Box 1: Relationship between ‘evasion landscapes’ and ‘landscapes of fear’
Each prey individual’s habitat domain can be characterized by an ‘evasion landscape’, or spatial variability in its probability of evading a predator during an encounter situation (e.g., as a function of background features, proximity to refugia, terrain). Upon perceiving predation risk (from background to immediate), prey individuals whose evasion landscapes are heterogeneous during a given time period may therefore move to locations that facilitate their likelihood of predator evasion (e.g., successfully hiding). These locations would generally correspond to regions of the prey individual’s ‘landscape of fear’ (LOF, the mapping of predation cost of foraging to the physical landscape; Laundré et al. 2001; van der Merve & Brown 2007) where its perceived predation cost of foraging is relatively low, at least with respect to the costs associated with the conditional probability of capture given an encounter. All else equal, in other words, we would expect peaks in the topographic visualization of the predation cost of foraging (LOF) to tend to match areas of the evasion landscape where the prey individual has relatively low probability of evading a predator. Note, however, that the true predation cost of foraging at any location on the LOF is complex. It is the product of the risk of predation and the marginal rate of substitution of energy for survivorship (Brown 1988, 1992). The risk of predation itself is a product of the probability of encountering a predator (which depends on where an individual is on the ‘encounter landscape’) and the conditional probability of capture given an encounter (which depends on where an individual is on the evasion landscape and its means of resistance, if any). Both of these can be altered by the prey’s risk management strategies (time allocation and vigilance behavior) and the derring-do (willingness to risk injury to better able prey capture) of the predator (Brown et al. 2016). Thus, for any prey species, measurement of both the encounter landscape and inverse of the evasion landscape assist in delineating the LOF (Gaynoret al . 2019).
Box 2 : State dependent foraging games between gerbil prey and owl predators
The interaction of predator and prey is a state-dependent foraging game where the prey must manage risk using time allocation and vigilance (Brown 1999), and the predators must manage fear: as prey become more afraid, they become less catchable. The predator’s tools include time allocation and derring-do; a more daring predator is more willing to risk injury in order to capture its prey (Brown et al . 2016). Here we focus on Allenby’s gerbil (Gerbillus andersoni allenbyi ), a nocturnal rodent of sand dunes in the Middle East, and its barn owl (Tyto alba ) predator. Within an outdoor vivarium (17 x 34 x 4.5 m), it is possible to manipulate the energetic states, and subsequently quantify the foraging behavior, of both gerbils and owls (Kotleret al . 2004).
In theory, a forager should exploit depletable resource patches until the benefits of its harvest rate no longer exceed the sum of energetic, predation, and missed opportunity costs of foraging (Brown 1988). The food density at which this occurs is called the giving-up density (GUD) and is a behavioral indicator of foraging costs for that context. Energetic costs of foraging and risk factors should all lead to higher GUDs, and do so in gerbils (Kotler et al . 1991; Kotler et al . 1993). The predation cost is highly state-dependent as it equals predation risk multiplied by the survivor’s fitness divided by the marginal value of the food. Hungry animals and those in a low state or with poor prospects should be less fearful and have lower GUDs.
In vivarium experiments, gerbils that received supplemental food, relative to those that did not, used food patches less intensively, had higher GUDs, and avoided risky open microhabitat (Kotler 1997; Kotleret al . 2004). These effects carried over into the subsequent night when no gerbils received supplemental food. Gerbils that had received supplemental food previously responded more strongly to owls than those that did not (Kotler 1997). These results show how a higher energetic state acts to magnify foraging costs and alter behaviors, ultimately leading to diminished risk taking during phase two.
Tracking gerbil foraging over the course of lunar cycles revealed the dynamic nature of risk management and feedbacks with state (Kotleret al . 2010). Starting at new moon, as the moon waxes, gerbils increased vigilance to counter the greater ease of predator encounter, and reduced their time allocation to limit their exposure to predators; they sacrificed state to buy safety. By full moon, the gerbils upped vigilance even more, but increased time foraging; they defended state to guard against starvation. As the moon waned, gerbils decreased vigilance and increased foraging time to rebuild state. By new moon, vigilance was at a minimum, and foraging time began to decline; state had been rebuilt in time for another cycle (Kotler et al . 2010).
