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\begin{document}

\title{The context dependence of non-consumptive predator effects}



\author[1]{Aaron Wirsing}%
\author[2]{Michael Heithaus}%
\author[3]{Joel Brown}%
\author[4]{Burt Kotler}%
\author[5]{Oswald Schmitz}%
\affil[1]{University of Washington}%
\affil[2]{Florida International University}%
\affil[3]{University of Illinois at Chicago}%
\affil[4]{Ben-Gurion University of the Negev}%
\affil[5]{Yale University}%


\vspace{-1em}



  
  \date{June 17, 2024}


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\selectlanguage{english}
\begin{abstract}
Non-consumptive predator effects (NCEs) are now widely recognized for
their capacity to shape ecosystem structure and function. Yet,
forecasting the propagation of these predator-induced trait changes
through particular communities remains a challenge, in part because we
lack a predictive framework that accounts for environmental and species
context. Accordingly, focusing on plasticity in prey anti-predator
behaviors, we conceptualize the multi-stage process by which predators
trigger direct and indirect NCEs, review and then distill potential
drivers of NCE contingencies into three key categories (properties of
the prey, predator, and setting), and conduct a meta-analysis to
quantify the extent to which prey behavioral plasticity in response to
predation risk hinges on a well-studied driver -- prey energetic state.
Our synthesis underscores the myriad factors that can generate NCE
contingencies while guiding how research might better anticipate and
account for them. We highlight two key knowledge gaps that continue to
hinder development of a comprehensive framework for exploring
non-consumptive predator-prey interactions. These are insufficient
exploration of 1) context-dependent indirect NCEs and 2) the ways in
which direct and indirect NCEs are shaped interactively by multiple
drivers of context dependence.%
\end{abstract}%



\sloppy


\textbf{REVIEW AND SYNTHESIS}

\textbf{The context dependence of non-consumptive predator effects}

Aaron J. Wirsing\textsuperscript{1}, Michael R.
Heithaus\textsuperscript{2}, Joel S. Brown\textsuperscript{3,4}, Burt P.
Kotler\textsuperscript{5}, Oswald J. Schmitz\textsuperscript{6}

\textsuperscript{1}School of Environmental and Forest Sciences, Box
352100, University of Washington

Seattle, WA 98195, USA, wirsinga@uw.edu (corresponding author)

\textsuperscript{2}Department of Biological Sciences, Marine Sciences
Program, 3000 NE 151st St, Florida International University, North
Miami, FL 33181, USA, heithaus@fiu.edu

\textsuperscript{3}Department of Biological Sciences, University of
Illinois at Chicago, 845 West Taylor Street, Chicago, IL 60607, USA,
squirrel@darwiniandynamics.org

\textsuperscript{4}Department of Integrated Mathematical Oncology,
Moffitt Cancer Center, 12902 Magnolia Dr., Tampa, FL, 33613, USA

\textsuperscript{5}Mitrani Department of Desert Ecology, Jacob Blaustein
Institutes for Desert Research

84990 Midreshet, Ben-Gurion University of the Negev, Ben-Gurion, Israel,
kotler@bgu.ac.il

\textsuperscript{6}School of Forestry and Environmental Studies, Yale
University, 195 Prospect Street, New Haven, CT 06511, USA,
Oswald.schmitz@yale.edu

\emph{Running head} : Context dependence of NCEs

\emph{Statement of authorship} : AW and MH conceived of the idea for the
paper. All authors wrote the manuscript as a team and contributed
substantially to revisions.

\emph{Data accessibility statement} : Should the manuscript be accepted,
the data supporting the results will be archived in the dryad digital
repository.

\emph{Counts} : Abstract (195); Main text (7489); Cited references
(162); Tables (0); Figures (4); Text boxes (3)

\emph{Key Words} : anti-predator behavior; contingency; escape; evasion
landscape; habitat domain; hunting mode; predation risk; predator-prey
interactions; risk effects; top-down effects; trade-offs

\emph{Abstract.} -- Non-consumptive predator effects (NCEs) are now
widely recognized for their capacity to shape ecosystem structure and
function. Yet, forecasting the propagation of these predator-induced
trait changes through particular communities remains a challenge.
Accordingly, focusing on plasticity in prey anti-predator behaviors, we
conceptualize the multi-stage process by which predators trigger direct
and indirect NCEs, review and distill potential drivers of contingencies
into three key categories (properties of the prey, predator, and
setting), and then provide a general framework for predicting both the
nature and strength of direct NCEs. Our review underscores the myriad
factors that can generate NCE contingencies while guiding how research
might better anticipate and account for them. Moreover, our synthesis
highlights the value of mapping both habitat domains and prey-specific
patterns of evasion success (``evasion landscapes'') as the basis for
predicting how direct NCEs are likely to manifest in any particular
community. Looking ahead, we highlight two key knowledge gaps that
continue to impede a comprehensive understanding of non-consumptive
predator-prey interactions and their ecosystem consequences; namely,
insufficient empirical exploration of 1) context-dependent indirect NCEs
and 2) the ways in which direct and indirect NCEs are shaped
interactively by multiple drivers of context dependence.

\textbf{Introduction}

The idea that predators might influence prey non-consumptively by
eliciting trait changes has a long history. Indeed, Darwin (1839)
hypothesized that prey escape responses cost time and energy to maintain
and, consequently, should attenuate in the absence of predators. Today,
these predator-induced trait changes, or non-consumptive effects (NCEs),
have a strong conceptual basis (Charnov \emph{et al} . 1976; Lima \&
Dill 1990; Lima 1998) and may rival or even exceed direct predation in
terms of their impacts on prey populations and ecosystems (Kotler \&
Holt 1989; Peacor \& Werner 2001; Schmitz \emph{et al} . 2004;
Preisser\emph{et al} . 2007). Once the purview of laboratory and
short-term field experiments involving small-bodied taxa (Kotler 1984;
Preisser \emph{et al} . 2005; Weissburg \emph{et al} . 2014), NCEs and
their broader consequences are increasingly being explored in large
vertebrate systems (e.g., Willems \& Hill 2009; Burkholder \emph{et al}
. 2013; Basille\emph{et al} . 2015; Moll \emph{et al} . 2016; Le Roux
\emph{et al} . 2018; Smith \emph{et al} . 2019; Valeix \emph{et al} .
2019). This expansion has shed new light on how NCEs manifest in
communities of larger-bodied species. Yet, it has also revealed the
challenges associated with their prediction in the field, underscoring
the need for standardized methodology for evaluating these phenomena
across species and environmental contexts (Ford \& Goheen 2015; Prugh
\emph{et al} . 2019) and conceptual clarity (Peacor \emph{et al} . 2013;
Gaynor \emph{et al} . 2019) to guide research.

