Properties of the prey
Within prey guilds, species employ various means to detect (Weissburget al . 2014), evade (Moore & Biewener 2015), and resist (Creel
2011) predators. Modes of detection (acoustic, chemical, olfactory,
visual, tactile) enable prey to identify risky places, for example by
quantifying spatial variation in the intensity of persistent predator
cues; and risky times, as when a predator’s approach is observed (Creelet al . 2008). Sensory modalities for perceiving and responding to
risk are a critical source of contingency during phase one (Fig.
1 ). Prey species may lack the capacity to detect persistent evidence of
a predator’s presence and thus to prepare for encounters, or instances
when predator-prey spatial overlap is such that detection of one by the
other is possible (Lima & Dill 1990). Alternatively, their preparation
for encounters may be continuous and generalized, leading to high
fitness costs and reduced efficacy (Creel et al . 2008; Creel
2018). Similarly, inability to sense the approach of a predator limits
reactive responses to those triggered by an attack (e.g., physical
resistance; Creel 2018). In sum, consideration of sensory biology should
aid in predicting which members of prey guilds are least likely to be
subject to non-consumptive (versus consumptive) effects of a predator,
and which kinds of risk stimuli (background versus immediate) are most
likely to induce defensive responses by a given prey species.
The kinds of sensory modalities used to perceive predation risk should
also shape the propagation of NCEs during phases two and three
(Fig. 1 ). First, different sensory modalities may mediate the
type and intensity of information transferred from a risk cue to prey
(Weissburg et al . 2014). Thus, sympatric prey species that use
different senses to detect the same predator may respond with divergent
intensity and/or specificity depending upon the pathway through which
they receive and process the information. The threat level and predator
identity perceived by a given prey species could influence its response
and any associated risk effects (including from stress) during phase
two, as well as any indirect interactions precipitating during phase
three. Second, prey with multiple sensory modalities may be better able
to detect predators and have an anti-predatory advantage (Munoz &
Blumstein 2012). For example, access to both visual and chemical cues
allowed for more accurate detection and appropriate responses to
predators by mosquito fish (Gambusia holbrooki ) (Ward & Mehner
2010). Thus, members of prey guilds with multiple sensory modalities may
exhibit more striking and appropriate anti-predator responses, higher
vulnerability to risk effects, and greater capacity to transmit indirect
NCEs to other community members than sympatric heterospecifics relying
on a single means of detection.
Although some may double as routine safeguards, tactics for evading and
resisting predator attacks are typically reactive countermeasures
triggered by encounters with predators (Creel 2018). Thus, these ‘escape
behaviors’ (Wirsing et al . 2010) usually act as drivers of
contingency during the latter two phases of non-consumptive
interactions, after risk is perceived. Evasive behaviors are diverse and
include altered activity (Schmitz 2007), body part autotomy (Maginnis
2006), dynamic flash coloration (Murali 2018), feigning death (Humphreys
& Ruxton 2018), fleeing (Moore & Biewener 2015), grouping (to confuse
predators or dilute risk; Lehtonen & Jaatinen 2016), hiding/crypsis
(Caro 2014), and seeking a refuge (Sih 1987). Their efficacy can be
prey- and predator-specific and hinge on environmental features (Wirsinget al . 2010; Schmitz 2017). The effectiveness of flash coloration
as a means of visually confusing predators, for example, can depend on
visual obstructions, light levels, and background colors (Murali 2018).
To the extent that prey can modify the effectiveness of their evasion
strategies, interspecific variation in evasive behaviors may lead to
differences in anti-predator responses to the same risk stimuli during
phase two. For example, sympatric prey species that flee predators with
disparate means of locomotion may respond divergently to a shared
predator by proactively seeking areas that suit their respective
movement styles in preparation for an encounter or reactively shifting
to these areas after an encounter has occurred. Consistent with this
expectation, mule deer (Odocoileus hemionus ) and white-tailed
deer (O. virginianus ) exhibited divergent proactive shifts to
terrain suiting their respective running gaits when exposed to gray
wolves (Canis lupus ) (Dellinger et al . 2019; Fig.
