Oecologia DOI 10.1007/s00442-014-2956-0
Behavioral ecology - Original research
To dare or not to dare? Risk management by owls in a predator–prey foraging game Keren Embar · Ashael Raveh · Darren Burns · Burt P. Kotler
Received: 20 November 2013 / Accepted: 16 April 2014 © Springer-Verlag Berlin Heidelberg 2014
Abstract In a foraging game, predators must catch elusive prey while avoiding injury. Predators manage their hunting success with behavioral tools such as habitat selection, time allocation, and perhaps daring—the willingness to risk injury to increase hunting success. A predator’s level of daring should be state dependent: the hungrier it is, the more it should be willing to risk injury to better capture prey. We ask, in a foraging game, will a hungry predator be more willing to risk injury while hunting? We performed an experiment in an outdoor vivarium in which barn owls (Tyto alba) were allowed to hunt Allenby’s gerbils (Gerbillus andersoni allenbyi) from a choice of safe and risky patches. Owls were either well fed or hungry, representing the high and low state, respectively. We quantified the owls’ patch use behavior. We predicted that hungry owls would be more daring and allocate more time to the risky patches. Owls preferred to hunt in the safe patches. This indicates that owls manage risk of injury by avoiding the risky patches. Hungry owls doubled their attacks on gerbils, but directed the added effort mostly toward the safe patch and the safer, open areas in the risky patch. Thus, owls dared by performing a risky action—the attack maneuver—more times, but only in the safest places—the open areas. We conclude that daring can be used to manage risk of injury
Communicated by Peter Banks. Electronic supplementary material The online version of this article (doi:10.1007/s00442-014-2956-0) contains supplementary material, which is available to authorized users. K. Embar (*) · A. Raveh · D. Burns · B. P. Kotler Mitrani Department of Desert Ecology, Blaustein Institutes for Desert Research, Ben Gurion University of the Negev, 84990 Midreshet Ben‑Gurion, Israel e-mail: [email protected]; [email protected]
and owls implement it strategically, in ways we did not foresee, to minimize risk of injury while maximizing hunting success. Keywords Behavioral indicators · Predator–prey interactions · Daring · Risk of injury · Tradeoffs of food and safety
Introduction Perhaps all organisms face tradeoffs of food and safety. While we are well aware that prey manage this tradeoff, there may be less awareness that predators have their own food and safety issues. In a foraging game, predators must catch elusive prey while avoiding injury. Risks of injury may rise from predation, dangerous prey, and the inherent risk in the environment. Small- and medium-sized predators suffer predation from bigger predators, either during the vulnerable stage of their youth or throughout their entire lives. Juvenile predators may escape predation by outgrowing the capacity to be eaten (Damsgard and Dill 1998), and other preyed-upon predators are forced to adopt prey tactics such as predator avoidance (Laroche et al. 2008), which disrupt their own hunting efforts. Some prey evolved to be dangerous, possessing weapons such as horns, mechanical and chemical defenses such as spines and poisons, and aggressive behavior fostering counter attacking the predators (see Mukherjee and Heithaus 2013 for review). And finally there is always the environment. Accidents happen, and accidents may be more prone in some environments than others. For example, long-eared owls may suffer eye injury when chasing prey into thorny dense thickets (Holt and Layne 2008). Predators may tend to avoid hunting in these risky environments, and this
13
Oecologia
can affect their interactions with their prey. For example, wolves avoid steep slopes (Kauffman et al. 2007) where the risk of injury from falling is high, and in the presence of wolves, elk use these habitats as refuge (Mao et al. 2005). Thus, the predator-prey interactions can be affected by the predator’s considerations of risk of injury. Predator-prey interactions are often evolutionary games in which the players trade off food and safety (Sih et al. 1998). While foraging, the prey alter their exposure to risk by using behavioral tools such as time allocation and habitat selection to determine where, when, and for how long to forage. The prey can further manage risk within the foraging patch by using vigilance and apprehension to enhance predator detection, albeit at the cost of lower harvest rates (Dall et al. 2001; Kotler et al. 2004). The use of these tools make prey harder to capture. Predators, faced with clever prey able to react in this sophisticated manner to their presence, also use the behavioral tools of time allocation and habitat selection to manipulate the level of fear they impose in their prey (Brown et al. 1999). In addition, they may alter their selection of hunting tactics (Kullberg 1995; Wignall and Taylor 2009) and use of daring to manage risk of injury and predator lethality (Berger-Tal et al. 2009). Why should predators manage fear? Prey respond behaviorally to predators by becoming less active, less exposed through habitat or microhabitats shifts, more wary and likely to detect attacks, and in extreme cases, foraging all together in favor of the