A growing literature suggests that contingency in NCEs hinges on key
properties of the organisms involved as well as the environments in
which they interact (e.g., Preisser \emph{et al} . 2007;
Heithaus\emph{et al} . 2009; Creel 2011; Schmitz \& Trussell 2016).
Accordingly, there have been several recent calls for these properties
to be characterized and leveraged to improve our understanding of the
nature and consequences of NCEs within ecological communities (e.g.,
Cresswell 2008; Creel 2011; Peacor \emph{et al} . 2013; Moll \emph{et
al} . 2017). Here, we address this need by: (i) conceptualizing the
multi-stage process by which predators may trigger direct and indirect
NCEs; (ii) reviewing key drivers of context dependence in NCEs; and
(iii) synthesizing these drivers into a general framework for predicting
both the nature and strength of direct NCEs. We then (iv) conclude with
a prospectus for future work. Our review spans aquatic and terrestrial
ecosystems, addresses invertebrates and vertebrates, and focuses on a
prevalent form of prey trait plasticity that is often implicated in the
transmission of NCEs, \emph{anti-predator behaviors} . We emphasize,
however, that many of the sources of context dependencies that we
address likely also apply to other forms of predator-induced trait
modification (e.g., prey development, morphology, and physiology).

\textbf{Propagation of NCEs in communities}

Predation risk is typically defined as the probability of an individual
becoming prey within a given place and time (Lima \& Dill 1990).
However, predation risk could just as easily be conceptualized as the
probability of an individual becoming prey at a given place and time
assuming no, or some set amount of, anti-predator investment (Lank \&
Ydenberg 2003). Predation risk under the former definition is more
intuitive, given its direct link to observable patterns of mortality,
and therefore lends itself to estimation via the combination of
spatiotemporal probabilities of encountering and being killed by
predators (Lima \& Dill 1990; Lima 1992). Estimates of risk based on
this definition reflect inherent properties of the location and time of
interest and dynamic properties of the predator (e.g., decisions about
whether to attack in response to prey behavior) and the prey (e.g.,
defensive investment). Accordingly, we view them as measures
of\emph{realized predation risk} . By contrast, predation risk under the
latter definition, termed \emph{intrinsic predation risk}
or\emph{danger} , is harder to measure because it is an abstract
construct (Hugie \& Dill 1994; Lank \& Ydenberg 2003). Nevertheless,
this latter conceptualization importantly decomposes the process by
which prey individuals experience and respond to the threat of predation
into a series of steps beginning with exposure to risk stimuli and
ending with changes to prey numbers and traits (e.g., behavior) that may
affect additional species within the community (\textbf{Fig. 1} ).
Consequently, it provides a clearer mechanistic basis for understanding
when and how various sources of contingency might direct the propagation
of NCEs through communities than does the former definition. Hence,
while acknowledging the validity of both approaches to defining
predation risk, we focus on intrinsic risk for the remainder of our
review.

Propagation of NCEs consists of three phases (\textbf{Fig. 1} ) within a
context of intrinsic risk. Every point in space and time is
characterized by some value of intrinsic predation risk, which includes
spatial properties of the situation that influence the likelihood of
predator-induced mortality but that prey cannot easily modify through
behavioral changes. These properties include availability of refuges,
presence of escape impediments, dilution of risk by conspecifics and by
other species, and the abundance of predators and species that might
inhibit predator effectiveness (Lank \& Ydenberg 2003). Collectively,
they are often viewed as determinants of the background pattern of risk
for a given location. Areas with elevated background risk are sometimes
called \emph{risky places} (Creel \emph{et al} . 2008). Intrinsic risk
is also influenced temporally by whether predators, and other species or
environmental conditions (e.g., moonlight) that might influence the
predator's efficacy, are currently present at a location. Periods when
the presence of predators or conditions heighten prey vulnerability are
considered to be \emph{risky times} (Creel \emph{et al} . 2008).

Within the setting of intrinsic risk, phase one concerns whether the
forager perceives any cues related to the current level of intrinsic
risk. Prey may either detect spatiotemporal cues that reflect intrinsic
predation risk (including an attack itself), setting up the possibility
of NCEs, or fail to detect appropriate risk stimuli, in which case no
NCEs will result (from the cue in question) and mortality from the
predator will be more likely. Thus, factors influencing prey detection
of intrinsic risk cues may operate as key sources of context
dependencies in NCEs.

Foragers that perceive intrinsic cues can then respond to them in phase
two. Perceived danger may or may not elicit prey responses of sufficient
magnitude to precipitate NCEs. In response to background risk and risky
times, prey individuals may manage this risk proactively. In response to
immediate threats (including attacks), prey may respond reactively
through behavioral countermeasures (Creel 2018). The energetic,
reproductive, and opportunity costs that ensue from these adjustments
determine the magnitude of any associated risk effects (Creel \&
Christianson 2008). Thus, the type of anti-predator behavior exhibited
by a prey individual in any situation is crucial to whether and to what
extent it will experience fitness penalties. Prey individuals that
perceive danger may also experience stress, which may affect fitness
(Clinchy \emph{et al} . 2013) and thereby precipitate risk effects
either alone or in concert with other (e.g., lost opportunity) costs of
anti-predator behaviors. Accordingly, during phase two, factors that
influence the strength of responses to perceived risk, the form of
anti-predator behaviors, and the amount of associated stress could act
as important drivers of contingency in associated predator risk effects
experienced directly by prey and ensuing propagation of NCEs.

In phase three, the responses of the forager to intrinsic risk can give
rise to indirect effects on other species. Risk effects from
predator-induced risk management and stress can reduce prey population
size (Creel \& Christianson 2008) and thereby trigger indirect
interactions if prey abundance drops enough to affect other community
members. Moreover, the nature of prey risk management can determine
whether and how other species in the community are affected indirectly.
Some behavioral adjustments may only affect the prey species that
responds to perceived risk, potentially leading to direct risk effects,
whereas others may further (or exclusively) influence third parties and
thereby propagate through ecological communities as indirect
interactions. Therefore, any factor that modulates the impacts of
perceived risk on prey population size and anti-predator behaviors also
has the potential to shape indirect NCEs.

