Running away or running to? Do prey make decisions solely based on the landscape of fear or do they also include stimuli from a landscape of safety?

Predator–prey interactions are composed of two major impacts on the prey: consumptive effects, where the predator consumes the prey, and non-consumptive effects, where the predator causes alterations in the prey's behavior, physiology, morphology or life history (Matassa and Trussell, 2011; Weissburg et al., 2014). While the consumptive effects are obvious when studying the interactions between predator and prey, the non-consumptive effects can remain more shadowed (Lima, 1998). One well-established concept in the field of non-consumptive effects is the concept of the landscape of fear (Laundré et al., 2001).

The landscape of fear is the sensory landscape composed of aversive cues associated with the presence of predators (Laundré et al., 2001; Luttbeg and Trussell, 2013). The aversion to these cues has been termed ‘fear’ and may result in significant changes to a prey's behavior, physiology, morphology or even their evolutionary trajectory (Brönmark and Miner, 1992; Brown and Chivers, 2005; Peckarsky et al., 2008). The landscape element refers to the spatial and temporal distribution as well as the specific dynamics of predatory cues within a habitat (Hernández and Laundré, 2005). Shifts in behavior, such as foraging choices and habitat use, can often have broadscale ecological impacts that can alter ecosystem function and services (Arias-Del Razo et al., 2012; Laundré et al., 2014; Gallagher et al., 2017). These changes, independent of the biological level of response, are dictated by the landscape of aversive signals (Gaynor et al., 2019; Leavell and Bernal, 2019). Yet, the landscape of fear is only one of a number of sensory landscapes from which prey make behavioral decisions (Leavell and Bernal, 2019).

These sensory landscapes include multiple sensory modalities that vary in their spatial and temporal dynamics (Jordan and Ryan, 2015; Kohl et al., 2018). The landscape of fear is often visualized as a three-dimensional plane with peaks and valleys (Fig. 1; Gaynor et al., 2019). Within this plane, the peaks represent some form of aversion or fear. Depending on the specific usage within a paper, the peak can represent the intensity of the predatory cue in nature, the perceived intensity after neural filtering, or even the perceived risk of predation (Fig. 1, left-hand side; Jordan and Ryan, 2015; Gaynor et al., 2019; Leavell and Bernal, 2019). In response to the detection and perception of this landscape, prey can evoke two common options by either hiding in refuges or moving to habitats with lower levels of perceived risk (Lima and Dill, 1990; Lima and Bednekoff, 1999; Martín and López, 1999; Kobak et al., 2014). Of course, other anti-predatory behavior exists such as jamming of bat sonar in moths (Corcoran et al., 2009) or mobbing in magpies (Koboroff et al., 2013). The relocation of prey to areas of lower perceived risk (or movement away from aversive stimuli) led to the coining of the concept of fear (Laundre et al., 2010). Integrated upon this landscape of fear are other sensory landscapes from which animals extract useful ecological knowledge (Luttbeg and Trussell, 2013).

Probably the most commonly researched landscape (beside fear) is that of food resources (Sih, 1982; Sih et al., 1990; Brown et al., 1999). In contrast to the aversive response to predatory cues, food patches or resources are attractive stimuli and create within prey an easily researched decision-making point: foraging versus predation (Sih et al., 1990; Shrader et al., 2008; Iribarren and Kotler, 2012; Luttbeg and Trussell, 2013). By modulating relative risk or resource value, estimates of decision-making paradigms used by prey can be produced (Křivan, 2007; Ferrari et al., 2009). At the base of these experimental and theoretical projects is the interplay between the spatial and temporal dynamics of aversive stimuli (landscape of fear) and attractive stimuli (resource patchiness). During predator–prey interactions, prey are faced with far more complex sensory landscapes composed of a multitude of aversive and attractive stimuli, which produce an umwelt of their habitat (Partan and Marler, 2002; Van Dyck, 2012; Leavell and Bernal, 2019). While prey are unlikely to integrate all aversive and attractive stimuli within a landscape in their decision making, prey are likely to incorporate both multimodal sensory information (vision and olfaction) as well as sensory signals and cues related to both fear and safety.