safety of the refuge (Brown et al. 2001). That is, wary prey become harder to catch. Consequently, the longer a predator remains in the current patch, the lower are its chances of catching anything more there (Charnov et al. 1976). It is therefore to its benefit not to scare prey too much or for too long. This can be accomplished by reducing hunting effort, changing tactics, or moving to other patches where prey are less wary (Roth and Lima 2007). Remaining in the current patch where prey are becoming increasingly uncatchable instead of moving to patches with less wary prey represents wasted capture opportunities (Kotler 1992). Similarly, easing up on hunting effort creates opportunities to capture less wary prey, but easing up too much allows too many of those opportunities to go to waste. Therefore, a predator must manage fear in order to maximize its hunting returns. How should a predator do this? For predators hunting in patches where prey become increasingly wary the longer the predator stays in a patch, a predator should quit the patch when the prey become so wary that the expected energetic gain from hunting, E(H), falls to equal the summed costs of energy used in searching and capturing, C, the cost arising from risk of sustaining an injury in the course of the hunt, RI, the cost arising from the risk of predation when appropriate, P, and missed opportunity costs,
13
including that of not hunting in other patches where prey are less wary, MOC (Brown et al. 2001; Berger-Tal et al. 2009).
E(H) = C + RI + P + MOC.
(1)
This is analogous to the rule governing patch use by foragers (Brown 1988). Two distinctive features of this equation include the use of expected return rather than the actual instantaneous value and the inclusion of risk of injury. In their study with red foxes (Vulpes vulpes), Berger-Tal et al. (2009) showed that risk of injury gives rise to a foraging cost, with predators allocating less effort to risky patches and demanding a higher harvest rate to exploit compared to patches where the risk of injury was lower. Furthermore, patch use was affected by the predator’s state, with foxes allocating more time to both safe and risky patches when in poorer state. Daring can alter the risk of injury as well as the chances of prey capture, E(H), and the amount of food procured. Consider daring as a tool for fear management. We can define “daring” as the willingness to incur risk of injury. One way in which a predator can manage fear is by modulating its tactics. Some tactics may be more effective in capturing prey, yet also be more dangerous for the practitioner (Brown and Kotler 2004). These tactics should be more valuable when energy is more valuable. When this is the case, predators should be more daring when hungry and in low state and less daring when well fed (Berger-Tal et al. 2009). The frequency of attacks, speed of attack, willingness to pursue prey into physical refuges, patience displayed in ambushing prey, and disregard for prey weapons may all reflect daring. Hunger can greatly affect daring behavior in both predators and prey. Hungry prey are known to take more predation risks, leaving refuge earlier and approaching to shorter distances from predators (Sih 1992; Godin and Crossman 1994). Likewise, hungry spiders (Pardosa) under predation risk consumed as many prey as spiders not under predation risk (Walker and Rypstra 2003), implying they increased their daring and shifted the tradeoff of food and safety in favor of food. When hungry, predators may also dare to attack prey they previously avoided. For example, mice were more likely to attack bird nestlings when hungry (Bradley and Marzluff 2003), implying an increase in daring against the potential wrath of the parents and the struggle with nestlings that are often bigger than the mice. Hungry and daring predators may also reduce the effectiveness of the prey’s anti-predator behaviors. Altwegg (2003) found that against hungry predators, the prey’s attempts at predator avoidance were essentially ineffective. Hungry predators are more likely to attack (McCarty and Southwick 1981), their attacks are more lethal (Polsky 1975), and they are less willing to withdraw defensively from their
Oecologia
prey (Adamec et al. 1980), another indication of daring— willingness to risk. We ask, does a predator use daring to manage both the risk of injury to itself and the fear in the prey in a foraging game with behaviorally responsive prey? Also, does a predator’s hunger and energetic state affect its daring? We assume that hungry predators are in a lower energetic state than well-fed predators, and therefore we predict they are more willing to take greater risks to capture prey and to show more daring. The prey in turn should react to predator state by greater avoidance of hungry predators and by directing their activity more toward habitats that are riskier to the predator. To examine this, we presented owls in low or high state with gerbil prey foraging in two patch types, one with small, soft bushes representing safe habitat for the owls to hunt in and the other with robust, spiky bushes representing a more risky habitat. This allowed the owl to choose freely where to forage. More daring predators should direct more attacks and a higher proportion of their attacks toward prey in the more dangerous context.