2 ). A similar scenario characterizes NCEs of tiger sharks
(Galeocerdo cuvier ) on several vertebrates in an Australian
seagrass ecosystem (Heithaus et al . 2012; Fig. 3 ). These
studies highlight mapping ‘evasion landscapes’, or spatial variability
in the effectiveness of prey evasion strategies for a given time period
(Box 1 ), as a means of forecasting behavioral responses to
perceived risk cues (e.g., where a camouflaged individual goes to best
match the background when it senses a threat). They also raise the
intriguing, but as yet untested, possibility that a predator targeting
more than one sympatric prey species could impose multiple indirect
effects on other community members (e.g., basal resources for the
different prey species) that occur because of prey-specific forms of
evasion with divergent consequences for distribution (Wirsing & Ripple
2011).
Forms of prey resistance may discourage predators prior to an attack or
repel an attacker. Resistance may include cooperative defense (Lehtonen
& Jaatinen 2016), induced chemical defense (Mukherjee & Heithaus
2013), fighting back (Mukherjee & Heithaus 2013), and honest (e.g.,
aposematism, pursuit deterrence; Harvey & Paxton 1981; Caro 1995) and
deceptive signaling (e.g., actions making individual seem more difficult
to capture such as increases in apparent size, mimicry; Caro 2014). As
with evasion, the efficacy of resistance may be predator- and
setting-specific (Mukherjee & Heithaus 2013). Chemical defenses of
herbivorous insects, for example, are more effective against vertebrate
than invertebrate predators, perhaps because of the latter group’s
enhanced capacity to develop adaptations to tolerate or overcome prey
defenses (Zvereva & Kozlov 2016). Unlike evasive behaviors, resistance
usually manifests after the predator detects the prey, and often after
an attack has been initiated. Rough-skinned newts (Tarichia
granulosa ), for instance, show little behavioral response to predators
(Murray et al . 2004) save to honestly signal by displaying the
bright coloration of their underbelly when confronted by a would-be
attacker. Hence, these countermeasures are less likely than evasion to
result in either costly risk effects (e.g., diminished condition after
prolonged foraging disruption) or in changes to prey activity budgets
and distributions during phase two (e.g., displacement) that could
indirectly affect other species during phase three. For example, adult
moose (Alces alces ), which can fight back effectively against
wolves, show little spatial response to wolf presence (Nicholsonet al . 2014). Not surprisingly, indirect effects of wolves on the
plants that moose consume appear to be transmitted primarily by
numerical effects of direct predation rather than NCEs (Post et
al . 1999). By implication, prey species relying on resistance should
respond differently to predation risk, and to be less likely to be
vectors of indirect NCEs, than those depending on evasive behaviors.
There are studies supporting the former expectation (e.g., Lingle &
Pellis 2002) but it has not been addressed broadly. The latter remains
untested.
Within populations, prey state may shape individual responses to
predation risk and, consequently, propagation of NCEs (Sih et al .
2015; Schmitz 2017). States can be relatively stable (e.g., sex,
behavioral type, and epigenetically or genetically derived morphs) or
dynamic (e.g., age/developmental stage, current behavior, disease state,
learning, nutritional condition, residual reproductive value, and stress
level). An individual’s state can influence its risk-taking behaviors in
any of three ways. First, an individual’s capacity to recognize danger
may be state-dependent, as when prey acquire the capacity to detect and
respond appropriately to cues via development/growth and learning
(Kavaliers & Choleris 2001). For example, large bumblebees
(Bombus terrestris ) are more sensitive to spider risk while
visiting inflorescences, likely (at least in part) because they possess
eyes with greater visual acuity than smaller conspecifics (Gaviniet al . 2019). Ferrari et al . (2006) showed that fathead
minnows (Pimephales promelas ) learned to recognize northern pike
(Esox lucius ) as predators from a paired exposure to conspecific
alarm pheromones and pike odor. Once learned, a minnow’s fear response
increased with the concentration of pike odor alone. Not surprisingly,
therefore, naïve individuals often differ markedly from experienced
conspecifics in terms of whether (phase one) and how (phase two) they
respond to predation risk (Sih et al . 2010). This form of
experience-driven contingency in defensive behaviors could give rise to
differences in the extent to which individuals (and populations) with
divergent amounts of prior predator conditioning transmit indirect NCEs
(phase three).