In relation to predator–prey interactions and the landscape of fear, prey may seek out refuges as sources of safety or protection against predation (Cressman and Garay, 2009; Orrock et al., 2013; Donelan et al., 2017). Just as decision making using information about the landscape of fear and distribution of resources can serve as an experimental model, seeking of refuges by prey is additionally useful as an experimental model of predator–prey dynamics (Sih, 1987; Sih et al., 1988; Wang and Wang, 2012). Integrating safety information may even provide better estimates of predation risk (Luttbeg et al., 2020). In a broader sense, refuges can be defined as any strategy invoked by prey to reduce predation risk (Sih, 1987). In simplistic terms, the cost of safety is a loss of potential energy gained from not foraging weighed against the benefit of reduced predation risk. The spatial distribution of refuges is yet another sensory landscape that prey need to extract meaningful information from to make decisions on behavioral actions (Dill, 1987; Brown and Kotler, 2004; Chivers et al., 2001). Shifts in foraging to less risky habitats are one form of refuge from predation (Hixon and Beets, 1993; Hernández and Laundré, 2005). Additionally, changes in activity patterns (Kohl et al., 2018), or even shifts in body morphology such as deeper bodies to escape gape-limited predators (Brönmark and Miner, 1992) can also create refuges from predation.

Some organisms have the ability to create their own physical space of refuge such as burrows, dens or shelters. These distinct locations serve as a safe location to hide themselves or their offspring from predators. Thus, animals with these home bases must have some perception of safety (Bayoumi and Meguid, 2011). For example, snails Nucella lapillus utilize rock crevices to hide from predatory green crabs Carcinus maenas (Donelan et al., 2017). Similar to the landscape of fear, these physical refuges provide sensory stimuli which prey can utilize to navigate their environment, but unlike the landscape of fear, these burrows could create a landscape of safety. The difference between refuges dispersed throughout an ecosystem, such as habitats with high cover, and burrows or shelters is the spatial specificity of the landscape because organisms create the refuges in a distinct location.

A landscape of safety with distinct burrows or shelters can provide animals with a sensory landscape that allows a goal orientation toward a known spot of safety (Fig. 1, right-hand side; Hansson and Åkesson, 2014; Schone, 2014). Landscapes of fear are composed of aversive stimuli where behavioral responses involve moving away from regions with a higher intensity predator cues to areas with a lower intensity of cues (Fig. 1, left-hand side: Jordan and Ryan, 2015). As opposed to a general movement ‘downhill’ from predatory peaks, burrows and shelters provide organisms with the opportunity to extract spatial information and perform a navigation strategy to locate a haven of safety (Schone, 2014). The landscape of safety can be visualized as a singular valley in a landscape, where the risk to the prey decreases the closer the prey approaches their refuge (Fig. 1, right-hand side). Signal detection theory within sensory ecology would predict that prey integrate these two landscapes (fear and safety) when responding behaviorally in threatening situations. Yet, in experimental or even field situations, the spatial and temporal representation of these two landscapes relative to each other are either unknown or conflated. In a conflated umwelt, the ‘downhill’ of the landscape of fear aligns with the ‘downhill’ of the landscape of safety. As such, movement along this path could be guided by aversion to predators, attraction to safety or some combination of those two.

To answer questions regarding the presence of safety cues and whether prey are using the spatial distribution of safety cues for movement, two distinct sensory landscapes that are orthogonal to each other must be created. When done correctly, prey can move along one axis (e.g. fear) independent of the sensory landscape along the other axis (e.g. safety). Movement and behavioral responses can be analyzed along each axis of the landscape separately. We have created just such an environment to evaluate whether crayfish can detect and use a landscape of safety along with a landscape of fear. In addition, we have created these orthogonal landscapes using two different types of aversive cues, one from a predator and one from injured conspecifics. Crayfish have been shown to react to predatory bass and alarm cues as aversive or fearful stimuli (Beattie and Moore, 2018; Wood and Moore, 2020) and both built shelters or used natural crevices as locations of refuge (Martin and Moore, 2008; Florey and Moore, 2019). We hypothesized that crayfish will integrate both the aversive stimuli that compose the landscape of fear and attractive stimuli in the landscape of safety to navigate an experimental arena. We created a novel experimental design, which simultaneously and orthogonally presents both fear and safety stimuli to crayfish, thus allowing us to determine whether crayfish movement was based on either the aversive stimuli presented through cues or the attractive stimuli presented through shelters.

