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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{May 25, 2024}


\begingroup
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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, and AW performed the meta-analysis. 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 (184); Main text (7495); Cited references
(164); Tables (0); Figures (4); Text boxes (3)

\emph{Key Words} : anti-predator behavior; contingency; energetic state;
meta-analysis; predation risk; predator-prey interactions; risk effects;
state-dependence; 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, 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.

\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, Creel \& Christianson 2008) and are thought to
rival or even exceed direct predation in terms of their impacts on prey
populations and ecosystems (Kotler \& Holt 1989; Peacor \& Werner 2001;
Werner \& Peacor 2003; 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., Dill \emph{et al} . 2003; Willems \& Hill 2009;
Burkholder\emph{et al} . 2013; Middleton \emph{et al} . 2013; Basille
\emph{et al} . 2015; Moll \emph{et al} . 2016; Le Roux \emph{et al} .
2018; Courbin\emph{et al} . 2019; Smith \emph{et al} . 2019; Valeix
\emph{et al} . 2019). This expansion has shed new light on the extent to
which NCEs scale up to communities of larger-bodied species. Yet, it has
also drawn more attention to contingencies in NCEs. Such contingencies
currently defy a coherent explanation, 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 (Schmitz \emph{et al} . 2017a; 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 (Preisser \emph{et al} . 2007; Heithaus \emph{et al}
. 2009; Wirsing \emph{et al} . 2010; Creel 2011; Schmitz \& Trussell
2016). Accordingly, there have been several recent calls for these
properties to be characterized and incorporated into a general framework
for predicting the nature and consequences of NCEs within ecological
communities (e.g., Cresswell 2008; Heithaus \emph{et al} . 2009; Creel
2011; Moll \emph{et al} . 2017). Here, to facilitate the development of
such a framework, we (i) conceptualize the multi-stage process by which
predators may trigger direct and indirect NCEs; (ii) review key drivers
of context dependence in NCEs; and (iii) quantify the extent to which
prey trait plasticity in response to predation risk hinges on prey
energetic state to demonstrate how considerations of contingency are
critical to understanding the role of NCEs in complex communities. We
then (iv) conclude with a synthesis and 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 (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 a prey response of
sufficient magnitude to precipitate a NCE. 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, 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
(\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 (Weissburg \emph{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
(\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' (Heithaus \emph{et al} . 2009; 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 (Heithaus \emph{et al} . 2009; Wirsing \emph{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 (\emph{Odocoileus
hemionus} ) and white-tailed deer (\emph{O. virginianus} ) exhibited
divergent proactive shifts to terrain suiting their respective running
gaits (bounding and galloping) 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 a community of vertebrates in the seagrass ecosystem of Shark Bay,
Australia (Heithaus \emph{et al} . 2012; \textbf{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
(\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
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 (\emph{Alces alces} ), which can fight
back effectively against wolves (\emph{C. lupus} ), show little spatial
response to wolf presence (Nicholson \emph{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 \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 expectation remains
unexplored.

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). Working with fathead minnows
(\emph{Pimephales promelas} ), Ferrari \emph{et al} . (2006) showed that
individuals 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, consequently, respond
differently to perceived risk than more vulnerable conspecifics during
phase two. For example, Christensen (1996) observed that juvenile roach
(\emph{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 \emph{et al} . (2019) found that blue
wildebeest (\emph{Connochaetes taurinus} ) eschewed chewing while being
vigilant following lion (\emph{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 (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 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 \& \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 (R\selectlanguage{ngerman}éale \emph{et al} .
2007; Mittlebach \emph{et al} . 2012; 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 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 (\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;
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 \emph{et al} . 1999). 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, under some circumstances, 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, Murie \& Bourdeau (2019) observed that 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. 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, Schmitz \emph{et al} .
(2017b) found that 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.

