically, and that competition can restrict the numbers of habitatsused 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 foragingfrom the perspective of niches and niche partitioning Much of an animal’sniche involves where it lives and how it feeds Foraging theory connects thecharacteristics and behavior of organisms with population dynamics, speciescoexistence, and community dynamics It provides the tools for revealing themechanisms by which species coexist and by which communities are struc-tured through the behaviors of the individuals Foraging theory provides awindow 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 theirpopulation densities are dynamically stable, or at least bounded away from

zero Dynamic stability occurs when a system at equilibrium returns to itsequilibrium point following small perturbations (i.e., has a stable equilibriumpoint) For pairs of interacting species, dynamic stability arises when intraspe-cific interactions are stronger than interspecific ones (e.g., May 1973) Mutualinvasibility 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 theother Chesson (2000) provides an outstanding review of these mechanisms.Species coexistence can be promoted by resource partitioning (when speciesutilize different food types), frequency-dependent predation (when the rate atwhich predators kill individuals of different species depends on their relativeabundances; Holt 1977; Holt et al 1994), nonlinear competition combinedwith resource variability (when the per capita growth rate of a competitor spe-cies increases nonlinearly with resource availability; Armstrong and McGehee1980), and storage effects (when temporal variation leads species to be moresuccessful in some seasons or years than in others; Chesson 1990, 2000) Thesemechanisms can stabilize communities whenever intraspecific interactionsare stronger than interspecific ones They are typically modeled as mass actionmodels 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 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

be-We can introduce foraging behavior into mass action models via functionalresponses (see chap 5) Adding foraging decisions to these models generallyaffects community stability Functional responses sometimes destabilize com-munities (e.g., Gleeson and Wilson 1986; Fryxell and Lundberg 1994; Krivan1996), but they can stabilize communities when predators avoid or ignoreprey species that are at low population densities Patch use decisions and con-straints on digestion or handling time can both stabilize communities in thisway (Holt 1983; Schmitz, Beckerman, and O’Brien 1997) Here we examinehow feeding behaviors shape species interactions and coexistence from theground 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 theforaging decisions of individuals requires an understanding of how behaviorinfluences fitness Testing such models requires methods that lead animals to

reveal aspects of their fitness through their behavior Such methods are based

on the costs and benefits of foraging when the forager experiences diminishingreturns

For example, Kotler and Blaustein (1995) examined microhabitat selectionand 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 forgerbils to value them equally with safer microhabitats under bushes Kotlerand Blaustein conducted their experiment in a large aviary where gerbilscould forage on artificial patches (trays filled with seeds mixed into sand)placed in bush and open microhabitats The gerbils experience diminishingreturns 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 exploitsthe 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 ties in the open than under bushes, revealing that owls pose a greater threat inthe open Then Kotler and Blaustein added seeds to the open trays until thegerbils were harvesting the same amount of seed from open and bush trays

densi-G pyramidum needed 4 times and densi-G a allenbyi needed 8 times as much initial

seed in the open trays to make the open microhabitats of equal value to thebush 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 box10.1) The experiment offered guppies a choice between two patches differing

in danger (one side of the aquarium contained a predator) Most guppiesavoided 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 thedangerous habitat was then increased to the level required to equalize thenumber of guppies on each side

We call studies like these “behavioral titrations” (Kotler and Blaustein1995) Foraging theory tells us that a forager should perform an activity(feeding, hiding) so long as the marginal benefit it derives from this activityexceeds its marginal cost A forager should continue with the activity untilthe marginal benefit falls to equal the marginal cost When choosing whichactivities to perform, a forager should allocate more time to activities with

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 tats (After Kotler and Blaustein 1995.)

microhabi-higher net marginal values and reduce time spent on activities with lower netmarginal values Hence, a forager’s optimal allocation of time among activitiesshould equilibrate the marginal values of the activities Behavioral titrationexperiments provide a window into this equilibration Researchers can takeadvantage of the animal’s natural tendency to perform fitness titrations byconducting 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 toquantify 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 tions that involve foraging behaviors

interac-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 poles’ behavior: in general, the tadpoles moved more slowly, which affectedtheir 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

tad-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 dents of indirect effects typically focus on effects mediated through changes inpopulation densities and population growth rates, but one can consider othertraits, including activity times, foraging speeds, and individual growth rates.When changes in behavior cause an indirect effect (e.g., as in our example

