Behavioral predictability, i.e., short-term intra-individual variability under relatively constant envi- ronmental conditions, has only recently begun to gain attention. It is unknown, however, whether predictability of individuals with distinct mean behavior changes differently as a response to ecological factors such as resource availability. Moreover, the response might be affected by anthropogenic contaminants that are ubiquitous in the environment and that can affect animals’ variability in behavior. Here, we investigated the relationship between mean predatory activity and predictability in predatory activity along a prey density gradient in the lynx spider Oxyopes linea- tipes. We further examined how this relationship is inﬂuenced by insecticides, azadirachtin, and a plant extract from Embelia ribes. We found that all studied variables affected the predictability. In the control and Embelia treatments, that did not differ signiﬁcantly, the predictability decreased with increasing prey density in a mean behavior-speciﬁc way. Individuals with low mean predatory activity were relatively less predictable than were those with high activity from low to moderate prey densities but more predictable at high prey densities. Azadirachtin altered this pattern and the individuals with low predatory activity were less predictable than were those with high predatory activity along the whole gradient of prey density. Our results show that predictability can change along an environmental gradient depending on a mean behavior. The relative predictability of the individuals with distinct mean behavior can depend on the value of the environmental gradient. In addition, this relationship can be affected by anthropogenic contaminants such as pesticides. Key words: azadirachtin, environmental gradient, intra-individual variability, pesticide, resource availability. Investigating the causes and consequences of behavioral variability behaves differently in identical situations. Predictability has started is the keystone of behavioral ecology. However, behavioral variabil- to gain more attention only recently and therefore its adaptive func- ity in a population is highly complex and involves several compo- tion and the factors that influence it remain poorly understood nents (Westneat et al. 2015; Stamps 2016). One component is (Pruitt et al. 2011; Stamps et al. 2012; Biro and Adriaenssens 2013; behavioral predictability, which refers to short-term intra-individual Westneat et al. 2015; Okuyama 2015; Stamps 2016). variability in behavior under relatively constant conditions (Stamps Predictability can, for example, influence predator–prey interac- et al. 2012). In other words, this is the degree to which an individual tions (Stamps et al. 2012; Briffa 2013; Chang et al. 2017). V C The Author(s) (2017). Published by Oxford University Press. 1 This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact email@example.com Downloaded from https://academic.oup.com/cz/advance-article-abstract/doi/10.1093/cz/zox075/4682791 by Ed 'DeepDyve' Gillespie user on 08 June 2018 2 Current Zoology, 2017, Vol. 0, No. 0 Unpredictable behavior in prey can reduce the probability of being the other hand, will still be motivated to prey. Therefore, at high captured, because a predator is unable to learn the prey’s activity prey density, the predatory activity might fluctuate in the shy indi- pattern (Briffa 2013). On the other hand, prey can alter the timing viduals but it might remain constantly high in the bold individuals. of their own foraging activity to correspond with times they expect As a consequence, the differences in the predictability between the a predator’s activity to be low and thereby minimize their probabil- individuals with different mean predatory activity might increase ity of encountering predators (Tambling et al. 2015). Therefore, with growing prey density because the predictability of shy and unpredictable predators might enjoy higher predation success than bold individuals will decrease rapidly and slowly, respectively do predictable predators, because prey may be unable to learn such (Figure 1B). predators’ activity patterns. However, if we want to know the adap- The way in which individuals with distinct mean behavior adjust tive function of predictability, we first need to know which factors their repeatability according to prey density might be affected by the influence it so that we can generate testable hypotheses about its sub-lethal doses of pesticides, which can have various effects on adaptiveness. behavior (Peka ´ r 2012). For example, the sub-lethal doses of pesticides Predictability of a behavior can be influenced by various internal can reduce mean predatory activity of pests’ natural enemies and external factors. The internal factors can be related to the