were the predators. Barn owls are common in the Negev Desert. For ethical reasons and permit conditions, we used adult owls that hatched and matured in captivity. The owls were habituated to the vivarium for several months before participating in the experiment. We used three owls in this experiment, one at a time in nightly rotation. Allenby’s gerbils (Gerbillus andersoni allenbyi; 23–30 g) were the prey. These gerbils are small, nocturnal granivores prevalent throughout stabilized and semi-stabilized sandy areas in the Negev Desert. Gerbil behavior adheres to the predictions of optimal foraging and optimal patch use theories, habitat selection, and use of behavioral tools such as vigilance, apprehension, and time allocation for managing risk of predation (e.g., Kotler et al. 2002). Gerbils were trapped from the field before the experiment, marked with uniquely numbered electronic passive induction transponder (PIT) tags, and released into the vivarium. We maintained constant populations of six gerbils per sector within the vivarium during the experiment, for a total of 24, with an equal sex ratio. Treatment
Methods Location We conducted this research in a large, 17 × 34-m outdoor vivarium, fully exposed to the natural environmental conditions. The vivarium floor is natural loess soil, into which gerbils readily dig their burrows. The vivarium is located on the Sede Boker Campus of Ben-Gurion University in the Negev Desert of Israel. The vivarium is subdivided into four 8.5 × 17-m sectors. Gerbils could not pass between the four sectors while the owls could fly freely throughout the entire vivarium. Each sector has 18 evenly distributed stations for a total of 72. Each station contained a low-lying, 76 × 60 × 16-cm wooden trellis topped with cut branches and foliage to create an artificial bush. Under the artificial bush at 12 of the stations per sector, we placed an assay tray for rodents, for a total of 48 trays. Each tray measured 28 × 38 × 8 cm and was filled with 3 l of sifted sand into which we mixed 3 g of millet seeds to form resource patches for the gerbils. Under a third (16) of these seed trays, we placed an electronic PIT tag reader that logged the identity of each gerbil to visit the tray (Model SQID, Vantro Systems, Burnsville, MN, USA), along with the time and the duration of the visit. Study animals As a system for studying predator foraging strategies, we chose two common desert species. Barn owls (Tyto alba)
To create hunting patches of low or high risk of injury for the owl, we created soft bushes (low risk) and spiky bushes (high risk of injury) and placed them such that all of the stations in two sectors contained only soft bushes and all of the stations in the other two sectors contained only spiky bushes. We made the soft bushes by laying thin, green, leaf-laden branches horizontally across the wooden trellis. We made the spiky bushes by tying to the trellis several dry whole tumble weeds in which we cut branches of equal lengths at angles to form sharp points. We arranged the four sectors in a checkered design (see illustrative chart and pictures in Online Resource 2) so no two adjacent sectors would have the same bush treatment. In preliminary trials in which owls had to retrieve food from the top of soft and spikey bushes, owls treated spikey bushes as more risky. In those preliminary trials owls avoided landing on spikey bushes and retrieved the food from them by carefully climbing through the sharp branches instead of landing in the bush as they did for the soft treatment. To generate high and low energetic states in the owls, we subjected them to different feeding regimes. Fed owls in high state were owls we that fed ad libitum for 48 h prior to the experimental night. We also provided additional food for the owl at the beginning of the experimental night. Hungry owls in low state were owls that we starved for 48 h before the experimental night. We did not provide ad libitum food until the following morning. After each experimental night, we let the owl rest for at least two nights to recover its state. During the experiment we
13
Oecologia
alternated between fed and hungry owls nightly. No owls were injured in the course of the experiment. Gerbil data collection Following each experimental night, we recorded which trays had been foraged by gerbils, and sifted and weighed the remaining seeds from those tray to obtain the givingup density (GUD) for each. The GUD is a measure of foraging costs and reflects the foraging efficiency of the last gerbil to thoroughly exploit the resource patch (Brown 1988). Electronic readers under 1/3 of the trays supply data about the identity of each gerbil to visit the patch during the night, the time of the visit, and its duration. They also provide a measure of the cumulative duration of time that gerbils spent in each tray each night. Using the initial amount of food in a tray (3 g), final amount (GUD), and activity duration, we can construct harvest rate curves and estimates of quitting harvest rates (QHRs) for both hungry and well-fed treatment gerbils using Holling’s disc equation (Kotler and Brown 1990; Kotler et al. 2010). Using these, we can deconstruct risk management behavior into time allocation and vigilance (Kotler et al. 2010; Raveh et al. 2011).