Stu-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 herbivoreprey, 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 Becausethe small tadpoles eat less, each one has less of a negative effect on bothits competitors and its periphyton food Reduced feeding by small tadpolesallows for greater periphyton density The effect of predators on the tadpolesthus “cascades” down to lower trophic levels

To see how behaviorally mediated indirect effects can affect communitystructure and coexistence, consider an environment with two equally productive

habitat types One habitat provides more protection from potential predators.Two species that share a common predator and a common resource live in thishypothetical environment The two species compete for the limited resource,but one is more vulnerable to predation than the other In the absence of thepredator, we expect the two species to compete intensely in both habitats,depleting all the available resources We expect coexistence only if the twospecies 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 safetybut little food, and the other offers more food that comes at a cost (recall ourdiscussion of behavioral titrations in section 12.4) As the foragers balance thecosts and benefits of each habitat and adjust their activities and habitat useaccordingly, competition intensifies in the safe habitat, but weakens in 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 interactionbecause 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 onthe habitat choices of the prey promote species coexistence, provided that thebetter 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 foragerscombined to create the observed behaviors that led to the indirect effects.They had their model foragers select swimming speed and proportion of timespent 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 mortalityrisk, harvest rate, and individual growth rate In general, both competition forresources 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 behaviorallymediated 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 ineach 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

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 trophiclinks)

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 riskand thus spent less time active and grew more slowly (another indirect effectspanning three trophic links)

This example demonstrates how behavioral responses to predators can altercompetitive 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, ing substratum, and types and numbers of competitors and predators are justsome of the things that can vary in space or time Specializations that increase

forag-a forforag-ager’s forag-ability to exploit pforag-articulforag-ar conditions often come forag-at the expense

of decreasing its ability to exploit others Consequently, selection can favorthe ability of a forager to direct its activity to situations where it profits most.This coadaptation of ability and behavior can affect species interactions andcommunity structure For example, habitat selection can reduce competition

if two species select different habitats In fact, the strengths of species tions emerge from the optimal behaviors of the interacting individuals Box12.1 explains two graphical tools (isodars and isolegs) that reveal properties ofhabitat selection as well as community organization based on habitat selection.The following examples apply these tools

interac-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 Chipmunksare diurnal, forest-dwelling ground squirrels that larder-hoard seeds and nuts.Deer mice are nocturnal caching omnivores that climb well, while red-backedvoles are terrestrial herbivores that are active day and night and eat seeds and

The ideal free distribution (IFD) of Fretwell and Lucas (1969) providesthe basis for understanding how individuals should distribute themselvesamong habitats in response to habitat quality and population density TheIFD is described in box 10.1 Isodars (Morris 1988) and isolegs (Rosen-zweig 1981) link the habitat choices of individuals with the dynamics ofpopulations and communities.

Isodars

The ideal free distribution assumes that foragers can change habitats out cost Individuals choose the habitat that offers the highest fitness, andindividuals can enter a habitat on an equal basis with those already there.Furthermore, the ideal free distribution assumes that fitness (per capitapopulation growth rate) in a habitat declines with the habitat’s populationdensity (fig 12.1.1) For example, the relationship between density andfitness may be linear:

capita population growth rate in habitat A, and bAis the strength of densitydependence in habitat A

Consider two habitats, A and B If habitat A offers higher fitness atlow population density, then all individuals should choose habitat A atlow density As density in A increases, fitness decreases for each individualthere Eventually, fitness in habitat A drops to the point at which fitness in acrowded habitat A equals fitness in an unoccupied habitat B At that point,individuals should be indifferent to habitat choice because both habitatsoffer equal returns As population density grows further, individuals shoulddistribute themselves such that fitnesses across the two habitats are equal:

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

Morris (1988) noted that this equation can be rewritten as

popu-Figure 12.1.2 Isodars The solid lines show the relationship between the numbers of individuals

in habitat A and in habitat B such that individuals experience the same fitness in each habitat (A) Habitat A offers twice the productivity of habitat B (same parameters as in fig 12.1.1A) (B) Habitat B offers twice the ease of encountering prey as habitat A (same parameters as in fig 12.1.1B) The dashed line (“centrally planned”) represents the distribution that maximizes total

