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Home , Behavioral ecology, Predation, Theoretical ecology, The Predator (novel)

13

Predation Risk and Behavioral History

PETER NONACS AND DANIEL T. BLUMSTEIN

t is generally good to be afraid because hard outer coverings, growing spines or thorns, or I risk is omnipresent. If we extend our defi nition producing or sequestering poisonous substances. of predator to disease organisms, parasites, and Nevertheless, such defense mechanisms are (for plants), then the vast majority of clearly not without costs and demand trade-offs of organisms end their by falling victim to preda- energy or time allocated to defense or other activi- tion. Indeed, most organisms will be killed before ties. A hiding or fl eeing is not a they manage to produce even one surviving descen- animal. Group living can also create competitors dant offspring. The odds for a leaving for scarce or mates. Energy channeled her to begin a new colony, or for an acorn into armaments or defensive structures is energy navigating toward becoming an oak, are probably unavailable for attracting mates, reproducing, or in excess of 10,000 to 1 against success. Even in . Any organism that makes a poor allo- humans, it is estimated that in an ancestral type of cation among these functions suffers on the lathe hunter-gatherer society the average reproductively of . Indeed, given the ubiquity of mature woman gave birth to 8.09 children in her predation across all life stages, one can argue that lifetime (Hill & Hurtado 1996). Assuming a stable natural selection acts more strongly on antipreda- (i.e., each woman needs to produce 2 tor than anything else in the organism’s replacement adults for herself and her mate), it is repertoire. This is what makes studying predation obvious that the majority of children never sur- and responses to predation risk so fascinating. To vived to become either the “average” reproducing make an analogy to life history theory, those traits woman or man. that are expressed earlier in life are likely under All is not lost, however. Predation may be almost stronger selection than traits expressed late in life inevitable, but predators can be successfully avoided, (Roff 1992). Thus, for all the fundamental selective misdirected, or repelled for some time if act importance of reproductive behavior (see chapters in an appropriate manner. Appropriate behavior 20–26), many never have an opportunity will vary across . For some, it will be hid- to choose mates or produce offspring. Yet almost ing in safe refuges. For others, it will be increased every animal is likely to be under some predation vigilance and increased proclivity to fl ee when per- risk. ceiving danger. In some cases, social behavior will The effects of predation risk are evident in both create effective group defense mechanisms. Finally, fl exible and fi xed traits of organisms. Flexible traits for many organisms, the proper response may be a (plasticity; see chapter 6) are evident in decisions morphological one, such as making shells or other about where and how long to forage, willingness

207 208 of Behavior to tolerate the presence of others, and the amount the gain in survival decreases and asymptotes to a invested in mate choice and parental care (Lima & maximum. Obviously the relationship between Dill 1990). Fixed characteristics can be general per- survival and investment need not be S-shaped, but sonality types (e.g., bold or shy; Sih et al. 2004b; it is a reasonable starting point. Furthermore, to chapter 30, this volume), developmental describe such a relationship, we can use the general of behavior, physiology, and other morphological logistic curve, which has a long history in ecology features. The underlying assumption is that both and behavior (Richards 1959). Thus, we can map types are shaped by natural selection. Flexible traits survival for our trade-off as follows: imply that the ability to evaluate risk and modify actions is adaptive (Nonacs & Dill 1993) and plas- P(survival) = 0.01 + [0.98 / (1 + e-b(x - m))]n (13.1) ticity is favored (chapter 6). Fixed traits imply that predation risk is pervasive and constant in expres- where b and m are constants. The former deter- sion across multiple generations and plasticity adds mines how rapidly the curve rises, and the latter little to no benefi t. determines the level of investment that produces Through the evolutionary perspective, we can then the most rapid gain in survival. Primarily by vary- ask, “When does a given predation risk select for any ing m, we can produce functional relationships in type of antipredator defense?” The simple answer which low, intermediate, or high levels of invest- is that responding to predation risk is selectively ment in antipredator defense are required to signifi - advantageous when it produces higher fi tness than cantly increase expected survival for each predator ignoring predation risk. This seems like an obvious encounter (fi gure 13.1). Expected overall survival truism, but there may be many instances when ignor- will depend on the number of predator encounters ing predation risk is the better option. Even if being (n). Notice that we have added the constants 0.01 aware of or defending against predators increases and 0.98 to the equation. We do this in order to survival, the functional form of that increase could have survival across all predator encounters range have critical evolutionary implications. between 0.01 and 0.99. Thus, no matter how little Consider an organism with a fi xed pool of or great the investment in defense or how many energy, resources, or time that it can devote to predators are encountered, neither nor sur- either or antipredator function. If it vival are ever completely certain. Using equation invests a proportion (x) in antipredator defense, it 13.1, we can estimate fi tness as a multiplicative will have 1 - x remaining for reproduction. Before relationship between overall expected survival and we go any further, it is valuable to realize how fl ex- the proportion of resources left to allocate to off- ible the variable x can be. The investment can be spring production, such that: a fi xed cost for morphological features such as spines, horns, shells, or . It can be a behav- = (1 - x)P(survival) (13.2) ioral energetic cost such as for fl eeing from a real or perceived predator or giving loud alarm calls. Using equation 13.2, we can calculate the Finally, the cost can be lost opportunities such as a expected fi tness for any level of x across the sur- reduced foraging rate due to vigilance behavior or vival functions in fi gure 13.1 when the animal foraging in safer but less rewarding food patches to expects to encounter 1, 10, or 100 predators (fi gure avoid predators. In all cases, antipredator defenses 13.2). Figure 13.2 suggests two predictions. First, or behavior take away energy that could be con- that fi tness is highest when effective antipreda- ceivably used for increasing reproductive output. tor defense has low costs. This, in and of itself, is From a standpoint, a most rather obvious and therefore unsurprising. There interesting question is, how can we fi nd what the are some evolutionary implications. Consider that optimal level of investment, x, should be? To do so, a behavioral antipredator defense may we need to quantify the trade-off between gain in have lower maintenance costs (i.e., behavior can survival and loss in reproduction. Let’s begin with be turned on and off as needed, whereas a mor- a simple assumption: investment in antipredator phological response is not so fl exible). Thus, one function has an S-shaped payoff in survival. Initial would predict that antipredator would investments do not increase survival greatly. How- likely be more common in nature than would ever, as investment amount increases, survival starts antipredation physical morphologies. The latter to rapidly rise. Finally, at higher levels of investment, would likely appear only when predation risk is Predation Risk and Behavioral Life History 209