Owl data analysis We analyzed owl attacks and owl time allocation using a mixed effect model. We set the variable Owl ID as a random factor to better control for individual variability. To control for possible non-independence of owl attacks in neighboring stations, we performed our analysis on nightly totals for the two observational sessions. For each of the eight combination of owl state, bush type treatments, and attack location (bush—open), we summed the number of owl attacks and time allocation per observation session. The two observation sessions within the same night did not significantly differ from each other. Gerbil data analysis We analyzed gerbil GUD data from seed trays and cumulative duration data from PIT tag readers using ANOVA. The two sides of the vivarium did not significantly differ from each other, and therefore we pooled both sides. (See complete analysis result tables in Online Resource 1 for owl attacks, owl foraging time, gerbil GUDs, and gerbil cumulative foraging time.) Gerbil harvest rate curves
Owl data collection To quantify owl foraging behavior and hunting effort, we video-taped the owl using an Infra-Red camera system (8 CH 1 TB H.264 SECURITY DVR SYSTEM + (8) 1/3 Sony COLOR CCD IR CAMERAS). The eight cameras plus supplemental IR lighting are positioned around the vivarium, allowing us to see every sector of the vivarium clearly, including where the owls land, where they perch, or if they returned to the nest box. We equipped the owls with leather leg straps holding three battery-powered IR LEDs (5 g weight) to augment the visibility of the owls in the videos. Owls and gerbils are blind to IR light, so this treatment should not affect their foraging behavior. We sampled the videos for 2 h every experimental night, one starting an hour after sunset and one around midnight, quantifying how much time the owl spent in each sector of the vivarium, the number of attacks it made, the type of bush in the sector where the attack occurs, and whether the attacking owl landed on bushes or on the open ground between them.
Using the GUD and foraging time data, we plotted gerbil harvest rate curves. Harvest rate curves graph the relationship between foraging success and patch resource density and allow us to visually portray the gerbils’ use of their behavioral tools for risk management of time allocation and vigilance (Brown 1999; Kotler et al. 2010). In particular, harvest rate curves for more vigilant animals are shallower as a consequence of slower harvest due to shifting attention from harvesting activities to predator detection. Thus, shallow curves indicate greater use of vigilance in risk management. Also, patch depletion can be tracked on the harvest rate curve by following the curve from initial resource density at the upper right hand corner down along the curve toward the origin at the lower left. Plotting mean GUD for a particular set of conditions reflects time allocation. Points lying closer to the origin reflect greater time allocation (for more information, please see Embar et al. 2011 Appendix 1, available online as Appendix O19278 at http://www.oikosoffice.lu.se/appendix).
Results Duration Owl behavior We ran a total of 12 experimental nights centered on the new moon during October–November 2011, 6 with a lowstate owl and 6 with a high-state owl.
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Owl state affected owl foraging decisions (Fig. 1). Hungry owls performed significantly more attacks (F1,42 = 9.6,
Oecologia Fig. 1 The effect of owl state (a) and bush type (b) on the number of owl attacks. Bars represent standard errors
10 9
a
b
8
Owl Attack (#)
7 6 5 4 3 2 1 0 Fed
Soft
Hungry
Owl State
Gerbil behavior Owl state affected gerbil foraging decisions (Fig. 3). Gerbils harvested significantly fewer seeds when hunted by a hungry owl (F1,479 = 7.2, p = 0.007, Fig. 3a) than a wellfed one. Gerbils harvested significantly more seeds in the sectors with soft bushes than spiky bushes (F1,479 = 9.9, p = 0.002, Fig. 3b). This behavioral response implies that gerbils can respond to owl state and recognize differences in environmental features such as the type of bush they forage under, but are unable to factor bush spikiness into account when determining safety from owls. Surprisingly, gerbils did not alter time allocation to foraging patches in response to either the state of the owls, bush type, or the interaction between them (Online Resource 1, Table 2b). To make inferences about the relative foraging effort of the owls, we compared the ratio of owl activity (attacks) to gerbil activity (GUDs) for the soft and spikey bush types (F1,22 = 6.7, p = 0.013, Fig. 4). If the owl selects
a
a
Hungry Fed
Owl Attack (#)
10 9 8 7 6 5 4 3 2 1 0 300
Owl Time Allocation (s)
p = 0.004, Fig. 1a) than well-fed owls. Habitat structure also affected owl hunting behavior in two ways. First, owls preferred hunting in areas with the safe bushes over areas with the risky ones, directing significantly more of their attacks to the sectors with soft bushes (F1,42 = 7.3, p = 0.011, Fig. 1b). Second, within a sector, owls directed more attacks toward (F1,42 = 18.37, p
Behavioral ecology - Original research
To dare or not to dare? Risk management by owls in a predator–prey foraging game Keren Embar · Ashael Raveh · Darren Burns · Burt P. Kotler