inter- (Michalko and Ko sulic2016). The research on the sub-lethal effect of individual differences in mean behavior (Stamps et al. 2012; Chang pesticides traditionally focused on the mean behavior (Peka ´ r 2012). et al. 2017; He et al. 2017). For example, bolder individuals can be Recently, some authors (Montiglio and Royaute´2014; Royaute´et al. more predictable than shy individuals (Chang et al. 2017). The 2015) emphasized that the research on the sub-lethal effects of pesti- external factors that have been found to influence the predictability cides should focus also on the variability in behavior. For example, are temperature (Briffa et al. 2013) and pesticides (Royaute ´ et al. Royaute ´ et al. (2015) found that an insecticide did not affect the pop- 2015). To date, however, no study has investigated how the predict- ulation mean in a set of behaviors in a salticid spider. However, the ability changes along an ecological gradient in relation to mean insecticide reduced inter-individual variability and increased intra- behavior. individual variability, i.e. reduced predictability (Royaute´et al. A common ecological gradient, which the foragers encounter in 2015). As both, the mean and the variability in behavior, influence nature, is the spatio-temporal gradient of food availability (Stephens the predator–prey interactions (Pruitt et al. 2016; Chang et al. 2017; et al. 2007). Prey of predators has patchy occurrence in nature, and Michalko and Peka ´ r 2017), it is necessary to investigate the effect of its densities are highly changeable among these patches (Stephens pesticides on the behavioral variability if we are to evaluate their tox- et al. 2007). Predictability in predatory activity may, consequently, icity and predict their possible impact on the ecological dynamics. change in response to different prey densities (Figure 1A). There is, In the present work, we investigated the relationship between for instance, a natural fluctuation in predatory activity of spiders mean predatory activity and predictability in predatory activity that follows the satiation–hunger dynamics (e.g., Michalko and along the prey density gradient in the lynx spider Oxyopes lineatipes Ko suli c 2016). The prey density may influence the amplitude of the (C.L. Koch, 1847). We further investigated how this relationship is fluctuations (Figure 1A). At low prey densities, a predator’s state of influenced by exposure to two pesticides, namely azadirachtin and a satiation can switch to the state of hunger before it encounters plant extract from Embelia ribes (Burm f.) (Primulaceae). Oxyopes another prey item. Consequently, the predator will be motivated to lineatipes is a cursorial spider that is highly abundant within various prey during each encounter with a prey item (Holling 1965). This agroecosystems in South-East Asia; it also occurs in higher vegeta- will result in a relatively constant predatory activity and low intra- tion, on branches, and in canopies (Barrion and Litsinger 1995; individual variability, i.e., high predictability (Figure 1A). At high Murphy and Murphy 2000). We expected that (1) the predictability prey densities, the encounter rate can be so high that it enables feed- will decrease with increasing prey density, but (2) the predictability ing ad libitum and the foraging bouts to be followed by relatively will decrease more slowly in those individuals with high mean pred- long periods of predatory inactivity (Figure 1A). This will result in atory activity than in those individuals with low mean predatory relatively large fluctuations in predatory activity and low predict- activity, and (3) the pesticides will alter the relationship between ability in predatory activity (Figure 1A). The predictability may mean behavior, predictability, and prey density. therefore decrease with prey density (Figure 1B). The individuals with distinct mean behavior may, however, adjust their repeatability as a response to the changing prey density Materials and Methods differently (Figure 1). For example, the individuals with different Spider collection level of predatory activity often differ also in their level of shyness, We collected 75 subadult and adult females of O. lineatipes (mean i.e., willingness to take a risk (Pruitt and Riechert 2012). The shy carapace length ¼ 2.2 mm, SE ¼ 1.3, range¼ 0.9–2.7) by sweeping individuals with low predatory activity invest more into maintaining and visually searching herbaceous vegetation from crop fields on the vigilance against enemies rather than into foraging and they are experimental farm of Kamphaeng Saen Campus in Nakhon Pathom motivated to prey at lower energy states than the bold individuals (Thailand) at the end of June 2014. The crop fields had not been with high predatory activity do (Riechert and Hedrick 1993; Pruitt sprayed with