1

0.9

0.8

0.7

0.6

0.5

0.4

Probability of escape 0.3

0.2

0.1

0 00.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.91 Antipredator investment

FIGURE 13.1 Probability of escaping a predator’s attack per unit of investment into antipredator defenses (x). Strategies refl ect where minimal investment (triangular points, b = -25; m = 0.2 in the logistic equation), moderate investment (circles, b = -15; m = 0.5), or high investment (squares, b = -25; m = 0.8) in defense assures a high probability of successful escape. For all lines, n = 1. constantly signifi cant (i.e., when antipredator the central prediction from our simple model: that defense should never be turned “off”) and behav- investment in antipredator defense is highest when ioral options produce limited success. Therefore, individuals expect to encounter an intermediate the world should have far more animal species that number of predators. When predators are rare, are alert, wary, and observant of their environment defense investment can be lower because it is less rather than loaded with sharp spines, , and needed. There is a point, however, when predation hard shells. risk becomes so pervasive that no defense The second prediction is that as more predators is likely to be effective. Then it becomes better to are likely to be encountered, the optimal level of simply ignore predation risk and put all resources defense generally increases (and not surprisingly, into (rapid) reproduction. expected fi tness declines because more is invested in Therefore, fi gure 13.3 can be considered to rep- defense and survival is lower). However, looking at resent what we would call the intermediate preda- fi gure 13.2, one can see that this is not always true. tion risk hypothesis (IPRH): antipredator behaviors For example, when the survival function requires a and morphologies are most likely to evolve and be high level of investment to be effective, high preda- maintained under intermediate levels of predation tor encounter rates (n = 100) produce a radical shift risk. A corollary prediction of the IPRH is that the in strategies: the optimal policy invests nothing in intermediate zone is generally wider for behavioral antipredator defense. Because expected survival is traits than morphological traits. This follows from so low, the best evolutionary response may be to behavioral traits having less of a fecundity cost at invest entirely in early reproduction and little, if low levels of investment because they may still yield any, in defense against predation. high-effi cacy payoffs. The results from our simple We can better visualize the shifts in investment model are also very similar to a more detailed strategy by plotting the predicted optimal level of approach employed by Lima and Bednekoff (1999). investment (for the three survival functions from They also predict that antipredator investment can fi gure 13.1) against the expected number of preda- be increased to be effective against any single pre- tor encounters (fi gure 13.3). This fi gure illustrates dation attempt. If a large number of such attempts (a) 0.7 0.6 0.5 0.4 0.3 Fitness 0.2 0.1 0 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 (b) 0.35 0.3 0.25 0.2 0.15 Fitness 0.1 0.05 0 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 (c) 0.14 0.12 0.1 0.08 0.06 Fitness 0.04 0.02 0 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 Antipredator investment

FIGURE 13.2 Fitness payoff (fecundity ´ survival) for various levels of investment into anti-predator defense. Panels refl ect strategies that require (a) low, (b) medium, or (c) high investment to be effective (see fi gure 13.1). The symbols indicate where individuals expect 1 (triangles), 10 (circles) or 100 (squares) encounters with potential predators (n). The optimal investment level is found at the highest point of each curve.

1 0.9 0.8 0.7 0.6 0.5 0.4 0.3

Antipredator investment 0.2 0.1 0 0 25 50 75 100 125 150 175 200 225 250 Predator encounters

FIGURE 13.3 The optimal level of investment into antipredator defenses as the number of expected encounters with predators increases. Lines with square, circular, or triangular points refl ect strategies that require low, medium, or high investment to be effective (see fi gure 13.1). All strategies predict an initial increase in antipredator defense as more encounters are expected. However, for all levels of defense effectiveness, there is a point at which no investment in defense becomes the optimal strategy.

210 Predation Risk and Behavioral Life History 211 are expected, however, then long-term survival will 1 High risk not increase enough to offset the cost of the added defense. The concept of a trade-off between antipredator defense and other biological goals forms the basis for our review of the behavioral ecology of preda- tion risk. First, we consider evolutionary responses to predation risks that can change on a daily or moment-to-moment basis, or remain constant across many generations. Second, we consider how Optimal vigilance both the immediate behavior and the evolutionary Low risk history of species might affect the conservation and preservation of in a changing world. 0 1 Attack ratio 10 EVOLUTIONARY BEHAVIORAL FIGURE 13.4 The optimal vigilance model. As risk ECOLOGY OF PREDATION RISK in the high-risk situation relative to the low-risk situation (i.e., ratio) increases, animals Vigilance should become more vigilant in the high-risk situation and less vigilant in the low-risk situation. Prey cannot detect predators without some level of Redrawn from Lima and Bednekoff (1999). vigilance, but vigilance can take away from other activities. If predation risk is not constant over time, antipredator vigilance should vary dynamically and ultimately can be related to the relative time indi- (1 - p) = the amount of time spent in the relatively viduals are in high- and low-risk situations (Lima safe situation. In these equations, optimal time for- & Bednekoff 1999). Envision a ground squirrel or aging is expressed as a function of need (R) divided a living beneath the path of a raptor migra- by a ratio of attack rates combined with the time tory route. During the migration season, prey may spent in the different patches. By doing so, our encounter many thousands more raptors than dur- units work out to time foraging. ing the rest of the year. Should animals vary vigi- The key to understanding risk allocation is seen lance and foraging during each season? Or consider by setting aH = aL, in which case, fH = fL = R. This the variation in risk that darkness and moon cycles shows that the average foraging rate required to create. How should animals allocate time to vigi- meet energetic demand is not infl uenced by the lance and foraging during risky moonlit nights? actual level of risk, but rather the relative differ- Lima and Bednekoff’s (1999) risk allocation ence in risk between the low- and high-risk times hypothesis predicts that animals will be the most (or environments). Thus, the attack ratio, (aH / aL), vigilant when high-risk situations are rare. Con- determines variation in vigilance and antipreda- sider two environments, a high-risk one (H) and tor behavior. As the attack ratio increases, animals a low-risk one (L). The optimal times allocated to should feed more in the low-risk situations and the optimal level of vigilance should decrease in the feeding in the high- and low-risk environments (fH) low-risk situation (fi gure 13.4). and (fL) are as follows: The situation is more complex when the relative * = R fH (13.3) time spent in different risk situations begins to vary. (/)()αα1 −+pp HL Figure 13.5 illustrates the predicted optimal levels of vigilance when the proportion of time spent in *= R fL (13.4) the high-risk situation is 0.3, 0.5, and 0.7. For any ()(/)1−+ααpp LH given attack ratio, vigilance in the low-risk situa- where R = the average rate of foraging required tion (solid lines) is affected more by the proportion to meet an energetic demand, a = the attack rates of time in the high-risk situation than is vigilance in in the different environments, p = the proportion the high-risk situation. Thus, when risks are sub- of time spent in the dangerous situation, and thus stantial and rare, it pays to invest a lot in vigilance. 212 Ecology of Behavior

