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, then, 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 (Weissburg et al . 2014).
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’ (Heithaus et al . 2009; 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 (Heithaus et al . 2009; Wirsing et
al . 2010; Schmitz 2017; Creel 2018). 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 have scope to modify the
effectiveness of their escape tactics, 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 (bounding and galloping) when exposed to gray wolves (Canis
lupus ) (Dellinger et al . 2019; Fig. 2 ). A similar
scenario characterizes NCEs of tiger sharks (Galeocerdo cuvier )
on a community of vertebrates in the seagrass ecosystem of Shark Bay,
Australia (Heithaus et al . 2012; Fig. 3 ). By
implication, during phase three, 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). This possibility
has not been tested.
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 that make an 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, however,
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
accosted 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 (C. lupus ), show little spatial
response to wolf presence (Nicholson et al . 2014). Not
surprisingly, observed indirect effects of wolves on the plants that
moose consume appear to be transmitted primarily by the 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 expectation remains
unexplored.
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). Working with fathead minnows (Pimephales
promelas ), Ferrari et al . (2006) showed that individuals 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, consequently, respond differently
to perceived risk than more vulnerable conspecifics during phase two.
For example, Christensen (1996) observed that juvenile roach
(Rutilus rutilus ) that were beyond the gape limits of their
predators invested less in defense (time spent near the surface and
jumping out of the water when at risk) than smaller (ingestible)
conspecifics. Similarly, Dannock et al . (2019) found that blue
wildebeest (Connochaetes taurinus ) eschewed chewing while being
vigilant following lion (Panthera leo ) playbacks, presumably
because mastication hampers predator detection. 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 (Ferrariet 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
yet to be 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 (Réale et al .
2007; Mittlebach et al . 2012; Sih et al . 2015; Moranet 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 1 ). 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;
Matassa & Trussell 2014). 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
advertisments of similarity to toxic/unpalatable heterospecifics
(Tollrian & Harvell 1999). In theory, the effectiveness of these
defenses should be inversely proportional to the need for anti-predator
behavior (Dewitt et al . 1999). 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, under some circumstances, breach their defenses.