pesticides. Size did not influence any of the studied and Riechert 2012). At low prey densities, after consuming one prey parameters (see below). After collection, spiders were kept individu- item, the state of satiation may switch to that of hunger before ally in plastic tubes with a half of a facial tissue at the bottom that encountering another prey item in all individuals, regardless of their was periodically moistened. mean behavior. The predatory activity may remain constant and consequently the predictability may remain relatively high and will Tested chemicals not differ between the individuals with different mean predatory activities (Figure 1). At high prey densities, the encounter rate with We evaluated the effect of two agrochemicals: an extract from dried prey can be greater than the decrease in energy states to the motiva- E. ribes leaves and azadirachtin. We chose to compare these two tion level for foraging in the shy individuals. Bold individuals, on insecticides because Embelia is a potential new biopesticide while Downloaded from https://academic.oup.com/cz/advance-article-abstract/doi/10.1093/cz/zox075/4682791 by Ed 'DeepDyve' Gillespie user on 08 June 2018 Michalko et al. Predictability in predatory activity 3 Patch with high prey density Patch with low prey density 1 2 3 4 5 High mean predatory activity Observation # Low mean predatory activity Prey density Figure 1. Conceptual ﬁgure explaining how IIV, i.e., behavioral predictability, of individuals with different mean predatory activities may change with prey density. The panel (A) shows the temporal courses of predatory activities of individuals with different mean predatory activities throughout ﬁve observations in patches with low and high prey density. At low prey density, there may be small ﬂuctuations in predatory activity among observations because the encounter rate is small and the predators are motivated to prey upon encounter with each prey item. At high prey density, the encounter rate can be high, and it enables the foraging bouts to be followed by relatively long periods of resting. This may result in relatively large ﬂuctuations in predatory activity. Individuals with different levels of mean predatory activity often differ in their level of shyness, i.e., willingness to take a risk. At low prey densities, after consuming one prey item, the state of sati- ation may switch to that of hunger before encountering another prey item in all individuals, regardless of their mean behavior. Consequently, predatory activity may be relatively constant in all individuals. At high prey densities, the encounter rate with prey can be greater than the decrease in energy states to the motivation level for foraging in the shy individuals with low mean predatory activity. Bold individuals with high mean predatory activity, on the other hand can be often moti- vated to prey. At high prey density, therefore, the predatory activity might ﬂuctuate in the shy individuals, but it might remain constantly high in the bold individu- als. As a consequence, the IIV and differences in IIV between the individuals with distinct mean behaviors might increase with growing prey density (B). the azadirachtin is a commercial pesticide widely used across various (Noosidum et al. 2007; Insung et al. 2008; Noosidum and agroecosystems (Stark 2013). Ideally, the pesticides used in the agro- Chandrapatya 2015). ecosystems with integrated pest management should have no or min- Azadirachtin, a chemical compound belonging to the limonoid imal effect on the non-target organisms. group, is a secondary metabolite and is present in seeds from the The plant extract was prepared as follows. Leaf samples of neem tree (Azadirachta indica A. Juss., 1830). Azadirachtin is an Embelia ribes were air-dried at room temperature (28–32 C) for insecticide acting as an antifeedant, repellent, and deterrent to egg- 24 h and ground to a powder before extraction. The fixed-bed laying. Azadirachtin is highly effective against thrips, hemipterans, method was employed to extract plant metabolites. Fixed-bed and lepidopterans (Sundaram 1996; Kumar and Poehling 2006). extraction (hot extraction) was done in a Soxhlet extractor, where It works as a contact and systemic food poison (Stark 2013). We tested the lower rate of the recommended field dose for aza- the samples were sequentially extracted with n-hexane for 8 h. The V 4 extracts were then vacuum-filtered through Whatman No. 1 filter dirachtin (1.25 10 %), which is much lower than the lethal con- paper and the residue was consequently extracted in dichlorome- centration required to kill 50% of the population, the so-called LC for O. lineatipes (0.045; authors, submitted). For Embelia,we used thane and methanol by the same procedure. Solvents were removed on a rotary evaporator and the crude