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Crabs interpret the threat of predator body size and biomass via cue concentration and diet
Animal Behaviour 92 (2014) 117e123
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Animal Behaviour journal homepage: www.elsevier.com/locate/anbehav
Crabs interpret the threat of predator body size and biomass via cue concentration and diet Jennifer M. Hill*, Marc J. Weissburg Georgia Institute of Technology, Atlanta, GA, U.S.A.
a r t i c l e i n f o Article history: Received 6 December 2013 Initial acceptance 9 January 2014 Final acceptance 24 February 2014 Published online MS. number: A13-01014R Keywords: antipredator behaviour blue crab mud crab nonconsumptive effect trait-mediated interaction
Greater predator body size is often associated with greater predation risk. According to the threatsensitive predator avoidance hypothesis, prey should display graded responses to increasing predator body size; in turn, these differences in behaviour should also cause differing indirect effects. Yet, in aquatic systems, where prey often use chemical cues to judge predator threats, the role of chemically mediated perception of predator body size and the propagation of indirect effects are still unclear. To differentiate intraspecific predator size via chemical cues, prey must judge predator threat quantitatively (i.e. via concentration) or qualitatively (i.e. via differing cues and/or diets). We investigated the role of individual and aggregate predator body size (i.e. biomass, cue concentration) and qualitative diet cues in antipredator behaviour and indirect interactions by examining the behavioural responses of the mud crab Panopeus herbstii and the survival of oyster prey (Crassostrea virginica) in response to various blue crab, Callinectes sapidus, biomass and diet treatments. Mud crabs increased their refuge use and decreased foraging in response to chemical cues from large, but not small, individual blue crabs. The perception of predator size appeared to be related to predator biomass as multiple small blue crabs and large crabs elicited similar foraging responses in mud crabs. However, multiple small blue crabs failed to affect mud crab refuge use, indicating that some measures of behaviour may not always be predictive of indirect effects. Predator diet also influenced mud crab behaviour and foraging: predators fed mud crabs elicited a greater antipredator response than crushed conspecifics or predators fed oyster diets, suggesting that qualitative cues also influence intraspecific threat perception and indirect interactions. These experiments demonstrate that we cannot successfully predict indirect interactions without considering predator population size structure and measuring indirect effects. Ó 2014 The Association for the Study of Animal Behaviour. Published by Elsevier Ltd. All rights reserved.
Predation is an important force in structuring ecological communities and imposes high selection pressure on prey to develop strategies to avoid being eaten (Dawkins & Krebs, 1979). One such strategy is predator threat assessment, in which prey use a variety of sensory cues to determine the likelihood of danger from an individual predator and the need to perform antipredator behaviours that increase survivorship (Lima, 1998). Yet, assessing predator threats can be complex as predation risk can vary both between predators and between individuals of a single predator species. For instance, snails differentiate between predator cues and crawl underneath leaf litter to escape sunfish, but crawl towards the water surface in response to crayfish cues (Turner, Fetterolf, & Bernot, 1999). Within predator species, predation threat can change based on life history stage (i.e. juvenile/adult, moulting,
* Correspondence and present address: J. M. Hill, Dauphin Island Sea Lab, 101 Bienville Blvd, Dauphin Island, AL 36528, U.S.A. E-mail address: [email protected] (J. M. Hill).
hibernation), time of year when predators are most abundant or most active and/or location (Basille, Fortin, Dussault, Ouellet, & Courtois, 2012; Griffen, Toscano, & Gatto, 2012; Lipcius & Herrnkind, 1982; Sheriff, Krebs, & Boonstra, 2011; Yen, 1983). Differences in threat-specific behaviours are important because they can greatly influence community structure by altering foraging behaviour and fitness of prey (Ferrari, Wisenden, & Chivers, 2010; Kats & Dill, 1998; Stankowich & Blumstein, 2005), which can indirectly affect the survival of the prey’s resource (often called traitmediated indirect interactions, or nonconsumptive effects: Abrams, 2007; Preisser, Bolnick, & Benard, 2005; Werner & Peacor, 2003). Therefore, understanding how prey assess predator threat is essential to predict outcomes for prey survival and cascading indirect effects. Predator body size is an intraspecific trait often associated with greater predatory threat and frequently determines where predators feed, as well as their feeding rates and diet choices (Cohen, Pimm, Yodzis, & Saldana, 1993; Werner & Gilliam, 1984). Furthermore, size-based predator traits lead to numerous cascading
http://dx.doi.org/10.1016/j.anbehav.2014.03.025 0003-3472/Ó 2014 The Association for the Study of Animal Behaviour. Published by Elsevier Ltd. All rights reserved.
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indirect interactions in communities (Dodson, 1970; Rudolf, 2006; Werner & Gilliam, 1984). However, despite its importance in predicting predatoreprey outcomes, the role of predator body size in threat assessment and its propensity to propagate indirect effects are still understudied, especially in aquatic communities and for nonvisual modalities. According to the threat-sensitive predator avoidance hypothesis (Helfman, 1989), prey will respond to predators with antipredator behaviours that match predator threat levels. Thus, if increasing predator size is indicative of greater threat, prey should display greater antipredator responses to larger predators, and this would translate into stronger cascading effects on prey resources. In many aquatic communities, the perception of predator threat often is determined by chemical cues emanating from predators or crushed conspecifics (Kats & Dill, 1998) since the transmission of acoustic or visual cues is more limited (Dusenberry, 1992). Predator body size may also be chemically perceived through differing cues (Kusch, Mirza, & Chivers, 2004) or through greater cue release (i.e. concentration; Chivers, Mirza, Bryer, & Kiesecker, 2001; Pettersson, Nilsson, & Bronmark, 2000) from larger predators. For example, Kusch et al. (2004) found that minnows responded to sympatric small pike predators more than they did to allopatric large pike predators, suggesting that differing cues were responsible for sizebased perception. In contrast, Chivers et al. (2001) and Pettersson et al. (2000) found that prey fishes avoided large, but not small, fish predators, suggesting concentration-dependent responses. Importantly, the manner in which size is chemically communicated (i.e. either quantitatively or qualitatively) may cause differing behavioural responses and indirect effects on prey resources depending on predator population size structure and/or predator density. For instance, if the perception of size is concentration based, small predators at high densities may elicit behavioural responses and indirect effects similar to those of a single large predator. In contrast, if prey respond to qualitative differences in predator chemical cues, then predators will cause size-specific behavioural effects and indirect effects regardless of predator density. Thus, the perception of size will not only have consequences for antipredator behaviour, but also for numerous cascading interactions that affect community structure. In systems where predator body size dictates the trophic level where predators feed (i.e. intraguild predation systems or systems containing predators that shift their diet during maturation), diet cues may be beneficial in determining the threat of oncoming predators. Predator diet often affects a prey’s evaluation of threat, and diets including conspecifics often result in the greatest antipredator responses as a result of qualitative differences in predator diet cues (Chivers & Mirza, 2001a, 2001b; Ferrari et al., 2010; Schoeppner & Relyea, 2005; Smee & Weissburg, 2006; Turner, 2008). Some prey species show antipredator responses to predators fed a diet of heterospecific prey, but these responses can often decrease with increasing phylogenetic distance of prey in the predator’s diet (Schoeppner & Relyea, 2005). However, dietdependent antipredator behaviours are not ubiquitous (Chivers & Mirza, 2001a, 2001b). Furthermore, similar to studies on chemically mediated body size, studies examining behavioural responses of prey to diet cues of predators often have not examined whether differences in prey behaviour result in differing indirect (cascading) effects. As the magnitude and direction of antipredator behaviour may vary based on predator traits such as size and diet, indirect effects on prey resources may also vary based on predator threat assessment. Yet, how changes in behaviour directly translate into indirect interactions is often inferred but not directly tested. As predator size and diet may affect predator threat assessment and indirect interactions, we had multiple objectives in our study. Our goals were to (1) investigate the role of predator size and