1 hypothesis to pulses of high risk. In another experi- High risk ment, Van Buskirk et al. (2002) kept tadpoles in artifi cial ponds with either many or few caged dragonfl y larvae. Although tadpoles responded aversively to the predators, they did not vary their feeding behavior as predicted by the risk allocation hypothesis. Finally, Sundell et al. (2004) did not Low risk fi nd strong support for the risk allocation hypoth- esis when they manipulated predation risk to

Optimal vigilance by exposing them to weasels in large outdoor enclosures. Again, variation in feeding behavior was not infl uenced by pulses of risk, leading these authors to suggest limitations in the ability of the 0 voles to properly assess risk. These studies clearly 1 Attack ratio 10 indicate that more work is required to understand how animals assess risk and then to manipulate FIGURE 13.5 Optimal vigilance as a function of time risk to adequately test the risk allocation hypoth- spent at high risk (p). Optimal vigilance is plotted esis. Failure to fi nd support for the risk allocation for individuals that spend 30% (solid lines), 50% hypothesis could also stem from limited pheno- (dashed lines), or 70% (dotted lines) of their time typic plasticity. Future comparative studies could in the high-risk environment. As the proportion of time spent in the high-risk state decreases, the risk explore the situations under which species should allocation hypothesis predicts more vigilance for respond to temporally variable risk. individuals who live in low-risk situations than for However, before we reject empirical tests of the those in high-risk situations. Redrawn from Lima hypothesis, a recent study by Creel et al. (2008) and Bednekoff (1999). provides perhaps the best support for the risk allo- cation hypothesis under natural conditions. They focused on elk (Cervus elaphus) that had to sur- The consequence of this is to bias foraging toward vive snowy winters as well as the risk of predation lower-risk locations or at lower-risk times. There- by (Canis lupus). In Yellowstone National fore, predation risk, particularly temporally vari- Park, elk lived in areas with and without wolves able risk, impacts other activities and otherwise and thus experienced different amounts of back- should structure time budgets. Although the risk ground predation risk. In areas with wolves, elk allocation hypothesis applies generally to any anti- encountered wolves periodically. Therefore, there predator behavior, behavioral correlations (i.e., were pulses of predation risk. They evaluated a set syndromes, discussed below) may prevent adaptive of alternative statistical models that contrasted the allocation of antipredator behavior as predicted by risk allocation hypothesis against a null model (vigi- the hypothesis (Slos & Stoks 2006). lance was not infl uenced by predation risk) and two Strong empirical support for the risk alloca- alternative models (a risky times model and a risky tion hypothesis is lacking. Some of the best sup- places model). The risk allocation model explained port comes from Sih and McCarthy (2002), who the data the best: elk modifi ed their vigilance based exposed to chemical cues associated with on both background levels of predation risk and their predatory crayfi sh in different temporal sce- the temporal change in predation risk. narios. Snails living in high-risk situations were exposed to a pulse of low risk, whereas other Flight snails typically living in low-risk situations were exposed to a pulse of high risk. behavior var- Once detected, prey may or may not elect to fl ee. The ied based on the temporal pattern of risk. Snails decision to fl ee upon detecting a predator depends typically living under high risk were relatively inac- on both the costs and benefi ts of fl ight and thus is tive but when risk was suddenly decreased, they another antipredator behavioral trade-off (Yden- foraged a lot more. Snails from low-risk situations berg & Dill 1986). Eventually, all individuals will were moderately active. Unexpectedly, these snails fl ee an approaching threat (Blumstein 2006a), and did not respond as predicted by the risk allocation this deceptively simple observation has generated a Predation Risk and Behavioral Life History 213

(a)

High cost

Low cost

Cost of remaining = benefit of flight

(b) D-hc D-lc

Cost of flight

Cost of fleeing or cost remaining High benefit

low benefit

D-lb D-hb Distance to predator

FIGURE 13.6 A summary of Ydenberg and Dill’s (1986) model of fl ight initiation distance (FID). The intersection of cost and benefi t of fl ight curves predicts the optimal FID. In (a), FID is found for fi xed benefi ts of fl ight and variable costs of remaining. D-hc and D-lc are the optimal distances to fl ee when there are high or low costs of remaining, respectively. In this case, animals with high costs (e.g., those less able to move, or who will lose valuable resources by fl eeing) will tolerate closer approaches. In (b), FID is found for fi xed costs of fl ight and variable benefi ts from fl eeing that could refl ect the different risks from different predators. D-hb and D-lb are the optimal distances to fl ee when there are high or low benefi ts of fl ight, respectively. Thus, for a fast-moving, high-risk predator, for any distance, there would be a higher benefi t of fl ight compared to a slower-moving, lower risk predator. Therefore, animals should fl ee at greater distances from higher-risk predators. rich literature that identifi es a variety of factors that close and lower the farther away the predator infl uence fl ight (Stankowich & Blumstein 2005). is. Therefore, the cost of fl ight is 0 if the preda- Following Ydenberg and Dill (1986), we present tor is on top of the prey, and cost increases with a simple economic model of fl ight (fi gure 13.6). This increasing distance. The cost of fl ight might be lost model assumes that animals should minimize over- foraging opportunities. Like many such graphical all fi tness costs. The costs of fl eeing or remaining, optimality models, the crossover point is where c and b, are both affected by distance to predator. fl ight distance is optimized (fi gure 13.6). Thus, The cost of remaining equals the benefi t of fl ight. prey should let predators get closer as the cost Therefore, as this cost goes up, so does the benefi t of fl ight decreases. Similarly, the relative benefi t of fl ight. In other words, the benefi ts of fl eeing are will also infl uence the optimal distance to fl ee the increased fi tness relative to not fl eeing, whereas the predator. For any given cost of fl ight, fl ight ini- costs of fl eeing equals energy, opportunity, and any tiation distance (FID) will vary based on the cost increased conspicuousness to predators. of remaining. If some predators are more effective Realistically, the cost of remaining (i.e., the risk than others at a given distance, then this should of capture) is highest when the predator is very also infl uence FID. 214 Ecology of Behavior

