Current Zoology
60 (3): 323–332, 2014
Compensatory foraging in Trinidadian guppies: Effects of acute and chronic predation threats Chris K. ELVIDGE1, Indar RAMNARINE2, Grant E. BROWN1* 1 2
Department of Biology, Concordia University, 7141 Sherbrooke St. West, Montreal, QC, H4B 1R6, Canada Department of Life Sciences, University of the West Indies, St. Augustine, Trinidad and Tobago
Abstract In response to acute predation threats, prey may sacrifice foraging opportunities in favour of increased predator avoidance. Under conditions of high or frequent predation risk, such trade-offs may lead to reduced fitness. Here, we test the prediction that prey reduce the costs associated with lost opportunities following acute predation threats by exhibiting short-term compensatory foraging responses. Under semi-natural conditions, we exposed female guppies Poecilia reticulate from high and low predation risk sites to one of three levels of acute predation threat (high, intermediate or low concentrations of conspecific alarm cues). Our results confirm previous reports, demonstrating that guppies from a high predation site were consistently ‘bolder’ (shorter escape latencies) and exhibited graded threat-sensitive responses to different simulated threat levels while those from the low predation site were ‘shyer’ and exhibited non-graded responses. Most importantly, we found that when guppies from low predation sites resumed foraging, they did so at rates significantly lower than baseline rates. However, guppies from high predation sites resumed foraging either at rates equal to baseline (in response to low or intermediate risk stimuli) or significantly increased relative to baseline rates (in response to high risk stimuli). Together, these results highlight a complex compensatory behavioral mechanism that may allow prey to reduce the long-term costs associated with predator avoidance [Current Zoology 60 (3): 323–332, 2014 ]. Keywords
Compensatory behaviour, Predator-prey interactions, Personality, Threat-sensitivity, Trade-offs
The non-consumptive effects of predation exert considerable selection pressure on the behaviour, morphology and life history of prey populations (Preisser et al., 2005; Walsh, 2013), as individuals must balance the conflicting needs of predator avoidance and energy intake (Lima and Dill, 1990; Brown, 2003). Reduced foraging rates as a result of increased predation pressure, especially under conditions of variable foraging opportunities, would lead to reduced fitness (Preisser et al., 2005; 2009). Presumably, flexible response patterns to acute predation threats should allow prey to maintain some foraging benefits while reducing their overall level of risk (Lima and Dill, 1990; Lima and Bednekoff, 1999; Brown, 2003). However, predation risk is often spatially and temporally variable (Sih, 1992; Dall et al., 2005), leading to increased costs associated with risk assessment and predator avoidance. The effects of high and/or unpredictable predation risk on the foraging patterns of prey have been widely hypothesized (Lima and Bednekoff 1999; Sih et al. 2000). For example, Trinidadian guppies Poecilia reticulata from high predation risk populations exhibit Received June 26, 2013; accepted Sept.17, 2013. Corresponding author. E-mail:
[email protected] © 2014 Current Zoology
lower foraging rates in the absence of acute predation threats compared to those from low predation sites (Botham et al., 2008). Moreover, guppies under high risk conditions exhibit reduced foraging rates at night (Fraser et al., 2004), in the absence of potentially valuable visual threat cues (Cronin, 1997). These lost foraging opportunities measurably reduce both individual growth rates and the amount of time spent courting by male guppies (Fraser et al., 2004), both of which are likely to have negative population-level consequences. How, then, could prey rely on flexible behavioral response patterns to limit the negative impacts of lost foraging opportunities associated with high and/or unpredictable predation risk? One previously explored behavioral strategy involves prey exhibiting graded or threat-sensitive trade-offs, in which the intensity of antipredator responses are proportional to the level of perceived threat (Helfman and Winkelman, 1997; Brown et al., 2006a). Background levels of predation risk have been shown to shape the threat-sensitive trade-offs of prey. For example, guppies from a high predation risk site exhibit predator avoid-
324
Current Zoology
ance response patterns proportional to the relative concentration of conspecific alarm cues (Brown et al., 2009). Conspecifics from a low predation site, however, exhibited non-graded responses to the same range of cue concentrations. Moreover, the response towards a high intensity threat is greater among guppies from high vs. low predation sites (Botham et al., 2008; Brown et al., 2009). Thus, differences in threat-sensitive response patterns may arise from the increased cost of lost foraging opportunities to prey under high risk conditions relative to low risk conditions as suggested by Brown et al. (2009). However, these differences may be confounded by individual risk-taking tactics. Background levels of predation risk have recently been shown to influence more general risk-taking tactics (i.e., ‘boldness’) of prey (Brown et al., 2005; Thomson et al., 2012). Such tactics are typically referred to as ‘shy’ vs. ‘bold’ behavioral strategies, personalities or syndromes (Sih et al., 2004; Réale et al., 2007). These personalities fall along a continuous range of response intensities and represent different tactics towards the optimization of behavioral trade-offs (Budaev and Brown, 2011). Recent studies have shown, perhaps counter-intuitively, that prey from high predation risk populations are consistently ‘bolder’ than conspecifics from low risk populations. For example, bishopfish Brachyraphis episcopi from high risk populations are consistently bolder (measured as latency to escape from an enclosure) than were their low risk counterparts (Brown et al., 