\textbf{Potential drivers of context dependence in NCEs}

\emph{Properties of the prey}

Within prey guilds, species employ various means to detect
(Weissburg\emph{et 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 (Creel\emph{et al} . 2008). Sensory modalities for perceiving
and responding to risk are a critical source of contingency during phase
one (\textbf{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
\emph{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
(\textbf{Fig. 1} ). First, different sensory modalities may mediate the
type and intensity of information transferred from a risk cue to prey
(Weissburg \emph{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 (\emph{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 \emph{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
(Wirsing\emph{et 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 (\emph{Odocoileus hemionus} ) and
white-tailed deer (\emph{O. virginianus} ) exhibited divergent proactive
shifts to terrain suiting their respective running gaits when exposed to
gray wolves (\emph{Canis lupus} ) (Dellinger \emph{et al} . 2019;
\textbf{Fig. 2} ). A similar scenario characterizes NCEs of tiger sharks
(\emph{Galeocerdo cuvier} ) on several vertebrates in an Australian
seagrass ecosystem (Heithaus \emph{et al} . 2012; \textbf{Fig. 3} ).
These studies highlight mapping `evasion landscapes', or spatial
variability in the effectiveness of prey evasion strategies for a given
time period (\textbf{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 (\emph{Tarichia
granulosa} ), for instance, show little behavioral response to predators
(Murray \emph{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 (\emph{Alces alces} ), which can fight back effectively against
wolves, show little spatial response to wolf presence (Nicholson\emph{et
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 \emph{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 \emph{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
(\emph{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
(Gavini\emph{et al} . 2019). Ferrari \emph{et al} . (2006) showed that
fathead minnows (\emph{Pimephales promelas} ) learned to recognize
northern pike (\emph{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, na\selectlanguage{ngerman}ïve individuals often differ markedly from
experienced conspecifics in terms of whether (phase one) and how (phase
two) they respond to predation risk (Sih \emph{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
(Diamond\emph{et 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 (\emph{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
(\emph{Connochaetes taurinus} ) eschew chewing while being vigilant
following lion (\emph{Panthera leo} ) playbacks, presumably because
mastication hampers predator detection (Dannock \emph{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 \emph{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 \& \selectlanguage{polish}Ř\selectlanguage{english}e\selectlanguage{polish}ž\selectlanguage{english}ucha
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., R\selectlanguage{ngerman}éale \emph{et al}
. 2007; Sih \emph{et al} . 2015; Moran \emph{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., \textbf{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 (\emph{Panopeus sapidus} ) exhibit lower refuging times
relative to shyer conspecifics following exposure to predator cues, and
consequently experience higher predation from blue crabs
(\emph{Callinectes sapidus} ) (Belgrad \& Griffen 2016). Rainbow trout
(\emph{Onchorhynchus mykiss} )~with reduced access to food take greater
risks to achieve growth and, consequently, suffered increased predation
mortality (Biro \emph{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 \emph{et al} .
2004; Moran \emph{et al} . 2017) or its mean energetic state (Anholt \&
Werner 1995; Heithaus \emph{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 (\emph{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 (\emph{Orconectes rusticus} ) (Dewitt \emph{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 (\emph{Erethizon dorsatum} ) altered
their movements to reduce risk from fishers (\emph{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.

\emph{Properties of the predator}

The means by which predators capture their prey, or their hunting modes,
are a pervasive driver of context dependence in NCEs (Preisser \emph{et
al} . 2007). Hunting predators, and their prey, are also characterized
by a habitat domain, or the spatial extent over which individuals move
while foraging (Schmitz 2005; Schmitz \emph{et al} . 2017a). Together,
these properties form the `hunting mode-habitat domain concept', which
aims to explain spatiotemporal contingency in the nature of
predator-prey interactions. It can predict how foraging predators and
prey should interact during the three phases as a consequence of
contingencies in their spatial movement and overlap, the nature of which
depends on how prey respond to the threat of predation across space.

Habitat domain size appears to be consistent among predators with
similar hunting modes (Miller \emph{et al} . 2014). At one extreme of a
continuum, actively roaming/coursing predators typically have large
habitat domains; at the other, sit-and-wait/ambush predators usually
exhibit smaller domains. Notably, predators may switch hunting modes
(Helfman 1990; Olson \& Eklov 2005; Donihue 2016), which can change
space use, habitat domain size, and contingency in the nature of
interactions. Smaller prey may forage locally, whereas larger prey may
roam widely depending on their forage requirements in relation to the
distribution of plant (or other resource) quality and productivity
(Haskell \emph{et al} . 2002), creating contingency in prey movement and
habitat domain size. Further contingencies could arise if prey have
different habitat domain sizes as they adjust their movement behaviours
to the type of predator they face (Fischhoff \emph{et al} . 2007;
Merrill\emph{et al} . 2010; Miller \emph{et al} . 2014).

The spatiotemporal nature of predator-prey movement and overlap may
determine prey perception of predation risk (phase one). Sit-and-wait
predators, by remaining sedentary in fixed locations, create a
continuous presence within a narrow habitat domain (Schmitz 2007;
Schmitz \emph{et al} . 2017a). Consequently, prey facing sit-and-wait
predators may have a heightened perception of risk because of the
persistent point-source cue of predator presence. Actively hunting
predators roam widely and thereby often produce diffused, moderate cues
in any given location within their broad habitat domain, resulting in
lower perception of risk by prey (Schmitz 2007). Consistent with this
framework, herbivorous snails (\emph{Tegula funebralis} ) altered their
distribution in an intertidal ecosystem in response to the purple sea
star \emph{Pisaster ochraceus} , which moves slowly within a narrow
domain producing an acute and spatially localized acute risk signature
(Murie \& Bourdeau 2019). These snails did not alter their distribution
when exposed to crab and octopus predators that hunt actively within
larger domains and generate diffuse risk profiles. Hence, relative to
sedentary predators occupying narrow habitat domains, active predators
with large domains may be less likely to initiate direct and indirect
NCEs that play out during phases two and three (e.g., Schmitz 2008).

Prey responding to predator cues (phase two) must weigh potentially
considerable opportunity costs, in terms of energy and nutrient intake
(up to 25\% of daily energy expenditure: Schmitz {[}2005{]}) and
survival, of remaining continuously vigilant given the likelihood of
encountering and being captured by a predator. Thus, prey occupying
landscapes with sit-and-wait predators may accept those costs and
respond with chronically heightened apprehension. This response could
involve heightened vigilance at the expense of reduced foraging, or
seeking safety in refuges, or both, depending on the sizes of their
habitat domain relative to their predator's (Schmitz 2005).
Alternatively, prey facing active hunting predators may encounter
predators infrequently. Under these circumstances the prey should not be
chronically apprehensive and incur a large energetic penalty. Rather,
prey under these conditions should react acutely to imminent risk by
simply evading predators upon encounter (Schmitz 2005). There is
evidence that these divergent phase two scenarios can govern the nature
of indirect NCEs in phase three. For example, chronic avoidance of
sit-and-wait spider predators by grasshoppers increased plant diversity
while decreasing soil carbon retention, whereas a predator guild
dominated by actively-hunting spiders failed to elicit grasshopper
anti-predator behavior and, consequently, did not indirectly affect
plant composition and soil carbon via a non-consumptive pathway
(Schmitz\emph{et al} . 2017b).