We can construct isodars from census data (e.g., Morris et al 2000) byplotting estimated density in habitat A against estimated density in habitat

B By convention, we plot the density of the habitat with the higher

produc-tivity on the y-axis The isodar’s intercept [(AA− AB)/bA] gives the ence between the habitats in per capita growth rate at low population densi-ties (i.e., in the productivities of the habitats) Morris refers to differences in

differ-habitats revealed by nonzero y-intercepts of the isodar as quantitative

dif-ferences The isodar’s slope is the ratio of the terms that describe the sity of density-dependent effects in habitats A and B (often due to differ-ences in risk of predation) Morris refers to differences in habitats revealed

inten-by slopes different from 1 as qualitative differences

We can extend isodars to examine species interactions If two species,

1 and 2, share habitats A and B, then we can rewrite equation (12.1.3) asfollows:

N1A+ αN2A= [C + β(N1B+ βN2B)],

where α= b11A/b12Aand gives the average competitive effect of one

indi-vidual of species 2 on species 1 in habitat A; C = (A1A − A1B)b1Aand givesthe quantitative differences between the two habitats; and β= (b12B/b12A)and gives the average competitive effect of one individual of species 2 onspecies 1 in habitat B Or, more conveniently, we can rewrite equation(12.1.3) as

N1A= C − αN2A+ β(N1B+ βN2B) (12.1.1)

We can use multiple regression to estimate the parameters in this tionship [eq (12.1.4)] Isodar analysis accurately detects exploitative com-petition (Morris 1988), but may fail to detect interference competition(Ovadia and Abramsky 1995)

rela-Isolegs

Isolegs provide a different perspective on habitat selection (Rosenzweig1981) (fig 12.1.3) Again, the ideal free distribution provides the conceptualfoundation Isolegs give combinations of population densities at whichtwo habitats provide equal fitness Again, consider two species, 1 and 2, thatshare habitats A and B The two species can either show a shared preferencefor 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

Figure 12.1.3 Isolegs and isoclines (A) The isolegs and isoclines for distinct-preference, species, density-dependent habitat selection Below species 1’s isoleg (solid, positively sloped line), species 1 resides in both habitats, while above its isoleg it occupies habitat A only Below species 2’s isoleg (dashed, positively sloped line), species 2 resides in habitat B only, while above its isoleg it occupies both habitats Each species’ isocline (thinner lines) has a negative slope in region I (species 1 is opportunistic and species 2 is selective), a vertical (species 2) or zero (species 1) slope in region II (both species are selective on their preferred habitat type), and a negative slope in region III (species 1 is selective and species 2 opportunistic) The point where the two isoclines cross in region II indicates the ghost of competition past—neither species appears to have a negative effect on the other at the equilibrium point (B) Isolegs for shared- preference habitat selection where species 1 is the superior competitor in the preferred habitat Species 1 and 2’s isolegs have the same interpretation as in part A, with the addition of a second isoleg for species 2 (the short negative line) Inside this second isoleg, species 2 is selective on habitat 1 This creates a fourth region in the state space, IV, where both species are selective on

two-habitat preferences Assume that species 1 does best in two-habitat A, and species

2 does best in habitat B There are two important isolegs, one for species

1 and one for species 2 The isoleg for species 1 maps where species 1 goesfrom being selective on its best habitat (to the left of the isoleg) to beingopportunistic in its use of both habitats (to the right of the isoleg) This issimply the effect of density dependence that we have seen previously inchapter 10, in the ideal free distribution (see box 10.1), and above Theother isoleg maps the same for species 2

Consider the problems of species 1 without species 2 At low density, allmembers of species 1 select their preferred habitat A As population densityincreases, fitness in A drops to the same level as fitness in B This gives the

x-intercept of species 1’s isoleg At this point, individuals can choose either

habitat with the same consequences, and they should be indifferent If densityincreases beyond this point, foragers should choose habitats opportunisti-cally Thus, the isoleg separates a region of selectivity (species 1 resides only

in habitat A) from a region of opportunism (species 1 occupies both habitats

A and B)