biomass (i.e. cue concentration) in chemically mediated threat perception, (2) examine whether predator diet influences the threat response and (3) determine whether differences in antipredator behaviour due to predator size and diet also translate into indirect effects on prey resources. METHODS Model System To examine the influence of predator biomass and diet in predator threat assessment and the resulting indirect effects, we chose an intraguild predation system consisting of both adult and juvenile blue crabs, Callinectes sapidus, the mud crab Panopeus herbstii, and their shared oyster prey, Crassostrea virginica. This system is ideal for examining size-based interactions because blue crabs are generalist predators, predation by crabs is crush limited (i.e. larger body sizes have greater crush strength), prey size scales with predator body size, and size classes co-occur. The blue crab, which is the top predator in this system, is an important predator and scavenger of estuarine environments (Micheli, 1997) and has been shown to prey on a variety of bivalve and crustacean species (Eggleston, 1990a, 1990b; Fitz & Weigert, 1991; Micheli, 1997). The intermediate prey, mud crabs, are small cryptic xanthid crab predators found in both oyster reef and salt marsh habitats. Mud crabs occupy the interstices of oyster beds at high densities (Hollebone & Hay, 2007; Lee & Kneib, 1994) and prey on a number of bivalve species (Bisker & Castagna, 1987; Seed, 1980). Xanthid and other nonportunid crabs make up approximately 43% of the diet of blue crab (Fitz & Weigert, 1991). Furthermore, risk of predation to mud crabs varies as a function of blue crab predator size; large adult blue crabs (>100 mm carapace width; CW) are voracious predators on mud crabs in laboratory mesocosms, whereas small juvenile blue crabs (40e60 mm CW) rarely present a threat to mud crabs greater than 15 mm CW (Hill & Weissburg, 2013b). Thus, according to the threat-sensitive predator avoidance hypothesis (Helfman, 1989), mud crabs should show stronger antipredator responses to larger blue crabs. A priori, this also suggests that blue crab body size may propagate differing indirect effects. Animal Collection and Maintenance All experiments were performed at the Skidaway Institute of Oceanography (SkIO), Skidaway Island, Georgia, U.S.A. over summer months in multiple years from 2008 to 2010. Both blue crabs and mud crabs were collected from Wassaw Sound and associated tributaries with permits from the Georgia Department of Natural Resources. Blue crabs were collected by commercial crab pot and seine net. Mud crabs were collected by hand from loose oyster reef. Hatchery-reared oysters (10e15 mm in length) were obtained from Bay Shellfish (Tampa, FL, U.S.A.). All animals were maintained in covered outdoor flowthrough sea water tanks (0.62 0.50 0.27 m) at the SkIO for a minimum of 48 h before experiments began. Blue crabs were maintained on a diet of shrimp and/or clams and were fed an ad libitum diet of shrimp and oysters once a day for 48 h prior to experiments. Mud crabs were maintained on a clam diet and were starved 48 h prior to experiments. Blue crabs were not used in experiments if they were premoult. No ovigerous female crabs were utilized in experiments. Animals were held no longer than 2 months and all animals were released into an estuary adjacent to SkIO following the experiments. Investigating How Prey Encode Size-based Threat To examine the ability of prey to distinguish predator body size via chemical cues, we monitored mud crabs foraging on oysters in
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response to differing combinations of individual predator size and aggregate biomass. These manipulations allowed us to address the role of cue concentration (predator biomass) versus size-specific chemical cues in predator threat assessment. Experiments took place in laboratory mesocosms (0.7 0.4 0.3 m) containing artificial oyster reefs constructed over approximately 2.5 cm of sand and shell hash supplied with flow-through sea water. We constructed artificial reefs by gluing 10 oyster shells (obtained from natural shell banks) to create similar small clusters (approximately 6 cm in diameter) and then bundling clusters with rubber bands to build a reef (21 clusters per tank). We secured one juvenile oyster (10e15 mm in length) on the face of each cluster using cyanoacrylate glue (21 oysters total). Once the reef structure was created, we scattered approximately 1 litre of shell hash around reef edges to mimic the natural structure of the habitat and to help hold the reef in place. We then added 20 marked mud crabs (similar to natural size class densities: 15e20 mm CW, N ¼ 13; 20e25 mm CW, N ¼ 4; 25e30 mm CW, N ¼ 3; Lee & Kneib, 1994) to the tank. Each mud crab received a painted fluorescent mark on its carapace to allow us to monitor its behaviour with a black light wand at night, when mud crabs are typically more active (Grabowski, 2004). We then submerged one of four caged predator treatments into each tank: one large blue crab (>100 mm CW, approximately 130e180 g), one small blue crab (40e60 mm CW, 8e20 g), multiple small blue crabs (40e60 mm CW, totalling 130e180 g), or no predator (control). Predator cages consisted of semitranslucent plastic containers (0.34 0.20 0.12 m) with multiple holes drilled through the side and covered with vexar mesh, which allowed movement of chemical cues into mesocosms but prevented direct contact between blue crabs and mud crabs. The predator containers allowed mud crabs to see slight shadows of the blue crabs inside, and also transmitted auditory cues produced by blue crab movements. Blue crabs were fed an ad libitum diet of shucked oysters every day as this diet is common to multiple sizes of blue crab predators (Eggleston, 1990b). Mud crabs were allowed to forage on oysters in mesocosms for 2.5 days. The total number of oysters eaten was recorded at the conclusion of the experiment, as well as the number of surviving mud crabs. Mud crab survival was high in all experimental runs (91%). We monitored the night-time refuge use of mud crabs in mesocosm reefs using a black light wand to illuminate the fluorescent paint on mud crab carapaces (Grabowski, 2004). The number of mud crabs visible in tanks was counted once every 15 min for 30 min each night of the experiment (9 observations total). Mud crabs that were not visible were either buried underneath sand or hidden within oyster reef structure. We calculated the average percentage of mud crabs visible on reefs by dividing the average number of mud crabs visible over three nights by the total number of mud crabs surviving experiments. Because of the limited mesocosm space, we ran three replicates at once in a randomized block design, with six runs in all, totalling 18 replicates for each treatment. We used run (time) as a factor in analysis as preliminary analysis demonstrated that run (time) significantly affected the behaviour and percentage of oysters eaten. Thus, the average percentage of mud crabs visible was analysed by two-factor ANOVA for run and predator biomass treatment. The percentage of oysters eaten was arcsine transformed to meet assumptions of normality and analysed by a twofactor ANOVA for the effects of run and predator treatment. We also analysed the number of mud crabs surviving via a two-factor ANOVA (run, predator size) to determine whether significant differences in mud crab survival over time or by treatment affected oyster survival. When significant treatment differences were detected, we ran Tukey post hoc tests to examine significant differences between groups.
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The Role of Predator Diet To examine the effect of blue crab diet on the level of mud crab antipredator response, we investigated mud crab foraging behaviour and oyster survival in response to a caged large blue crab fed one of two diets, shucked oysters or mud crabs. Since previous studies have demonstrated that injured conspecific cues alone can induce antipredator activity (Ferrari et al., 2010), we used crushed mud crab treatments to determine whether responses to blue crabs could be attributable to alarm cues from crushed conspecifics alone or were additive with blue crab cues. Mesocosms were set up as in previous experiments containing artificial oyster reef, 42 oysters (two 10e15 mm oysters per artificial cluster) and 20 mud crabs. Blue crab diet treatments consisted of large blue crabs caged in predator boxes (as in predator size experiments) and placed in mesocosms. Blue crabs were fed an ad libitum diet of shucked oysters or crushed mud crabs once a day while predator boxes were submerged in mesocosms. All mud crabs were chilled prior to crushing. Crushed mud crab treatments were applied once a day and consisted of one large (25e30 mm CW), two medium (20e 25 mm CW) and two small (15e20 mm CW) crushed mud crabs. Multiple sizes of mud crabs were used as differing sizes of conspecifics may elicit different reactions by prey (Mirza & Chivers, 2002). Crushed mud crab treatments were prepared by quickly culling/crushing crabs in 0.7 litres of sea water and then after death, splitting them in half to mimic a blue crab predation event. Crushed conspecifics were then added immediately to a predator box within the mesocosm. Remnants of crushed mud crabs were removed directly before the next application of crushed conspecifics cue. We recorded the number of oysters eaten every 24 h for 48 h. We also monitored the presence of mud crabs on reefs at night as in previous experiments. Because of the limited mesocosm space, we ran three replicates at once for four runs, totalling 12 replicates per treatment. As predation rates were high during the experiment, we analysed the percentage of oysters eaten after 24 h, following square-root transformation of the data to meet assumptions of normality, by a two-factor ANOVA (run, diet) since preliminary analysis indicated that run (time) affected the percentage of oysters eaten. The average percentage of mud crabs visible at night over the course of the experiment analysed by two-factor ANOVA (run, predator diet). We used Tukey post hoc tests to examine differences between specific treatment groups when main effects were significant. RESULTS How Prey Encode Size-based Threat The presence of mud crabs on mesocosm reefs during nighttime observations was significantly affected by blue crab predator size (ANOVA: F3,48 ¼ 9.14, P < 0.001; Fig. 1a) and run (ANOVA: F5,48 ¼ 4.77, P ¼ 0.001), but there was no significant interaction (treatment*run: F15,48 ¼ 1.59, P ¼ 0.110). Large blue crabs suppressed the percentage of mud crabs visible on reefs by approximately 10%, indicating that large blue crabs caused mud crabs to seek refuge deeper within reefs where they could not be observed (Fig. 1a; Grabowski, 2004). The presence of mud crabs was not diminished in response to either small or multiple small blue crabs. Chemical cues from blue crab predators had a significant biomass-dependent effect on the percentage of oysters eaten by mud crabs (ANOVA: F3,48 ¼ 7.75, P < 0.001; Fig. 1b). Cues from both large and multiple small caged blue crabs diminished the percentage of oysters eaten by mud crabs by approximately 20e25%. The reduction in foraging caused by multiple small blue crabs was unexpected as multiple small blue crabs did not increase mud crab
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Figure 1. Mean SE percentage of (a) mud crabs visible outside of reef refuges and (b) oysters eaten in response to a single large blue crab, a single small blue crab, multiple small blue crabs and a control without predators (N ¼ 18). Differing letters denote significant differences based on Tukey post hoc tests (P < 0.05).