Predator state is also a factor that can shape NCEs. State variation can
drive differences in a predator's detectability (Scherer \& Smee 2016)
and motivation to seek (i.e., its activity and, consequently,
spatiotemporal pattern of cue generation) and/or successfully attack
(i.e., its lethality) prey (Brown \& Kotler 2004; Brown \emph{et al} .
2016). This variation can range from being highly dynamic, as when
predator hunger elicits increased foraging activity (Hooten \emph{et al}
. 2019), to persistent, as when aggressive behavioral types are more
likely to attack prey given an encounter (e.g., Michalko \& \selectlanguage{polish}Ř\selectlanguage{english}e\selectlanguage{polish}ž\selectlanguage{english}ucha
2018). During phase one, dynamic changes to a predator's feeding states
can alter its detectability to prey that rely on cues from depredated
conspecifics as signals of danger. For instance, mud crabs
(\emph{Panopeus herbstii} ) detected and responded to predatory blue
crabs (\emph{Callinectes sapidus} ) that had recently been fed a mud
crab diet at a greater distance than food-restricted blue crabs
(Weissburg \& Beauvais 2015). By implication, predator populations that
rely on such prey species may be more likely to initiate NCEs that
cascade through to phase three. For instance, blue crabs that had fed
recently on mud crabs indirectly reduced consumption of a basal resource
(oysters) by mud crabs to a greater degree than their hungry
counterparts (Weissburg \& Beauvais 2015).

During phase two, dynamic predator state changes can influence the
strength of anti-predator responses by prey over short intervals (e.g.,
when hungry predators are perceived as more threatening; \textbf{Box 2}
). Thus, prevalence of certain states within predator populations (e.g.,
compromised energetic state) could drive changes to overall prey risk
taking that are large enough to affect propagation of indirect NCEs
during phase three. More persistent differences in predator state can
give rise to marked inter-individual variation in the anti-predator
behavior induced by predators during phase two (Sih \emph{et al} .
2012). For example, goldfish (\emph{Carassius auratus} ) with aggressive
temperaments elicited greater reduction in newt (\emph{Lissotriton
helveticus} ) foraging than less aggressive conspecifics (Winandy \&
Deno\selectlanguage{ngerman}ël 2015). By implication, the temperamental mix of predator
populations could influence the nature of prey defenses during phase two
and the likelihood of cascading NCEs in phase three.

\emph{Properties of the setting}

The propagation of NCEs depends on the setting in which the
predator-prey interaction takes place. During phase one, changes to the
environment may impair prey detection of predator cues by disrupting
acoustic (e.g., owing to anthropogenic noise; Chan \emph{et al} . 2010),
chemosensory (e.g., because of pollution; Lürling \& Scheffer 2007), or
visual (e.g., via increased turbidity; Abrahams \& Kattenfield 1997)
systems. These environmental changes may reduce the likelihood of, or
even preclude, anti-predator behavior. For example, predator avoidance
by freshwater snails (\emph{Physa acuta} , \emph{Helisomatrivolvis} )
disappeared when eutrophication of their outdoor mesocosms led to
chemosensory impairment (Turner \& Chislock 2010). Environmental
features that impede predator detection may themselves be recognized as
risk cues by prey and thereby result in elevated defensive investment.
For example, gerbils (\emph{Gerbillus andersoni allenbyi} ) reduced
their foraging activity (measured by GUDs) when landscape features
blocked sightlines that were necessary for anti-predator vigilance
(Embar \emph{et al} . 2011). In general, then, environmental
heterogeneity with respect to properties that influence predator
detection may mediate substantial inter-individual and population
variation in the degree to which prey recognize predation danger and
subsequently experience and transmit NCEs.

During phase two, properties of the setting may influence the scope for
prey anti-predator behavior in several ways. First, predator and prey
habitat domains are shaped in part by environmental context (e.g.,
thermal conditions, vegetative structure; Schmitz \& Barton 2014).
Accordingly, environmental factors constraining prey movement or the
amount of predator-free space could dictate the extent to which prey use
refugia and thus suffer non-consumptive instead of consumptive effects.
Barton \& Schmitz (2009) showed, for example, that experimental warming
created enemy-free space by shifting the environment from one where two
spider predators were spatially complementary to overlapping. This led
to a strictly non-consumptive interaction whereby grasshoppers avoided
predators rather than a composite scenario where they avoided the
sit-and-wait predator but experienced consumptive effects of the active
hunter.

Second, even when predator and prey domains are unaffected by the
setting, landscape features can shape NCEs by modifying the efficacy of
prey escape behavior. The ability of an individual to escape a predator
following an encounter can depend on environmental factors that
influence mobility (e.g., terrain) or visibility (e.g., when the
background affects prey camouflage) (Wirsing \emph{et al} . 2010). Thus,
areas with properties that render prey escape tactic(s) less effective
are likely to be avoided, at least when predators are present, or to
elicit other countermeasures that enhance the probability of early
predator detection (e.g., vigilance). For instance, reef habitat
complexity enhanced and dampened anti-predator behaviors of large and
small fishes, respectively, likely because large-bodied fish are less
able to flee from predators through obstacle-rich reefscapes than
smaller conspecifics (Catano \emph{et al} . 2016).

Third, food quantity or quality at the landscape scale can shape NCEs by
influencing the mean energetic state of prey populations
(Heithaus\emph{et al} . 2008; Wirsing \& Ripple 2011). In depauperate
landscapes, average energetic states will be depressed and the
overwhelming necessity of food should drive foraging decisions (Chesson
\& Kuang 2008), whereas anti-predator investments should increase when
resources are plentiful and prey have nutritional reserves (Hopcraft
\emph{et al} . 2010; Matassa \& Trussell 2014). For example, elevated
resource (barnacle, \emph{Semibalanus balanoides} ) density strengthened
anti-predator investment (refuge use) by sub-adult snails (\emph{Nucella
lapillus} ) exposed to risk cues from predatory crabs (\emph{Carcinus
maenas} ), presumably by augmenting prey state (Matassa \emph{et al} .
2016).