Wild-caught rusty crayfish, Faxonius rusticus (Girard 1852), were exposed to two different types of fear-inducing chemical stimuli within a landscape of differing safety cues to determine whether behavioral decisions are based primarily on the perception of fear or safety cues. A fear gradient was constructed perpendicular to a safety gradient within a behavioral arena to spatially differentiate between these two cues. The fear gradient was created using chemical stimuli that relayed information of either predator presence or conspecific injury cues, which were delivered at different concentrations across the behavioral arena (Fig. 2). Previous work has shown fear or aversive responses to both of these cues in crayfish (Hazlett, 1994; Jurcak and Moore, 2018). Safety cues were based on a spatial distribution of shelters of varying values based on the number of openings (1–4). Previous research has demonstrated that crayfish prefer shelters with fewer openings (Matin and Moore, 2008). Crayfish were allowed to explore the arena for 15 min (called the exploratory phase) prior to one of the two chemical cue introductions. Crayfish were briefly removed from the arena and the appropriate stimulus was delivered into each of the shelters through a small hole in the top of the shelter. Once the chemical stimulus was delivered, crayfish was placed back into the middle of the arena were allowed an additional 15 min to navigate the arena (called the stimulus phase), now with an altered landscape of chemical cues. Crayfish position within the arena was tracked at a rate of 1 point per second using Xcitex ProAnalyst^® motion tracking system.

The design of the experiment comprises a fully factorial 2×2 repeated design, as the same crayfish were used in the exploratory phase and given an additional stimulus phase of the experiment. The second factor was the two different fear cues. Treatment: alarm – phase: exploratory, N=20; phase: stimulus, N=20 (same 20 crayfish used in the previous phase). Treatment: bass – phase: exploratory, N=20; phase: stimulus, N=20 (same 20 crayfish used in the previous phase). Henceforth, we will refer to each of the four conditions by combining the phase followed by the treatment. Thus, the experiment consists of these four conditions: exploratory alarm, stimulus alarm, exploratory bass, stimulus bass.

Both male and female F1 (reproductive) and F2 (non-reproductive) rusty crayfish, F. rusticus, were collected via kick seining on the Portage River (41.3618, −83.5007) in Bowling Green, OH, USA. After collection, crayfish were brought back to the Laboratory of Sensory Ecology at Bowling Green State University, where experimental crayfish were individually housed in plastic containers (25.2×16.2×11.8 cm, l×w×h). Each container was connected by recirculating aged tap water, but the individuals were mechanically and visually isolated. Crayfish used to make injury cues were communally housed in a modified steel cattle tank (119.4×55.9×77.5 cm, l×w×h). All crayfish were fed Manna Pro™ Small World™ Complete guinea pig pellets 3 times a week. Only crayfish with fully intact appendages and chelae were used in the behavioral trials. Crayfish had an average intraorbital carapace length of 2.2±0.1 cm.

Largemouth bass Micropterus salmoides were purchased from Hills Trout Farm LLC (Harrietta Hills, MI, USA). Bass were kept in a flume (243.8×58.4×55.9 cm, l×w×h) of aged tap water and fed a fish food diet (Sportsman's Choice TrophyFish Feed, High-Protein, Multi-Species Fish Formula).

All bass used to collect bass cue were kept following the established animal care and used procedures approved by the Institutional Care and Use Committees at Bowling Green State University (Protocol: 856543-5).

The predator chemical cue was generated by placing three largemouth bass M. salmoides in a 102 l plastic storage bin filled with 68 l of tap water that was aged a minimum of 24 h. The total length of the three fish placed within the odor collection bin was 59.8±5 cm (mean±s.e.m.) for the trials. Fish were fed prior to introduction into the storage bin but were not fed within the bin. Water from the bin was collected fresh before each trial in which bass cue was needed. Bass were chosen such that each individual animal exceeded the gap ratio needed to consume the crayfish used in the trial (Wood and Moore, 2020).

The alarm cue was created by macerating 15–18 g wet mass of whole F. rusticus crayfish in 400 ml of deionized water (Hazlett, 1994). The alarm cue was used within 6 h of creation, as previous studies have shown that crayfish are less reactive to older chemical stimuli (Hazlett, 1994).