What might infl uence the relative cost of an optimal solution because prey were assumed to remaining versus leaving? Let us fi rst focus on break even (i.e., costs would equal benefi ts), rather what might cause fl ight distance to increase with than maximize their fi tness (i.e., the location where risk. Some species are sensitive to the speed with benefi ts most greatly exceeded costs). They devel- which a predator approaches. For species that use oped two optimality models for maximizing fi tness. refugia, distance to protective cover should infl u- One had prey losing all residual reproductive value ence FID. Interestingly, some studies have found an upon death, and the other retained it after death. effect of distance to , whereas others have Overall, they concluded that optimality models are not (Stankowich & Blumstein 2005). The positive better than break-even models in explaining the results are consistent with the hypothesis that prey existing data on fl ight. reduce risk of predation by modifying their FID. Although quite variable, fl ight initiation distance However, not all animals seem to do this, and a can be viewed as a species-specifi c trait (Blumstein more complex consideration of the costs and ben- et al. 2003). For instance, in a comparative study of efi ts of fl ight may be needed. For instance, as ani- 150 species of , body size explains a substan- mals are farther from refuge, the cost to escaping tial portion of the variation in FID. Other variation increases because they have to expend more energy was explained by diet (species eating living prey both moving between the cover and the presum- fl ee at greater distances) and (cooperative ably good foraging area. breeders fl ee at greater distances). These results The Ydenberg and Dill (1986) model also suggest some carry-over effects from foraging and assumes that prey are cost minimizers. Thus, sociality on FID, such that species eating living prey they move when the benefi ts of fl eeing exceed the have better motion detection and social species are costs. However, animals that maximize the differ- vigilant for reasons other than predator detec- ence between the benefi ts and costs of fl eeing will tion (Blumstein 2006a). Additional studies have do better. The point is that both costs and benefi ts explored whether birds with larger are more increase as the distance to increases. If costs likely to detect a threat, but in a phylogenetically differ across species, this might account for some of corrected analysis, no signifi cant relationship was the variation in the response of distance to safety found (Blumstein et al. 2004a). Some variation in and FID. A positive relationship between FID and FID was also explained by the age at fi rst breeding distance to safety would be consistent with benefi ts (species that need to live longer to fi rst reproduc- of fl ight exceeding the costs. tion fl ee at greater distances). Thus, FID is a trait What about costs of leaving? For an animal to infl uenced by many aspects of life history. leave a good patch could be costly. If you lived in a A number of recent studies also focus on dynamic , it may take a lot to get you to leave an oasis! “hiding games” between prey and predators (Cooper It turns out that birds on productive wetlands in & Frederick 2007a). For species that use refuges generally desert-like Southern California typically (e.g., ) to reduce predation risk, the benefi ts are very tolerant of human approaches (Blumstein of hiding eventually are outweighed by the risk of unpublished data). An individual’s relative compet- starvation and prey must eventually emerge. Like itive ability can also infl uence the cost of leaving. FID, the dynamics of hiding should be sensitive to Subordinate animals might not be able to forage the risk of predation and the benefi ts and costs of at equally high rates before a as domi- hiding. Anything reducing cost for remaining in a nants and may be more likely to tolerate closer refuge (e.g., lowering metabolic rate while in a bur- approaches. All animals are neither created equal row) would presumably enable prey to reduce the nor remain equal throughout their lives. risks of both starvation and predation. Blumstein (2003) modifi ed Ydenberg and Dill’s model in a manner consistent with the intermediate Alarm Calling predation risk hypothesis. Specifi cally, if the risk is too great, all animals should immediately fl ee. And Alarm calls are signals emitted when prey detect a if the risk is too low, animals should never fl ee. It is predator and are remarkably plastic. Not all indi- only in a zone of intermediate risk that the dynam- viduals produce alarm calls and calling appears ics of fl ight become relevant and animals make sensitive to the trade-off between increased detect- trade-offs. Cooper and Frederick (2007b) further ability to the predator and escape benefi ts (Blumstein argued that the Ydenberg and Dill model was not 2007). What is particularly attractive to studying Predation Risk and Behavioral Life History 215 alarm calls is that we can study their trade-offs at emit “seet” calls when they encounter an aerial either an ultimate or proximate level. predator (Marler 1955). These high-frequency, nar- At level, how does giving an alarm row bandwidth calls are diffi cult to localize because call increase fi tness? Fitness can be increased through of their frequency and because they also fade in and two nonmutually exclusive pathways. First, calling out. Therefore, by examining the acoustic structure may increase the caller’s likelihood of survival. For of alarm signals, we can infer something about the example, calls can signal predator detection and risk associated with producing them. therefore discourage active pursuit. Or calling can A second line of proximate investigation create pandemonium among other potential prey involves the energetic and opportunity costs of call- that then allows the caller to escape. The second ing. At one level, calls are usually brief and there fi tness-enhancing pathway is through warning kin, is no convincing evidence that they have an ener- either one’s own offspring or other nondescendent getic cost (Blumstein 2007). Opportunity costs, kin. Because alarming calling is often assumed to be however, may be quite real and must be evaluated altruistic (i.e., callers increase exposure to predators from the signaler and recipient’s perspective. If the while warning others), many studies have focused signaler has already increased its vigilance, calling on the conspecifi c warning function of alarm calls. has a limited opportunity cost. From the recipient’s Although the importance of signaling to preda- perspective, this is an information problem. We can tors is often downplayed, in alarm calling examine two aspects of information: (1) is a preda- seems to have initially evolved as detection signal- tor truly present, and (2) does the call identify a ing toward predators (Shelley & Blumstein 2005). specifi c type of predation risk? Thus, in rodents, the conspecifi c warning functions Suppose there is a situation in which Nervous may be exaptations (chapter 2). Nelly calls at the drop of a leaf and Cool Hand Independent of its ultimate function, the prop- Lucy calls only when certain of a predator (Blum- osition that alarm calls increase predation risk is stein 2008). We should expect receivers to use often assumed but rarely tested. This involves a this difference in caller reliability (Blumstein et al. proximate question of call detectability. Because 2004b). In some species of and sciurid researchers often use alarm calls to locate callers, rodents, calls from unreliable individuals are dis- they assume that if it helps them, it should also counted and individuals reduce their vigilance after help the predators (Blumstein 2007). Tests of rap- repeated calls from unreliable individuals tors’ ability to localize alarm calls, however, dem- (following the fable of “the boy who cried ”). onstrate that some calls may be diffi cult to localize In contrast, yellow-bellied marmots (Marmota fl a- (e.g., Klump et al. 1986; Wood et al. 2000). In viventris) respond to unreliable calls by increasing contrast, hungry are attracted to the foot vigilance (Blumstein et al. 2004b). It might be that thumps of banner-tailed rats (Randall & unreliable marmots provide limited, but possibly Matocq 1997). Other evidence for a cost of calling true, information about predation. Thus, those comes from Sherman’s (1985) study of Columbian hearing them might need to independently validate ground squirrels (Spermophilus columbianus), the information. which scurry for cover before or while calling More controversial in terms of communication when pursued by a rapidly moving aerial predator, is whether alarm callers specifi cally identify the but call in place when responding to a terrestrial predator. Calls that function like basic words and predator. communicate information about external objects or Interestingly, the structure of calls shows that events are functionally referential alarm calls (Evans some are cryptic, whereas others are easy to localize. 1997). Marler et al. (1992) defi ned functional refer- For instance, mobbing calls (not, strictly speaking, ence so as to avoid implications about higher-level an alarm call) of many birds are rapidly paced with a cognitive abilities. Evidence of functional reference wide frequency band. Their bandwidth makes them requires that the calls be produced only to a spe- easy to localize. High frequencies predictably atten- cifi c set of stimuli and that playbacks in the absence uate to allow distance estimation, and broadband of a predator elicit the appropriate response. This have more “” present to stand out may not immediately seem that interesting, but ref- against background noise—increasing their detect- erential ability is a key feature of human language ability. Thus, these calls function, in part, to recruit (Hockett 1960) and once thought to be a uniquely others to mob the predator. By contrast, many birds human attribute. Subsequent research has shown 216 Ecology of Behavior