extracts were weighed and the recommended dose (0.75%) because the LC for predacious ben- eficial mites is 0.83% for residual effect and 0.67% for direct con- refrigerated at 10 C for further experimentation. The crude extract from dry E. ribes leaves (hereinafter Embelia) tact, respectively (Leelawan et al. 2010). It is desirable to determine contains a rich array of bioactive chemicals. The main active ingre- the most effective combination of the plant extract concentration and dient, embelin, is considered to have pesticidal and/or repulsive activities of possible biocontrol agents. We used distilled water as the effects against broad mites, spider mites, and common cutworm solvent for pesticide dilutions and as a control treatment. Downloaded from https://academic.oup.com/cz/advance-article-abstract/doi/10.1093/cz/zox075/4682791 by Ed 'DeepDyve' Gillespie user on 08 June 2018 Intra-individual variability [IIV] Number of prey killed Differences in mean behavior Intra-individual variability [IIV -Predictability 4 Current Zoology, 2017, Vol. 0, No. 0 Experimental design of IIV instead of a population-level estimate of IIV (Stamps et al. Spiders were acclimated in the laboratory for 1 week. Laboratory 2012; Cleasby et al. 2015; Chang et al. 2017). We therefore used the residual individual standard deviation (i.e., riSD index) as a measure conditions were 226 1 C, 706 5% relative humidity, and a natural of IIV and the method to obtain it proposed by Stamps et al. (2012). photoperiod (Light: Dark ¼ 12: 12). Spiders were fed ad libitum First, we fitted GLMM for each pesticide and control treatment. with laboratory-reared fruit flies Drosophila melanogaster (Diptera, The response variable was the number of killed prey, the fixed Drosophilidae) 1 week before the experiments to standardize their effects were represented by prey density and by time, and the ran- hunger level. dom effects were then represented by ID as a random intercept and We used only females because the effect of chemicals can be sex- by time as a random slope. We used GLMM with gamma error specific (e.g., Royaute ´ et al. 2015). Spiders were first sorted into five structure and inverse link (GLMM-g) and we also inversely trans- size categories. Thereafter, the individuals within the size categories formed the prey density before the analysis to account for the were assigned to the treatment/prey density (see later) randomly asymptotic relationship between predatory activity and prey density without replacement. We sprayed 50 ml of one or the other of the (Juliano 2001; Peka ´ r and Brabec 2016). In addition, as gamma dis- two tested solutions or water as a control directly onto each spider tribution works only with the positive values, the response variable from a distance of 10 cm using a pharmaceutical pump sprayer. was x þ 1 transformed. We next extracted residuals for each Direct exposure is one of the common modes of exposure in agroe- individual and each time point and then computed the riSD index cosystems. After 10 s, spiders were removed from the plastic con- (Stamps et al. 2012). The higher the value of IIV, the lower the tainer to Petri dishes (diameter 8.5 cm, height 1.5 cm) containing a predictability. wet cotton ball to maintain humidity. The spiders were allowed to acclimate for 30 min. Flightless fruit flies, D. melanogaster, main- Relationship between prey density and IIV tained on agar medium, were used as prey. The flies were untreated To investigate the relationship between IIV, prey density, and mean to prevent any effect of contaminated prey. Each spider was exposed predatory activity, we used generalized linear models (GLM) with to one of the following prey densities: 1, 3, 6, 12, or 25 fruit flies. gamma error structure and inverse link function (GLM-g). The We conducted five replicates per density/treatment (N ¼ 75). The response variable was IIV, while the explanatory variables were experiments were run for 3 days. The number of killed flies was treatment, standardized mean predatory activity (see later), prey checked every 8 h and the killed flies were replaced with living ones density, and all their 2-fold and the 3-fold interactions. We used this to ensure constant prey densities. We thus obtained eight observa- full model because we were specifically investigating how BTs influ- tions per each individual. Spiders that did not accept prey and ences the change in predictability along the prey density gradient molted within 24 h (N ¼ 2) were excluded from further statistical and how this relationship is affected by the two pesticides. analyses. All animal experimentation met the ABS/ASAB guidelines The two explanatory variables, predatory activity and prey den- for ethical treatment of animals. sity, required transformations before the analysis. First, as predatory activity