Received: 20 November 2013 / Accepted: 16 April 2014 © Springer-Verlag Berlin Heidelberg 2014
Abstract In a foraging game, predators must catch elusive prey while avoiding injury. Predators manage their hunting success with behavioral tools such as habitat selection, time allocation, and perhaps daring—the willingness to risk injury to increase hunting success. A predator’s level of daring should be state dependent: the hungrier it is, the more it should be willing to risk injury to better capture prey. We ask, in a foraging game, will a hungry predator be more willing to risk injury while hunting? We performed an experiment in an outdoor vivarium in which barn owls (Tyto alba) were allowed to hunt Allenby’s gerbils (Gerbillus andersoni allenbyi) from a choice of safe and risky patches. Owls were either well fed or hungry, representing the high and low state, respectively. We quantified the owls’ patch use behavior. We predicted that hungry owls would be more daring and allocate more time to the risky patches. Owls preferred to hunt in the safe patches. This indicates that owls manage risk of injury by avoiding the risky patches. Hungry owls doubled their attacks on gerbils, but directed the added effort mostly toward the safe patch and the safer, open areas in the risky patch. Thus, owls dared by performing a risky action—the attack maneuver—more times, but only in the safest places—the open areas. We conclude that daring can be used to manage risk of injury
Communicated by Peter Banks. Electronic supplementary material The online version of this article (doi:10.1007/s00442-014-2956-0) contains supplementary material, which is available to authorized users. K. Embar (*) · A. Raveh · D. Burns · B. P. Kotler Mitrani Department of Desert Ecology, Blaustein Institutes for Desert Research, Ben Gurion University of the Negev, 84990 Midreshet Ben‑Gurion, Israel e-mail: [email protected]; [email protected]
and owls implement it strategically, in ways we did not foresee, to minimize risk of injury while maximizing hunting success. Keywords Behavioral indicators · Predator–prey interactions · Daring · Risk of injury · Tradeoffs of food and safety
Introduction Perhaps all organisms face tradeoffs of food and safety. While we are well aware that prey manage this tradeoff, there may be less awareness that predators have their own food and safety issues. In a foraging game, predators must catch elusive prey while avoiding injury. Risks of injury may rise from predation, dangerous prey, and the inherent risk in the environment. Small- and medium-sized predators suffer predation from bigger predators, either during the vulnerable stage of their youth or throughout their entire lives. Juvenile predators may escape predation by outgrowing the capacity to be eaten (Damsgard and Dill 1998), and other preyed-upon predators are forced to adopt prey tactics such as predator avoidance (Laroche et al. 2008), which disrupt their own hunting efforts. Some prey evolved to be dangerous, possessing weapons such as horns, mechanical and chemical defenses such as spines and poisons, and aggressive behavior fostering counter attacking the predators (see Mukherjee and Heithaus 2013 for review). And finally there is always the environment. Accidents happen, and accidents may be more prone in some environments than others. For example, long-eared owls may suffer eye injury when chasing prey into thorny dense thickets (Holt and Layne 2008). Predators may tend to avoid hunting in these risky environments, and this
13
Oecologia
can affect their interactions with their prey. For example, wolves avoid steep slopes (Kauffman et al. 2007) where the risk of injury from falling is high, and in the presence of wolves, elk use these habitats as refuge (Mao et al. 2005). Thus, the predator-prey interactions can be affected by the predator’s considerations of risk of injury. Predator-prey interactions are often evolutionary games in which the players trade off food and safety (Sih et al. 1998). While foraging, the prey alter their exposure to risk by using behavioral tools such as time allocation and habitat selection to determine where, when, and for how long to forage. The prey can further manage risk within the foraging patch by using vigilance and apprehension to enhance predator detection, albeit at the cost of lower harvest rates (Dall et al. 2001; Kotler et al. 2004). The use of these tools make prey harder to capture. Predators, faced with clever prey able to react in this sophisticated manner to their presence, also use the behavioral tools of time allocation and habitat selection to manipulate the level of fear they impose in their prey (Brown et al. 1999). In addition, they may alter their selection of hunting tactics (Kullberg 1995; Wignall and Taylor 2009) and use of daring to manage risk of injury and predator lethality (Berger-Tal et al. 2009). Why should predators manage fear? Prey respond behaviorally to predators by becoming less active, less exposed through habitat or microhabitats shifts, more wary and likely to detect attacks, and in extreme cases, foraging all together in favor of the safety of the refuge (Brown et al. 2001). That is, wary prey become harder to