Behavioral assays were conducted in a black (6 mm thick) Plexiglas arena (74.3×62×13 cm, l×w×h) (Fig. 2). Aquarium rocks (approximately 0.5 cm in diameter) were attached to the bottom of the arena using silicone to increase traction and aid in crayfish movement. The arena was divided into 16 equally sized quadrants in a 4×4 (rows×columns) fashion using fishing line that was affixed to the top of the arena, 5.0 cm above the waterline. Crayfish shelters were constructed using PVC pipe with an inner diameter of 3.2 cm that had been sawed in half lengthwise. A total of 16 shelters were used for the experiment: 4 with a single opening, 4 with two openings, 4 with three openings and 4 with four openings (Fig. 2). These shelters were equally placed in four rows of four. This created a pattern such that the shelters in each row had the same number of openings, and the shelters in each column had one more opening than the row above it (Fig. 2). Each shelter also had a small hole (0.6 cm in diameter) drilled in the top for delivery of the appropriate chemical stimulus. Each chemical stimulus was pipetted into these holes so that the stimulus remained within each shelter. The chemical cues were delivered in four different aliquots: 5, 2.5, 1.25 and 0 ml. Chemical stimuli were pipetted into the shelters such that each column of shelters received the same amount of stimulus, thus creating a grid within the arena in which the crayfish could choose between shelters with one to four openings, which were injected with 5 to 0 ml of aversive stimulus (Fig. 2). The placement of the shelters and delivery of the stimuli created two different landscapes with increasing perceptual levels perpendicular to each other. Along the x-axis, the intensity of the predator or alarm cue increased, but was uniform perpendicular to the axis. In a similar manner, the safety of the shelter increased along the y-axis but remained uniform along the x-axis (Fig. 2).

Prior to the assays, the crayfish's carapace was painted white using correction fluid (BIC^® White-Out^® Quick-Dry Correction Fluid), which does not affect the behavior of the crayfish (Edwards et al., 2018; Wood et al., 2018). One crayfish was placed in the middle of the arena and allowed to explore for 15 min. During the exploration phase, the shelters were in place in the arena, but no chemical stimulus had been added to the shelters. After 15 min, the crayfish was removed from the arena and the chemical stimulus was added to the shelters in the manner described above. Removing crayfish in this fashion has little effect on their behavior (Edwards et al., 2018). The same crayfish was then replaced in the center of the arena and allowed to navigate the arena for an additional 15 min. Both the exploration and stimulus phases were recorded as MP4 files using a Sony Handycam HDR-CX405. Between each trial, the entire arena and all shelters were rinsed to remove any odors from the previous trials.

Videos of each trial were analyzed in Xcitex ProAnalyst^® software. The videos were digitized at a rate of 1 point per second, as crayfish movement behavior can be analyzed every second (Kamran and Moore, 2015; Moore et al., 2021). The video was calibrated so that the output of the tracking would give an x,y coordinate in centimeters from the origin of the behavioral arena. The origin was set as the corner of the tank which contained the shelter with four openings (lowest safety) and no chemical stimulus (lowest fear). Thus, the origin reflected the part of the behavioral arena where both safety and fear stimuli were the lowest and thus was set to zero. The x-axis represented an increase in either the alarm or predator cues, and the y-axis represented an increase in the safety provided by the shelter. The calibration of each video was set by choosing the edges of the Plexiglas and setting the distance between the two edges as 6 mm. After each video was calibrated in millimeters, the crayfish's carapace was manually tracked for the entire duration of the trial. This tracking gave an x,y coordinate position of the crayfish within the arena. Thus, each individual crayfish had a total of 900 points throughout the 15 min exploration phase and a subsequent 900 points during the 15 min stimulus phase. This track was exported (as calibrated millimeter units) to an Excel file for further processing by an R code (https://www.r-project.org/).

After all of the tracks were digitized, each individual track was processed using an R code to extract relevant behavioral parameters. As the arena was moved and cleaned between all of the trials, the exact distance between the camera and arena was subtly different between trials. Despite trials being calibrated to known locations and distances in the arena, there were small scale differences in the real-world coordinates of the arena. These differences varied no more than 5% of the total arena size. To account for subtle differences between trials and prior to the extraction of any behavioral parameters, both the x- and y-axes were normalized to run from 0 to 100 by subtracting the minimum x-axis value from all x coordinates and the minimum y-axis value from all y coordinates. These new coordinates were divided by the maximum value for the x- and y-axes. This procedure produced x and y values that ranged from 0 to 100 along each axis. After normalizing x,y positions of the crayfish, behavioral parameters were extracted from the digitized pathways. The x- and y-axes were divided into four different zones based on the concentrations of alarm and bass stimulus for the fear axis and based on the safety provided by the number of different shelter openings. Based on this, we created four different fear zones (0–25, 25–50, 50–75, 75–100 along the x-axis) and four different safety zones (0–25, 25–50, 50–75, 75–100 along the y-axis). The increasing numbers also indicate increasing levels of fear (x-axis) and increasing levels of safety (y-axis). Finally, the overall walking speed of the animal, as well as the walking speed in the x dimension (fear) and the y dimension (safety), were calculated. A negative walking speed indicates that the mean walking speed of the animal is toward the origin of the arena or toward lower safety or fear values.