increases with prey density (Holling 1965), we had to trans- Statistical analyses form the mean predatory activities of individuals from the different All analyses were performed within the R environment prey densities onto the same scale. We range-standardized the mean (R Development Core Team 2017 ). We conducted three groups of predatory activities within each prey density between 0 and 1 sepa- analyses, namely 1) estimation of repeatability to investigate whether rately for each treatment. Consequently, those individuals scored there are consistent inter-individual differences in behavior, 2) estima- with 0 have the lowest predatory activity within the prey density/ tion of intra-individual variability (IIV) as a measure of predictability treatment, while those with 1 have the highest predatory activity. for each individual, and 3) investigation of how IIV is influenced by Second, the prey density was inversely transformed because the rela- mean behavior, prey density, and pesticides. tionship between predictability and prey density seemed to be asymptotic during the data exploration (Peka ´ r and Brabec 2016). Estimation of repeatability The post hoc comparisons were made by the treatment contrasts To investigate whether there are consistent interindividual differen- and if the pesticide treatments proved not to differ significantly, we ces in predatory activity, we computed the adjusted repeatability to pooled the levels for the parameters estimation (Peka ´ r and Brabec avoid potential pseudo-repeatability caused by differences in prey 2016). density and size differences (Stoffel et al. 2017). Because the response variable was counts, we used generalized linear mixed Results effects models (GLMM) with Poisson error structure and log link (GLMM-p) (Stoffel et al. 2017). The fixed effects were represented Size did not influence any of the studied parameters, i.e., repeatabil- by prey density, size, and the random effects represented the ID. As ity or IIV (P> 0.205, Supplementary Table S1). There was signifi- we investigated whether there is a significant repeatability in each cant repeatability in predatory activity in all treatments (GLMM-p; treatment, we estimated the repeatability for each experimental 1000 permutations; Control: 0.37, CI ¼ 0.13–0.52, P ¼ 0.001; 95% treatment separately. The statistical significance was tested by per- Embelia: 0.47, CI ¼ 0.23–0.63, P ¼ 0.001; azadirachtin: 0.14, 95% mutations while the 95% confidence intervals were obtained by CI ¼ 0–0.27, P ¼ 0.028). 95% parametric bootstrapping, both with 1000 iterations. The analysis The IIV changed along the prey density gradient in a mean of repeatability was performed within the R package ‘rptR’ (Stoffel behavior-specific way and this mean behavior-specific change dif- et al. 2017). fered among the treatments because there was significant three-fold interaction among treatment, prey density, and mean predatory Estimation of predictability activity (GLM-g, P ¼ 0.039; Tables 1 and 2; Figure 2). According to As we were interested in the interaction among mean beahavior, our hypothesis, IIV increased with prey density in all treatments predictability, and prey density, we needed an individual-level index (contrasts, P< 0.045; Figure 2). With respect to the pesticide Downloaded from https://academic.oup.com/cz/advance-article-abstract/doi/10.1093/cz/zox075/4682791 by Ed 'DeepDyve' Gillespie user on 08 June 2018 Michalko et al. Predictability in predatory activity 5 treatment, the Embelia treatment did not differ significantly from Discussion the control treatment in any parameters (contrasts, P> 0.260). We investigated how mean predatory activity of the lynx spider In contrast, the azadirachtin treatment differed significantly from O. lineatipes influences the relationship between predictability in both control and Embelia treatments and it altered how the individ- predatory activity and prey density and how this relationship is uals with distinct mean predatory activities adjusted their IIV as a influenced by two insecticides, azadirachtin and plant extract from response to the prey density (contrasts, P< 0.001; Figure 2). In the Embelia ribes. Firstly, in line with our hypothesis, the predictability control and Embelia treatments, there was a significant interaction of predatory activity decreased (IIV increased) with prey density. between density and mean predatory activity (contrasts, P< 0.003, Secondly, mean behavior influenced the relationship in an unex- Table 2; Figure 2A). Consequently, the IIV increased asymptotically pected way. In the control and Embelia treatments, that did not dif- with prey density in those individuals with low predatory activity fer significantly, IIV of individuals with low predatory activity but linearly in the individuals with high predatory activity. In