some degree of referential abilities in a number of group status to reduce predation risk, it is more dif- , , and species. fi cult to show that such trade-offs maximize fi tness. Macedonia and Evans (1993) provided perhaps This is known as the common currency problem the best explanation for the of function- (McNamara & Houston 1986; Gilliam & Fraser ally referential alarm calls. Species that have unique 1987; Nonacs & Dill 1990). The currency for pre- and mutually incompatible escape strategies seem dation is being alive or dead. A currency for foraging especially likely to have referential alarm calls. For is energy collected over time. So how much energy instance, vervet monkeys (Cercopithicus aethiops) should an animal give up to reduce predation risk must deal with snakes, which elicit investigation by a given amount? One answer is to empirically and avoidance; raptors, which elicit taking cover ask animals themselves how much energy they are in the safety of a dense tree crown; and leop- willing to sacrifi ce for increased safety. These are ards, which elicit taking cover on peripheral tree known as behavioral titrations, in which two fac- branches (Cheney & Seyfarth 1990). Such mutually tors such as food availability and predation risk are incompatible escape strategies create an opportu- allowed to covary (Abrahams & Dill 1989). When nity to communicate specifi cally about them. Thus, animals are trading off across the factors, it allows vervet monkeys have functionally referential calls. experimenters to measure the value of one factor in Playback of -elicited calls in the absence of the currency of the other factor. snakes causes monkeys to stand on their toes and Abrahams and Dill (1989) did a behavioral titra- look around. Playback of raptor-elicited calls causes tion for the value of predation risk in terms of food monkeys to run to trees and hide near the trunk. for (Poecilia reticulata). The fi sh were placed And playback of -elicited calls produces in an aquarium with two separated feeders. Initially, hiding on peripheral branches of trees. Once such without a predator present, the fi sh arranged across referential abilities evolve, more complex cognitive the two feeders as predicted by the ideal free dis- processes may follow. Therefore, selection to avoid tribution, such that all fi sh fed at about equal rates predation may be an integral pathway to complex (fi gure 13.7). A predation risk was associated with cognitive abilities. How such abilities might be one feeder and this caused a shift in preference, costly is unknown, although some interesting pos- measured as shift in fi sh distribution, toward the sibilities are being tested (chapter 10). safer feeder. This shift created a difference in feed- ing rates across the two feeders that could be used Multiple Goals and Common to estimate the energetic value guppies assigned to Currencies predation risk. This also allowed Abrahams and Dill to estimate in a second experiment how much The effect of predators on prey has fi sh would have to receive in food to return to the been part of for a long time (e.g., risky side in numbers equivalent to the initial ideal Volterra 1926; Lotka 1932). The incorporation of free distribution (fi gure 13.7). Interestingly, only behavior into predictive models, however, took sev- equated food with risks. For males, even eral decades longer until the rise of optimal foraging when one feeder was 17 times more rewarding, they theory (OFT; Schoener 1971; Charnov 1976; Pyke would not go to the risky feeder. This differ- et al. 1977). This body of work was the fi rst attempt ence in response to predation risk is probably due to to predict behavior in an economic context based in female guppies being closely generally on the maximization of the net rate of tied to their feeding rate. Access to females, rather energy intake. OFT was relatively quickly expanded than feeding rate, matters more to males. by the seminal work of Sih (1980, 1982) showing Researchers can also use patch giving-up density that animals could balance between two simultane- (GUD; the amount of food left behind in a patch at ous goals: the collection of food and the avoidance the point the forager leaves the patch) to estimate of predators. A multitude of studies followed that the food costs of predation risk (Brown & Kotler demonstrated trade-offs between avoiding preda- 2004). Thus, in the presence of , two species tors and gathering food, selection, prey of gerbils, Gerbillus a. allenbyi and G. pyramidum, choice, sociality, and group functioning (see reviews have far higher GUDs in seed trays that are placed by Lima & Dill 1990; Brown & Kotler 2004). in open rather than protected microhabitats (Kotler Although it is demonstrably obvious that ani- & Blaustein 1995). Furthermore, species-level dif- mals often trade off food, opportunities, and ferences are evident as G. a. allenbyi requires twice Predation Risk and Behavioral Life History 217

mortality risk is therefore a function of the expected gain in colony across patches. In conjunction with strictly empirical measures, theoretical models have been developed to pre- Risky dict multifaceted behavior. Particularly relevant are those that predict dynamic behavior. These models take into account that organisms have changing states, and optimal behavior should be infl uenced by those changes (e.g., hungry animals