refuge use (Fig. 1b). This suggests that increased oyster survival was not attributed to mud crab prey seeking refuge in response to predator threat. Single small blue crabs also did not suppress mud crab foraging, as oyster survival was not significantly different from zero crab controls (Fig. 1b). Experimental run (time) also influenced the percentage of oysters eaten (ANOVA: F5,48 ¼ 5.41, P ¼ 0.001), but there was no interaction between run and predator treatment (ANOVA: F15,48 ¼ 1.62, P ¼ 0.10). The number of mud crabs surviving the duration of the experiment was significantly affected by run (ANOVA: F5,48 ¼ 3.02, N ¼ 18, P ¼ 0.02) but not by predator treatment (ANOVA: F3,48 ¼ 0.73, P ¼ 0.539). Because of the variation in mud crab survival, we calculated the per crab oyster consumption rate by dividing the number of oysters consumed by the number of mud crabs alive at the end of the experiment. As the survivorship of mud crabs was high (estimate SE ¼ 91 0.7%), statistical analysis using per capita oyster consumption yielded the same results as when we examined total oyster consumption. The Role of Predator Diet The average percentage of mud crabs visible outside of reef refuges was significantly affected by run (ANOVA: F3,32 ¼ 5.01, P ¼ 0.006) and diet treatment (F3,32 ¼ 11.57, P < 0.001) and closely followed the patterns of predation (Fig. 2a). Crushed mud crabs and blue crabs fed oysters both suppressed the number of mud crabs visible by about 10e12% (Fig. 2a). The greatest suppression of mud crabs outside of reefs was about 20% and was caused by blue crabs
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Figure 2. Mean SE percentage of (a) mud crabs visible outside of reef refuges and (b) oysters eaten in response to crushed conspecifics and large blue crabs fed differing diets (N ¼ 12). Blue crabemud crabs denotes blue crabs fed a mud crab diet. Blue crabseoysters denotes blue crabs fed an oyster diet. Differing letters denote significant differences based on Tukey post hoc tests (P < 0.05).
fed mud crabs. The treatment*run interaction was not significant (F9,32 ¼ 0.66, P ¼ 0.737). Blue crab diet (ANOVA: F3,32 ¼ 4.85, P ¼ 0.007) and experimental run (ANOVA: F3,32 ¼ 3.89, P ¼ 0.018) both had a significant effect on the number of oysters eaten (Fig. 2b). The run*treatment interaction was not significant (F9,32 ¼ 0.40, P ¼ 0.925). Both blue crabs fed oysters and crushed mud crabs alone suppressed the percentage of oysters eaten by approximately 15% relative to controls. Blue crabs fed mud crabs suppressed predation on oysters the greatest amount, on average, by 30% (Fig. 2b). DISCUSSION In turbid aquatic environments where vision is limited, animals obtain a great deal of information from chemosensory cues. Chemical cues carry qualitative (i.e. differing cues and diets) and quantitative (i.e. concentration) information about predators that allow prey to assess the associated levels of threat (Ferrari et al., 2010; Kats & Dill, 1998). Our results demonstrate that mud crab prey use both the qualitative (i.e. chemical composition) and quantitative aspects (i.e. concentration) of chemical cues to assess the threat associated with blue crab predators. These chemical cues affected both the measured antipredator behaviour of mud crabs and the survival of mud crab’s oyster prey. Responding to qualitative differences in predator cues allows prey to distinguish between different predators and predators with
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different diets (Ferrari et al., 2010; Mirza & Chivers, 2001b; Schoeppner & Relyea, 2005, 2009; Turner, 2008). Mud crabs generally showed less activity on reefs in response to blue crabs fed mud crabs than they did to blue crabs fed oysters (Fig. 2), demonstrating that cues associated with consumed conspecifics represent a greater threat. Blue crabs less than 80 mm CW rarely successfully prey on mud crabs (>15 mm CW) and are instead restricted to bivalves and other infaunal prey (Hill & Weissburg, 2013b; O’Connor, Grabowski, Ladwig, & Bruno, 2008), suggesting that diet may be a dependable indicator of blue crab predator threat. Consistent with many previous diet studies from other organisms, alarm cues from crushed conspecifics alone was not sufficient to induce intense antipredator responses (Schoeppner & Relyea, 2005, 2009), and the combination of the two cues elicited a stronger response than either cue presented alone. This suggests that both blue crab predator cues and crushed conspecific metabolites may provide important nonredundant information about predation risk that reduces uncertainty (Munoz & Blumstein, 2012) and results in enhancement of antipredator behaviours. Importantly, the measured differences in mud crab behaviour and refuge use directly translated into similar patterns for oyster mortality (Fig. 2). In general, fewer oysters were eaten by mud crabs in response to cues from blue crabs fed mud crabs than in response to cues from blue crabs fed oysters. This suggests that our measure of behaviour was highly predictive of consequences to prey resources, at least for single predators, where distinguishing between individual and aggregate biomass was not a concern. Although many previous studies have found that diet cues affect prey perception of predator threat (reviewed in Ferrari et al., 2010), the response of prey to predator diet may be particularly apt in an intraguild predation systems where dietary metabolites can be indicative of either competitors or predators. Although our study supports the hypothesis that diet cues are important predictors of risk in intraguild predation systems, other studies have differing conclusions. Magalhaes et al. (2005) determined that intraguild prey respond to predators fed shared prey but not to predators fed conspecific prey. In contrast, Choh, van der Hammen, Sabelis, and Janssen (2010) concluded that although intraguild prey respond to predator presence, the antipredator response does not vary with diet. At this point, we can only speculate as to reason these responses vary; however, it is possible that responses to predator diet are based on the way that prey learn to associate cues with predators (Magalhaes et al., 2005). For instance, prey often learn to associate predators with risk after being exposed to combinations of odours from predators and from injured conspecifics (Mirza & Chivers, 2001a). The diet-dependent responses we measured may also be a consequence of our experimental design. One previous study found that clam prey respond equally to blue crab metabolites regardless of diet (Smee & Weissburg, 2006). Prey in our experiment were exposed to predators during a feeding event, so we cannot differentiate whether mud crabs were responding to the scent combination of crushed conspecifics and predator metabolites or to a different digestive metabolite specific to a mud crab diet (Schoeppner & Relyea, 2009). Regardless, our work suggests that the specificity of cues derived from predators consuming conspecific prey enhances prey responses relative to less specific cues derived from predators consuming heterospecific prey. Future studies should continue to examine the role of diet in predator threat assessment to determine general relationships between information-specific prey responses and uncertainty. Prey also may use quantitative differences in chemical cues to discern predator threat. High concentrations may indicate that predators are close. Concentration also may allow prey to differentiate between predator size classes because higher concentrations of chemical cues arise from larger predators that consume
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more prey. However, the use of chemical concentration as a property to encode predator threat is inherently ambiguous. A small close predator may be confused with a distant but larger one, since chemical signals disperse as they propagate away from the source. An aggregation of small predators may release approximately the same quantity of material as a single larger individual of the same total biomass. In our experiments, individual blue crab body size affected both mud crab behavioural responses and the magnitude of the indirect effects on mud crab prey (Fig. 1). Mud crabs differed in their refuge use when presented with chemical cues from individual small and large blue crabs fed the same diet. Our observations that large blue crabs suppressed the night-time activity of mud crabs and the percentage of oysters eaten on oyster reefs are similar to those of other studies (Fig. 1; Grabowski, 2004; Grabowski & Kimbro, 2005). However, mud crabs showed no measured response in behaviour or oyster predation to chemical cues from a single small blue crab. This response is consistent with Pettersson et al. (2000), where fish responded to large but not small pike predator cues. Thus, large blue crabs were correctly perceived as representing a greater threat, resulting in stronger behavioural suppression that indirectly increased oyster survival. The differentiation between small and large blue crab predators appears to be driven by the total biomass of predators and the chemical cue concentration, as multiple small blue crabs increased the survival of oyster prey; however, whereas multiple small blue crabs and a single large blue crab increased oyster survival, multiple small crabs did not similarly affect mud crab presence outside of reef refuges. This disparity probably resulted from extra sensory cues provided by multiple small blue crab treatments. The semitranslucent containers used to house predators allowed the perception of some visual cues (moving shadows) and auditory cues (pleopods scraping against the plastic containers