Fourth, interacting predator-prey pairs are unlikely to do so in
isolation from other species, which may alter the focal prey species'
responses to perceived risk. For instance, dwarf mongooses
(\emph{Helogale parvula} ) displayed lower rates of anti-predator
vigilance when in the presence of an avian co-forager, the drongo
(\emph{Dicrurus adsimilis} ) (Sharpe \emph{et al} . 2010). The presence
of other predators may also affect the transmission of NCEs if prey
species with conflicting predator-specific responses consequently reduce
their investment in defense (Sih \emph{et al} . 1998). In accord with
predator facilitation (Charnov \emph{et al} . 1976; Kotler \emph{et al}
. 1992), for example, larval mosquitoes (\emph{Culex pipiens} )
abandoned diving behavior normally deployed to escape surface-hunting
insect mesopredators when also exposed to a benthic predator (dragonfly
naiads,\emph{Aeshna} spp.) (Meadows \emph{et al} . 2017).

Lastly, landscape properties may mediate how prey are affected by
temporal variation in predation risk (\textbf{Box 3} ). Many nocturnal
animals, for example, decrease their activity on moonlit nights because
of their increased exposure to visually-orienting predators, and this
trend is accentuated in areas dominated by open habitats (Prugh \&
Golden 2014). Therefore, moonlight exacerbation of NCEs experienced by
nocturnal prey species is likely to be inversely proportional to
landscape cover availability. Landscapes also may influence temporal
patterns of predation risk, and thus NCEs, over longer intervals.
Seasonal variation in snow accumulation, for example, can give prey a
temporary refuge or heighten vulnerability to predation by restricting
mobility (Gorini \emph{et al} . 2011). Not surprisingly, snow depth has
been linked to prey risk taking (e.g., Nelson \& Mech 1991).

Any of these environmental attributes, alone or in concert, can
influence the kinds of anti-predator behaviors that manifest during
phase two and that precipitate as indirect NCEs during phase three
(Heithaus \emph{et al} . 2009; Wirsing \& Ripple 2011). Thus, direct and
indirect non-consumptive relationships between the same suites of
interacting predator and prey species may differ markedly as a function
of landscape type. For instance, habitat type (availability of refugia)
shaped how risk from crabs (\emph{Carcinus maenas} ) altered the
foraging intensity of a snail (\emph{Nucella lapillus} ) and,
consequently, the levels of consumption of the snails' resource
(\emph{S. balanoides} ) (Trussell \emph{et al} . 2006).

\textbf{Synthesis}

Woven together, the `hunting mode-habitat domain' and `evasion
landscape' concepts produce a general framework for predicting the
nature and strength of NCEs on prey behavior during phase two. This
framework predicts that four different patterns of anti-predator
behavior can emerge depending on the degree of overlap between the
habitat domains of the interacting predator and prey species and spatial
variability in the efficacy of the prey's evasion strategy (\textbf{Fig.
4} ). NCEs manifest in all four scenarios and are expected to be
especially strong in three of them.

When predator and prey exhibit narrow, overlapping habitat domains, and
consequently encounters between them should be common, prey individuals
are predicted to chronically invest in anti-predator countermeasures
(\textbf{Fig. 4a} ). The nature of the investment, however, should
depend on the prey's evasion landscape. If the prey's evasion landscape
is spatially heterogeneous, enabling modification of the probability of
surviving an encounter situation, then it should be both chronically
vigilant and use space in a way that promotes the efficacy of its
evasion strategy (e.g., by seeking backgrounds against which it is more
camouflaged or, if the landscape lacks physical structure, grouping with
conspecifics) (scenario one). If its evasion landscape is homogeneous,
whereby the effectiveness of its evasion tactic is independent of
location, then the prey individual should be chronically vigilant but
only engage in evasion behavior such as fleeing when perceived risk is
elevated (i.e., from an encounter situation up to an attack; scenario
two). Risk effects and cascading indirect NCEs (phase three) under both
of these scenarios are expected to be strong given the opportunity and
energetic costs (Creel \& Christianson 2008) and persistent changes to
prey foraging and distribution resulting from chronic defensive
investment.

When facing a predator with a narrow domain, prey individuals with broad
domains should seek predator-free space via spatial shifts (scenario
three; \textbf{Fig. 4b} ). These shifts should be chronic, given the
high potential for encounters associated with use of the predator's
domain, and independent of the prey individual's evasion landscape
because avoidance of predators in space obviates the need for escape
behaviors. This scenario should give rise to substantial risk effects
and cascading indirect NCEs because of marked increases in
intra-specific competition (e.g., from crowding in predator-free space)
and changes to prey distribution accompanying chronic predator
avoidance.

When a narrow prey domain falls within a broader predator domain, the
predator should converge on the prey species, leading to high encounter
rates (\textbf{Fig. 4c} ). Under these circumstances, prey individuals
whose evasion landscape is heterogeneous should invest chronically in
vigilance and use space in a way that facilitates their evasion strategy
(scenario one), whereas those with homogeneous evasion landscapes should
exhibit chronic vigilance and engage in evasion behavior only when
perceived risk is heightened (scenario two). Risk effects and indirect
NCEs under these conditions are probable. However, the degree to which
predators converge on prey should depend on the relative importance of
the prey in question to the energy budget of the predator. Hence,
relatively simple predator-prey systems or situations in which the prey
species is highly profitable to the predator should produce the
strongest NCEs.

When predator and prey share broad, overlapping domains, encounters
should be infrequent (\textbf{Fig. 4d} ). Given that anti-predator
investment is not expected if the likelihood of predator-induced
mortality in the absence of countermeasures is low (Peacor \emph{et al}
. 2013), joint investment in vigilance and evasive behavior is predicted
under these circumstances (scenario four) only when the immediacy of
perceived risk is elevated (i.e., a predator has been encountered)
irrespective of the evasion landscape. Accordingly, CEs should
predominate under this scenario, with ephemeral NCEs emerging as the
result of a temporally dynamic landscape of fear for the prey.

In all scenarios, individual prey responses will be contingent on their
defensive repertoire and state. For example, prey individuals relying
exclusively on resistance, or that are constitutively defended, should
invest minimally in behavioral countermeasures no matter how immediate
the perceived risk cue is, save perhaps during an attack. Similarly,
prey individuals that are naïve to predators or in compromised
nutritional condition lack the experience, capacity, or incentive to
respond behaviorally to perceived risk. Thus, well-defended populations
or those with constrained opportunities for anti-predator investment
(e.g., by low food supply; Bolnick \& Preisser 2005) should be subject
primarily to CEs.