Food/fish at feeder should take more risks for food than satiated ani- mals). Numerical solutions, using the techniques of dynamic programming (McNamara Safe & Houston 1986; Mangel & Clark 1988; Clark & Mangel 2000; chapter 8, this volume), have come to dominate the trade-off literature. This is true for Number of fish at feeder predicting specifi c behaviors such as female FIGURE 13.7 Measuring the energy equivalent of exploiting patches of eggs (Wajn- risk. In the experiments of Abrahams and Dill berg et al. 2006) and broad patterns of behavior (1989), guppies were allowed to distribute across numerous species. For example, optimal for- themselves across two feeders. The fi sh arranged aging models that do not simultaneously consider into an such that all the predation risk will consistently underestimate the fi sh at both feeders fed at similar rates (the two time animals stay in patches (Nonacs 2001a). open circles). In one experiment, a predator was Dynamic trade-off models have also been par- added to the one patch (which became the risky ticularly useful in understanding mass regulation patch). Thereupon more guppies started using the by animals. For example, for many species of birds, safe feeder, and thus intake rates were no longer individuals seem to be on a permanent diet: they equivalent across the two feeders (dotted arrows are skinnier than local food availability would pointing to black points). The difference between predict. This becomes obvious when the environ- intake rates (the dashed arrow) is a measure of the ment becomes worse or more variable. Under energetic equivalent of predation risk for guppies. such worsening conditions, birds get fatter (chap- This measure was then used in a second experiment ter 12). Mass regulation appears to be infl uenced as the amount of food/fi sh (solid black arrow and hatched point) that had to be added to the riskier by a trade-off between the risk of predation (a patch in order to have the same number of fi sh at heavier bird is slower) and the risk of starvation. the risky patch as were present in the original ideal In a good, predictable environment, a bird can free distribution. afford to be skinny because a meal is always read- ily available. Thus, starvation risk is low and birds can regulate their mass to maximize maneuverabil- ity. In poorer or unpredictable environments, birds as much food in risky patches as G. pyramidum to need to carry a fat buffer because the next meal is harvest the same number of seeds (i.e., it abandons uncertain (chapter 12). Therefore, birds will pay to patches with much more food left in them). Another reduce starvation risk by increasing predation risk solution with modular organisms, such as ant colo- ( Houston et al. 1997). nies, is to measure whether behavioral choices max- Nonacs (2001a) showed, however, that preda- imize growth and reproduction. Therefore, Nonacs tion risk could produce the same phenomenon in and Dill (1990, 1991) presented Lasius pallitarsis another way. Rather than assuming that weight cor- colonies with a dichotomous choice in foraging relates with predation vulnerability, Nonacs asked patches that varied inversely in food quality and whether spatial location mattered. For example, if mortality risk. The colonies preferentially foraged a predator employs a sit-and-wait ambush strat- in the patches that maximized colony growth rate egy, the time prey spent in a patch without being as measured by the net of the increase in biomass attacked increases the likelihood that this patch due to food collected minus the loss in biomass due is predator-free. Therefore, animals may remain to predation. Whether or not foraging brave far longer in patches than predicted by net-energy 218 Ecology of Behavior