addi- increased asymptotically while IIV of individuals with high activity tion, IIV was higher in individuals with low predatory activity than increased linearly. Consequently, those individuals with low preda- in individuals with high predatory activity at low and medium tory activity were less predictable than were the individuals with prey densities, but the opposite was true at high prey densities high activity from low to moderate prey densities but more predict- (Figure 2A). In the azadirachtin treatment, the IIV increased with able at high prey density. There was also a parameter space where foraging aggressiveness (contrasts, P ¼ 0.045) but foraging aggres- the mean behaviors did not differ in their predictability. Thirdly, siveness had only an additive effect on IIV as the interaction was not with respect to the effects of pesticides, only azadirachtin affected significant (contrasts, P ¼ 0.525). Thus, IIV increased asymptotically the predictability of Oxyopes. Where azadirachtin was applied, with prey density in individuals with low as well as high predatory mean behavior had only an additive effect and the individuals with activity in the azadirachtin treatment (Figure 2B). The temporal low predatory activity were less predictable than were individuals dynamics of predatory activity by individuals with the highest and with high predatory activity along the whole gradient of prey lowest predatory activities at low and high prey densities in the three density. treatments are shown in Figure 3. It needs to be noted that we used adult and sub-adult females, which means that the differences in mean predatory activity can be Table 1. Results of the GLM-g error structure and inverse link inves- caused by the differences in behavioral types or so-called animal per- tigating the effect of pesticide treatment, prey density, and mean sonality as well as by developmental flexibility/plasticity. The differ- predatory activity on the IIV in predatory activity in the lynx spider ent developmental stages can use different behavioral strategies Oxyopes lineatipes (Westneat and Fox 2010). However, age is highly correlated with size in spiders (Foelix 2011) and we accounted for the size differen- Term df F-statistic P ces in our analyses and therefore also, to a large extent, for the age Treatment 2, 71 7.0 0.002 differences. Nevertheless, we still interpret our results as differences 1/Density 1, 70 473.0 <0.001 in mean behavior, keeping in mind that they may be caused by the Activity 1, 69 3.3 0.074 differences in behavioral types as well as developmental flexibility. Treatment: 1/Density 2, 67 1.3 0.274 Our results have important implication for further studies on Treatment: Activity 2, 65 2.8 0.071 behavioral predictability. Studies investigating the relationship 1/Density: Activity 1, 64 15.1 <0.001 between mean behavior and predictability have uncovered various Treatment: 1/Density: Activity 2, 62 3.4 0.039 patterns. For example, there has been found a negative, a positive, AB Control and Embelia ribes Azadirachtin Low predatory activity High predatory activity 5 10152025 5 10152025 2 2 Prey density [inds / 154 cm ] Prey density [inds / 154 cm ] Figure 2. Relationship among prey density, mean predatory activity (range-standardized within prey density groups), and IIV in the lynx spider Oxyopes lineatipes exposed to water control and plant extracts from Embelia ribes (A) and the insecticide azadirachtin (B). Higher intra-individual variability indicates lower predictability. Fitted relationships are for those individuals with lowest (0) and highest (1) mean predatory activity. Point sizes correspond with the level of mean predatory activity, with larger points indicating greater predatory activity. The parameter estimates are stated in Table 1. Downloaded from https://academic.oup.com/cz/advance-article-abstract/doi/10.1093/cz/zox075/4682791 by Ed 'DeepDyve' Gillespie user on 08 June 2018 Intra-individual variability 012 33 45 0 1 2 3 4 5 6 Current Zoology, 2017, Vol. 0, No. 0 AB C Highest mean predatory activity Lowest mean predatory activity 8 1624324048 5664 8 1624324048 5664 8 1624324048 5664 Time [h] Time [h] Time [h] Figure 3. Temporal trend in predatory activity during 64 h by Oxyopes lineatipes individuals with the highest and lowest mean predatory activities within treat- ment/prey density exposed to low prey density (the lower clusters of lines in white ﬁelds; 3 ﬂies per 154 cm ) or to high prey density (upper clusters of lines in the gray ﬁelds; 25 ﬂies per 154 cm ) and exposed to water control (A), plant extract from Embelia ribes (B), or azadirachtin pesticide (C). Various individuals within the line clusters (prey density and pesticide treatments) are depicted by the different dashing of lines. The individuals differed in their mean predatory activity, which