An initial statistical analysis was done to ensure that there were no significant differences in responses due to sex or reproductive form of the crayfish. No differences were found, so these two factors were ignored in the subsequent analysis.

All data analysis took place within the programming language of R. The beginning steps of the data conditioning follow those typically done in mixed model analysis (Zuur et al., 2009). The first step in the analysis was to produce histograms, Q–Q plots and normality tests of all the behavioral variables. The variance of the overall walking speed and walking speed in the y dimension were not normally distributed. Given that most GLMMs are robust against violations of the underlying assumptions of data distributions, we chose not to transform any of our response variables before running the models (Schielzeth et al., 2020). Cleveland dot plots were used to examine both the behavioral and spatial data for outliers, and none were found. Finally, to check for collinearity within the behavioral variables, independent regressions were performed between all of the (behavioral) variables. None of the behavioral measures showed any significant correlations.

All statistical tests were performed using the generalized linear mixed models approach (Zuur et al., 2009). All statistical models were performed in R used the ‘lmer’ function from the lmerTest package in R (https://CRAN.R-project.org/package=lmerTest). Following model construction, the outputs were extracted using the ‘anova’ function from the car package in R and the ‘summary’ function (https://cran.r-project.org/web/packages/car/index.html). Within models that showed significant interactions, the ‘emmeans’ function with a Tukey adjustment was used to investigate where significant differences between treatments occurred (https://cran.r-project.org/web/packages/emmeans/index.html).

For the models, the behavioral movement measures (walking speed, walking speed along the fear dimension, and walking speed along the safety dimension) served as the dependent variable, and the phase of the experiment (exploration or stimuli) and type of stimuli (alarm or bass cue) as independent measures with full interactions between those two variables. Finally, because the exploration and stimulus phase of any individual trial included the same animal, the trial number was used as a random factor within each model. For the spatial data (duration in different fear or safety zones), duration served as the dependent measure, and phase of the experiment, type of stimuli and zone number served as independent measures with full interactions between those three variables. As in the first model, the trial number served as a random factor within each model.

Crayfish displayed a wide array of movement tracks in both phases of both stimulus treatments (Fig. 3). Crayfish typically spent a portion of their time traveling along the edges of the arena and exploring the entire tank. In some trials, some crayfish spent most of their time within one or more shelters regardless of the odors present in the arena (Fig. 3, bottom right). A more quantitative and statistical analysis is provided below.

Crayfish walking speed significantly increased in the presence of bass cues compared with their speed without the bass cue present, but walking speed significantly decreased when the alarm cue was added compared with that during the exploratory phase of the alarm treatment (overall model: F_1,38,0.05=18.9, P<0.001: Table 1, Fig. 4). When presented with the predatory cue of bass, crayfish increased their walking speed by approximately 68% from 1.25±0.13 cm s⁻¹ during the exploratory phase to 2.1±0.3 cm s⁻¹ during the odor phase (emmeans post hoc P=0.009). Conversely, crayfish reduced their walking speed by a third from 1.02±0.08 cm s⁻¹ during the exploratory phase to 0.34±0.08 cm s⁻¹ while the alarm cue was present (emmeans post hoc P=0.039). As expected, the walking speed during the two different exploratory phases was not significantly different (emmeans post hoc P=0.79), and the walking speed during the bass cue presentation was significantly higher than during the alarm cue presentation (emmeans post hoc P<0.001).

An analysis of the walking speeds along either the x-axis (fear) or y-axis (safety) reveals further differences (Figs 5 and 6). Crayfish did not exhibit any significant shift in walking speed along the x-axis under any of the treatments (overall model F_1,38,0.05=0.03, P=0.854; Fig. 5). Yet, crayfish significantly altered their walking speed along the y-axis (overall model F_1,38,0.05=5.9, P=0.017; Fig. 6). Crayfish exhibited a larger negative walking speed along the y-axis during the bass stimulus phase when compared with the exploratory phase of this experiment (emmeans post hoc, P=0.04, Fig. 6). Conversely, the only positive walking speed along the y-axis exhibited by crayfish during any phase of the experiment was when they were presented with the alarm cue in the stimulus phase. Under these conditions, crayfish exhibited a higher and positive walking speed along the y-axis compared with that with the bass odor in the stimulus phase of the experiments (emmeans post hoc, P=0.0095, Fig. 6).