Prey foraging behavior also depends on the interaction between the state of the prey and that of predator. Using vivarium experiments, Berger-Tal & Kotler (2010) showed that hungry barn owls (Tyto alba ) were 4-7 times more active than their satiated counterparts. Gerbils responded to this increase in predator activity by visiting fewer patches and leaving them at higher GUDs, but only when in high energetic state (Berger-Tal et al . 2010).
Predators, too, consider their state as well as that of their prey. Hungry owls, for example, showed derring-do by performing dangerous attack maneuvers (plunging into areas with stiff, spikey experimental shrubs) more than twice as often as well-fed conspecifics (Embaret al . 2014a). Moreover, owls choose between well-fed and hungry gerbils (Embar et al . 2014b). In spring when gerbils were reproductive, owls favored well-fed gerbils; in the summer when they were months away from breeding, owls favored hungry gerbils. That may seem odd, but well-fed gerbils are more active in spring when energy supports offspring, and hungry gerbils are more active than well-fed gerbils in summer when survivorship to the next reproductive season is paramount. Owls, when given the choice between gerbils with fleas and gerbils without, chose the more active flea-free gerbils (Raveh 2018). In all cases, then, owls sought more active prey.
In summary, foraging games between gerbils and their predators are contingent on environmental factors such as microhabitat and moon phase and biotic factors such as the energetic states of predators and prey. Prey manage risk, predators manage fear, and these actions feed back between the players and the environment throughout each night (Kotleret al . 2002), across moon phases (Kotler et al . 2002, 2010), and over the seasons (Kotler et al . 2004).
Box 3 : The timing of predation risk as an emergent driver of contingency in NCEs
How prey invest in defense at any given time during phase two (prey response to perceived risk) may depend on the temporal pattern of intrinsic predation risk. Namely, according to the risk allocation hypothesis, defensive investment should be greatest in response to transient pulses of high risk against a background of relative safety (given that periods during which safe feeding can occur should soon return), and reduced when pulses of safety occur against a background of elevated danger (Lima & Bednekoff 1999). By implication, prey in systems where predation danger is highly punctuated may be able to compensate for heavy anti-predator investment when predators are most active (and/or lethal) by feeding during periods of predator inactivity. For example, vicuñas (Vicugna vicugna ) exploit puma (Puma concolor ) downtimes (during the day) to utilize their feeding grounds but avoid these densely-vegetated areas when low light levels and ample stalking cover combine to enhance puma lethality (Smith et al . 2019). Under these circumstances, demographic risk effects experienced by prey populations and the potential for prey to transmit indirect NCEs during phase three may be limited.
To date, empirical support for the risk allocation hypothesis has been mixed (Ferrari et al . 2009), perhaps in part because prey condition in some assessments has been high enough to allow for continuous anti-predator investment even when risk is chronic (Matassa & Trussell 2014), or because some focal prey species were not given sufficient time to learn the risk regime (Moll et al . 2017). Our review offers an additional, non-mutually exclusive explanation. Namely, the temporal pattern of intrinsic risk experienced by a prey individual is an emergent outcome of the interaction between the properties (e.g., activity) of the predator(s) by which it is threatened and setting in which an encounter might take place. Moreover, as outlined earlier, the response of any prey individual/species to perceived intrinsic danger cues during phase two hinges on its own properties (e.g., escape tactics). Thus, proper quantification of the temporal pattern of risk and how prey should respond to perceived stimuli in any situation requires explicit consideration of each of these drivers of context dependence, as well as their interplay. It is possible that, lacking the capacity to be this comprehensive, some prior tests of the risk allocation hypothesis may have misrepresented the temporal pattern of risk. We view studies exploring this possibility as a fruitful line of inquiry. In the meantime, a recent investigation by Dröge et al . (2018) offers a path forward, at least in terms of accounting for predator properties. Namely, their ability to explain vigilance responses by African ungulates was greatest when immediate risk stimuli (predator proximity) were considered in relation to patterns of long-term risk associated specifically with the approaching predator species rather than the predator guild overall.