during movement) from blue crabs. These cues were particularly noticeable in treatments with multiple small blue crabs, where interactions between conspecifics increased blue crab movement. Consequently, high concentrations of predator scent characteristic of large predators accompanied by auditory and visual cues from small predators may have changed mud crab perceptions regarding the likelihood of a predation event, which led to a decrease in foraging (i.e. increased vigilance) but not increased refuge use. Importantly, when we modified the experimental design so that water flowed from the predator boxes into the tanks containing the mud crabs (i.e. removing all sensory cues but predator scent), the behavioural responses of mud crabs to both high biomass treatments (i.e. one large and multiple small predators) were similar (data not shown). This suggests that the concentration of chemical cues associated with predator biomass is responsible for the differentiation of predator body size. Furthermore, in highly turbid field experiments, where visual cues are limited, mud crabs also show biomass-dependent foraging responses, demonstrating that chemical cues are the most likely source of size and biomassdependent responses (Hill & Weissburg, 2013b). Consequently, our results indicate that (1) the perception of predator size is biomass based (i.e. chemical concentration) and (2) mud crabs may be able to disambiguate individual and aggregate biomass when other sensory information is available. The former is consistent with Chivers et al. (2001), where sculpin responses to chemical cues from large predatory brook trout and multiple small brook trout (equal in concentration to large brook trout) were not significantly different. As a result, when only chemical cues are available, mud crabs interpret aggregations of small blue crabs as a predator threat, even when no such threat exists. In support of the latter, previous studies have documented either differing antipredator responses when prey are presented with predator cues of differing modalities, or behavioural antagonism when prey are
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presented with multiple cues (Hartman & Abrahams, 2000; Hazlett & McLay, 2005; Martin, Fodrie, Heck, & Mattila, 2010; Munoz & Blumstein, 2012; Sih, 1992; Ward & Mehner, 2010). For instance, a combination of tactile cues and chemical cues from crushed conspecifics caused a reduction in crab catatonic antipredator responses when compared to either cue presented alone (Hazlett & McLay, 2005). Thus, changes in refuge use by mud crabs in our study may have resulted because of multimodal cue reception. The distinction between the effects of behaviour due to differences in predator size, diet and/or sensory information and the effects on downstream organisms (i.e. oyster prey) is important as experiments often address only prey activity level or refuge use. In turn, these changes in activity or refuge use are sometimes assumed to directly reflect potential effects on downstream organisms. However, as our examinations demonstrate, measuring activity or refuge use may not be sufficient to accurately interpret cascading interactions. This suggests that changes in prey populations as well as their indirect impacts should be measured to understand how antipredator behaviours influence predatoreprey dynamics and community structure. In addition, researchers should be increasingly cognizant of the role of experimental design in sensory processing of multimodal predator stimuli and its ability to modify experimental outcomes. In summary, our results suggest that mud crabs can differentiate predator threat based on qualitative and quantitative aspects of chemical signals. Predators capable of consuming mud crabs (e.g. large blue crabs) can be distinguished based on diet, and predator biomass can be assessed based on the quantity of released cues. Consequently, mud crabs cannot disambiguate individual biomass of large predators from the aggregate biomass of many small predators based on chemical cues from predators that have consumed heterospecific prey. Since multimodal sensory input may allow mud crabs to encode predator threat more accurately (e.g. distinguish aggregate from individual biomass), predicting how antipredator behaviours influence prey resources requires understanding the availability of different sensory cues in field environments. Hill and Weissburg (2013a) provided evidence that aggregations of small blue crabs or a single large blue crab produced similar effects on basal resources in the field, which may reflect the poor transmission or dissipation of visual or other cues in that specific environment. In general, the behavioural effects of environmental variation in cue availability remains sparsely documented, and understanding the behavioural and ecological effects of environmental gradients is an important future challenge (Smee et al., 2008). Acknowledgments We thank M. Wilson and K. Shafer for their assistance in animal collection and completing laboratory experiments, the editor, Ronald Rutowski, and two anonymous referees, whose comments significantly improved this manuscript, and the Skidaway Institute of Oceanography for being wonderful and gracious hosts. This work was funded by a National Science Foundation grant to M.J.W. 0424673 and an IGERT Fellowship in aquatic chemical signalling. References Abrams, P. A. (2007). Defining and measuring the impact of dynamic traits on interspecific interactions. Ecology, 88, 2555e2562. Basille, M., Fortin, D., Dussault, C., Ouellet, J. P., & Courtois, R. (2012). Ecologically based definition of seasons clarifies predatoreprey interactions. Ecography, 36, 220e229. Bisker, R., & Castagna, M. (1987). Predation on single spat oysters Crassostrea virginica (Gmelin) by blue crabs Callinectes sapidus and mud crabs Panopeus herbstii. Journal of Shellfish Research, 6, 37e40.
Chivers, D. P., & Mirza, R. S. (2001a). Predator diet cues and the assessment of predation risk by aquatic vertebrates: a review and prospectus. In A. MarchlewskaKoi, J. J. Lepri, & D. Müller-Schwarze (Eds.), Chemical signals in vertebrates (Vol. 9); (pp. 277e284). New York, NY: Kluwer Academic/Plenum. Chivers, D. P., & Mirza, R. S. (2001b). Importance of predator diet cues in responses of larval wood frogs to fish and invertebrate predators. Journal of Chemical Ecology, 27, 45e51. Chivers, D. P., Mirza, R. S., Bryer, P. J., & Kiesecker, J. M. (2001). Threat-sensitive predator avoidance by slimy sculpins: understanding the importance of visual versus chemical information. Canadian Journal of Zoology, 79, 867e873. Choh, Y., van der Hammen, T., Sabelis, M., & Janssen, A. (2010). Cues of intraguild predators affect the distribution of intraguild prey. Oecologia, 163, 335e340. Cohen, J. E., Pimm, S. L., Yodzis, P., & Saldana, J. (1993). 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Diet of intraguild predators affects antipredator behavior in intraguild prey. Behavioral Ecology, 16, 364e370. Martin, C. W., Fodrie, F. J., Heck, K. L., & Mattila, J. (2010). Differential habitat use and antipredator response of juvenile roach (Rutilus rutilus) to olfactory and visual cues from multiple predators. Oecologia, 162, 893e902. Micheli, F. (1997). Effects of predator foraging behavior on patterns of prey mortality in marine soft bottoms. Ecological Monographs, 67, 203e224. Mirza, R., & Chivers, D. P. (2001a). Learned recognition of heterospecific alarm signal: the importance of mixed predator diet. Ethology, 107, 1007e1018. Mirza, R., & Chivers, D. P. (2001b). Do juvenile yellow perch use diet cues to assess the level of threat posed by intraspecific predators? Behaviour, 138, 1249e1258. Mirza, R., & Chivers, D. P. (2002). Brook char can differentiate chemical alarm cues produced by different age/size classes of conspecifics. Journal of Chemical Ecology, 28, 555e564. 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J. M. Hill, M. J. Weissburg / Animal Behaviour 92 (2014) 117e123 O’Connor, N. E., Grabowski, J. H., Ladwig, L. M., & Bruno, J. F. (2008). Simulated predator extinctions: predator identity affects survival and recruitment of oysters. Ecology, 89, 428e438. Pettersson, L. B., Nilsson, P. A., & Bronmark, C. (2000). Predator recognition and defence strategies in crucian carp, Carassius carassius. Oikos, 88, 200e212. Preisser, E. L., Bolnick, D. I., & Benard, M. F. (2005). Scared to death? The effects of intimidation and consumption in predatoreprey interactions. Ecology, 86, 501e509. Rudolf, V. H. W. (2006). The influence of size-specific indirect interactions in predatoreprey systems. Ecology, 87, 362e371. Schoeppner, N. M., & Relyea, R. A. (2005). Damage, digestion, and defence: the roles of alarm cues and kairomones for inducing prey defences. Ecology Letters, 8, 505e512. Schoeppner, N. M., & Relyea, R. A. (2009). Interpreting the smells of predation: how alarm cues and kairomones induce different prey defences. Functional Ecology, 23, 1114e1121. Seed, R. (1980). Predatoreprey relationships between the mud crab Panopeus herbstii, the blue crab Callinectes sapidus and the Atlantic ribbed mussel Geukensia demissa. Estuarine and Coastal Marine Science, 11, 445e458. Sheriff, M. J., Krebs, C. J., & Boonstra, R. (2011). From process to pattern: how fluctuating predation risk impacts the stress axis of snowshoe hares during the 10-year cycle. Oecologia, 166, 593e605. Sih, A. (1992). Prey uncertainty and the balancing of antipredator and feeding needs. American Naturalist, 139, 1052e1069. Smee, D. L., Ferner, M. C., & Weissburg, M. J. (2008). Alteration of sensory abilities regulates the spatial scale of nonlethal predator effects. Oecologia, 156, 399e409.