Whether or not a predator-prey interaction during phase two is largely
consumptive or non-consumptive will depend on the relative habitat
domain sizes of predators and prey (Schmitz \emph{et al} . 2004; Schmitz
2005). There are at least four contingencies that can arise, with
non-consumptive effects being predominant in three of the four. Whenever
prey and predator have overlapping, narrow habitat domains, prey will
respond with chronic vigilance. Prey with narrow habitat domains that
face widely roaming predators with broad habitat domains will likewise
be chronically vigilant. Prey with broad habitat domains should seek
refuge by shifting habitat use when facing predators with a narrow
habitat domain. Finally, when prey and predators both have broad habitat
domains, prey are less prone to exhibit habitat shifts or chronic
vigilance, in which case consumptive effects (phase two) and their
indirect consequences (phase three) should predominate (Schmitz \emph{et
al} . 2004; 2017a).

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 change how detectable it is 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. In the Weissburg \& Beauvais (2015)
study, 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.

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 1}
). 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, Winandy \& Deno\selectlanguage{ngerman}ël (2015) found that goldfish
(\emph{Carassius auratus} ) with aggressive temperaments elicited
greater reduction in newt (\emph{Lissotriton helveticus} ) foraging than
less aggressive conspecifics. 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}

Both prey and predator traits crucially predict the outcome of their
non-consumptive interactions. Additionally, the propagation of NCEs
depends also on the setting in which the 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{Helisoma} \emph{trivolvis} ) 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, Embar
\emph{et al} . (2011) demonstrated that gerbils (\emph{Gerbillus
andersoni allenbyi} ) reduced their foraging activity (measured by GUDs)
when landscape features blocked sightlines that were necessary for
anti-predator vigilance. 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 whether prey manifest
chronic anti-predator vigilance, use refugia, or experience consumptive
effects under the four contingent scenarios described above. 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 their
smaller counterparts (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, Matassa
\emph{et al} . (2016) found that 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.

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; Korpimaki \emph{et al} . 1996), for example, Meadows \emph{et
al} . (2017) showed that 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.).

Lastly, landscape properties may mediate how prey are affected by
temporal variation in predation risk (\textbf{Box 2} ). 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., yarding in deer; 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. Trussell \emph{et al} . (2006) determined, for
example, that 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} ).

\textbf{The magnitude of contingency}

To quantify strength of contingency in NCEs, we conducted a
meta-analysis of the relationship between four types of prey risk taking
and a key hypothesized driver of context dependence: prey energetic
state (Kotler \emph{et al} . 2004; Schmitz \& Trussell 2016;
see\textbf{Box 3} for methods). Reduced energetic state resulted in
significantly elevated prey risk taking in each behavioral dimension
(\textbf{Fig. 4} ). When in a compromised energetic state, prey
subjected to predation risk tended to be more active (\emph{d} = 0.73, ±
95\% CI = 0.32-1.14; lnRR = 0.34, ± 95\% CI = 0.17-0.51), spend less
time in refugia (\emph{d} = 0.95, ± 95\% CI = 0.74-1.16; lnRR = 0.58, ±
95\% CI = 0.46-0.71), invest more in foraging versus apprehension
(\emph{d} = 0.84, ± 95\% CI = 0.50-1.19; lnRR = 0.49, ± 95\% CI =
0.22-0.77), and be more isolated from other group members (\emph{d} =
0.86, ± 95\% CI = 0.29-1.42; lnRR = 0.42, ± 95\% CI = 0.19-0.65) than
conspecifics in better nutritional condition. The mean estimates of
\emph{d} suggest a medium/strong effect of reduced energetic state on
prey activity, and strong effects on exposure, foraging investment, and
isolation. We detected significant heterogeneity among studies exploring
each risk-taking dimension: activity (\emph{d} : \emph{Q}
\textsubscript{33} = 178.39, p \textless{} 0.001; lnRR: \emph{Q}
\textsubscript{33} = 88.04, p \textless{} 0.001); exposure (\emph{d} :
\emph{Q} \textsubscript{70} = 213.60, p \textless{} 0.001; lnRR:
\emph{Q} \textsubscript{70} = 369.66, p \textless{} 0.001); foraging
investment (\emph{d} :\emph{Q} \textsubscript{40} = 186.19, p
\textless{} 0.001; lnRR:\emph{Q} \textsubscript{40} = 924.42, p
\textless{} 0.001); isolation ((\emph{d} : \emph{Q} \textsubscript{9} =
34.85, p \textless{} 0.001; lnRR: \emph{Q} \textsubscript{9} = 57.06, p
\textless{} 0.001).