is depicted by different colors. Table 2. Parameter estimates (SE) from the GLM-g error structure and inverse link investigating the effect of pesticide treatment, prey den- sity, and mean predatory activity on the IIV in predatory activity in the lynx spider Oxyopes lineatipes. Dash indicates a nonsigniﬁcant term. A common estimate for the control and Embelia treatments is shown because the two did not differ signiﬁcantly (P> 0.260). Treatment Intercept 1/Density Predatory activity 1/Density: Predatory activity Control þ Embelia 0.184 (0.040) 2.005 (0.346) 0.166 (0.066) 2.854 (0.599) Azadirachtin 0.153 (0.074) 2.729 (0.616) 0.307 (0.151) – and no relationship between boldness and predictability in boldness Effects of mean behavior on predictability (Stamps et al. 2012; Chang et al. 2017; He et al. 2017). Our results One possible explanation why the predictability changed in a mean behavior-specific manner might be that those individuals with low show that the distinct pattern might be obtained just because the experiments are conducted at different values of an environmental predatory activity can be shy while the individuals with high preda- tory activity can be bold, which relationship often is reported in spi- gradient and because the individuals with distinct mean behavior ders (Pruitt and Riechert 2012). The shy individuals invest more respond differently to the gradient, which can itself adjust its into enemy vigilance, while bold individuals invest more into forag- predictabilities. ing. Therefore, unlike the bold individuals, shy individuals are moti- Although the exact reason for the observed pattern is vated to prey at lower energy states (Pruitt and Riechert 2012). unknown, we suggest potential mechanisms that interactively Also, shy individuals might have slower metabolism than do the could generate the observed pattern and that can be tested in fur- bold individuals (Shearer and Pruitt 2014). Thus, at low to medium ther experiments. The observed pattern might be caused by 1) the prey density, the encounter rate with flies could be low for the bold interaction between encounter rate with prey and the hunger– individuals and their energy states might reach a level at which they satiation states dynamics, 2) mean predatory activity-specific are motivated to prey upon encounter with each prey. In contrast, energy level of motivation to prey and/or differences in metabo- the encounter rate for the shy individuals could still be sufficiently lism rate, 3) cognitive styles, and 4) the degree of wasteful killing high and their energy states would not decrease to such level where or gluttony. they would be motivated to prey upon encounter with each prey item. This could be the cause of why that the capture rate in the indi- Effect of prey density on predictability viduals with high mean predatory activity remained relatively con- The reason for the decrease of predictability with prey density is the stant, and was accompanied by relatively high predictability, and only one that is relatively straightforward. In spiders, there is a natu- meanwhile the capture rate fluctuated in the individuals with low ral fluctuation in capture rate that follows the satiation–hunger predatory activity and predictability was relatively low (Figure 3). dynamics (Michalko and Ko suli c 2016). At low prey densities, a spi- Another possible explanation for the mean predatory activity- der’s state of satiation after a prey capture could switch to a state of specific relationship between predictability and prey density is that hunger before it will encounter another prey item. That would result individuals with different predatory activity might employ different in a relatively constant capture rate among the time periods. The prey-sampling strategy and/or possess different cognitive styles predictability at high prey density could be lower because the (Mathot et al. 2012; Sih and Del Giudice 2012). Individuals with encounter rate of spiders and flies enabled the spiders to feed ad libi- low predatory activity can be choosy while the individuals with high tum during a foraging bout that was followed by a period of diges- predatory activity can be non-choosy (Riechert 1991; Michalko and tion and rest. Consequently, the prey capture relatively fluctuated Peka ´ r 2014, 2017). Various prey represent different quality for gen- among the time periods. eralist spiders, even at intra-specific level, and spiders are able to Downloaded from https://academic.oup.com/cz/advance-article-abstract/doi/10.1093/cz/zox075/4682791 by Ed 'DeepDyve' Gillespie user on 08 June 2018 No. of prey killed 05 10 15 20 25 05 10 15 20 25 0 5 10 15 20 25 Michalko et al. Predictability in predatory activity 7 recognize that (Toft 1999; Mayntz et al. 2005). In addition, spiders azadirachtin can restructure the