But what if species 2 is also present? At low density, individuals of species

2 will select their preferred habitat B With some individuals of species 2 inhabitat B, it now takes more individuals of species 1 in habitat A to reducethe value of habitat A to equal that of habitat B The point at which fitnessesequilibrate now occurs at a higher density of species 1 (in A), and the isolegmoves up and to the right: as species 2 increases in B, the point wherespecies 1 switches from being selective on A to being Isolegs and isoclines.opportunistic occurs at ever higher densities of species 1 This results in an

isoleg that intercepts the x-axis and has a positive slope We use a similar

argument to find the species 2 isoleg, which also has a positive slope, but

intercepts the y-axis The result is a system of two isolegs, both with a positive slope, that separate the state space of N1and N2into three regions(fig 12.1.3A) Above species 2’s isoleg (region III in fig 12.1.3A), species 1selects habitat A and species 2 is opportunistic; between the isolegs (regionII), both species select their own best habitat; to the right of species 1’sisoleg (region I), species 2 selects habitat B and species 1 is opportunistic

As optimal habitat selection behavior changes across these three regions,the intensity of competition also changes The two species compete mostintensely in the upper and lower regions (I and III), where one species selectsits preferred habitat and the other occupies both habitats opportunistically

In the central region (II), however, the two species do not compete, because

the two species avoid each other by selecting their own preferred, best tats If population densities typically fall in this “no competition” region,the two species may evolve fixed habitat selection behavior that no longerresponds to density When this occurs, not even removal experiments candetect the interspecific competition that produced each species’ habitatspecialization Rosenzweig (1991) calls this phenomenon “the ghost ofcompetition past.”

habi-Zero population growth rate isoclines give the combinations of densities

of each species at which the population growth rate for a species is zero.These isoclines reveal the dynamic stability properties of the ecologicalsystem of two interacting species and can show the ghost of competitionpast (see fig 12.1.3A) The resulting changes in optimal habitat selectionbehavior in the different regions also change the intensity of competitionbetween the species there The isoclines change slope as they pass from oneregion to the next This results in isoclines that kink as they cross the behav-ioral isolegs The isoclines are vertical or horizontal between the isolegs andhave negative slopes elsewhere (see fig 12.1.3A) The kinking of the iso-clines can produce a stable equilibrium point where one otherwise wouldnot exist Thus, the magnitudes of the competition coefficients emergefrom behavior, and in fact, change as behavior changes (compare this withthe models of mass action in which competition coefficients are givens)

In other cases, two species may prefer the same habitat (fig 12.1.3B).Assume that both species prefer habitat A, but that species 1 is more despoticand specialized while species 2 is more tolerant across habitats There can bethree isolegs in this system The dominant species has a single isoleg that, as

in shared preference habitat selection, has a positive slope, and for the samereason At low density, species 1 will inhabit habitat A exclusively, butincreasing population density will eventually reduce fitness in habitat A tothe level of habitat B, so species 1 will become opportunistic and begin touse habitat B The presence of species 2 decreases the quality of alternativehabitat B and leads to a positively sloped isoleg The subordinate species has

up to two isolegs One separates the lower densities at which the nate species selects the preferred habitat from the higher densities at which itbecomes opportunistic For species 2 by itself, individuals will select habitat

subordi-1, and as its density rises, there will come a point where fitness in habitats A

and B are equal This point forms the isoleg’s y-intercept Below this point,

species 2 selects habitat A; above this point, it chooses opportunistically.However, species 2’s isoleg has a negative slope: increases in the density of

species 1 (also inhabiting habitat A at low density) will decrease the quality

of habitat A and lower the point where habitats A and B are of equal quality

Species 2’s isoleg will intercept the x-axis at or below species 1’s isoleg,

and that is why we can assume that there will be at least some members ofspecies 2 in habitat B when we calculate the species 1 isoleg

Finally, another isoleg for the subordinate species may exist above thefirst At sufficiently high densities of species 1 (which mostly uses habitat

A and may interfere with species B there), species 2 may choose to avoidthe best habitat altogether due to intolerable costs of interference from thedominant species and instead select habitat B This creates the new species

2 isoleg (to the right of the original) that separates opportunistic choice ofthe two habitats from a region of high species 1 density where species 2should select the poorer habitat This isoleg has a positive slope becauseadding more species 2 individuals to habitat B reduces its quality and makeshabitat A more attractive