2005). One possible mechanism for this might be that prey exposed to high levels of risk must resort to bold behavioral tactics (i.e. higher risk taking) to meet daily foraging needs (Brown et al., 2005). Recent studies with juvenile European sea bass Dicentrachus labrax support this argument, showing increased risk-taking tactics following periods of food deprivation (Killen et al., 2011). Likewise, guppies reared under conditions of unpredictable foraging opportunities were consistently bolder than those reared under stable food conditions (Chapman et al., 2010). As a result of this increase in risk-taking tactics, bolder individuals may experience more frequent encounters with predators (Biro et al., 2004). Under high and/or unpredictable levels of predation risk, prey are more likely to be forced to sacrifice short term foraging gains for increased predator avoidance. Moreover, individual risk taking is likely to increase the risk of predation. How might prey under such conditions reduce the costs associated with non-consumptive effects of predation? Models of compensatory behavi-
Vol. 60 No. 3
our suggest that prey may shift their temporal patterning of foraging or microhabitat usage (Reebs, 2002; Kim et al., 2011b; Elvidge et al., 2013), or allocate greater foraging efforts to relatively safer periods (Lima and Bednekoff, 1999; Ferrari et al., 2009). However, under conditions of unpredictable predation threats, such strategies may be of limited value. Upon detecting a potential threat, prey commonly reduce their time moving and foraging and increase time spent hiding or sheltering. Prey may be able to compensate for lost foraging opportunities following an acute exposure to a perceived threat by increasing their foraging rate immediately following the initial antipredator behavioral response. Such compensatory foraging tactics may allow prey to accrue sufficient survival benefits whilst recouping some of the energy lost as a result of their predator avoidance behaviours. Here, we test the hypothesis that Trinidadian guppies exhibit compensatory foraging following exposure to an acute threat cue. We compare the predator avoidance response intensity of wild guppies from high vs. low predation sites by exposing individual female guppies to one of three concentrations of conspecific alarm cues simulating different levels of predation risk. To account for population differences in risk-taking tactics (‘boldness’), we recorded the latency to escape an opaque isolation chamber as a proxy for individual tactic. This measure was then used a covariate to examine the predator avoidance responses of guppies to the level of threat posed by the chemical cue, as well as to the rate of foraging following return to baseline behaviour after exposure to the threat cue. We predict that guppies in the high predation site should be bolder, demonstrate more pronounced threat-sensitive responses in proportion to the level of threat, and higher compensatory foraging levels than those from low predation risk conditions.
1
Materials and Methods
1.1 Study populations and stimulus collection We collected non-gravid female guppies using beach seines (3 m × 1 m; 3 mm mesh size) from two locations in the Aripo River, Northern Mountain Range, Trinidad and Tobago. The Lower Aripo (10° 39′ N, 61°13′ W) is characterized as a high predation site (Croft et al. 2006; Botham et al., 2008), containing several predators that prey on both juvenile and adult guppies, including the pike cichlid Crenicichla alta, blue acara cichlid Aequidens pulcher and brown coscorub Cichlasoma taenia. In addition, there are several predators that prey on smaller,
ELVIDGE CK et al.: Compensatory foraging in guppies
juvenile guppies, including incidental Hart’s rivulus Rivulus hartii and the twospot sardine Astyanax bimaculatus (Elvidge and Brown, 2012). The Upper Aripo site (10°41′ N, 61°14′ W) is located above a series of barrier waterfalls and contains only Hart’s rivulus and a predatory freshwater prawn Macrobrachium crenulatum, both of which can prey on small, juvenile guppies (Endler and Houde, 1995). As such, the Upper Aripo is characterized as a low-predation site (Croft et al., 2006; Botham et al., 2008). To control for potential confounding effects associated with population bias in the behavioral response to alarm cues (Brown et al., 2010), we blended equal quantities of alarm cue from Upper and Lower Aripo guppies and used this combined solution as our alarm cue stimulus. Donors (Upper Aripo River, n = 11, mean ± SD, SL = 29.0 ± 3.8 mm; Lower Aripo River, n = 23, 20.6 ± 2.1 mm) were killed via cervical dislocation (in accordance with Concordia University Animal Research Ethics Protocol #AREC-2010-BROW). We immediately removed the head and tail (at the caudal peduncle) and all internal visceral tissue. The remaining carcass (skin and skeletal muscle) was immediately placed into 100 mL of chilled, aged tap water. We mechanically homogenized the tissue samples, filtered the remaining tissue through polyester floss and diluted the solution to a final concentration of 0.1 cm2 skin per mL-1 with the addition of aged tap water. This initial concentration of alarm cues has been shown to elicit consistent antipredator responses in laboratory (Brown and Godin, 1999; Brown et al., 2009) and field (Brown and Godin, 1999; Brown et al., 2010) trials. We froze the alarm cue in 20 mL aliquots at -20°C and stored them on ice for transportation to the field sites. 1.2 Experimental protocol Test arenas consisted of two clear Plexiglas boxes, (60 cm × 30 cm × 30 cm, L × W × H) bound at the corners with plastic zip-ties in order to allow the exchange of water between the enclosure and the stream. We positioned an isolation chamber (15 cm × 10 cm × 30 cm, L × W × H) constructed of opaque dark-grey Plexiglas near the upstream wall of the test arenas. The isolation chamber was equipped with a dark Plexiglas cover (6 cm × 6 cm, L × W) and a hinged opening along the bottom edge. Prior to observations, we positioned the test arenas in shaded pools, at least 1 m from the shore. Water depth within the pools ranged between 15–20 cm. Prior to behavioral observations, we collected nongravid (assessed visually) female guppies from the pool using the seine net described above and placed them