\textbf{Moving forward}

In a recent review, Sheriff et al. (2020) emphasized the need to better
understand how ecological and environmental context interact with prey
responses to predation risk. Focusing on anti-predator behavior, we
address this knowledge gap in two ways. First, our review sheds new
light on NCEs by showing when and how contingency can arise from
properties of the prey, the predator, and the setting as these effects
unfold across three phases (prey risk perception; prey responses to
perceived risk; impacts of these responses on other species). Second,
our synthesis of the `hunting mode-habitat domain' and `evasion
landscape' concepts offers a unified framework for predicting the form
and magnitude of anti-predator behavior during phase two. Looking ahead,
we highlight two knowledge deficiencies that require attention if we are
to develop a coherent framework for predicting how NCEs propagate
through ecosystems. First, there is insufficient exploration of
context-dependent indirect NCEs during phase three. Second, there is
need for research focused on the ways in which direct and indirect NCEs
are shaped simultaneously, or even interactively, by multiple drivers of
context dependence.

Drawing from a broad literature spanning diverse taxa and ecosystems,
our review reveals how contingencies in NCEs can arise as a result of
many factors. It is hardly surprising, then, that studies have revealed
so much variation with respect to whether, and in what way, NCEs
manifest in communities (Moll \emph{et al} . 2016; Gaynor \emph{et al} .
2019; Prugh \emph{et al} . 2019). We clarify these factors by grouping
them into three broad categories: (1) prey properties influencing
detection of and responses to risk; (2) predator properties shaping
their detectability and lethality; and (3) properties of the setting
influencing the prey's scope for predator detection and countermeasures.
We also emphasize that there is great potential for interplay among
them. For example, divergent responses to predators with disparate
hunting modes could disappear if declining food supply limits prey
capacity for defensive investment. Similarly, because prey often have
multiple defenses whose efficacies are context-specific (Britton\emph{et
al} . 2007; Wirsing \emph{et al} . 2010; Creel 2018), sympatric prey may
respond divergently to a shared predator in one setting but similarly in
another, depending on the availability of landscape features
facilitating particular responses (i.e., the evasion landscape).
Moreover, the latter two give rise to an emergent fourth driver, (4) the
timing of predation risk, and prey properties then determine how
individuals respond to this temporal dimension of danger (\textbf{Box 3}
). By implication, predictions based on one driver of contingency, or a
single NCE pathway (Preisser \& Bolnick 2008), may provide an incomplete
picture of the impacts of predation risk on prey populations and
communities. Rather, examination of NCEs requires thorough consideration
of the functional properties of interacting predator and prey species,
as well as the circumstances under which these interactions occur
(Heithaus \emph{et al} . 2009; Creel 2011; Schmitz 2017). Fortunately,
many of these natural history or environmental details are attainable
(Wirsing \emph{et al} . 2010), especially given new approaches (e.g.,
animal-borne video, camera traps, drones) that facilitate placing
behavioral data in context (Moll\emph{et al} . 2007; Wirsing \& Heithaus
2014).

Our review also highlights the staged manner in which NCE contingencies
can manifest. Namely, prey anti-predator investment may vary intra- and
inter-specifically as a function of differences in sensory perception
(phase one) and the form of any deployed countermeasures (phase two);
contingent outcomes during either of the first two phases then determine
if, and how, indirect NCEs emerge during phase three. Across taxa, then,
prey with greater sensory ability should experience and transmit larger
NCEs. Furthermore, the phase in which context dependence arises shapes
how the outcome of non-consumptive predator-prey interactions will
respond to perturbation. For example, landscape changes that reduce prey
sensory ability are likely to diminish NCEs, whereas those raising the
frequency of encounters with predators by restricting prey habitat
domains may elicit increased anti-predator defense during phase two
(Schmitz \emph{et al} . 2004) and elevate the potential for indirect
NCEs in phase three. Thus, studies exploring phase-specific mechanisms
by which prey, predator, and landscape properties shape anti-predator
investment are crucial to forecasting NCEs in a changing world.

By synthesizing the work and concepts of Heithaus \emph{et al} . (2009)
and Schmitz \emph{et al} . (2017a), we present a new framework that
integrates prey, predator, and landscape traits to anticipate the form
and magnitude of anti-predator behavior. This framework is broadly
applicable, as evidenced by its ability to retrospectively explain
differences in behavioral countermeasures that have been observed in the
field across a range of taxa. Consistent with scenario one (\textbf{Fig.
4c} ), for example, prey species whose habitat domains are nested within
those of tiger sharks manifest chronic vigilance and space use that
facilitates their escape strategies (Heithaus \emph{et al} . 2012), save
when in depressed energetic states (Heithaus \emph{et al} . 2007).
Similarly, white-tailed deer whose domains fall within the larger
movements of gray wolves exhibit space use changes within their home
ranges facilitating their means of predator evasion (Dellinger \emph{et
al} . 2019). By contrast, sympatric mule deer practice chronic predator
avoidance by shifting to refugia within their domains that are little
used by wolves (scenario three; \textbf{Fig. 4b} ). For both ungulates,
the consumptive effects of wolves appear to be limited
(Dellinger\emph{et al} . 2018). In the Greater Yellowstone Ecosystem,
USA, elk (\emph{Cervus canadensis} ) and wolves have large, overlapping
domains, leading to low encounter rates (Cusack \emph{et al} . 2020).
Thus, consistent with scenario four (\textbf{Fig. 4d} ), elk in this
system appear to predominantly experience the consumptive effects of
wolves (Peterson \emph{et al} . 2014) and typically exhibit evasive
behavior only during risky times (e.g., Cusack \emph{et al} . 2020).
Larger elk survive many encounters with wolves via resistance (Mech
\emph{et al} . 2015), further contributing to their tendency to
experience consumptive rather than non-consumptive wolf impacts. In an
African system with multiple sympatric predators, prey consistently
select for habitats offering a lower probability of lethal predator
encounters, suggesting that chronic evasive behavior (under scenarios
one and three) may be common where overlapping predator domains preclude
outright avoidance (Thaker \emph{et al} . 2011). Accordingly, it
underscores characterization of habitat domains and evasion landscapes
as a critical first step in forecasting the extent to which, and how,
prey should respond behaviorally to perceived risk during phase two and
transmit indirect NCEs in phase three. Our framework also highlights the
need to discriminate among prey individuals relying principally on
evasion versus resistance, given that prey expressing the latter group
of behaviors are less likely to respond to the threat of predation
unless the cue is acute and, consequently, to experience and transmit
NCEs. Finally, it gives rise to new hypotheses. For example, in any
scenario where predators cannot be avoided spatially and encounters are
high enough to warrant anti-predator investment, we might nevertheless
expect vigilance and space use that facilitates evasion to relax in prey
species that are instead able to avoid the predator(s) temporally
(Kohl\emph{et al} . 2019).