Second, prey state may affect vulnerability, as when individuals in
different growth stages are differentially able to outpace (Diamondet al . 2019) or resist (Schmitz 2017) predators. Thus, against
any predator, individuals in less susceptible states should have reduced
need to invest in countermeasures and respond differently to perceived
risk than more vulnerable conspecifics during phase two. For example,
juvenile roach (Rutilus rutilus ) that are beyond the gape limits
of their predators invest less in defense (time spent near the surface
and jumping out of the water when at risk) than smaller (ingestible)
conspecifics (Christensen 1996). Similarly, blue wildebeest
(Connochaetes taurinus ) eschew chewing while being vigilant
following lion (Panthera leo ) playbacks, presumably because
mastication hampers predator detection (Dannock et al . 2019).
Thus, the overall pattern of anti-predator behavior characterizing a
prey population during phase two, and the degree to which it transmits
indirect NCEs during phase three, could hinge on the distribution of
states manifested by its constituents. Indeed, where prey switch
ontogenetically from being the prey to being the predator of another
species (Ferrari et al . 2010), relative abundance of different
developmental stages within a population could mediate the extent to
which it experiences and transmits versus initiates NCEs. These
hypotheses have not been evaluated systematically.
Third, a prey’s state may influence its willingness to respond to
perceived risk, as when individuals with risk-prone behavioral types are
less likely to invest in anti-predator behavior (Michalko & Řežucha
2018) or those with compromised energetic state are more willing to
expose themselves to danger to avoid starvation (Clark 1994). The former
mechanism is gaining support in the literature (e.g., Réale et
al . 2007; Sih et al . 2015; Moran et al . 2017). The
latter, known as state-dependent risk taking, has long been recognized
and is thoroughly explored in a range of taxa (e.g., Box 2 ).
Both have consequences for levels of anti-predator investment and
subsequent predation rates experienced by prey during phase two. For
example, bold mud crabs (Panopeus sapidus ) exhibit lower refuging
times relative to shyer conspecifics following exposure to predator
cues, and consequently experience higher predation from blue crabs
(Callinectes sapidus ) (Belgrad & Griffen 2016). Rainbow trout
(Onchorhynchus mykiss ) with reduced access to food take greater
risks to achieve growth and, consequently, suffered increased predation
mortality (Biro et al . 2005). Thus, the extent to which any prey
population is subject to consumptive versus non-consumptive predator
effects may depend on its average behavioral type (Sih et al .
2004; Moran et al . 2017) or its mean energetic state (Anholt &
Werner 1995; Heithaus et al . 2008). These scenarios have only
rarely been assessed under large-scale field conditions (e.g., Sinclair
& Arcese 1995). The additional inference that mean temperamental or
energetic states should influence the transmission of indirect NCEs in
communities has, to our knowledge, not been addressed.
Finally, prey may possess constitutive (permanent) defenses that
influence risk-taking behavior including armor, harmful morphology
(e.g., spines), toxicity/unpalatability, and honest or deceptive
advertisements of similarity to toxic/unpalatable heterospecifics
(Tollrian & Harvell 1999). Theoretically, the effectiveness of these
defenses should be inversely proportional to the need for anti-predator
behavior. Freshwater snails (Physa gyrina ) with vulnerable shell
shapes, for instance, exhibited greater behavioral responses (refuging,
avoidance) than harder-to-kill conspecifics when confronted by cues from
crayfish (Orconectes rusticus ) (Dewitt et al . 1999). By
implication, taxa that are well defended constitutively should exhibit
weaker anti-predator responses than other community members with less
effective constitutive defenses during phase two, whether or not cues
are detected in phase one, and be less likely to transmit indirect NCEs
during phase three. However, the effectiveness of any constitutive
defense is, itself, context dependent. For example, Pokallus & Pauli
(2016) observed that, despite possessing a well-developed predator
deterrent (quills), porcupines (Erethizon dorsatum ) altered their
movements to reduce risk from fishers (Pekania pennanti ), a
specialized porcupine predator. Hence, even prey with generally
effective constitutive protections may react to and transmit indirect
NCEs elicited by predators that can breach their defenses.