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1 Biological Control 47 (2008) Contents lists available at ScienceDirect Biological Control journal homepage: Prey killing without consumption: Does Macrolophus pygmaeus show adaptive foraging behaviour? q A.A. Fantinou a, *, D.Ch. Perdikis b, D.A. Maselou a, P.D. Lambropoulos a a Laboratory of Ecology and Environmental Sciences, Agricultural University of Athens, Iera Odos 75, Athens, Greece b Laboratory of Agricultural Zoology and Entomology, Agricultural University of Athens, Iera Odos 75, Athens, Greece article info abstract Article history: Received 28 February 2008 Accepted 6 August 2008 Available online 14 August 2008 Keywords: Macrolophus pygmaeus Myzus persicae Functional response Predatory adaptation Wasteful killing Several predators exhibit a killing behaviour that might not result in prey consumption after prey death. This behaviour includes partial prey consumption and/or killing without consumption. This study was undertaken to elucidate the factors that may influence consumptive and non-consumptive prey mortality in a predator prey system. The hypotheses tested included whether or not the predatory behaviour of Macrolophus pygmaeus is affected by the density or size of prey. As prey, the nymphal instars of the aphid Myzus persicae were used. Additionally, to determine if this behaviour was constrained by temperature, the experiments were conducted at three different temperatures. Data was obtained showing that the frequency of non-consumptive mortality was higher with larger and overall less preferred prey instars. Predators that foraged at low temperatures appeared to be less selective, killed more frequently, and left more prey unconsumed. Killing behaviour, however, was not found to increase with prey density. Instead, non-consumptive prey mortality was associated with intermediate prey densities and was dependent on temperature and the prey instar. We conclude that the factors leading to non-consumptive prey killing behaviour are affected by internal and external elements. Additionally, we believe that this behaviour corresponds to a foraging predator s strategy for optimal exploitation of the available prey. Ó 2008 Elsevier Inc. All rights reserved. 1. Introduction Predator prey interactions have been a research topic in population ecology since the early theoretical works of Lotka (1925) and Volterra (1926). As a foraging predator searches for a potential victim, the following sequenced steps are expected: searching, detecting, capturing, killing, consuming and digesting the prey (Johnson et al., 1975). However, during this sequence of events, the predator might exhibit a modified behaviour, such as the abandonment of an already killed prey without consuming it. This behaviour includes either partial prey consumption or killing without consumption and is referred to as wasteful killing or superfluous killing (Reichert, 1999). Partial prey consumption has been recorded in several orders of animals including zooplankton (Conover, 1966), mantids (Holling, 1966), beetles (Hackett, 1968), damselfly naiads (Johnson et al., 1975), predatory flies (Uygun, 1971; Barlow and Whittingham, 1986), anthocorids (Campbell, 1977; Meyling et al., 2003), and carabids (Lang and Gsodl, 2003), as well as in predatory mites (Hoyt, 1970; Sandness and McMurtry, 1972), spiders (Samu and Biro, 1993; Maupin and Reichert, 2001) q Funding: The current work was co-funded by the European Union European Social Fund & National Resources O.P. EDUCATION II. * Corresponding author. Fax: address: argyr@aua.gr (A.A. Fantinou). and wolves (Miller et al., 1985). This behaviour generally increases with prey availability (Bailey, 1986) and is internally motivated by the predator s hunger status and/or gut-filling (Holling, 1966; Samu, 1993). Furthermore, the rate of resource acquisition declines with the time invested per prey and therefore, partial prey consumption has been attributed to an optimization process that aims to maximize the food intake rate, when foraging at high prey densities (Dudgeon, 1990). This conforms well to the predictions of the optimal foraging theory (Charnov, 1976). On the other hand, prey killing without consumption, has been recorded in anthocorids (Campbell, 1977; Meyling et al., 2003), carabids (Lang and Gsodl, 2003), predatory flies (Uygun, 1971), predatory mites (Hoyt, 1970; Sandness and McMurtry, 1972) and spiders (Samu and Biro, 1993). This particular behaviour has attracted interest since it is difficult to envision an adaptive function of a predator s decision to abandon captured prey. This foraging strategy results in the wasting of the already invested searching time and handling efforts and poses the risk of incomplete prey patch exploitation. However, evidence on the factors that may affect this behaviour is scarce and it is probably more common under high prey availability (Sandness and McMurtry, 1972; Maupin and Reichert, 2001; Lang and Gsodl, 2003) or to a low quality of prey (Lang and Gsodl, 2003; Meyling et al., 2003). Killing without consuming the prey could also occur if the predator can evaluate the quality of the prey only after killing it (Meyling et al., 2003). Nev /$ - see front matter Ó 2008 Elsevier Inc. All rights reserved. doi: /j.biocontrol