Our survey revealed two knowledge gaps that represent fruitful
directions for future research. First, whereas there is ample evidence
for context dependence during phases one and two, few studies have
rigorously examined contingency in the propagation of indirect NCEs.
There are notable examples, including the role of predator hunting mode
in shaping indirect NCEs of spiders on plant and soil properties
(Schmitz \emph{et al} . 2017b), and the impact of prey refugia on
indirect non-consumptive relationships between crabs and barnacles
(Trussell \emph{et al} . 2006). These studies offer a template for
expanded scrutiny of contingencies in NCEs during phase three, which
will improve our understanding of when and how predators initiate
indirect effects by altering prey traits.

Second, a growing literature underscores the importance of
simultaneously considering multiple drivers of contingency in NCEs. For
example, anti-predator investment by mud crabs varied with their
personality (bold versus shy) and predator hunting mode (actively
hunting blue crabs versus sit-and-wait toadfish, \emph{Opsanus tau} )
(Belgrad \& Griffen 2016). Thaker \emph{et al} . (2011) showed that
small members of an African ungulate guild avoided all predators whereas
their larger counterparts avoided sit-and-pursue but not active hunters.
More work is needed, however, particularly on the importance of
three-way interactions among factors drawn from the aforementioned
groups.

There are also studies suggesting that interactive impacts of multiple
contingent drivers may act collectively to shape indirect NCEs during
phase three. For example, Murie \& Bourdeau (2019) speculated that,
compared to the strong effects initiated by slow-moving sea stars, the
absence of direct and indirect non-consumptive effects of crabs and
octopuses on snail grazing and kelp, respectively, might owe to the
inability of snails to escape these vagile predators. Thus, more mobile
prey species with greater scope for avoidance may have responded
equivalently to all three predators, yielding similar rather than
predator-specific cascades of NCEs. The possibility that interactions
between context dependent factors might modify cascading NCEs has not
been tested empirically, however, and thus remains as an exciting
research frontier.

\textbf{Acknowledgements}

This review was made possible by NSF DEB grants 1145902 (to A. Wirsing)
and 1145522 (to M. Heithaus). We are grateful to two anonymous referees
for constructive comments on a previous version of the manuscript. This
is publication xx of the Mitrani Department of Desert Ecology, xx of the
Shark Bay Research Project, and xx of the Coastlines and Oceans Division
of FIU's Institute of Environment.

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\textbf{Figure Legends}

\textbf{Fig. 1} . Flow chart, adapted from Figure 1 in Lima \& Dill
(1990), conceptualizing the process by which direct and indirect
non-consumptive predator effects (NCEs) may manifest. (\emph{a} ) Phase
one. Each point in space and time is characterized by some value of
intrinsic predation risk, or danger, defined after Lank \& Ydenberg
(2003) as the inherent probability that an individual will become a prey
item given no, or a standard amount of, anti-predator investment. Danger
may or may not be perceived; in the latter case, no NCE will precipitate
from the danger cue in question. (\emph{b} ) Phase two. Given that the
forager perceives risk cues, does it respond? Danger that is perceived
may nevertheless fail to elicit a response of sufficient magnitude to
trigger a NCE. Though not the focus of this review, prey individuals
that do respond to perceived danger may experience stress, which may in
turn affect fitness and consequently lead to risk effects. Furthermore,
prey individuals that perceive danger may seek to manage their risk of
predation through behavioral modifications, whose costs in terms of time
and energy determine the magnitude of any associated risk effects.
(\emph{c} ) Phase three. Given that the forager responds to the cues,
does the response induce an indirect interaction? Risk effects flowing
from predator-induced stress and risk management can reduce prey
population size and, in turn, trigger indirect interactions if changes
to prey abundance affect other members of the community. The nature and
strength of predator-induced risk management by prey can also determine
whether and how other species in the community are affected indirectly;
namely, if additional species are impacted by prey risk management, then
NCEs can propagate through communities in the form of indirect
interactions that are transmitted by prey behavior.

\textbf{Fig. 2} . Observed (solid arrows) and hypothesized (dashed
arrows) relationships between gray wolves (\emph{Canis lupus} ) and two
sympatric ungulates -- mule deer (\emph{Odocoileus hemionus} ) and
white-tailed deer (\emph{O. virginianus} ) -- in areas of eastern
Washington, USA, located outside (\emph{a} , \emph{c} ) and inside
(\emph{b} , \emph{d} ) wolf pack territories. Non-consumptive effects of
wolves on prey behavior (relative to wolf-free sites; \emph{a} ) are
depicted in \emph{b} , whereas \emph{c} and \emph{d} display baseline
and wolf-influenced trophic relationships between the herbivores and the
plants they target, respectively. Increasing effect size corresponds
with arrow thickness. Mule and white-tailed deer are morphologically
similar but have different running gaits (Lingle 1993). When threatened,
mule deer flee by stotting, a bounding gait that limits speed on flat
ground but facilitates navigation of uneven terrain and obstacles.
White-tailed deer flee danger by galloping, a swift means of moving over
gentle terrain that is less effective where the ground is more sloped or
broken. This disparity explains differences in the space use of these
two deer species that emerge when they are exposed to the risk from wolf
predation during phase two (\emph{b} versus \emph{a} ). Working in a
system in eastern Washington, USA, Dellinger \emph{et al} . (2019) found
that wolf presence elicited elevated use of sloped terrain by mule deer
(\emph{b} ; heavy arrow), presumably because the uneven ground
characterizing these uplands confers an advantage to bounding prey
seeking to escape coursing wolves. White-tailed deer space use differed
comparatively little as a function of wolf presence, with individuals
exposed to wolf risk manifesting small-scale shifts within their home
ranges toward flat ground and roads that actually led to increased
overlap with wolves (\emph{b} ; thin arrow). By inference, white-tailed
deer were able to manage risk `in place' because of spatial synchrony
between the effectiveness of their galloping means of escape and the
space use pattern of their coursing predator. Notably, this form of risk
management is expected whenever the safety benefits of matching predator
distribution that accrue from escape facilitation outweigh the costs
associated with elevated encounter probability (Lima 1992). These
divergent anti-predator responses raise the possibility of recolonizing
wolves triggering prey-specific indirect NCEs on plants during phase
three (\emph{c} versus \emph{d} ). In this ecosystem, mule and
white-tailed deer exhibit considerable dietary overlap, though mule deer
rely more heavily on upland shrubs (e.g.,
serviceberry;\emph{Amelanchier} spp), and white-tailed deer exploit
lowland riparian vegetation (e.g., willow; \emph{Salix} spp.) to a
greater degree (A. Craig, \emph{unpublished data} ). Given that they
elicit broad-scale spatial shifts by mule deer, wolves may dampen the
impact of mule deer on lowland plant species (\emph{d} ; thin dashed
arrow) while strengthening this species' effects on upland plants
growing in areas with steeper slopes (\emph{d} ; thick dashed arrow). By
contrast, the absence of a strong spatial response by white-tailed deer
in areas occupied by wolf packs suggests that wolves may have modest and
localized (i.e., within existing home ranges) indirect effects on the
plants exploited by this deer species (similarity in the thickness of
the solid and dashed arrows in \emph{c} and \emph{d} ).