These results underscore the marked differences that can characterize
risk taking by prey individuals with divergent energetic states, and by
extension the pivotal role that prey condition plays as a mediator of
direct NCEs in communities. Most of the studies included in this
analysis examined small-bodied taxa in laboratory or small-scale
mesocosm settings (Appendix S1 in Supporting Information), and we found
few investigations of the indirect community consequences of
state-mediated variation in prey risk taking. Accordingly, there remains
need for studies quantifying the influence of energetic state on risk
taking under field conditions, especially with larger-bodied species,
and the degree to which disparate responses of prey in divergent
nutritional states shape indirect predator NCEs. Furthermore, prey state
was the only potential driver of contingency in prey anti-predator
behavior subject to enough empirical investigation to support
meta-analysis. Thus, we also encourage further work addressing other
potential drivers of context dependent NCEs.

Our findings also revealed considerable variability in the magnitude and
direction of the effect of prey condition on anti-predator behavior.
Some studies in each of the four behavioral categories showed effects
opposite the majority of studies. In these studies, anti-predator
behavior increased with decreasing body condition or state of the prey
(7 of 34 studies of activity; 7 of 71 for exposure; 5 of 41 for foraging
investment; 1 of 10 for isolation). This variability may, in part, stem
from the myriad means by, and circumstances under, which the experiments
of the meta-analyses were conducted. The variability in results is also
consistent with the idea that, during phase two, anti-predator behavior
and the way it is shaped by context (including prey state) hinge on the
relative safety benefits conferred by different behavioral options
available to the prey species. Either increased or decreased activity,
for example, can serve as effective anti-predator tactics depending on
the hunting mode of the predator against which they are deployed
(Schmitz 2007). Furthermore, if individuals in a low state are more
vulnerable to predation, then they may adopt a higher level of
antipredator investment (Raveh \emph{et al} . 2011; Makon \emph{et al}
.\emph{in press} ). It is not surprising, then, that some studies we
assessed found that sated prey exposed to predation risk became more
active, and that food restriction diminished rather than exacerbated
this response (e.g., Hossie \& Murray 2011). Though beyond the scope of
this review, meta-regressions with covariates potentially influencing
the efficacy of different anti-predator behaviors (e.g., predator type)
would likely reduce some of the uncertainty characterizing our findings.

\textbf{Conclusions and future directions}

In a recent review of context-dependent species relationships,
Chamberlain \emph{et al} . (2014) called for increased scrutiny of the
factors contributing to, and the ecological and evolutionary
consequences of, variation in interaction outcomes. Focusing on
non-consumptive predator-prey interactions, we address both of these
knowledge gaps. First, we present new insights into 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, while recognizing that there is more work to
be done, we help to unravel the consequences of contingency by
quantifying the extent to which prey energetic state drives differences
in anti-predator behavior, and by spotlighting cases where variation in
the outcome of non-consumptive predator-prey interaction has shaped the
indirect NCEs experienced by other community members. Our synthesis also
highlights two knowledge deficiencies -- insufficient exploration of
context-dependent indirect NCEs during phase three and the ways in which
direct and indirect NCEs are shaped simultaneously, or even
interactively, by multiple drivers of context dependence -- that must be
remedied if we are to develop a coherent framework for predicting NCEs.