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Smee, D. L., & Weissburg, M. J. (2006). Hard clams (Mercenaria mercenaria) evaluate predation risk using chemical signals from predators and injured conspecifics. Journal of Chemical Ecology, 32, 605e619. Stankowich, T., & Blumstein, D. T. (2005). Fear in animals: a meta-analysis and review of risk assessment. Proceedings of the Royal Society B: Biological Sciences, 272, 2627e2634. Turner, A. M. (2008). Predator diet and prey behaviour: freshwater snails discriminate among closely related prey in a predator’s diet. Animal Behaviour, 76, 1211e1217. Turner, A. M., Fetterolf, S. A., & Bernot, R. J. (1999). Predator identity and consumer behavior: differential effects of fish and crayfish on the habitat use of a freshwater snail. Oecologia, 118, 242e247. Ward, A. J. W., & Mehner, T. (2010). Multimodal mixed messages: the use of multiple cues allows greater accuracy in social recognition and predator detection decisions in the mosquitofish, Gambusia holbrooki. Behavioral Ecology, 21, 1315e 1320. Werner, E. E., & Gilliam, J. F. (1984). The ontogenetic niche and species interactions in size structured populations. Annual Review of Ecology, Evolution and Systematics, 15, 393e425. Werner, E. E., & Peacor, S. D. (2003). A review of trait mediated interactions in ecological communities. Ecology, 84, 1083e1100. Yen, J. (1983). Effects of prey concentration, prey size, predator life stage, predator starvation, and season on predation rates of the carnivorous copepod Euchaeta elongata. Marine Biology, 75, 69e77.
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Animal Behaviour journal homepage: www.elsevier.com/locate/anbehav
Crabs interpret the threat of predator body size and biomass via cue concentration and diet Jennifer M. Hill*, Marc J. Weissburg Georgia Institute of Technology, Atlanta, GA, U.S.A.
a r t i c l e i n f o Article history: Received 6 December 2013 Initial acceptance 9 January 2014 Final acceptance 24 February 2014 Published online MS. number: A13-01014R Keywords: antipredator behaviour blue crab mud crab nonconsumptive effect trait-mediated interaction
Greater predator body size is often associated with greater predation risk. According to the threatsensitive predator avoidance hypothesis, prey should display graded responses to increasing predator body size; in turn, these differences in behaviour should also cause differing indirect effects. Yet, in aquatic systems, where prey often use chemical cues to judge predator threats, the role of chemically mediated perception of predator body size and the propagation of indirect effects are still unclear. To differentiate intraspecific predator size via chemical cues, prey must judge predator threat quantitatively (i.e. via concentration) or qualitatively (i.e. via differing cues and/or diets). We investigated the role of individual and aggregate predator body size (i.e. biomass, cue concentration) and qualitative diet cues in antipredator behaviour and indirect interactions by examining the behavioural responses of the mud crab Panopeus herbstii and the survival of oyster prey (Crassostrea virginica) in response to various blue crab, Callinectes sapidus, biomass and diet treatments. Mud crabs increased their refuge use and decreased foraging in response to chemical cues from large, but not small, individual blue crabs. The perception of predator size appeared to be related to predator biomass as multiple small blue crabs and large crabs elicited similar foraging responses in mud crabs. However, multiple small blue crabs failed to affect mud crab refuge use, indicating that some measures of behaviour may not always be predictive of indirect effects. Predator diet also influenced mud crab behaviour and foraging: predators fed mud crabs elicited a greater antipredator response than crushed conspecifics or predators fed oyster diets, suggesting that qualitative cues also influence intraspecific threat perception and indirect interactions. These experiments demonstrate that we cannot successfully predict indirect interactions without considering predator population size structure and measuring indirect effects. Ó 2014 The Association for the Study of Animal Behaviour. Published by Elsevier Ltd. All rights reserved.
Predation is an important force in structuring ecological communities and imposes high selection pressure on prey to develop strategies to avoid being eaten (Dawkins & Krebs, 1979). One such strategy is predator threat assessment, in which prey use a variety of sensory cues to determine the likelihood of danger from an individual predator and the need to perform antipredator behaviours that increase survivorship (Lima, 1998). Yet, assessing predator threats can be complex as predation risk can vary both between predators and between individuals of a single predator species. For instance, snails differentiate between predator cues and crawl underneath leaf litter to escape sunfish, but crawl towards the water surface in response to crayfish cues (Turner, Fetterolf, & Bernot, 1999). Within predator species, predation threat can change based on life history stage (i.e. juvenile/adult, moulting,
* Correspondence and present address: J. M. Hill, Dauphin Island Sea Lab, 101 Bienville Blvd, Dauphin Island, AL 36528, U.S.A. E-mail address: [email protected] (J. M. Hill).
hibernation), time of year when predators are most abundant or most active and/or location (Basille, Fortin, Dussault, Ouellet, & Courtois, 2012; Griffen, Toscano, & Gatto, 2012; Lipcius & Herrnkind, 1982; Sheriff, Krebs, & Boonstra, 2011; Yen, 1983). Differences in threat-specific behaviours are important because they can greatly influence community structure by altering foraging behaviour and fitness of prey (Ferrari, Wisenden, & Chivers, 2010; Kats & Dill, 1998; Stankowich & Blumstein, 2005), which can indirectly affect the survival of the prey’s resource (often called traitmediated indirect interactions, or nonconsumptive effects: Abrams, 2007; Preisser, Bolnick, & Benard, 2005; Werner & Peacor, 2003). Therefore, understanding how prey assess predator threat is essential to predict outcomes for prey survival and cascading indirect effects. Predator body size is an intraspecific trait often associated with greater predatory threat and frequently determines where predators feed, as well as their feeding rates and diet choices (Cohen, Pimm, Yodzis, & Saldana, 1993; Werner & Gilliam, 1984). Furthermore, size-based predator traits lead to numerous cascading
http://dx.doi.org/10.1016/j.anbehav.2014.03.025 0003-3472/Ó 2014 The Association for the Study of Animal Behaviour. Published by Elsevier Ltd. All rights reserved.