catch. Consequently, the longer a predator remains in the current patch, the lower are its chances of catching anything more there (Charnov et al. 1976). It is therefore to its benefit not to scare prey too much or for too long. This can be accomplished by reducing hunting effort, changing tactics, or moving to other patches where prey are less wary (Roth and Lima 2007). Remaining in the current patch where prey are becoming increasingly uncatchable instead of moving to patches with less wary prey represents wasted capture opportunities (Kotler 1992). Similarly, easing up on hunting effort creates opportunities to capture less wary prey, but easing up too much allows too many of those opportunities to go to waste. Therefore, a predator must manage fear in order to maximize its hunting returns. How should a predator do this? For predators hunting in patches where prey become increasingly wary the longer the predator stays in a patch, a predator should quit the patch when the prey become so wary that the expected energetic gain from hunting, E(H), falls to equal the summed costs of energy used in searching and capturing, C, the cost arising from risk of sustaining an injury in the course of the hunt, RI, the cost arising from the risk of predation when appropriate, P, and missed opportunity costs,
13
including that of not hunting in other patches where prey are less wary, MOC (Brown et al. 2001; Berger-Tal et al. 2009).
E(H) = C + RI + P + MOC.
(1)
This is analogous to the rule governing patch use by foragers (Brown 1988). Two distinctive features of this equation include the use of expected return rather than the actual instantaneous value and the inclusion of risk of injury. In their study with red foxes (Vulpes vulpes), Berger-Tal et al. (2009) showed that risk of injury gives rise to a foraging cost, with predators allocating less effort to risky patches and demanding a higher harvest rate to exploit compared to patches where the risk of injury was lower. Furthermore, patch use was affected by the predator’s state, with foxes allocating more time to both safe and risky patches when in poorer state. Daring can alter the risk of injury as well as the chances of prey capture, E(H), and the amount of food procured. Consider daring as a tool for fear management. We can define “daring” as the willingness to incur risk of injury. One way in which a predator can manage fear is by modulating its tactics. Some tactics may be more effective in capturing prey, yet also be more dangerous for the practitioner (Brown and Kotler 2004). These tactics should be more valuable when energy is more valuable. When this is the case, predators should be more daring when hungry and in low state and less daring when well fed (Berger-Tal et al. 2009). The frequency of attacks, speed of attack, willingness to pursue prey into physical refuges, patience displayed in ambushing prey, and disregard for prey weapons may all reflect daring. Hunger can greatly affect daring behavior in both predators and prey. Hungry prey are known to take more predation risks, leaving refuge earlier and approaching to shorter distances from predators (Sih 1992; Godin and Crossman 1994). Likewise, hungry spiders (Pardosa) under predation risk consumed as many prey as spiders not under predation risk (Walker and Rypstra 2003), implying they increased their daring and shifted the tradeoff of food and safety in favor of food. When hungry, predators may also dare to attack prey they previously avoided. For example, mice were more likely to attack bird nestlings when hungry (Bradley and Marzluff 2003), implying an increase in daring against the potential wrath of the parents and the struggle with nestlings that are often bigger than the mice. Hungry and daring predators may also reduce the effectiveness of the prey’s anti-predator behaviors. Altwegg (2003) found that against hungry predators, the prey’s attempts at predator avoidance were essentially ineffective. Hungry predators are more likely to attack (McCarty and Southwick 1981), their attacks are more lethal (Polsky 1975), and they are less willing to withdraw defensively from their
Oecologia
prey (Adamec et al. 1980), another indication of daring— willingness to risk. We ask, does a predator use daring to manage both the risk of injury to itself and the fear in the prey in a foraging game with behaviorally responsive prey? Also, does a predator’s hunger and energetic state affect its daring? We assume that hungry predators are in a lower energetic state than well-fed predators, and therefore we predict they are more willing to take greater risks to capture prey and to show more daring. The prey in turn should react to predator state by greater avoidance of hungry predators and by directing their activity more toward habitats that are riskier to the predator. To examine this, we presented owls in low or high state with gerbil prey foraging in two patch types, one with small, soft bushes representing safe habitat for the owls to hunt in and the other with robust, spiky bushes representing a more risky habitat. This allowed the owl to choose freely where to forage. More daring predators should direct more attacks and a higher proportion of their attacks toward prey in the more dangerous context.