325
into a covered bucket, filled with stream water. Guppies do not appear to experience any stress while being held in the bucket, swimming and feeding normally and not displaying any signs of changes in colouration. Capturing guppies prior to testing ensured that we would not be observing individual guppies repeatedly. Focal guppies were tested within 2 hours of collection. Previous studies have shown significant relationships between size and escape latencies (i.e. Brown et al., 2005). We sampled ‘intermediate’ sized guppies in an attempt to reduce this relationship as a potential confounding factor. Mean (± SD, SL = 26.14 ± 2.50 and 22.80 ± 2.85 mm, Upper and Lower Aripo respectively). Before a trial, we placed individual female guppies into the isolation boxes, closed the cover and allowed a 5 min acclimation period. We stocked ample food (freeze-dried Tubifex worms, approximately 0.1 g) into the arena such that food remained available at the end of the trial. Tubifex worms were chosen as a food item as it was easily visible and ensured accurate quantification of foraging attempts. Trials began when the door was raised remotely via metal wire. We initially recorded that latency for an individual to leave the isolation chamber, defined as the time when more than half of its body was outside of the chamber. As we did not test for repeatability of individual escape latencies, this is a measure of ‘boldness’ at the time of testing and not a comprehensive measure of personality per se. Following the escape, we immediately closed the escape door and began the pre-stimulus observation period at this point. The pre-stimulus period lasted five minutes, during which we recorded time moving and number of foraging attempts. A foraging attempt was recorded when the guppy made a pecking movement towards the substrate or at a visible food item in the water column. Immediately following the pre-stimulus observation period, we introduced 10 ml of one of three stimuli: a high relative concentration of guppy alarm cue (10 ml of stock solution, 100% concentration), a low concentration of guppy alarm cue (2.5 ml alarm cue + 7.5 ml stream water, 25% concentration) or a 10 ml stream water control. During the five-minute post-stimulus injection observation period, we recorded time moving and foraging as above. We also recorded the latency to resume foraging (time until the first foraging attempt was observed) following stimulus exposure. All observations were made blind to chemical stimulus. Following each observation, the focal guppy was released and the observation arena was rinsed with stream water and repositioned. Behavioral observations were conducted between 25 April
326
Current Zoology
and 10 May, 2010. We conducted equal numbers of Upper and Lower Aripo observations each day, alternating between starting at each site. We collected a total of 15 replicates for each of the three stimulus treatments in the Lower Aripo and 14 replicates for each stimulus in the Upper Aripo sites. The order of treatments within a site was randomized. 1.3 Statistical analysis 1.3.1 Risk-taking tactics In order to assess population differences in boldness (risk- taking tactics), we initially tested the effects of population (Upper vs. Lower Aripo) on escape latency (square root transformed) using standard length as a covariate. Standard length was included as previous work has shown that larger poecillids may exhibit ‘shyer’ tactics (i.e. Brown et al., 2005). In order to assess potential differences in baseline activity attributable to individual risk-taking tactics, we used univariate GLMs to test differences between populations in pre-stimulus foraging attempts and time spent moving between populations, using escape latency (square root transformed) as a covariate. 1.3.2 Antipredator response For the change in time moving and number of foraging attempts, we calculated the difference between observation periods (post – pre) and used these difference scores as dependent variables in all analyses. For latency to resume foraging, we square-root transformed the data to ensure normality. As these measures of antipredator behaviours are likely correlated, we tested for the overall effects of population (Upper vs. Lower Aripo) and cue (high, low or control) using a multivariate GLM. To account for any effects of individual risk-taking tactics (i.e. shy vs. bold), we included escape latency (square root transformed) as a covariate. In the event of significant population × cue interactions, we tested each population independently using multivariate GLMs. As escape latency was found to be not significant in our overall model (see below), we did not include it as a factor in the population-specific GLMs. Post-hoc comparisons were made using Fisher’s Probability of Least Squared Differences. 1.3.3 Compensatory foraging In order to assess any change in the rate of foraging (number of attempts per minute), we calculated the prestimulus and post-stimulus foraging rates. The poststimulus foraging period was defined as the time from the resumption of active foraging (latency to resume foraging) to the end of the five minute post-stimulus
Vol. 60 No. 3
observation. The difference between the pre- and poststimulus observation periods was used as the dependent variable in a univariate GLM, with population and cue concentration as independent variables and escape latency (square root transformed) as a covariate. As above, populations were further tested separately in the event of significant population × cue concentration interactions. As escape latency was found to be not significant in our overall model (see below), we did not include it as a factor in the population-specific GLMs. Post-hoc comparisons were based on Fisher’s Probability of Least Squared Differences. All analyses were conducted with SPSS v.21.