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indirect interactions in communities (Dodson, 1970; Rudolf, 2006; Werner & Gilliam, 1984). However, despite its importance in predicting predatoreprey outcomes, the role of predator body size in threat assessment and its propensity to propagate indirect effects are still understudied, especially in aquatic communities and for nonvisual modalities. According to the threat-sensitive predator avoidance hypothesis (Helfman, 1989), prey will respond to predators with antipredator behaviours that match predator threat levels. Thus, if increasing predator size is indicative of greater threat, prey should display greater antipredator responses to larger predators, and this would translate into stronger cascading effects on prey resources. In many aquatic communities, the perception of predator threat often is determined by chemical cues emanating from predators or crushed conspecifics (Kats & Dill, 1998) since the transmission of acoustic or visual cues is more limited (Dusenberry, 1992). Predator body size may also be chemically perceived through differing cues (Kusch, Mirza, & Chivers, 2004) or through greater cue release (i.e. concentration; Chivers, Mirza, Bryer, & Kiesecker, 2001; Pettersson, Nilsson, & Bronmark, 2000) from larger predators. For example, Kusch et al. (2004) found that minnows responded to sympatric small pike predators more than they did to allopatric large pike predators, suggesting that differing cues were responsible for sizebased perception. In contrast, Chivers et al. (2001) and Pettersson et al. (2000) found that prey fishes avoided large, but not small, fish predators, suggesting concentration-dependent responses. Importantly, the manner in which size is chemically communicated (i.e. either quantitatively or qualitatively) may cause differing behavioural responses and indirect effects on prey resources depending on predator population size structure and/or predator density. For instance, if the perception of size is concentration based, small predators at high densities may elicit behavioural responses and indirect effects similar to those of a single large predator. In contrast, if prey respond to qualitative differences in predator chemical cues, then predators will cause size-specific behavioural effects and indirect effects regardless of predator density. Thus, the perception of size will not only have consequences for antipredator behaviour, but also for numerous cascading interactions that affect community structure. In systems where predator body size dictates the trophic level where predators feed (i.e. intraguild predation systems or systems containing predators that shift their diet during maturation), diet cues may be beneficial in determining the threat of oncoming predators. Predator diet often affects a prey’s evaluation of threat, and diets including conspecifics often result in the greatest antipredator responses as a result of qualitative differences in predator diet cues (Chivers & Mirza, 2001a, 2001b; Ferrari et al., 2010; Schoeppner & Relyea, 2005; Smee & Weissburg, 2006; Turner, 2008). Some prey species show antipredator responses to predators fed a diet of heterospecific prey, but these responses can often decrease with increasing phylogenetic distance of prey in the predator’s diet (Schoeppner & Relyea, 2005). However, dietdependent antipredator behaviours are not ubiquitous (Chivers & Mirza, 2001a, 2001b). Furthermore, similar to studies on chemically mediated body size, studies examining behavioural responses of prey to diet cues of predators often have not examined whether differences in prey behaviour result in differing indirect (cascading) effects. As the magnitude and direction of antipredator behaviour may vary based on predator traits such as size and diet, indirect effects on prey resources may also vary based on predator threat assessment. Yet, how changes in behaviour directly translate into indirect interactions is often inferred but not directly tested. As predator size and diet may affect predator threat assessment and indirect interactions, we had multiple objectives in our study. Our goals were to (1) investigate the role of predator size and
biomass (i.e. cue concentration) in chemically mediated threat perception, (2) examine whether predator diet influences the threat response and (3) determine whether differences in antipredator behaviour due to predator size and diet also translate into indirect effects on prey resources. METHODS Model System To examine the influence of predator biomass and diet in predator threat assessment and the resulting indirect effects, we chose an intraguild predation system consisting of both adult and juvenile blue crabs, Callinectes sapidus, the mud crab Panopeus herbstii, and their shared oyster prey, Crassostrea virginica. This system is ideal for examining size-based interactions because blue crabs are generalist predators, predation by crabs is crush limited (i.e. larger body sizes have greater crush strength), prey size scales with predator body size, and size classes co-occur. The blue crab, which is the top predator in this system, is an important predator and scavenger of estuarine environments (Micheli, 1997) and has been shown to prey on a variety of bivalve and crustacean species (Eggleston, 1990a, 1990b; Fitz & Weigert, 1991; Micheli, 1997). The intermediate prey, mud crabs, are small cryptic xanthid crab predators found in both oyster reef and salt marsh habitats. Mud crabs occupy the interstices of oyster beds at high densities (Hollebone & Hay, 2007; Lee & Kneib, 1994) and prey on a number of bivalve species (Bisker & Castagna, 1987; Seed, 1980). Xanthid and other nonportunid crabs make up approximately 43% of the diet of blue crab (Fitz & Weigert, 1991). Furthermore, risk of predation to mud crabs varies as a function of blue crab predator size; large adult blue crabs (>100 mm carapace width; CW) are voracious predators on mud crabs in laboratory mesocosms, whereas small juvenile blue crabs (40e60 mm CW) rarely present a threat to mud crabs greater than 15 mm CW (Hill & Weissburg, 2013b). Thus, according to the threat-sensitive predator avoidance hypothesis (Helfman, 1989), mud crabs should show stronger antipredator responses to larger blue crabs. A priori, this also suggests that blue crab body size may propagate differing indirect effects. Animal Collection and Maintenance All experiments were performed at the Skidaway Institute of Oceanography (SkIO), Skidaway Island, Georgia, U.S.A. over summer months in multiple years from 2008 to 2010. Both blue crabs and mud crabs were collected from Wassaw Sound and associated tributaries with permits from the Georgia Department of Natural Resources. Blue crabs were collected by commercial crab pot and seine net. Mud crabs were collected by hand from loose oyster reef. Hatchery-reared oysters (10e15 mm in length) were obtained from Bay Shellfish (Tampa, FL, U.S.A.). All animals were maintained in covered outdoor flowthrough sea water tanks (0.62 0.50 0.27 m) at the SkIO for a minimum of 48 h before experiments began. Blue crabs were maintained on a diet of shrimp and/or clams and were fed an ad libitum diet of shrimp and oysters once a day for 48 h prior to experiments. Mud crabs were maintained on a clam diet and were starved 48 h prior to experiments. Blue crabs were not used in experiments if they were premoult. No ovigerous female crabs were utilized in experiments. Animals were held no longer than 2 months and all animals were released into an estuary adjacent to SkIO following the experiments. Investigating How Prey Encode Size-based Threat To examine the ability of prey to distinguish predator body size via chemical cues, we monitored mud crabs foraging on oysters in
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response to differing combinations of individual predator size and aggregate biomass. These manipulations allowed us to address the role of cue concentration (predator biomass) versus size-specific chemical cues in predator threat assessment. Experiments took place in laboratory mesocosms (0.7 0.4 0.3 m) containing artificial oyster reefs constructed over approximately 2.5 cm of sand and shell hash supplied with flow-through sea water. We constructed artificial reefs by gluing 10 oyster shells (obtained from natural shell banks) to create similar small clusters (approximately 6 cm in diameter) and then bundling clusters with rubber bands to build a reef (21 clusters per tank). We secured one juvenile oyster (10e15 mm in length) on the face of each cluster using cyanoacrylate glue (21 oysters total). Once the reef structure was created, we scattered approximately 1 litre of shell hash around reef edges to mimic the natural structure of the habitat and to help hold the reef in place. We then added 20 marked mud crabs (similar to natural size class densities: 15e20 mm CW, N ¼ 13; 20e25 mm CW, N ¼ 4; 25e30 mm CW, N ¼ 3; Lee & Kneib, 1994) to the tank. Each mud crab received a painted fluorescent mark on its carapace to allow us to monitor its behaviour with a black light wand at night, when mud crabs are typically more active (Grabowski, 2004). We then submerged one of four caged predator treatments into each tank: one large blue crab (>100 mm CW, approximately 130e180 g), one small blue crab (40e60 mm CW, 8e20 g), multiple small blue crabs (40e60 mm CW, totalling 130e180 g), or no predator (control). Predator cages consisted of semitranslucent plastic containers (0.34 0.20 0.12 m) with multiple holes drilled through the side and covered with vexar mesh, which allowed movement of chemical cues into mesocosms but prevented direct contact between blue crabs and mud crabs. The predator containers allowed mud crabs to see slight shadows of the blue crabs inside, and also transmitted auditory cues produced by blue crab movements. Blue crabs were fed an ad libitum diet of shucked oysters every day as this diet is common to multiple sizes of blue crab predators (Eggleston, 1990b). Mud crabs were allowed to forage on oysters in mesocosms for 2.5 days. The total number of oysters eaten was recorded at the conclusion of the experiment, as well as the number of surviving mud crabs. Mud crab survival was high in all experimental runs (91%). We monitored the night-time refuge use of mud crabs in mesocosm reefs using a black light wand to illuminate the fluorescent paint on mud crab carapaces (Grabowski, 2004). The number of mud crabs visible in tanks was counted once every 15 min for 30 min each night of the experiment (9 observations total). Mud crabs that were not visible were either buried underneath sand or hidden within oyster reef structure. We calculated the average percentage of mud crabs visible on reefs by dividing the average number of mud crabs visible over three nights by the total number of mud crabs surviving experiments. Because of the limited mesocosm space, we ran three replicates at once in a randomized block design, with six runs in all, totalling 18 replicates for each treatment. We used run (time) as a factor in analysis as preliminary analysis demonstrated that run (time) significantly affected the behaviour and percentage of oysters eaten. Thus, the average percentage of mud crabs visible was analysed by two-factor ANOVA for run and predator biomass treatment. The percentage of oysters eaten was arcsine transformed to meet assumptions of normality and analysed by a twofactor ANOVA for the effects of run and predator treatment. We also analysed the number of mud crabs surviving via a two-factor ANOVA (run, predator size) to determine whether significant differences in mud crab survival over time or by treatment affected oyster survival. When significant treatment differences were detected, we ran Tukey post hoc tests to examine significant differences between groups.