were the predators. Barn owls are common in the Negev Desert. For ethical reasons and permit conditions, we used adult owls that hatched and matured in captivity. The owls were habituated to the vivarium for several months before participating in the experiment. We used three owls in this experiment, one at a time in nightly rotation. Allenby’s gerbils (Gerbillus andersoni allenbyi; 23–30 g) were the prey. These gerbils are small, nocturnal granivores prevalent throughout stabilized and semi-stabilized sandy areas in the Negev Desert. Gerbil behavior adheres to the predictions of optimal foraging and optimal patch use theories, habitat selection, and use of behavioral tools such as vigilance, apprehension, and time allocation for managing risk of predation (e.g., Kotler et al. 2002). Gerbils were trapped from the field before the experiment, marked with uniquely numbered electronic passive induction transponder (PIT) tags, and released into the vivarium. We maintained constant populations of six gerbils per sector within the vivarium during the experiment, for a total of 24, with an equal sex ratio. Treatment
Methods Location We conducted this research in a large, 17 × 34-m outdoor vivarium, fully exposed to the natural environmental conditions. The vivarium floor is natural loess soil, into which gerbils readily dig their burrows. The vivarium is located on the Sede Boker Campus of Ben-Gurion University in the Negev Desert of Israel. The vivarium is subdivided into four 8.5 × 17-m sectors. Gerbils could not pass between the four sectors while the owls could fly freely throughout the entire vivarium. Each sector has 18 evenly distributed stations for a total of 72. Each station contained a low-lying, 76 × 60 × 16-cm wooden trellis topped with cut branches and foliage to create an artificial bush. Under the artificial bush at 12 of the stations per sector, we placed an assay tray for rodents, for a total of 48 trays. Each tray measured 28 × 38 × 8 cm and was filled with 3 l of sifted sand into which we mixed 3 g of millet seeds to form resource patches for the gerbils. Under a third (16) of these seed trays, we placed an electronic PIT tag reader that logged the identity of each gerbil to visit the tray (Model SQID, Vantro Systems, Burnsville, MN, USA), along with the time and the duration of the visit. Study animals As a system for studying predator foraging strategies, we chose two common desert species. Barn owls (Tyto alba)
To create hunting patches of low or high risk of injury for the owl, we created soft bushes (low risk) and spiky bushes (high risk of injury) and placed them such that all of the stations in two sectors contained only soft bushes and all of the stations in the other two sectors contained only spiky bushes. We made the soft bushes by laying thin, green, leaf-laden branches horizontally across the wooden trellis. We made the spiky bushes by tying to the trellis several dry whole tumble weeds in which we cut branches of equal lengths at angles to form sharp points. We arranged the four sectors in a checkered design (see illustrative chart and pictures in Online Resource 2) so no two adjacent sectors would have the same bush treatment. In preliminary trials in which owls had to retrieve food from the top of soft and spikey bushes, owls treated spikey bushes as more risky. In those preliminary trials owls avoided landing on spikey bushes and retrieved the food from them by carefully climbing through the sharp branches instead of landing in the bush as they did for the soft treatment. To generate high and low energetic states in the owls, we subjected them to different feeding regimes. Fed owls in high state were owls we that fed ad libitum for 48 h prior to the experimental night. We also provided additional food for the owl at the beginning of the experimental night. Hungry owls in low state were owls that we starved for 48 h before the experimental night. We did not provide ad libitum food until the following morning. After each experimental night, we let the owl rest for at least two nights to recover its state. During the experiment we
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Oecologia
alternated between fed and hungry owls nightly. No owls were injured in the course of the experiment. Gerbil data collection Following each experimental night, we recorded which trays had been foraged by gerbils, and sifted and weighed the remaining seeds from those tray to obtain the givingup density (GUD) for each. The GUD is a measure of foraging costs and reflects the foraging efficiency of the last gerbil to thoroughly exploit the resource patch (Brown 1988). Electronic readers under 1/3 of the trays supply data about the identity of each gerbil to visit the patch during the night, the time of the visit, and its duration. They also provide a measure of the cumulative duration of time that gerbils spent in each tray each night. Using the initial amount of food in a tray (3 g), final amount (GUD), and activity duration, we can construct harvest rate curves and estimates of quitting harvest rates (QHRs) for both hungry and well-fed treatment gerbils using Holling’s disc equation (Kotler and Brown 1990; Kotler et al. 2010). Using these, we can deconstruct risk management behavior into time allocation and vigilance (Kotler et al. 2010; Raveh et al. 2011).