2
Results
2.1 Risk-taking tactics We found that the Upper Aripo (low predation) population guppies took significantly longer to emerge from the start box compared to the Lower Aripo population (F1,84 = 12.74, P < 0.001, Fig. 1A). Standard length was found to have no effect on escape latency (F1,84 = 0.08, P = 0.77; Fig. 1B) and was not included in subsequent models. When comparing the time spent moving during the pre-stimulus period, we found no difference between populations (F1,83 = 0.10, P = 0.75) nor a population × escape latency interaction (F1,83 = 0.28, P = 0.60; Fig. 1C). We did, however, find an effect of escape latency on pre-stimulus time spent moving (F1,83 = 5.46, P = 0.022; Fig. 1C) with shyer guppies, regardless of population, spending less time moving during the pre-stimulus observation period. We found no effect of population (F1,83 = 0.41, P = 0.52), escape latency (F1,83 = 0.97, P = 0.33), nor an interaction (F1,83 = 0.006; P = 0.94; Fig. 1D) on the pre-stimulus foraging rates (attempts per minute). 2.2 Predator avoidance behaviour: Our overall MANCOVA revealed a significant effect of cue (F3,79 = 22.56, P < 0.001) and a significant cue × population interaction (F3,79 = 4.41, P = 0.009; Fig. 2). There was no significant main effect of population (F3,78 = 0.24, P = 0.87) nor an effect of escape latency (F3,78 = 1.17, P = 0.20). As escape latency was not significant, we omitted it from subsequent population-specific comparisons. For the Upper Aripo population, there was a significant effect of cue (F3,38 = 7.64, P = 0.002). Post-hoc comparisons demonstrate that for all three behavioral measures (change in time moving, change in number of foraging attempts and latency to resume foraging), Upper Aripo guppies responded with similar in-
ELVIDGE CK et al.: Compensatory foraging in guppies
tensities to high vs. low concentration of alarm cues (Fig. 2). When considering the Lower Aripo guppies, we found a significant effect of cue (F3,41 = 22.21, P < 0.001). However, the post-hoc comparisons revealed a very different pattern (Fig. 2), as the greatest response was demonstrated by guppies exposed to the high concentration of alarm cue and the lowest among those exposed to water, with intermediate responses to low concentration of alarm cue (Fig. 2). 2.3 Compensatory foraging When we calculate the change in foraging rate by factoring in the latency to resume foraging, there is a significant effect of population (F1,80 = 8.22, P = 0.005) and of the population × cue interaction (F2,80 = 5.17, P = 0.008; Fig. 3). As above, there was no effect of escape
327
latency on the change in foraging rate (F1,80 = 0.37, P = 0.55) and escape latency was removed from subsequent tests. For Upper Aripo guppies, we found a significant effect of cue (F2, 39 = 3.28, P = 0.048) with guppies foraging at reduced rates compared to controls (Fig. 3). For Lower Aripo guppies, we also found a significant effect of cue (F2,42 = 3.77, P = 0.031), but with a very different response pattern. Despite showing a decrease in the foraging rate (attempts min-1) when exposed to low concentrations of alarm cue (Fig. 3), they foraged at a rate similar to baseline when they did return to active foraging (Fig. 3). However, when exposed to the high risk cue, guppies significantly increased their rates of foraging (relative to the low risk or control stimuli), despite showing a stronger initial anti-predator response.
Fig. 1 Mean (± SE) latency to escape from an opaque start box (A); latency to escape (square-root transform) plotted as a function of standard length (B); pre-stimulus time moving (square-root transform) plotted against latency to escape (square-root transform; C) and pre-stimulus foraging rate (attempts per minute) plotted against latency to escape (square-root transform; D) for female guppies tested in the Upper (low predation, open circles) and Lower (high predation, solid circles) Rivers n = 42 and 45 for Upper and Lower Aripo Rivers respectively. Best fit line plotted for panel C, y = 14.62x – 0.22, R2 = 0.06, P = 0.022. No lines are presented for panels B or D, as no significant relationship was found, see text for details.