\textbf{Fig. 3} . Observed (solid arrows) and hypothesized (dashed
arrows) relationships between tiger sharks (\emph{Galeocerdo cuvier} ),
their air-breathing prey -- dugongs (\emph{Dugong dugon} ), dolphins
(\emph{Tursiops} cf. \emph{aduncus} ), green turtles (\emph{Chelonia
mydas} ), sea snakes (\emph{Disteria major} ; not pictured), pied
cormorants (\emph{Phalacrocorax varius} ) -- omnivorous fish
(\emph{Pelates octolineatus} ), and seagrasses within shallow
(\textless{}4.5m water depth) habitats in Shark Bay, Western Australia.
Species interactions are depicted during times when tiger sharks are
present and absent from the bay, and interaction effect sizes correspond
with arrow thickness. When tiger sharks are present, they preferentially
spend time over shallow banks (Heithaus \emph{et al} . 2002). Within
these shallow habitats, they spend more time over bank edges compared to
interior areas of banks (Heithaus \emph{et al} . 2006). Non-consumptive
direct effects of sharks on prey behavior (phase two) are black lines,
whereas indirect relationships between tiger shark prey and lower
trophic levels are gray lines (phase three). Dugongs (Wirsing \emph{et
al} . 2007), cormorants (Heithaus \emph{et al} . 2009), dolphins
(Heithaus \& Dill 2006), and sea snakes (Wirsing \& Heithaus 2009)
distribute themselves between edge and interior portions (microhabitats)
of shallow banks roughly proportional to food abundance when tiger
sharks are absent. When sharks are present, by contrast, these species,
along with green turtles (Heithaus \emph{et al} . 2007), shift among the
two microhabitats to enhance safety. Their spatial shifts during phase
two, however, are based on species-specific escape tactics. Green
turtles, dugongs, and dolphins escape through sub-surface flight and
rely on maneuverability that is constrained over interior portions of
banks. Accordingly, these species move into bank edges when tiger sharks
are present to facilitate escape even at the cost of higher encounter
rates with sharks (Heithaus \emph{et al} . 2009). Conversely, sea
snakes, which are unlikely to escape a tiger shark, and cormorants,
which escape by flying away, shift toward interior areas of banks where
shark encounters are minimized. For green turtles, habitat use is
state-dependent with turtles in better condition selecting safer areas
of banks with less food (Heithaus \emph{et al} . 2007). Experimental
studies of herbivory (Burkholder \emph{et al} . 2013; Bessey \emph{et
al} . 2016) show that these spatial shifts cascade to seagrass
communities during phase three.

\textbf{Fig. 4.} Framework integrating the `hunting mode-habitat domain'
and `evasion landscape' concepts to predict the nature and strength of
direct NCEs on prey populations. Once predation risk is perceived (phase
two), four different patterns of anti-predator behavior can emerge
depending on the overlap between predator ( ) and prey ( ) domains
across the landscape ( ) and spatial variability in the effectiveness of
the evasion tactic used by the prey species (i.e., the evasion landscape
, with darker interior colors representing higher evasion efficacy). For
each combination of domain overlap and evasion landscape (\emph{a}
-\emph{d} ), magnitudes of different anti-predator responses --
vigilance only ( ), vigilance plus evasive behavior ( ), spatial shifts
( ), and resistance or responses of individuals in compromised or naïve
states, ) as a function of the immediacy of perceived predation risk
(from low to high with intermediate values representing an encounter
situation) are depicted under `Anti-predator Response'. (\emph{a} ) When
predator and prey habitat domains are narrow and overlapping, and the
probability of predator encounter is high, two scenarios are possible:
if a prey's evasion landscape is heterogeneous, allowing it to spatially
modify the efficacy of its evasion tactic, then it should exhibit
chronic vigilance coupled with evasion facilitation by moving to space
where its tactic is most effective; if its evasion landscape is
homogenous, then the prey individual should be vigilant when risk
immediacy is low and only engage in vigilance plus evasive behavior when
perceived risk is immediate (i.e., when a predator has been encountered
because the efficacy of evasion does not depend on the prey individual's
location at the time of the encounter). (\emph{b} ) Prey with broad
domains should seek refuge by shifting space use (dashed intermediate
gray line) when facing predators with a narrow domain, irrespective of
the type of evasion landscape. (\emph{c} ) Prey individuals with narrow
domains that fall within a broad predator domain should behave similarly
to those in (\emph{a} ), given the absence of a spatial refuge and that
the predator is likely to converge on the prey, leading to high
encounter rates. (\emph{d} ) When predator and prey share broad,
overlapping domains, low encounter rates lead prey to jointly invest in
vigilance and evasive behavior only when the immediacy of perceived risk
is elevated (a predator has been encountered), irrespective of the
evasion landscape. In all cases, prey individuals that 1) rely
exclusively on resistance to repel predators, 2) are constitutively
defended, 3) are in a compromised energetic state, or 4) are naïve with
respect to predators are expected to invest minimally in anti-predator
behavior ( ), save perhaps when perceived risk is acute (during an
attack; ).

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\textbf{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;
Laundr\selectlanguage{ngerman}é 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
(Gaynor\emph{et al} . 2019).

\textbf{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 \emph{et al} . 2016).
Here we focus on Allenby's gerbil (\emph{Gerbillus andersoni allenbyi}
), a nocturnal rodent of sand dunes in the Middle East, and its barn owl
(\emph{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 (Kotler\emph{et
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 \emph{et al} . 1991; Kotler \emph{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;
Kotler\emph{et 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
(Kotler\emph{et 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 \emph{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 (\emph{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 \emph{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 (Embar\emph{et
al} . 2014a). Moreover, owls choose between well-fed and hungry gerbils
(Embar \emph{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
(Kotler\emph{et al} . 2002), across moon phases (Kotler \emph{et al} .
2002, 2010), and over the seasons (Kotler \emph{et al} . 2004).

\textbf{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 (\emph{Vicugna vicugna} ) exploit puma (\emph{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 \emph{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 \emph{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 \emph{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 \emph{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.

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