maximizing criteria alone. A byproduct of staying Group living also allows the creation of a group where it is safe rather than always moving to fi nd phenotype that is unattainable by solitary indi- more rewarding patches is that animals stay skinny. viduals. The mutualistic benefi t gained through the Independent of whether location or weight itself interactions of genetically nonidentical individuals affects predation risk, the tendency to stay skinny is called social heterosis (Nonacs & Kapheim 2007, in the presence of plentiful food may be a quite 2008). If genetic diversity within groups increases common life history strategy in nature (although the variety of vigilance tactics or other antipredator our own species appears to be a very notable excep- techniques, this can reduce all group members’ per tion to this rule!). capita predation risk relative to living solitarily or living in a genetically homogenous group. A possi- Behavioral Syndromes ble example of the advantages of behavioral diver- sity could be a situation in which individuals adopt Animals may behave in predictable ways that defi ned roles such as “sentry” (Wang et al. 2009). indicate behaviors are not always independent of Indeed, a recent example with babblers (Turdoides one another (Sih et al. 2004b; chapter 30, this vol- bicolor) showed that sentry behavior was especially ume). Behavioral syndromes are seen when there effective at reducing predation-related costs and is a correlation across situations or contexts. For increasing foraging intake (Hollén et al. 2008). instance, if individuals that are bold around pred- ators are also bold when courting, a boldness syn- Sex and Alternative Reproductive drome has been identifi ed. Such syndromes may Tactics inhibit the ability of traits to track environmental variation if they create a trade-off. Assume, for Lima and Dill (1990) argued that the degree to example, that females prefer to mate with bold which predation risk might shape variation in males, but bold males are more likely to be killed reproductive tactics across species’ life histories by predators. Thus, the expression of boldness in was greatly underappreciated. We believe this still a population will be highly dependent upon the to be the case and that predation risk may a predation risk. If this syndrome did not exist, signifi cant role in exaggerating behavioral and mor- then the traits might vary independently accord- phological differences across the , alternative ing to variation in the costs and benefi ts of their strategies within a sex, and species-level differences. expression. Antipredator behavioral syndromes First, consider a species that catches prey in have been identifi ed in a number of species, and a fi xed web. For mating, males have to search for in the future we will have a better understand- females. Relative to the number of expected preda- ing of their importance. Describing and under- tor encounters (x-axis in fi gure 13.3), males would standing them can illustrate how trade-offs may inescapably experience considerably more risk. explain seemingly maladaptive behavior (Sih et al. Hence, males and females could differ dramatically 2004b). when females exhibit high investment into anti- predator defense and males invest next to nothing. Social Behavior and Group Living These differing optima could result in exaggerat- ing sexual dimorphism such that males, when com- Groups can form for selfi sh reasons, in which indi- pared to females, are (1) physically unimposing and viduals cluster in an attempt to reduce their own (2) behaviorally insensitive to risk to the point that exposure to predators (Hamilton 1971a). Group liv- they readily approach highly cannibalistic females ing, however, also has tremendous potential to cre- (Andrade 2003). ate effective antipredatory mechanisms (chapter 17). Second, in many species, males (and less com- Regardless of whether animals aggregate to increase monly females) exhibit alternative reproductive foraging effi ciency or to reduce the probability of tactics that can differ both behaviorally and mor- predation, once aggregated, novel antipredator phologically (Shuster & Wade 1991; Gross 1996; defenses can emerge. Consider the group defenses of Sinervo 2001; Oliveira et al. 2008; chapter 25, this muskox (Ovibos moschatus) that form a defensive volume). A key element across these tactics is that line against wolves or the group defenses of school- they impose signifi cantly different survival costs. ing fi sh, such as anchovies (family Engraulidae), that For example, a male salmon that follows the small form a constantly moving three-dimensional mass. jack strategy will forgo an extra year of foraging in Predation Risk and Behavioral Life History 219 the ocean. Such males expect to survive at a higher approached humans. Unarmed humans are of little average rate to breeding maturity, if for no other risk to an emu. The smallest birds (e.g., humming- reason than that they encounter fewer predators birds) also tolerated close approaches. The rela- (Gross 1996). Again, such differences in cumula- tionship between body size and vulnerability may tive predation risk would suggest that small jack explain a substantial amount of variation in risk. and large, nonjack males would reside at different Alternatively, body size may be associated with the points on the curves in fi gure 13.3. A (currently ability of animals to detect an approaching threat untested) prediction would then follow that jacks (e.g., Blumstein et al. 2004c). should be more likely to show antipredatory behav- In all of the above examples, predation risk ior and awareness of predation risk than their larger could create evolutionary feedbacks that increase male brethren. the effects of inter- and intrasexual selection and species . Thus, it would be interesting Species Differences to examine situations in which species have experi- enced prolonged evolutionary periods with reduced Some animals appear bolder in the face of pre- predation risk, such as isolated island faunas. Here dation risk than others. For example, you might all individuals may fi nd themselves at or near zero notice that you (or your dog) can get closer to the in fi gure 13.3, with low investment in antipreda- average chipmunk than to either a jay or a . tor defenses having the highest fi tness. Therefore, This certainly cannot be because the chipmunk is it would be predicted that (1) sexual dimorphism more able defend itself or escape your dog’s . would be reduced (e.g., Blondel et al. 2002), (2) However, the intermediate predation risk hypoth- alternative reproductive strategies within a sex esis may yield part of from where one would be rarer, and (3) species-level characters, would plot these species on fi gures 13.1–13.3. Jays such as size, would not predict the remaining levels have a much more effective escape mechanism than of antipredator awareness or fl ightiness. chipmunks (fl ying versus running). Hence, in fi gures 13.1 and 13.2 jays may lie on the triangles, whereas chipmunks lie on the squares. Thus even though PREDATION RISK AND both may have many potential predators, over a large range of predation risk one would expect the jay to invest more into vigilance and predator Predicting Successful Invasive avoidance (i.e., compare the squares and triangles Species in fi gure 13.3). In contrast, because a deer is a large animal, its size would reduce its number of poten- In 1890, Eugene Schiffelin felt that Americans tial predators relative to the chipmunk (hence the suffered in not being able to experience fi rsthand n for deer in equation 1 would be smaller than for all the birds mentioned in Shakespeare’s writings. chipmunks). Therefore, even if both hypothetically Therefore, he released bevies of larks and other lie on the same line for “running” animals in fi gure nonnative birds into the New York area. Thanks to 13.3, one might fi nd the deer on the hump of the Mr. Schiffelin’s efforts, we now have over 200 curve and the chipmunk at the bottom. million starlings in North America, but despite It turns out that size is a reasonably good predic- his efforts, no nightingales. This disparity of suc- tor of an animal’s potential life span (Roff 1992). cess highlights a major question in conservation To the degree to which predation causes this rela- biology. Why do some survive tionship, the IPRH predicts that animals of an inter- and become pests, whereas others do not (see mediate size would invest the most in antipredatory chapter 29)? behavior. Very small or large animals may experi- Following from the intermediate predation risk ence so many or few predators, respectively, that hypothesis, successful may be those high levels of vigilance would either be of little that have evolved life histories that invest relatively effect or not worth the cost. Blumstein (2006a) less in antipredator defenses because they are on demonstrated this empirically with species-specifi c the right-hand side of the curves in fi gure 13.3. fl ight behavior in birds. Large birds generally fl ee a Effective invasiveness could occur for two reasons. human at a greater distance. However, the largest First, such species are released from their natural birds (emus, Dromaius novaehollandiae) actually predators. This is an obvious advantage for any 220 Ecology of Behavior