328
Current Zoology
Vol. 60 No. 3
Fig. 3 Mean (± SE) change in foraging rate (attempts per minute) based on actual foraging periods for guppies exposed to stream water (open bars), low concentration alarm cues (hatched bars) or high concentration alarm cues (solid bars) in the Upper (low predation) and Lower (high predation) Rivers n = 14 (Upper Aripo) or 15 (Lower Aripo) per stimulus. Different letters denote significant differences (P < 0.05) based on Fisher’s Probability of Least Squared Differences.
3
Fig. 2 Mean (± SE) change in time spent moving (A) and total foraging attempts (B), and latency to resume foraging (square root transformed; C) for guppies exposed to stream water (open bars), low concentration alarm cues (hatched bars) or high concentration alarm cues (solid bars) in the Upper (low predation) and Lower (high predation) Rivers n = 14 (Upper Aripo) or 15 (Lower Aripo) per stimulus. Different letters denote significant differences (P < 0.05) based on Fisher’s Probability of Least Squared Differences.
Discussion
Our results confirm previous findings that prey individuals from high risk populations are generally bolder than conspecifics from low risk populations (Brown et al., 2005), as guppies from the Lower Aripo (high predation) site had significantly shorter escape latencies than those in the Upper Aripo (low predation) site. We found no significant relationship between body size (standard length) and escape latency, which is in agreement with recent laboratory studies involving wild-caught Trinidadian guppies (Harris, 2010). By contrast, correlations between size and personality have been previously demonstrated in a variety of taxa (Dewitt et al.,1999; Brown et al., 2005; López et al., 2005; Wilson et al., 2010). For example, Brown et al. (2005) found that for bishopfish, smaller individuals were typically bolder than larger conspecifics in both high and low predation populations. However, they sampled individuals between ~ 15–50 mm standard length, a considerably greater size range than tested here. It is important to note that we attempted to reduce possible size related effects by sampling female guppies of a median size range. The choice of ‘average’ sized guppies may have reduced or eliminated any size-related effects on boldness.
ELVIDGE CK et al.: Compensatory foraging in guppies
Our current results are also consistent with previous findings that Lower Aripo guppies exhibit graded threatsensitive responses to varying concentrations of conspecific alarm cues, while those from the Upper Aripo were decidedly non-graded (Brown et al., 2009). In addition, the maximal intensities of observed antipredator responses were highest among the Lower Aripo (high predation risk) population. It has been previously demonstrated that long term levels of background predation risk can shape threat-sensitive response patterns (Botham et al., 2008; Brown et al., 2009). Most importantly, our results demonstrate that there exist significant population-based differences in compensatory foraging patterns following exposure to an acute predation threat. When latency to resume foraging is taken into account, very different patterns of post-threat foraging emerge for Upper vs. Lower Aripo guppies. Despite showing strong non-graded responses to conspecific alarm cues, Upper Aripo guppies had significantly lower foraging rates (i.e., post-stimulus rate was lower that pre-stimulus rate, compared to the stream water control; Fig. 3) following the resumption of normal feeding. Such a pattern is consistent with the prediction of increased levels of caution following a short-term response, as might be predicted for prey exhibiting ‘shyer’ or risk averse behavioral phenotypes (Frost et al., 2007; Jones and Godin, 2010). Conversely, Lower Aripo guppies exhibited a graded response to alarm cue concentrations and a different pattern of foraging following the resumption of feeding. Lower Aripo guppies exposed to low concentrations of alarm cue demonstrated foraging rates (postresumption) that were similar to those shown by guppies exposed to the stream water control (i.e. resumed foraging at the baseline rate). However, Lower Aripo guppies exposed to the high risk cue, despite showing the strongest overall intensity of predator avoidance response, significantly increased their foraging rates following the resumption of active foraging. This pattern of compensatory foraging might be considered consistent with ‘bolder’, or risk-prone behavioral phenotypes. One likely explanation for our observed differences in compensatory foraging may rest with the relative cost-benefit trade-offs between foraging and predator avoidance. Presumably, increasing foraging rates immediately following a predation threat would incur a high cost associated with foraging under risky conditions. Prey under lower risk conditions have been shown to devote a greater proportion of time to foraging than do conspecifics from higher risk sites (Botham et
329