The three isolegs create four regions with different combinations of timal habitat selection behaviors (see fig 12.1.3B) In region IV at the bot-tom left, both species select the best habitat, A In region III, species 1 se-lects habitat A, but species 2 is opportunistic In region I, species 1 choosesopportunistically, while species 2 shows an apparent preference for habitat

op-B The species compete most intensely in this region because both speciesoccupy both habitats and population densities are high And finally, in re-gion II, species 1 chooses habitat A, but species 2 selects the poorer habitat

As in the case of distinct preference, shared preference habitat selectioncauses the zero population growth rate isoclines to kink as they pass fromone region to the next

We derive isodars and isolegs from the ideal free distribution, and we usethem to reveal aspects of population growth, population regulation, speciesinteractions, and community organization Although they both explorehabitat selection, notice that they consider different quantities When weplot isodars, we plot density in habitat A versus density in habitat B; when

we plot isolegs, we plot density of species 1 versus density of species 2 Wecan find both isodars and isolegs from simple census data Additionally, ex-periments that give foragers a choice between habitats at different com-petitor densities can reveal the zero population growth rate isoclines ofthe system (e.g., Rosenzweig and Abramsky 1997) Thus, the ideal freedistribution forms the basis for a comprehensive analysis of populationsand ecological communities

vegetation Figure 12.2 shows the isodars for each species (Morris 1996).The isodars reveal a habitat generalist (chipmunk) and two habitat specialists(xeric habitat: deer mouse; mesic habitat: vole) Habitat selection responds tointraspecific density only, though the opportunism of the chipmunk occurs

at a fine scale, and the habitat selection of the deer mouse and red-backed voleoccur at a coarse scale Theory suggests that a generalist and two specialists cancoexist if the generalist experiences the environment as relatively fine-grained(Brown 1996), as do these rodents

Morris et al (2000) calculated isodars for two competing herbivorousrodents from the wet heathlands of eastern Australia The heathlands are

seasonally dry and burn frequently There, the swamp rat (Rattus lutreolus) occurs with the eastern chestnut mouse (Pseudomys gracilicaudatus) in habitats

co-that vary in age and edaphic conditions The eastern chestnut mouse is cially common in recently burned sites, but is gradually replaced by the swamprat as the effects of fire recede In intermediate-aged stands, the two species co-occur across the range of edaphic conditions The isodar analysis confirmedthe asymmetric competitive dominance of the swamp rat over the easternchestnut mouse in both wet and dry heath habitat, with stronger effects in

espe-drier sites Isodars also revealed the superiority of P gracilicaudatus in recently

burned areas Applying principles of density-dependent habitat selection roborated the results of previous removal experiments that revealed muchthe same information, at much greater cost and effort (Higgs and Fox 1993).Although isodars can reveal aspects of community organization, they arebetter suited for studying intraspecific behavior In contrast, isolegs are definedonly for two or more interacting species in heterogeneous environments Wecan use experimental manipulations of population densities to find isolegs.The isoleg for a species gives all combinations of the densities of two (or more)species such that the species is indifferent in its use of the two habitats Usuallythis isoleg considers the point at which a species goes from being selective onone habitat to being opportunistic on two habitats There is a separate isolegfor each species The isolegs exist in the same state space of species densities

cor-as the population growth rate isoclines from ecology (see box 12.1)

Abramsky, Rosenzweig, and colleagues manipulated the densities of bils in 1 ha field enclosures where two gerbil species, Allenby’s gerbil and thegreater sand gerbil, could choose between stabilized and semi-stabilized sanddunes within a mosaic of habitats (Abramsky et al 1990, 1991; Rosenzweigand Abramsky 1997) The results supported the shared preference model (see

ger-fig 12.1.3B) and the existence of a single isoleg for the dominant species, buttwo isolegs for the subordinate species More importantly, the investigatorsdeduced the general shapes of the zero population growth rate isoclines (in-dicators of the dynamic stability of the system, i.e., whether the two species

species of montane rodents in the Rocky Mountains of southern Alberta, Canada: (A) deer mouse, (B) red-backed vole, and (C) pine chipmunk Isodars are based on the ideal free distribution and are ob- tained by regressing population densities in one habitat versus the other Isodar intercepts that differ from