Crayfish significantly altered their movement patterns along the fear axis in response to the different odor treatments (overall fear model: F_3,304,0.05=5.16; Fig. 7). Crayfish significantly increased their use of the lower alarm cue areas when compared with both exploratory phases as well as the stimulus bass phase (exploratory bass, P=0.003; exploratory alarm, P=0.025; stimulus alarm, P=0.001; Table 2). This change in habitat usage is further supported by a shift away from zones with higher alarm cues to zones with lower alarm cues exhibited by crayfish during the stimulus alarm treatment. During the stimulus alarm treatment, crayfish spent more time in zone 2 along the fear axis than in zones 3 and 4 (P<0.001 for both; Table 2). Crayfish also exhibited an increase in the use of zone 1 of the fear axis as compared with the two higher fear zones 3 and 4 during the stimulus alarm phase (P=0.055 for zone 1 compared with zone 4; P=0.019 for zone 1 compared with zone 3; Table 2). There were no significant shifts in zone use during any of the exploratory phases or during the stimulus phase with bass treatment.

In a more dramatic result, crayfish shifted their zone usage along the safety axis during the alarm stimulus phase of the experiment compared with the other three treatments (overall model: F_3,304,0.05=4.45, P=0.004; Fig. 8). Crayfish significantly increased their use of the zone with the highest safety when presented with the alarm odor (Table 2). Crayfish exhibited higher use of zone 4 compared with zone 3 (P=0.042), zone 2 (P<0.001) and zone 1 (P<0.001) (Table 2). This statistical finding is even more evident in the pairwise comparisons of zone 4 use across the different treatments. Crayfish increased their use of the zone with the highest safety, zone 4, when the alarm cue was present, compared with other treatments (alarm stimulus compared with alarm exploratory, P=0.004; compared with bass exploratory, P<0.001; compared with bass stimulus, P=0.003). This shift in zone use was followed by the expected decrease in zone 1 use compared with the bass stimulus treatment (P=0.003).

Our results showed that crayfish movement kinetics and habitat use were influenced by the perception of both aversive (fear) and attractive (safety) stimuli within the behavioral arena (Tables 1 and 2, Figs 4–8). The degree to which both of these sensory landscapes (attractive and aversive) shape movement and habitat choices was based, in part, on the identity of the aversive stimuli (i.e. bass or alarm) (Table 1, Figs 4–8). While predatory cues provide information about the location of potential threats, alarm cues indicate that an immediate injury or predation event of a conspecific has occurred. Because of the differences in perceived risk between these two aversive stimuli for crayfish, alarm cues carry a more threatening message than bass cues (Hazlett, 1999).

In the presence of predatory cues, crayfish increased their overall walking speed and displayed an increased average walking speed toward lower safety environments (Table 1, Figs 4 and 6). There was no statistical change in walking speed along the fear gradient (Fig. 5). Despite these changes, crayfish did not alter their overall habitat use along either the fear or safety gradients when confronted with predator cues (Table 2). In contrast, in the presence of the more threatening cue (alarm), crayfish decreased their overall walking speed and habitat use, which strongly indicates that crayfish were responding to the sensory landscape of alarm cues (landscape of fear) as well as the sensory landscape of attractive shelters (landscape of safety) (Tables 1 and 2, Figs 4, 7 and 8). Thus, crayfish integrated these two related landscapes when making movement and habitat usage decisions. These movement decisions alter how predator and prey interact (Shrader et al., 2008; Iribarren and Kotler, 2012).