al., 2008). In addition, under high risk conditions, guppies may shift foraging efforts to nighttime and show significantly lower growth rates in the presence vs. absence of predators (Fraser et al., 2004). As such, under low risk conditions, the cost of infrequent predation threats would exceed any marginal increase in foraging opportunities, resulting in prey exhibiting little compensatory foraging. Conversely, under high risk conditions, prey would likely have a greater need for temporally consistent foraging. This may result from reduced opportunities under high risk conditions (Fraser et al., 2004; Botham et al., 2008) or increased physiological costs (Cooke et al., 2003) associated with elevated predation risk, or a combination of the two mechanisms. Nevertheless, the energy demands of individual prey would likely outweigh the survival benefits of a prolonged antipredator response. Despite showing strong reductions in the number of total foraging attempts (Fig. 2B), guppies exposed to high risk cues in the Lower Aripo actually show evidence of short-term compensatory foraging. While our data provide strong evidence for population differences in compensatory foraging, we cannot make similar statements regarding the role of ‘boldness’. Ideally, a comparison of individual body condition, rather than simple standard length, would provide additional support for this hypothesis. Future studies incorporating detailed body condition measures are required. Within aquatic ecosystems, prey may rely on a multitude of sensory inputs to assess local predation threats, including chemosensory (Brown, 2003; Ferrari et al., 2010), and visual (Helfman and Winkelman, 1997; Kim et al., 2011a) public information. The sensory complementation hypothesis (Ferrari et al., 2008; Kim et al., 2009; Elvidge et al., 2013) predicts that the reliability of risk assessment by prey is enhanced through the integration of complementary information sources. By extension, additional sensory inputs may provide confirming information regarding an individual’s initial assessment of local risk. For example, prey responding to chemosensory cues may initiate an increased predator avoidance response (i.e. reduced foraging), but in the absence of complementary visual information (i.e., they do not see a predator), they may judge the situation sufficiently safe to return to baseline activity levels. In the context of our current results, the guppies tested in the Lower Aripo site may judge the lack of secondary information (i.e. visual cues) as indicating a ‘safe’ period and increase foraging rates to compensate for short-term energy losses. The predation risk allocation hypothesis
330
Current Zoology
(Lima and Bednekoff, 1999; Ferrari et al., 2009) predicts that when faced with frequent and/or unpredictable predation threats, prey will: 1) reduce their intensity of predator avoidance and 2) allocate more foraging effort to the time predators are not present. To date, the model has received mixed support (Ferrari et al., 2009; Beauchamp and Ruxton, 2011), particularly for the second prediction of increased foraging during safe periods. Though not a direct test of the model, our results are in agreement with the risk allocation hypothesis and extend the predictions to include acute shifts in foraging patterns. Populations differ both in genetic variance and experience. As such, our observed results may be the result of differential selection on behavioral phenotypes (O'Steen et al., 2002), individual experience (Kelley and Magurran, 2003), or some combination of the two. Initially, while directional gene flow likely exists within the Aripo River system (Crispo et al., 2006), genotypic differences have been demonstrated between the Upper and Lower sites (Carvalho et al., 1991; van Oosterhout et al., 2006; Tezuka et al., 2011). Genetic similarity is highest within a local population (van Oosterhout et al., 2006), potentially giving rise to population-specific response patterns. Conversely, Kelley and Magurran (2003) demonstrated that guppies from high-risk populations were more sensitive to acute predation threats (i.e. greater response intensity to standardized threat) than were guppies from low-risk populations. However, this difference was absent among laboratory-reared offspring from high vs. low-risk source populations. These results demonstrate that differential experience also plays an important role in the predator avoidance response patterns. Regardless of the underlying mechanism, the ability to exhibit graded threat-sensitive responses under high risk conditions is argued to provide considerable functional benefits to potential prey (Brown et al., 2006b; Botham et al., 2008; Brown et al., 2009). Interestingly, while we found significant differences between the high and low predation sites in overall risk taking tactics, we found no effect of behavioral tactic on the response to differing levels of risk either between or within a population. Recently, (Couchoux and Cresswell, 2012) found that vigilance among redshanks Tringa totanus, was highly repeatable and correlated with the intensity of general disturbances, the response to an acute predation threat showed little relationship with individual personality. Given that individual risk-taking tactics may be shaped by ecological situation (i.e ambient risk; Réale et al., 2007; Dingemanse et al., 2010),
Vol. 60 No. 3
personality may shape the ‘risk management strategies’ of individuals (Couchoux and Cresswell, 2012), but not the responses to an acute threat. Our current experiment was designed to compare threat-sensitive decisions while controlling for individual risk-taking tactics. Clearly, future studies should investigate the link between personality and behavioral response patterns to acute risk in greater detail. Acknowledgments We thank Drs. Jean-Guy Godin and James Grant for their valuable comments and the Director of Fisheries, Ministry of Agriculture, Land and Marine Resources, Trinidad and Tobago for permission to conduct this work. Financial support was provided by Fonds des recherche du Quebec, Nature et technologies to C.K.E. and Concordia University and the Natural Sciences and Engineering Council to G.E.B. All work was conducted in accordance with Concordia University Animal Research Ethics Committee approval AREC-2010-BROW.