introduced species. Second, if the introduced spe- evolutionarily short-term response over relatively cies is evolutionarily in an intermediate zone of few generations. If predators and parasites were predation risk, it still may considerably invest in absent for many generations, the good genes func- antipredatory defense against absent predators. tion within mate choice for male coloration would This cost would not be borne by species in which become less relevant. Whether a larger, drabber, low levels of antipredatory defense have been evo- longer-lived, more female-like male would eventu- lutionarily favored. This saved investment could tip ally evolve in this context is an interesting, open the competitive balance against natives. Simply not question. investing in antipredatory defenses, however, can- When antipredator behavior is lost, species may not by itself determine a successful invasive (oth- become particularly vulnerable to new predators erwise continents would overrun by pests from and such species may be particularly vulnerable to distant oceanic islands rather than vice versa!). exploitation and accidental . However, in Instead, successful invasives may come from the some cases, we see remarkable persistence of anti- right-hand side of fi gure 13.3 curves. That is, these predator behavior. Antisnake persist in species have adapted to strong predation pressures ground squirrels, wallabies retain group size effects by evolving life histories that may maximize repro- and predator discrimination abilities, and prong- duction at the expense of long-term survival. Thus, horn antelope (Antilocapra americana) retain their even if such species are susceptible to novel preda- remarkable athletic abilities long after the extinc- tors in an introduced range, it may not offset their tion of important predators (Blumstein 2006b). intrinsic fecundity. Therefore, in predicting which Thus, understanding the specifi c conditions under species are likely to become future economically which antipredator behavior is lost has important damaging invasives, it may be helpful to gauge their conservation implications. boldness and their response to predation risk. It is important to realize that prey species sel- dom have only one species of predator (Lima 1992; The Loss of Antipredatory Sih et al. 1998). The multipredator hypothesis Behavior capitalizes on this truism and therefore expects the evolution of linked, pleiotropic, or potentially fi xed If you are lucky enough to spend time on small antipredator traits when the costs of expressing oceanic islands, you may fi nd that ground-dwell- them in the absence of a predator are not extreme ing species are very tolerant of your presence. The (Blumstein 2006b). Specifi cally, the multipredator intermediate predation risk hypothesis may provide hypothesis predicts that for species with multiple one clue in that there is little predation here and predators, the loss of a single predator may have no therefore there will be little fear. But another clue effect on its antipredator behavior for that preda- may come from the observation that although most tor. Why? Imagine a young that relies species have more than one predator, some species on both camoufl age and immobility to hide from live in virtually predator free environments. predators. Individuals not possessing both of these In general, if we assume that predators have traits would be at a selective disadvantage. Now let selected for a variety of antipredator behaviors, opti- us elaborate on this theme. Imagine a population mality theory would predict antipredator behavior of prey that had to avoid both foxes and eagles. to disappear when the predators are no longer pres- Some could be super fox avoiders, whereas others ent. The loss of predators occurs naturally via colo- could be super eagle avoiders. However, in an envi- nization and extinction, and unnaturally when prey ronment with both types of predators, we would species are moved to predator-free locations. Gup- expect that only those individuals that were good pies for which predation pressure was eliminated at avoiding both predators would persist. Thus, we illustrate this nicely; they rapidly become sexier expect the presence of multiple predators to select (or at least more colorful), and we infer that this for suites (or syndromes) of antipredator behavior. is because the expression of this sexually selected Some of these suites are likely to result from linkage trait is no longer traded off against predation risk or pleiotropy. If so, we would expect a limited evo- (Endler 1995). We note that this seems at odds with lutionary response if suddenly one predator went our above suggestion that loss of predators should extinct. In support of the multipredator hypoth- disfavor sexual dimorphism. The discrepancy may esis, tammar wallabies (Macropus eugenii) were be that the observed changes in guppies are an found to retain group size effects and some degree Predation Risk and Behavioral Life History 221 of predator discrimination in populations in which implicated in their failure. Thus, the fi eld of con- there were some predators but not in a population servation behavior (Blumstein & Fernández-Juricic in which there were no predators (Blumstein & 2004) will profi tably benefi t from the cross-pollina- Daniel 2002; Blumstein et al. 2004a). tion of antipredator behavioral theory. For instance, The multipredator hypothesis has two important knowledge about the ontogeny and evolution of implications for future research. First, all predators antipredator behavior can inform captive rearing that a species encounters may have effects. An indi- programs when animals destined to be reintroduced vidual can have the best antipredator response to a in natural environments that contain predators. terrestrial predator, but if it never looks up in the sky it will be particularly vulnerable to aerial predators. We might expect selection to create suites of anti- predator behavior because being the best responder SUGGESTIONS FOR FURTHER to (Canis latrans) doesn’t count for much READING in an area with eagles. Second, are antipredator traits retained following the relaxation of selec- ’s (2005) recent book is the authoritative tion pressures located on the same chromosomes? go-to volume on antipredator behavior in birds Have these traits and genes been fi xed? Our grow- and . Curio (1976) provides a classical ing ability to identify and map signifi cant genes will ethological review of antipredator behavior. Lima ultimately answer this question and provide valu- (1998) and Preisser et al. (2005) provide a more able integration between phenotypic responses and ecological perspective on the consequences of anti- genotypic architecture. predator behavior. General foraging theory, includ- ing examples of how understanding predation risk is essential to understand foraging decisions, is FUTURE DIRECTIONS covered in Stephens and Krebs’ (1986) book. More recently, in the Stephens et al. (2007) book, chap- We believe that major advances in our understand- ters by Ydenberg et al., Bednekoff, and Brown and ing of how prey respond to predation risk will occur Kotler discuss numerous examples of how foraging in three areas. First, both the multipredator hypoth- gain and predation risk trade-offs can affect both esis and the intermediate predation risk hypothesis immediate behavior and structure. have yet to be tested in a variety of systems. If these predictive models work, then we have made a sig- Caro T (2005) Antipredator Defenses in Birds and nifi cant advance in understanding the evolution and Mammals. Univ Chicago Press, Chicago, IL. maintenance of antipredator behavior. Species like Curio E (1976) The of Predation. Springer-Verlag, Berlin, Germany. sticklebacks (family Gasterosteidae), in which pred- Lima SL (1998) Nonlethal effects in the ecology ator loss is replicated many times, and for which of predator-prey interactions. BioScience 48: there is great genomic knowledge, make an ideal 25–34. system to test the multipredator hypothesis. Second, Preisser EL, Bolnick DI, & Benard MF (2005) as suggested by Lima and Dill (1990), predation risk Scared to death? The effects of intimidation interacts with other processes that affect life history and consumption in predator-prey interac- evolution. The exact nature of these interactions tions. Ecology 86: 501–509. Stephens DW & Krebs JR (1986) Foraging remains a fertile ground for new research. Third, Theory. Princeton Univ Press, Princeton, NJ. predation risk has largely been ignored in both Stephens DW, Brown JS, & Ydenberg RC (eds) theory and practice of conservation biology. Many (2007) Foraging: Behavior and Ecology. Univ conservation actions fail, and predation is often Chicago Press, Chicago, IL.