behavioral architecture in the popu- do not know the complete prey offer and need to sample the prey lations of the pests’ natural enemies as it influences mean predatory offer first (Stephens et al. 2007). Therefore, the choosy individuals activity as well as variability in predatory activity. Azadirachtin might invest more time to evaluate the prey characteristics and/or therefore has a potential to disrupt the biocontrol services provided overall prey offer at first, then initiate the foraging bout after careful by the natural enemies. sampling (Mathot et al. 2012; Sih and Del Giudice 2012). The long In conclusion, we show for the first time that behavioral predict- time spent in prey evaluation followed by the time of foraging bout ability can change along an ecological gradient depending on the dif- might have caused the capture rate relatively to fluctuate among the ferences in mean behavior, which can be the behavioral type, for observations. In contrast, the non-choosy individuals decide quickly example. Consequently, the relative predictability of individuals and attack any prey item that they are able to overcome immediately with certain mean behavior can depend on the value of that gra- (Sih and Del Giudice 2012; Michalko and Peka ´ r 2017), and this dient. In addition, exposure to such anthropogenic contaminants as might cause a relatively constant rate of prey capture. These assump- pesticides can alter that relationship. tions might be supported by the fact that some individuals with low predatory activity killed a lower number of prey at first but then their capture rates increased while the individuals with high preda- Supplementary Material tory activity killed high numbers of prey immediately (Figure 3). Supplementary material can be found at https://academic.oup.com/cz The switch in relative predictability of the mean behaviors at high prey density can be explained by the differences in wasteful kill- ing and/or gluttony among the individuals (Samu and Bı´ro ´ 1993; Acknowledgments Maupin and Riechert 2001, Pruitt 2010). The individuals with high We would like to thank Chutima Phatjanhom, Yosita Sapongsa, and predatory activity can be aggressive and perform high wasteful kill- Witwisitpong Maneechan for their help in collecting spiders and conducting ing and/or consume larger amounts of prey than timid individuals laboratory experiments. We are grateful to the anonymous reviewers for their with low predatory activity (Samu and Bı´ro ´ 1993; Maupin and comments that signiﬁcantly improved the article. Riechert 2001; Pruitt 2010; Pruitt and Krauel 2010). The incidence of wasteful killing increases with prey density, as the encounter rate Funding with prey increases and spiders perceive more stimuli from prey (Samu and Bı ´ro ´ 1993). Therefore, the difference in numbers of killed The study was ﬁnancially supported by the Kasetsart University Research and prey between those periods, when the individuals with high preda- Development Institute Royal Project Thailand Foundation and by the Speciﬁc tory activity were in the state of relative satiation with low motiva- University Research Fund of the Faculty of Forestry and Wood Technology, tion to prey and periods of relative hunger with high motivation to Mendel University in Brno (Reg. No. LDF_PSV_2017004). The research was prey, might increase with prey density. The capture rate of individu- also supported in part by the European Social Fund and the state budget of als with low predatory activity that engage in low wasteful killing the Czech Republic under the project Indicators of Trees Vitality and Platform for Study and Inventory of Forest Ecosystems (Reg. No. CZ.1.07/ or gluttony might reach the asymptote sooner and the decrease 2.3.00/20.0265 and CZ.1.07/2.4.00/31.0214). in predictability would therefore also approach the asymptote. Consequently, the capture rate of individuals with high predatory activity might still be in its increasing phase due to wasteful killing References and the predictability might continue to decrease linearly below the Barrion AT, Litsinger JA, 1995. Riceland Spiders of South and Southeast Asia. asymptotic level of the predictability level of the individuals with Wallingford: CAB International. low predatory activity. Biro PA, Adriaenssens B, 2013. Predictability as a personality trait: consistent differences in intraindividual behavioral variation. Am Nat 182:621–629. Effects of pesticides on predictability Briffa M, 2013. Plastic proteans: reduced predictability in the face of predation Only azadirachtin affected the predictability of the oxyopids while risk in hermit crabs. Biol Lett 9:20130592. 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Current Zoology – Oxford University Press
Published: Nov 30, 2017
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