0 reveal quantitative differences between habitats, and slopes that differ from 1 reveal qualitative ences (see box 12.1) For the deer mouse, the xeric habitat is both quantitatively and qualitatively supe- rior; for the vole, the mesic habitat is quantitatively superior; for the chipmunk, the habitats are equally

differ-coexist) through the application of the ideal free distribution They did so

by connecting pairs of enclosures with gates By allowing only one species

to pass through the gates, Abramsky and Rosenzweig could fix competitordensities in the two connected enclosures while allowing the target species

to adjust its distribution and activity Using this technique, Abramsky andRosenzweig measured the effect of the species with a fixed density on thelevel and distribution of foraging activity of the species that could move freelybetween enclosure halves In this way, the species that is free to move revealsthe effect of competition with the other species on it (the competition coeffi-cient) through its habitat selection behavior By repeating this treatment over

a range and combination of competitor densities, Abramsky and Rosenzweigcould render the shape of the isoclines Remarkably, their data support thenonlinear isoclines that foraging theory predicts (Abramsky et al 1991, 1994;Abramsky, Rosenzweig, and Subach 1992; fig 12.3)

The data from these experiments can also be examined with isodar analysis(Ovadia and Abramsky 1995) The isodars confirm shared preference habitat

selection for the semi-stabilized habitat, with G pyramidum experiencing the stabilized and the semi-stabilized sand as qualitatively similar, but G a allenbyi

experiencing the stabilized sand as qualitatively superior The isodars reveal a

flip-flop in the habitat preferences of G a allenbyi At low population densities

it prefers the semi-stabilized sand habitat, but at high densities it prefers thestabilized habitat The isodars also revealed resource competition between thetwo species, but failed to detect interference Abramsky and Rosenzweig’sability to set conditions in different enclosures and then allow the animals toperform their own titrations made this a successful experiment

We can use this approach to address other questions in community ecology.Abramsky et al (2000), for example, used it to measure the energetic cost of

interspecific competition They established four G pyramidum individuals in one of two connected enclosures, along with 40 or 50 G a allenbyi individu- als The G a allenbyi could move freely between the two enclosures (through species-specific gates); the G pyramidum could not (as in the above experi- ments) G a allenbyi individuals adjusted their enclosure-specific activities in

response to the differing competitive regimes in the two enclosures More

G a allenbyi activity occurred in the enclosure without the competitor Next,

Abramsky et al carried out an experimental titration, adding seeds to the

enclosure with G pyramidum until G a allenbyi was equally active in both

enclosures Adding 4.5 g of seeds to each of 24 trays balanced the effect of fourcompetitors To date, similar titrations have measured the benefits of habitatselection, the cost of temporally partitioning the night, and the cost of appre-hensive foraging under predation risk (Abramsky et al 2001, 2002a, 2002b)

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 of

activity densities (i.e., activity as measured by tracking plots) of the two species The isolegs separate

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; in region IV, G a allenbyi continues to exhibit apparent preference for the stabilized habitats, and G pyramidum uses both

habitats The zero population growth rate isocline changes slope in the different regions as habitat tion behavior changes, and with it, the intensity of competition Note (1) the strong interactions between

selec-the gerbils when selec-the subordinate G a allenbyi and selec-the dominant G pyramidum occur at low densities

and both species forage mostly in the preferred semi-stabilized habitat; (2) the strong interactions when

G pyramidum is at high densities and using habitats more opportunistically; and (3) the less intense interactions at intermediate densities when the dominant G pyramidum is still selective on the preferred semi-stabilized habitat, but the subordinate G a allenbyi already favors the stabilized habitat (After

Abramsky et al 1991.)

12.7 Optimal Behavior and Consumer-Resource Models

Coexisting species often differ in body size, but such differences do not alwayslead to coexistence based on food size selection Coexisting species of graniv-orous desert rodents often differ in body size (e.g., Brown 1975), yet mayoverlap almost completely in the sizes of the seeds that they consume (e.g.,Lemen 1978) In contrast, coexisting species of Darwin’s finches may show dis-tinct differences in both their beak sizes and the seed sizes in their diets (Grant1986; see section 12.8) Can foraging theory illuminate the causes for such dif-ferent outcomes?