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; Schmitz 2017;
Gaynor\emph{et al} . 2019; Prugh \emph{et al} . 2019). We clarify these
factors by grouping them into three broad categories: (1) prey
properties that influence detection of and responses to risk; (2)
predator properties shaping their detectability and lethality; and (3)
properties of the setting that influence 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; Wirsing \&
Ripple 2011; Schmitz 2017; 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 that
facilitate particular responses (e.g., refuge space). 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 2} ). By
implication, predictions based on a single driver of contingency 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; Wirsing \emph{et}
\emph{al} . 2010; Creel 2011; Schmitz 2017). Fortunately, many of these
natural history or environmental details are available or 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 in what way, indirect NCEs emerge during phase three.
Identifying the phase(s) in which context dependence arises is therefore
crucial to predicting how the outcome of non-consumptive predator-prey
interactions will respond to perturbation. For example, landscape
changes that constrain prey habitat domains and raise the frequency of
encounters with predators may elicit increased anti-predator defense
during phase two (Schmitz \emph{et al} . 2004; 2017a) and thereby
elevate the potential for indirect NCEs in phase three. However, such
changes would be expected to have minimal non-consumptive impact on prey
species lacking the ability to detect risk cues during phase one, unless
they also reshape the sensory environment. Thus, studies exploring the
phase-specific mechanisms by which prey, predator, and landscape
properties shape anti-predator investment will strengthen a general
framework for forecasting NCEs in a changing world.

Recent syntheses have quantified the degree to which NCEs vary as a
function of resource competition (Bolnick \& Preisser 2005), predator
hunting mode and habitat domain (Preisser \emph{et al} . 2007), resource
dynamics (Preisser \emph{et al} . 2009), and refuge availability
(Orrock\emph{et al} . 2013). The results of our meta-analysis add prey
energetic state to the growing list of drivers whose strong impact on
the outcomes of NCEs has been quantified across systems and taxa.
Notably, our findings differ somewhat from those listed above because we
addressed multiple forms of antipredator behavior and only chose
experiments explicitly contrasting responses of prey individuals with
different energetic states to risk. Hence, we offer novel insights into
the manifold ways in which energetic state can shape patterns of
anti-predator investment under conditions where the potentially
confounding effects of differing environments and other prey traits have
been minimized. Our findings also highlight the varied ways by which
state-dependent variation in anti-predator behaviors might influence the
manifestation of indirect NCEs during phase three -- for example via
differences in vigilance or space use -- though these hypothetical
scenarios remain to be evaluated empirically.

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 the presence or
absence 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). Working in a large vertebrate system,
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. By inference, 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 yet to
be 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 L. Prugh for
statistical advice. 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 tropic 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} . Results of meta-analyses using two effect size
metrics, Hedges' \emph{d} (black boxes) and log response ratios (gray
boxes), to examine relationships between compromised energetic state and
four dimensions of risk taking by prey under threat of predation:
activity (n = 30 studies making 34 species-specific contrasts); exposure
(versus use of a refuge; n = 68 studies making 71 contrasts); foraging
investment (versus apprehension; n = 37 studies making 41 contrasts);
isolation (versus grouping/shoaling; n = 10 studies presenting 10
contrasts). For each dimension, positive effect size values quantify the
extent to which risk taking by energetically compromised prey
individuals subjected to a predation threat exceeded that of
conspecifics in better condition. Mean effect sizes and accompanying
95\% confidence intervals were calculated using random effects models in
\emph{OpenMEE} ; confidence intervals not overlapping zero were
considered to indicate significant effects.



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\textbf{Box 1} : 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 2} : 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, vicu\selectlanguage{ngerman}ñas (\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 (Kohl \emph{et al} . 2018).

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.