Within the field of predator–prey interactions, the theory of the landscape of fear has created room for a better understanding of the non-consumptive dynamics between prey and predator (Laundré et al., 2001; Preisser et al., 2005; Preisser and Bolnick, 2008; Laundre et al., 2010). Prey animals extract relevant information from their entire sensory landscape and use this information to make behavioral decisions (Lima and Dill, 1990; Wisenden, 2000). The spatial and temporal distribution of cues and signals within the landscape can provide information that alters prey behavior, including when and where they forage, mate or inhabit (Sih et al., 1990; Brown et al., 1999; Laundré et al., 2001; Sih and McCarthy, 2002; Hernández and Laundré, 2005). In addition to behavioral alterations, prey respond to the spatial distribution of predatory cues by altering their physiology, morphology or life history (Lima and Dill, 1990; Brönmark and Miner, 1992; Janssens and Stoks, 2013). This series of observations has led to the development of the landscape of fear theory, where prey behavioral, morphological and physiological changes are dictated by the perception of aversive or fearful stimuli within the entire sensory landscape (Peckarsky et al., 2008; for reviews, see Laundré et al., 2014; Gaynor et al., 2019). However, the sensory landscape of the prey contains both aversive cues about predation threats and attractive cues about potential areas of safety (shelters or refuges) that protect prey from predation (Hixon and Beets, 1993; Martín and López, 1999; Donelan et al., 2017). In the present study, crayfish used the landscape of fear differentially depending on the level of threat present in the arena (Figs 7 and 8). The attractive cues and their effect on the sensory ecology of prey have previously been underappreciated and often not integrated into the landscape of fear theory, leaving gaps in the existing predator–prey concepts (Wang and Wang, 2012; Jordan and Ryan, 2015; Donelan et al., 2017). Based on ambiguity and signal detection theory, prey should use multiple sources of information to respond appropriately to the threat of predation (Hazlett, 1999; Brown et al., 2006; Leavell and Bernal, 2019). Empirical evidence also suggests that the use of multidimensional integration, which is the neural integration of multiple stimuli along multiple dimensions, by prey increases the effectiveness of behavioral responses (Brown et al., 2006; Leavell et al., 2018; Ocasio-Torres et al., 2021). By extension, both theoretical and experiential studies in sensory ecology predict that prey would combine and use the information contained within both the well-established landscape of fear and the understudied landscape of safety (van der Merwe and Brown, 2008; Jordan and Ryan, 2015). Certainly, the results of this study show this is probably occurring in crayfish. During high threat events (alarm cues), crayfish use a combination of fear and safety to guide their movement patterns. In events that are less threatening, safety cues have an important role.

The use of safety cues for prey may be dependent upon habitat use of the prey species and their sensory capabilities (Sih and McCarthy, 2002; Orrock et al., 2013). Many prey species do not build specific protective structures such as burrows or dens but instead utilize natural variations in the landscape to hide from predators (Sih, 1987; Persson, 1993; Cressman and Garay, 2009). However, other prey create or use refuges that provide protection from predation and continually return to these refuges (Kobak et al., 2014; Pustilnik et al., 2021). Warthogs Phacohoerus africanus utilize burrows for both predator avoidance and communal nesting, though communal nesting might be a side effect of predator avoidance (White and Cameron, 2009). Golden jackals, Canis aureus, build burrows to rear pups and increase their guarding of these burrows at night, when predation risks are highest (Mukherjee et al., 2018). Eastern fox squirrels, Sciurus niger, occupy vacated burrows of the gopher tortoise, Gopherus polyphemus, to hide from predators and escape extreme temperatures (Potash et al., 2020). With specific and distinct spatial locations indicating safety, prey with refuges may utilize these safe zones and integrate the information into a landscape of safety (Weissburg et al., 2014; Jordan and Ryan, 2015). The sensory landscapes of fear and safety that refuge-building prey utilize to move through habitats is very different from the landscapes of prey that do not consistently return to these protective structures (Wilson and Weissburg, 2013; Gaynor et al., 2019).

The landscape of fear has often been visualized as a spatial distribution of peaks and valleys overlaid upon the three-dimensional habitat of the prey (Fig. 1: Jordan and Ryan, 2015; Gaynor et al., 2019). The peaks indicate intensity of the predatory cues and the valleys indicate lower intensity predatory cues (Relyea, 2003). While there is some confusion within the literature of whether the peaks and valleys indicate intensity of the cues or perception of those cues, the spatial variation remains poignant (Gaynor et al., 2019). Within this type of sensory landscape, prey supposedly move ‘downhill’ away from highly threatening areas to less threatening areas during threatening encounters (Fig. 1, left). This ‘downhill’ movement is driven by decreasing predatory cues as prey move away from a threatening stimulus, but the movement is not a goal-directed movement as defined by orientation literature (Hansson and Åkesson, 2014; Schone, 2014). Movement in any number of directions, as long as the movement is away from the aversive stimuli, results in relocation to a less threatening habitat. This movement can be directly contrasted with the goal-directed movement toward a refuge (Schone, 2014). The landscape of safety can be composed of specific locations of refuges (burrows, nests or shelters), where movement is only beneficial to the organism if that movement is in the specific direction of the refuge (Fig. 1, right). These refuges have distinct spatial locations within a habitat which would require a different set of orientation strategies from the non-goal-directed movement away from predatory cues (Moore and Crimaldi, 2004; Åkesson et al., 2014; Mulheim et al., 2014). In addition to the different strategies, the extraction of specific spatial locations and directional cues along with some measure of progress toward that location from the surrounding sensory landscape is needed to perform these behaviors (Lohmann et al., 2008; Geva-Sagiv et al., 2015; Kheradmand and Nieh, 2019). For example, insects can often measure distances, and hence progress toward safety, from their burrow during homing events (Wehner, 2003; Mandal, 2018). This distance of safety is often assessed by placing the animal in the center of its immediate landscape and is termed an egocentric frame of reference. For some prey, the movement toward the increased safety of a refuge can provide more certainty of reduced risk than simply moving away from an intense aversive stimulus.