References Beauchamp G, Ruxton GD, 2011. A reassessment of the Predation Risk Allocation Hypothesis: A comment on Lima and Bednekoff. Am. Nat. 177: 143–146. Biro PA, Abrahams MV, Post JR, Parkinson EA, 2004. Predators select against high growth rates and risk-taking behaviour in domestic trout populations. Proc. R. Soc. Lond. B 271: 2233– 2237. Botham MS, Hayward RK, Morrell LJ, Croft DP, Ward JR et al., 2008. Risk-sensitive antipredator behavior in the Trinidadian guppy Poecilia reticulata. Ecology 89: 3174–3185. Brown C, Jones F, Braithwaite V, 2005. In situ examination of boldness-shyness traits in the tropical poeciliid Brachyraphis episcopi. Anim. Behav. 70: 1003–1009. Brown GE, 2003. Learning about danger: Chemical alarm cues and local risk assessment in prey fishes. Fish Fish. 4: 227–234. Brown GE, Godin J-GJ, 1999. Chemical alarm signals in wild Trinidadian guppies Poecilia reticulata. Can. J. Zool. 77: 562– 570. Brown GE, Macnaughton CJ, Elvidge CK, Ramnarine I, Godin J-GJ, 2009. Provenance and threat-sensitive predator avoidance patterns in wild-caught Trinidadian guppies. Behav. Ecol. Sociobiol. 63: 699–706. Brown GE, Bongiorno T, Dicapua DM, Ivan LI, Roh E, 2006a. Effects of group size on the threat-sensitive response to varying concentrations of chemical alarm cues by juvenile convict cichlids. Can. J. Zool. 84: 1–8. Brown GE, Rive AC, Ferrari MCO, Chivers DP, 2006b. The dynamic nature of antipredator behavior: Prey fish integrate threat-sensitive antipredator responses within background levels of predation risk. Behav. Ecol. Sociobiol. 61: 9–16. Brown GE, Elvidge CK, Macnaughton CJ, Ramnarine I, Godin J-GJ, 2010. Cross-population responses to conspecific chemical alarm cues in wild Trinidadian guppies Poecilia reticulata: Evidence for local conservation of cue production. Can. J. Zool. 88: 139–147. Budaev S, Brown C, 2011. Personality traits and behaviour. In:
ELVIDGE CK et al.: Compensatory foraging in guppies
Brown C, Laland K, Krause J ed. Fish Cognition and Behavior. 2nd edn. West Sussex: Blackwell, 135–165. Chapman BB, Morrell LJ, Krause J, 2010. Unpredictability in food supply during early life influences boldness in fish. Behav. Ecol. 21: 501–506. Cooke SJ, Steinmetz J, Degner JF, Grant EC, Philipp DP, 2003. Metabolic fright responses of different-sized largemouth bass Micropterus salmoides to two avian predators show variations in nonlethal energetic costs. Can. J. Zool. 81: 699–709. Couchoux C, Cresswell W, 2012. Personality constraints versus flexible antipredation behaviors: How important is boldness in risk management of redshanks Tringa totanus foraging in a natural system? Behav. Ecol. 23: 290–301. Crispo E, Bentzen P, Reznick DN, Kinnison MT, Hendry AR, 2006. The relative influence of natural selection and geography on gene flow in guppies. Mol. Ecol. 15: 49–62. Croft DP, Morrell LJ, Wade AS, Piyapong C, Ioannou CC et al., 2006. Predation risk as a driving force for sexual segregation: A cross-population comparison. Am. Nat. 167: 867–878. Cronin TW, 1997. The visual ecology of predator-prey interactions. In: Godin J-GJ ed. Behavioral Ecology of Teleost Fishes. Oxford: University Press, 105–138. Dall SRX, Giraldeau L-A, Olsson O, McNamara JM, Stephens DW, 2005. Information and its use by animals in evolutionary ecology. Trends Ecol. Evol. 20: 187–193. Dewitt TJ, Sih A, Hucko JA, 1999. Trait compensation and cospecialization in a freshwater snail: Size, shape and antipredator behaviour. Anim. Behav. 58: 397–407. Dingemanse NJ, Kazem AJN, Réale D, Wright J, 2010. Behavioral reaction norms: animal personality meets individual plasticity. Trends Ecol. Evol. 25: 81–89. Elvidge CK, Brown GE, 2012. Visual and chemical prey cues as complementary predator attractants in a tropical stream fish assemblage. Int. J. Zool. 2012: 510920. Elvidge CK, Macnaughton CJ, Brown GE, 2013. Sensory complementation and antipredator behavioral compensation in acidimpacted juvenile Atlantic salmon. Oecologia 172: 69–78. Endler JA, Houde AE, 1995. Geographic variation in female preferences for male traits in Poecilia reticulata. Evolution 49: 456–468. Ferrari MCO, Vavrek MA, Elvidge CK, Fridman B, Chivers DP et al., 2008. Sensory complementation and the acquisition of predator recognition by salmonid fishes. Behav. Ecol. Sociobiol. 63: 113–121. Ferrari MCO, Sih A, Chivers DP, 2009. The paradox of risk allocation: A review and prospectus. Anim. Behav. 78: 579–585. Ferrari MCO, Wisenden BD, Chivers DP, 2010. Chemical ecology of predator-prey interactions in aquatic ecosystems: A review and prospectus. Can. J. Zool. 88: 698–724. Ferrari MCO, Chivers DP, 2009. Sophisticated early life lessons: Threat-sensitive generalization of predator recognition by embryonic amphibians. Behav. Ecol. 20: 1295–1298. Foam PE, Mirza RS, Chivers DP, Brown GE, 2005. Juvenile convict cichlids Archocentrus nigrofasciatus allocate foraging and antipredator behaviour in response to temporal variation in predation risk. Behaviour 142: 129–144. Fraser DF, Gilliam JF, 1987. Feeding under predation hazard:
331
Response of the guppy and Hart's rivulus from sites with contrasting predation hazard. Behav. Ecol. Sociobiol. 21: 203– 209. Fraser DF, Gilliam JF, Akkara JT, Albanese BW, Snider SB, 2004. Night feeding by guppies under predator release: Effects on growth and daytime courtship. Ecology 85: 312–319. Frost AJ, Winrow-Giffen A, Ashley PJ, Sneddon LU, 2007. Plasticity in animal personality traits: Does prior experience alter the degree of boldness? Proc. R. Soc. Lond, B 274: 333–339. Helfman GS, Winkelman DL, 1997. Threat sensitivity in bicolor damselfish: effects of sociality and body size. Ethology 103: 369–383. Jones KA, Godin J-GJ, 2010. Are fast explorers slow reactors? Linking personality type and anti-predator behaviour. Proc. R. Soc. Lond. B 277: 625–632. Kelley JL, Magurran AE, 2003. Effects of relaxed predation pressure on visual predator recognition in the guppy. Behav. Ecol. Sociobiol. 54: 225–232. Killen SS, Marras S, McKenzie DJ, 2011. Fuel, fasting, fear: routine metabolic rate and food deprivation exert synergistic effects on risk-taking in individual juvenile European sea bass. J. Anim. Ecol. 80: 1024–1033. Kim J–W, Brown GE, Dolinsek IJ, Brodeur NN, Leduc AOHC, Grant JWA, 2009. Combined effects of chemical and visual information in eliciting antipredator behaviour in juvenile Atlantic salmon Salmo salar. J. Fish Biol. 74: 1280–1290. Kim J-W, Grant JWA, Brown GE, 2011a. Do juvenile Atlantic salmon Salmo salar use chemosensory cues to detect and avoid risky habitats in the wild? Can. J. Fish. Aquat. Sci. 68: 655–662. Kim J-W, Wood JLA, Grant JWA, Brown GE, 2011b. Acute and chronic increases in predation risk affect the territorial behaviour of juvenile Atlantic salmon in the wild. Anim. Behav. 81: 93–99. Lima SL, Bednekoff PA, 1999. Temporal variation in danger drives antipredator behavior: the predation risk allocation hypothesis. Am. Nat. 153: 649–659. Lima SL, Dill LM, 1990 Behavioral decisions made under the risk of predation: A review and prospectus. Can. J. Zool. 68: 619– 640. López P, Hawlena D, Polo V, Amo L, Martín J, 2005. Sources of individual shy-bold variations in antipredator behaviour of male Iberian rock lizards. Anim. Behav. 69: 1–9. O'Steen S, Cullum AJ, Bennett AF, 2002. Rapid evolution of escape ability in Trinidadian guppies Poecilia reticulata. Evolution 56: 776–784. Preisser EL, Bolnick DI, Benard MF, 2005. Scared to death? The effects of intimidation and consumption in predator-prey interactions. Ecology 86: 501–509. Preisser EL, Bolnick DI, Grabowski JH, 2009. Resource dynamics influence the strength of non-consumptive predator effects on prey. Ecol. Lett. 12: 315–323. Quinn JL, Cresswell W, 2005. Personality, anti-predation behaviour and behavioral plasticity in the chaffinch Fringilla coelebs. Behaviour 9: 1377–1402. Reebs SG, 2002. Plasticity of diel and circadian activity rhythms in fishes. Rev. Fish Biol. Fish. 12: 349–371.
332
Current Zoology
Réale D, Reader SM, Sol D, McDougall PT, Dingemanse NJ, 2007. Integrating animal temperament within ecology and evolution. Biol. Rev. 82: 291–318. Sih A, 1992. Prey uncertainty and the balancing of antipredator and feeding needs. Am. Nat. 139: 1052–1069. Sih A, Bell AM, Johnson JC, Ziemba RE, 2004. Behavioral syndromes: An integrative overview. Quart. Rev. Biol. 79: 241– 277. Sih A, Ziemba R, Harding KC, 2000. New insights on how temporal variation in predation risk shapes prey behavior. Trends
Vol. 60 No. 3
Ecol. Evol. 15: 3–4. Thomson JS, Watts PC, Pottinger TG, Sneddon LU, 2012. Plasticity of boldness in rainbow trout Oncorhynchus mykiss: Do hunger and predation influence risk-taking behaviour? Horm. Behav. 61: 750–757. Walsh MR, 2013. The evolutionary consequences of indirect effects. Trends Ecol. Evol. 28: 23–29. Wilson ADM, Godin J-GJ, Ward AJW, 2010. Boldness and reproductive fitness correlates in the Eastern mosquitofish Gambusia holbrooki. Ethology 116: 96–104.