\textbf{Box 3} : Methodology for the meta-analysis exploring contingent
relationships between prey energetic state and risk taking

As the basis for our meta-analysis, we searched the literature using the
bibliographic sources BIOSIS Previews, Google Scholar, and Web of
Science using search terms for each engine were `predator' OR
`predation' AND `prey' AND `risk taking' OR `behavior' AND `state' OR
`condition' OR `hunger' OR `nutrition' OR `food'. We then screened all
resulting studies to ensure that they (i) manipulated prey state
directly (e.g., via starvation or nutritional supplementation of
individuals) or indirectly (e.g., by contrasting behavior of individuals
in environments offering different amounts of food); (ii) exposed prey
to direct (e.g., live or model predators) or indirect (different amounts
of cover) predation risk cues; (iii) presented comparisons of some form
of risk-taking behavior by prey individuals with divergent energetic
states that were not confounded by differences with respect to
development, experience/learning, parasite load,
personality/temperament, reproductive state, sex, or size; and (iv)
included means, measures of variance, and sample sizes required for
calculating effect sizes. Separate experiments addressing different
species within the same study were considered to be independent.

After filtering, our review of the literature yielded a total of 145
studies spanning a broad range of taxa and presenting 156 unambiguous,
species-specific contrasts of risk-taking behavior manifested by prey
individuals with disparate energetic states (see Appendix S1 in
Supporting Information). The risk-taking behaviors addressed by these
studies clustered into four categories, which we analyzed separately:
activity (n = 30 studies making 34 species-specific contrasts); exposure
(n = 68 studies making 71 contrasts); foraging investment (at the
expense of apprehension; n = 37 studies making 41 contrasts); and
isolation (versus grouping/shoaling; n = 10 studies presenting 10
contrasts). For the studies quantifying prey activity (movement),
response metrics included time spent active or latency to resume
activity after exposure to a predation risk cue for individuals, as well
as the proportion of individuals in a group engaged in activity.
Risk-taking responses addressed by studies of exposure were either time
spent or the proportion of individuals not hiding, outside of a refuge,
or in a dangerous versus safe area/patch. Studies addressing foraging
investment quantified several feeding behaviors expected to influence
vulnerability to predation: namely, feeding motivation (attack distance
or latency to feed), feeding time, giving-up densities (GUDS; with
greater amounts of food consumed corresponding to heightened foraging
activity), and the proportion of individuals feeding. Studies of
isolation quantified risk taking in terms of group cohesion (e.g.,
nearest neighbor distance) or size. For each of these four dimensions of
risk taking, we first scaled all measures such that higher values
corresponded with increased risk taking. Then, we assessed the
across-study effects of prey energetic state on risk taking using two
effect size metrics: Hedges' \emph{d} (Hedges \& Olkin 1985) and log
response ratios (lnRR, Hedges \emph{et al} . 1999). For Hedges' \emph{d}
, we interpreted effect sizes of 0.2 to be ``small'', 0.5 to be
``medium'', 0.8 to be ``large'', and any exceeding 1 to be ``very
large'' (Gurevitch \& Hedges 1993). Estimates of both effect sizes were
considered to be statistically significant if their 95\% confidence
intervals did not overlap zero. For each of the four dimensions of risk
taking, we calculated mean effect sizes for each effect size metric
using random effects models in \emph{OpenMEE} (Wallace \emph{et al} .
2017). We also tested for publication bias with Spearman's rank-order
correlations; no test was significant (all p \selectlanguage{english}[?] 0.076), so we did not use
funnel plots to remove outliers from any of our four sub-analyses (Begg
\& Mazumdar 1994). Finally, our meta-analyses of activity, exposure, and
foraging investment included a few papers presented behavioral contrasts
of more than one species. Given that these contrasts may not have been
independent (Gurevitch \& Hedges 1999), we conducted truncated
meta-analyses for the three aforementioned risk-taking dimensions that
included only one randomly-selected contrast from each study addressing
multiple species. Owing to close agreement between our full and
truncated analyses, we only present results from the full analyses
below.

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