Mapping prey responses onto solely a landscape of fear could create mismatches in predation risk and prey response (Abrams, 2000; Luttbeg and Trussell, 2013; Abom and Schwarzkopf, 2016). These mismatches can be indicators that other sensory stimuli in the environment are involved in the decision making and movement of animals (Ferrari et al., 2010; Neri et al., 2017). In these cases, prey are likely measuring trade-offs based on the sum total perception of risk, reward and safety in other sensory landscapes besides the landscape of fear (Ganson, 2018). The spatial distribution of foraging resources has been the most common landscape measured and assessed when considering prey habitat use under predation threats (Matsuda and Abrams, 1994; Křivan, 2007; Fleischer et al., 2018). Concepts such as giving-up density and resource patchiness are important pieces of information in the sensory landscape that prey utilize to assess risk and reward during foraging (Brown, 1988; Bedoya-Perez et al., 2013). While foraging is the most commonly researched motivator, other sensory landscapes such as water resources, mate distribution and refuges may be as important as the landscape of fear in the process of animal decision making. Indeed, recent modeling evidence has shown that the integration of public information about predation risk, which can be thought of as safety cues, can lead to greater fitness gains (Luttbeg et al., 2020).

The complete sensory landscape that prey experience is a series of different stimuli with varying meaning and intensity as well as spatial and temporal distribution (Wilson and Weissburg, 2013; Jurack and Moore, 2018). The meaning of these landscapes is connected to predation threats, foraging, mating and potentially refuges, and may vary in importance based on the motivational state of the prey (Jordan and Ryan, 2015). The perception of these landscapes may include a prey's major senses (e.g. vision or audition for terrestrial vertebrates) as well as their minor senses (e.g. vibration). These stimuli comprise multiple and potentially disparate landscapes which form a singular umwelt for the prey (Partan and Marler, 2002; Van Dyck, 2012; Jordan and Ryan, 2015). The landscape of fear and spatial distribution of foraging resources are two critical elements of this umwelt (Leavell and Bernal, 2019). The work in this paper would indicate that for animals that create spatially distinct refuges, safety is another key landscape that is integrated into that umwelt (Table 1 and Fig. 8). The degree that the landscape of safety plays in the decision making and movement of prey seems to change based on the type of fear signals that are present (Fig. 8).

The ecological impacts of the landscape of fear have been demonstrated rather thoroughly (Laundre et al., 2010). Alterations in prey behavior, morphology, physiology and habitat use in response to stimuli within the landscape of fear create changing dynamics in ecosystem function (Laundré et al., 2014; Schmitz et al., 2015; Gallagher et al., 2017). The reintroduction of wolves to Yellowstone had profound effects on the spatial distribution of elk, which led to significant changes in grazing and primary productivity (Laundré et al., 2001). An interesting addition to the landscape of fear concept would be the landscape of safety composed of specific and spatially distinct refuges within habitats. This safety landscape could also help explain non-consumptive effects at the ecosystem level. In these cases, prey may be motivated by the landscape of fear, but the behavioral patterns may be dictated by the landscape of safety. By integrating a landscape of safety with the landscape of fear, as well as other sensory landscapes, a deeper explanation of prey responses, non-consumptive effects and the ecological impacts may arise.

This project would not have been possible if not for the talk given by Doug Chivers at the 2020 Gordon Conference on predator–prey interactions and the subsequent discussion with Doug Chivers and Maud Ferrari on the topic. The authors would like to thank Jackson Doyel and Dani Saum for help in the collection of the data presented here. Finally, two anonymous reviewers provided thoughtful feedback that greatly improved the manuscript and the authors are indebted to their time and energy.