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The Role of Predator Diet To examine the effect of blue crab diet on the level of mud crab antipredator response, we investigated mud crab foraging behaviour and oyster survival in response to a caged large blue crab fed one of two diets, shucked oysters or mud crabs. Since previous studies have demonstrated that injured conspecific cues alone can induce antipredator activity (Ferrari et al., 2010), we used crushed mud crab treatments to determine whether responses to blue crabs could be attributable to alarm cues from crushed conspecifics alone or were additive with blue crab cues. Mesocosms were set up as in previous experiments containing artificial oyster reef, 42 oysters (two 10e15 mm oysters per artificial cluster) and 20 mud crabs. Blue crab diet treatments consisted of large blue crabs caged in predator boxes (as in predator size experiments) and placed in mesocosms. Blue crabs were fed an ad libitum diet of shucked oysters or crushed mud crabs once a day while predator boxes were submerged in mesocosms. All mud crabs were chilled prior to crushing. Crushed mud crab treatments were applied once a day and consisted of one large (25e30 mm CW), two medium (20e 25 mm CW) and two small (15e20 mm CW) crushed mud crabs. Multiple sizes of mud crabs were used as differing sizes of conspecifics may elicit different reactions by prey (Mirza & Chivers, 2002). Crushed mud crab treatments were prepared by quickly culling/crushing crabs in 0.7 litres of sea water and then after death, splitting them in half to mimic a blue crab predation event. Crushed conspecifics were then added immediately to a predator box within the mesocosm. Remnants of crushed mud crabs were removed directly before the next application of crushed conspecifics cue. We recorded the number of oysters eaten every 24 h for 48 h. We also monitored the presence of mud crabs on reefs at night as in previous experiments. Because of the limited mesocosm space, we ran three replicates at once for four runs, totalling 12 replicates per treatment. As predation rates were high during the experiment, we analysed the percentage of oysters eaten after 24 h, following square-root transformation of the data to meet assumptions of normality, by a two-factor ANOVA (run, diet) since preliminary analysis indicated that run (time) affected the percentage of oysters eaten. The average percentage of mud crabs visible at night over the course of the experiment analysed by two-factor ANOVA (run, predator diet). We used Tukey post hoc tests to examine differences between specific treatment groups when main effects were significant. RESULTS How Prey Encode Size-based Threat The presence of mud crabs on mesocosm reefs during nighttime observations was significantly affected by blue crab predator size (ANOVA: F3,48 ¼ 9.14, P < 0.001; Fig. 1a) and run (ANOVA: F5,48 ¼ 4.77, P ¼ 0.001), but there was no significant interaction (treatment*run: F15,48 ¼ 1.59, P ¼ 0.110). Large blue crabs suppressed the percentage of mud crabs visible on reefs by approximately 10%, indicating that large blue crabs caused mud crabs to seek refuge deeper within reefs where they could not be observed (Fig. 1a; Grabowski, 2004). The presence of mud crabs was not diminished in response to either small or multiple small blue crabs. Chemical cues from blue crab predators had a significant biomass-dependent effect on the percentage of oysters eaten by mud crabs (ANOVA: F3,48 ¼ 7.75, P < 0.001; Fig. 1b). Cues from both large and multiple small caged blue crabs diminished the percentage of oysters eaten by mud crabs by approximately 20e25%. The reduction in foraging caused by multiple small blue crabs was unexpected as multiple small blue crabs did not increase mud crab
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Figure 1. Mean SE percentage of (a) mud crabs visible outside of reef refuges and (b) oysters eaten in response to a single large blue crab, a single small blue crab, multiple small blue crabs and a control without predators (N ¼ 18). Differing letters denote significant differences based on Tukey post hoc tests (P < 0.05).
refuge use (Fig. 1b). This suggests that increased oyster survival was not attributed to mud crab prey seeking refuge in response to predator threat. Single small blue crabs also did not suppress mud crab foraging, as oyster survival was not significantly different from zero crab controls (Fig. 1b). Experimental run (time) also influenced the percentage of oysters eaten (ANOVA: F5,48 ¼ 5.41, P ¼ 0.001), but there was no interaction between run and predator treatment (ANOVA: F15,48 ¼ 1.62, P ¼ 0.10). The number of mud crabs surviving the duration of the experiment was significantly affected by run (ANOVA: F5,48 ¼ 3.02, N ¼ 18, P ¼ 0.02) but not by predator treatment (ANOVA: F3,48 ¼ 0.73, P ¼ 0.539). Because of the variation in mud crab survival, we calculated the per crab oyster consumption rate by dividing the number of oysters consumed by the number of mud crabs alive at the end of the experiment. As the survivorship of mud crabs was high (estimate SE ¼ 91 0.7%), statistical analysis using per capita oyster consumption yielded the same results as when we examined total oyster consumption. The Role of Predator Diet The average percentage of mud crabs visible outside of reef refuges was significantly affected by run (ANOVA: F3,32 ¼ 5.01, P ¼ 0.006) and diet treatment (F3,32 ¼ 11.57, P < 0.001) and closely followed the patterns of predation (Fig. 2a). Crushed mud crabs and blue crabs fed oysters both suppressed the number of mud crabs visible by about 10e12% (Fig. 2a). The greatest suppression of mud crabs outside of reefs was about 20% and was caused by blue crabs
0
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Figure 2. Mean SE percentage of (a) mud crabs visible outside of reef refuges and (b) oysters eaten in response to crushed conspecifics and large blue crabs fed differing diets (N ¼ 12). Blue crabemud crabs denotes blue crabs fed a mud crab diet. Blue crabseoysters denotes blue crabs fed an oyster diet. Differing letters denote significant differences based on Tukey post hoc tests (P < 0.05).
fed mud crabs. The treatment*run interaction was not significant (F9,32 ¼ 0.66, P ¼ 0.737). Blue crab diet (ANOVA: F3,32 ¼ 4.85, P ¼ 0.007) and experimental run (ANOVA: F3,32 ¼ 3.89, P ¼ 0.018) both had a significant effect on the number of oysters eaten (Fig. 2b). The run*treatment interaction was not significant (F9,32 ¼ 0.40, P ¼ 0.925). Both blue crabs fed oysters and crushed mud crabs alone suppressed the percentage of oysters eaten by approximately 15% relative to controls. Blue crabs fed mud crabs suppressed predation on oysters the greatest amount, on average, by 30% (Fig. 2b). DISCUSSION In turbid aquatic environments where vision is limited, animals obtain a great deal of information from chemosensory cues. Chemical cues carry qualitative (i.e. differing cues and diets) and quantitative (i.e. concentration) information about predators that allow prey to assess the associated levels of threat (Ferrari et al., 2010; Kats & Dill, 1998). Our results demonstrate that mud crab prey use both the qualitative (i.e. chemical composition) and quantitative aspects (i.e. concentration) of chemical cues to assess the threat associated with blue crab predators. These chemical cues affected both the measured antipredator behaviour of mud crabs and the survival of mud crab’s oyster prey. Responding to qualitative differences in predator cues allows prey to distinguish between different predators and predators with
J. M. Hill, M. J. Weissburg / Animal Behaviour 92 (2014) 117e123
different diets (Ferrari et al., 2010; Mirza & Chivers, 2001b; Schoeppner & Relyea, 2005, 2009; Turner, 2008). Mud crabs generally showed less activity on reefs in response to blue crabs fed mud crabs than they did to blue crabs fed oysters (Fig. 2), demonstrating that cues associated with consumed conspecifics represent a greater threat. Blue crabs less than 80 mm CW rarely successfully prey on mud crabs (>15 mm CW) and are instead restricted to bivalves and other infaunal prey (Hill & Weissburg, 2013b; O’Connor, Grabowski, Ladwig, & Bruno, 2008), suggesting that diet may be a dependable indicator of blue crab predator threat. Consistent with many previous diet studies from other organisms, alarm cues from crushed conspecifics alone was not sufficient to induce intense antipredator responses (Schoeppner & Relyea, 2005, 2009), and the combination of the two cues elicited a stronger response than either cue presented alone. This suggests that both blue crab predator cues and crushed conspecific metabolites may provide important nonredundant information about predation risk that reduces uncertainty (Munoz & Blumstein, 2012) and results in enhancement of antipredator behaviours. Importantly, the measured differences in mud crab behaviour and refuge use directly translated into similar patterns for oyster mortality (Fig. 2). In general, fewer oysters were eaten by mud crabs in response to cues from blue crabs fed mud crabs than in response to cues from blue crabs fed oysters. This suggests that our measure of behaviour was highly predictive of consequences to prey resources, at least for single predators, where distinguishing between individual and aggregate biomass was not a concern. Although many previous studies have found that diet cues affect prey perception of predator threat (reviewed in Ferrari et al., 2010), the response of prey to predator diet may be particularly apt in an intraguild predation systems where dietary metabolites can be indicative of either competitors or predators. Although our study supports the hypothesis that diet cues are important predictors of risk in intraguild predation systems, other studies have differing conclusions. Magalhaes et al. (2005) determined that intraguild prey respond to predators fed shared prey but