Owl data analysis We analyzed owl attacks and owl time allocation using a mixed effect model. We set the variable Owl ID as a random factor to better control for individual variability. To control for possible non-independence of owl attacks in neighboring stations, we performed our analysis on nightly totals for the two observational sessions. For each of the eight combination of owl state, bush type treatments, and attack location (bush—open), we summed the number of owl attacks and time allocation per observation session. The two observation sessions within the same night did not significantly differ from each other. Gerbil data analysis We analyzed gerbil GUD data from seed trays and cumulative duration data from PIT tag readers using ANOVA. The two sides of the vivarium did not significantly differ from each other, and therefore we pooled both sides. (See complete analysis result tables in Online Resource 1 for owl attacks, owl foraging time, gerbil GUDs, and gerbil cumulative foraging time.) Gerbil harvest rate curves
Owl data collection To quantify owl foraging behavior and hunting effort, we video-taped the owl using an Infra-Red camera system (8 CH 1 TB H.264 SECURITY DVR SYSTEM + (8) 1/3 Sony COLOR CCD IR CAMERAS). The eight cameras plus supplemental IR lighting are positioned around the vivarium, allowing us to see every sector of the vivarium clearly, including where the owls land, where they perch, or if they returned to the nest box. We equipped the owls with leather leg straps holding three battery-powered IR LEDs (5 g weight) to augment the visibility of the owls in the videos. Owls and gerbils are blind to IR light, so this treatment should not affect their foraging behavior. We sampled the videos for 2 h every experimental night, one starting an hour after sunset and one around midnight, quantifying how much time the owl spent in each sector of the vivarium, the number of attacks it made, the type of bush in the sector where the attack occurs, and whether the attacking owl landed on bushes or on the open ground between them.
Using the GUD and foraging time data, we plotted gerbil harvest rate curves. Harvest rate curves graph the relationship between foraging success and patch resource density and allow us to visually portray the gerbils’ use of their behavioral tools for risk management of time allocation and vigilance (Brown 1999; Kotler et al. 2010). In particular, harvest rate curves for more vigilant animals are shallower as a consequence of slower harvest due to shifting attention from harvesting activities to predator detection. Thus, shallow curves indicate greater use of vigilance in risk management. Also, patch depletion can be tracked on the harvest rate curve by following the curve from initial resource density at the upper right hand corner down along the curve toward the origin at the lower left. Plotting mean GUD for a particular set of conditions reflects time allocation. Points lying closer to the origin reflect greater time allocation (for more information, please see Embar et al. 2011 Appendix 1, available online as Appendix O19278 at http://www.oikosoffice.lu.se/appendix).
Results Duration Owl behavior We ran a total of 12 experimental nights centered on the new moon during October–November 2011, 6 with a lowstate owl and 6 with a high-state owl.
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Owl state affected owl foraging decisions (Fig. 1). Hungry owls performed significantly more attacks (F1,42 = 9.6,
Oecologia Fig. 1 The effect of owl state (a) and bush type (b) on the number of owl attacks. Bars represent standard errors
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Gerbil behavior Owl state affected gerbil foraging decisions (Fig. 3). Gerbils harvested significantly fewer seeds when hunted by a hungry owl (F1,479 = 7.2, p = 0.007, Fig. 3a) than a wellfed one. Gerbils harvested significantly more seeds in the sectors with soft bushes than spiky bushes (F1,479 = 9.9, p = 0.002, Fig. 3b). This behavioral response implies that gerbils can respond to owl state and recognize differences in environmental features such as the type of bush they forage under, but are unable to factor bush spikiness into account when determining safety from owls. Surprisingly, gerbils did not alter time allocation to foraging patches in response to either the state of the owls, bush type, or the interaction between them (Online Resource 1, Table 2b). To make inferences about the relative foraging effort of the owls, we compared the ratio of owl activity (attacks) to gerbil activity (GUDs) for the soft and spikey bush types (F1,22 = 6.7, p = 0.013, Fig. 4). If the owl selects
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p = 0.004, Fig. 1a) than well-fed owls. Habitat structure also affected owl hunting behavior in two ways. First, owls preferred hunting in areas with the safe bushes over areas with the risky ones, directing significantly more of their attacks to the sectors with soft bushes (F1,42 = 7.3, p = 0.011, Fig. 1b). Second, within a sector, owls directed more attacks toward (F1,42 = 18.37, p