not to predators fed conspecific prey. In contrast, Choh, van der Hammen, Sabelis, and Janssen (2010) concluded that although intraguild prey respond to predator presence, the antipredator response does not vary with diet. At this point, we can only speculate as to reason these responses vary; however, it is possible that responses to predator diet are based on the way that prey learn to associate cues with predators (Magalhaes et al., 2005). For instance, prey often learn to associate predators with risk after being exposed to combinations of odours from predators and from injured conspecifics (Mirza & Chivers, 2001a). The diet-dependent responses we measured may also be a consequence of our experimental design. One previous study found that clam prey respond equally to blue crab metabolites regardless of diet (Smee & Weissburg, 2006). Prey in our experiment were exposed to predators during a feeding event, so we cannot differentiate whether mud crabs were responding to the scent combination of crushed conspecifics and predator metabolites or to a different digestive metabolite specific to a mud crab diet (Schoeppner & Relyea, 2009). Regardless, our work suggests that the specificity of cues derived from predators consuming conspecific prey enhances prey responses relative to less specific cues derived from predators consuming heterospecific prey. Future studies should continue to examine the role of diet in predator threat assessment to determine general relationships between information-specific prey responses and uncertainty. Prey also may use quantitative differences in chemical cues to discern predator threat. High concentrations may indicate that predators are close. Concentration also may allow prey to differentiate between predator size classes because higher concentrations of chemical cues arise from larger predators that consume
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more prey. However, the use of chemical concentration as a property to encode predator threat is inherently ambiguous. A small close predator may be confused with a distant but larger one, since chemical signals disperse as they propagate away from the source. An aggregation of small predators may release approximately the same quantity of material as a single larger individual of the same total biomass. In our experiments, individual blue crab body size affected both mud crab behavioural responses and the magnitude of the indirect effects on mud crab prey (Fig. 1). Mud crabs differed in their refuge use when presented with chemical cues from individual small and large blue crabs fed the same diet. Our observations that large blue crabs suppressed the night-time activity of mud crabs and the percentage of oysters eaten on oyster reefs are similar to those of other studies (Fig. 1; Grabowski, 2004; Grabowski & Kimbro, 2005). However, mud crabs showed no measured response in behaviour or oyster predation to chemical cues from a single small blue crab. This response is consistent with Pettersson et al. (2000), where fish responded to large but not small pike predator cues. Thus, large blue crabs were correctly perceived as representing a greater threat, resulting in stronger behavioural suppression that indirectly increased oyster survival. The differentiation between small and large blue crab predators appears to be driven by the total biomass of predators and the chemical cue concentration, as multiple small blue crabs increased the survival of oyster prey; however, whereas multiple small blue crabs and a single large blue crab increased oyster survival, multiple small crabs did not similarly affect mud crab presence outside of reef refuges. This disparity probably resulted from extra sensory cues provided by multiple small blue crab treatments. The semitranslucent containers used to house predators allowed the perception of some visual cues (moving shadows) and auditory cues (pleopods scraping against the plastic containers during movement) from blue crabs. These cues were particularly noticeable in treatments with multiple small blue crabs, where interactions between conspecifics increased blue crab movement. Consequently, high concentrations of predator scent characteristic of large predators accompanied by auditory and visual cues from small predators may have changed mud crab perceptions regarding the likelihood of a predation event, which led to a decrease in foraging (i.e. increased vigilance) but not increased refuge use. Importantly, when we modified the experimental design so that water flowed from the predator boxes into the tanks containing the mud crabs (i.e. removing all sensory cues but predator scent), the behavioural responses of mud crabs to both high biomass treatments (i.e. one large and multiple small predators) were similar (data not shown). This suggests that the concentration of chemical cues associated with predator biomass is responsible for the differentiation of predator body size. Furthermore, in highly turbid field experiments, where visual cues are limited, mud crabs also show biomass-dependent foraging responses, demonstrating that chemical cues are the most likely source of size and biomassdependent responses (Hill & Weissburg, 2013b). Consequently, our results indicate that (1) the perception of predator size is biomass based (i.e. chemical concentration) and (2) mud crabs may be able to disambiguate individual and aggregate biomass when other sensory information is available. The former is consistent with Chivers et al. (2001), where sculpin responses to chemical cues from large predatory brook trout and multiple small brook trout (equal in concentration to large brook trout) were not significantly different. As a result, when only chemical cues are available, mud crabs interpret aggregations of small blue crabs as a predator threat, even when no such threat exists. In support of the latter, previous studies have documented either differing antipredator responses when prey are presented with predator cues of differing modalities, or behavioural antagonism when prey are
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presented with multiple cues (Hartman & Abrahams, 2000; Hazlett & McLay, 2005; Martin, Fodrie, Heck, & Mattila, 2010; Munoz & Blumstein, 2012; Sih, 1992; Ward & Mehner, 2010). For instance, a combination of tactile cues and chemical cues from crushed conspecifics caused a reduction in crab catatonic antipredator responses when compared to either cue presented alone (Hazlett & McLay, 2005). Thus, changes in refuge use by mud crabs in our study may have resulted because of multimodal cue reception. The distinction between the effects of behaviour due to differences in predator size, diet and/or sensory information and the effects on downstream organisms (i.e. oyster prey) is important as experiments often address only prey activity level or refuge use. In turn, these changes in activity or refuge use are sometimes assumed to directly reflect potential effects on downstream organisms. However, as our examinations demonstrate, measuring activity or refuge use may not be sufficient to accurately interpret cascading interactions. This suggests that changes in prey populations as well as their indirect impacts should be measured to understand how antipredator behaviours influence predatoreprey dynamics and community structure. In addition, researchers should be increasingly cognizant of the role of experimental design in sensory processing of multimodal predator stimuli and its ability to modify experimental outcomes. In summary, our results suggest that mud crabs can differentiate predator threat based on qualitative and quantitative aspects of chemical signals. Predators capable of consuming mud crabs (e.g. large blue crabs) can be distinguished based on diet, and predator biomass can be assessed based on the quantity of released cues. Consequently, mud crabs cannot disambiguate individual biomass of large predators from the aggregate biomass of many small predators based on chemical cues from predators that have consumed heterospecific prey. Since multimodal sensory input may allow mud crabs to encode predator threat more accurately (e.g. distinguish aggregate from individual biomass), predicting how antipredator behaviours influence prey resources requires understanding the availability of different sensory cues in field environments. Hill and Weissburg (2013a) provided evidence that aggregations of small blue crabs or a single large blue crab produced similar effects on basal resources in the field, which may reflect the poor transmission or dissipation of visual or other cues in that specific environment. In general, the behavioural effects of environmental variation in cue availability remains sparsely documented, and understanding the behavioural and ecological effects of environmental gradients is an important future challenge (Smee et al., 2008). Acknowledgments We thank M. Wilson and K. Shafer for their assistance in animal collection and completing laboratory experiments, the editor, Ronald Rutowski, and two anonymous referees, whose comments significantly improved this manuscript, and the Skidaway Institute of Oceanography for being wonderful and gracious hosts. This work was funded by a National Science Foundation grant to M.J.W. 0424673 and an IGERT Fellowship in aquatic chemical signalling. References Abrams, P. A. (2007). Defining and measuring the impact of dynamic traits on interspecific interactions. Ecology, 88, 2555e2562. Basille, M., Fortin, D., Dussault, C., Ouellet, J. P., & Courtois, R. (2012). Ecologically based definition of seasons clarifies predatoreprey interactions. Ecography, 36, 220e229. Bisker, R., & Castagna, M. (1987). Predation on single spat oysters Crassostrea virginica (Gmelin) by blue crabs Callinectes sapidus and mud crabs Panopeus herbstii. Journal of Shellfish Research, 6, 37e40.
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