2 188 A.A. Fantinou et al. / Biological Control 47 (2008) ertheless, the frequency of this behaviour is most likely associated with predatory gut satiation. This is a valid hypothesis since the gut limitation theory predicts that partial prey consumption will be more common at higher prey densities (Holling, 1966). In addition to its theoretical interest, non-consumptive prey mortality should be considered in modeling prey predator dynamics. If it was ignored, the energy obtained by predators would be overestimated. Additionally, it would complicate the interpretation of predation rates (Maupin and Reichert, 2001). It may also have significant implications on the assessment of the total mortality imposed by biological control agents. The goal of this study was to conduct a comprehensive evaluation of the possible factors that influence predation in a predator prey system. These factors were temperature, prey size and prey density. Emphasis was given to a detailed investigation of consumptive prey mortality and the estimation of the predator functional response. Non-consumptive prey mortality was also recorded and hypotheses were addressed whether the frequency of this behaviour is enhanced by increased prey density, prey size and temperature. 2. Materials and methods 2.1. Study organisms Macrolophus pygmaeus (Rambur) (Hemiptera: Miridae) is a polyphagous predator that develops and reproduces successfully by feeding on aphids and whiteflies found on vegetables (Perdikis and Lykouressis, 2000, 2002). It can also develop successfully and adults can survive and reproduce but at low rates feeding solely on leaves of vegetables (Perdikis and Lykouressis, 2000, 2004). This predator has been found to be an important natural enemy of aphids found on several vegetable crops in Greece (Lykouressis et al., 2000). For the purpose of the current study, M. pygmaeus rearings were initiated from adults and nymphs collected from a tomato field in Co. Boeotia, Central Greece. They were kept on potted eggplants (cv. Bonica) infested by Myzus persicae (Sulzer) (Homoptera: Aphididae). Myzus persicae rearings were established on eggplants. Both rearings were kept in wood-framed cages (80 cm length 80 cm width 70 cm height) in a glasshouse maintained at 22.5 ± 2.5 C (mean ± SD) under natural lighting Laboratory trials Experiments took place in plastic Petri dishes (9 cm diameter, 1.5 cm height) with mesh-covered holes in the lids (3 cm diameter) to reduce the accumulation of humidity. An eggplant leaf was placed on its abaxial surface on top of a layer of cotton wool, which was moistened with water, on the bottom of each dish. Myzus persicae nymphs were gently placed on each leaf, and left undisturbed for 1 h to settle. In all the experiments 5th instar nymphs of M. pygmaeus were used. Nymphs of 5th instar of the predator were obtained from nymphs of first or second instars that were transferred from the wood-framed rearing cages to potted caged eggplants infested with M. persicae. The predator nymphs were left to develop till the fifth instar while then caged on clean of aphids eggplants at 25 C, 65 ± 5% r.h. and 16L:8D photoperiod and deprived of prey for 24 h before the experiments. Each caged egg plant was covered with a plastic cylindrical cage (11 cm diameter, 30 cm height) bearing two rectangular openings (9 cm 9 cm) covered with fine muslin to allow for ventilation. A 24 h starved fifth instar M. pygmaeus nymph was introduced into each dish with aphids and its predation rate was recorded after a period of 24 h. Data from predator nymphs that moulted during the experiments were discarded, as moulting affects the feeding rates. After a 24-h period, the predators were removed from the dishes and the numbers of complete and non-consumed (sucked) aphids in each dish were recorded. Partial prey consumption was not observed. The completely consumed aphids were wholly emptied and their skin remained after the predators sucking. The biomass consumption of the predator was also estimated in each case. The average weight of each instar of M. persicae was estimated by weighing groups of 40 live nymphs of each aphid instar, which were previously anaesthetized by a low flow of carbon dioxide. The scale type used for weighting the aphids was Mettler Toledo AB204-S with a precision level of 0.1 mg. This procedure was replicated three times. The weight of the aphid remains was ignored for the estimation of consumption. The average weights of first, second, third and fourth instars of M. persicae were estimated to be , , and mg, respectively. The prey densities used in the experiments were 4, 8, 12, 16, 20, 24 or 32 aphids of each instar of M. persicae that were placed on the leaf in each dish. For each density level, 10 replicates (predators) were used. The experiments were carried out at 20, 25 and 30 C, under a photoperiod of L16:D8 and 65 ± 5% r.h. Preliminary experiments showed that manipulations caused negligible aphid mortality Statistical analyses The obtained data were analyzed by split split plot analyses with factors the temperature (plot), density (sub plot) and host size (aphid instar) (sub-sub plot). The response variables were the number of killed consumed or non-consumed prey and the biomass gain by the predator. The data were transformed using the Box Cox transformation prior to analysis (Box and Cox, 1964). Analyses were conducted with the JMP statistical package (v. 5.1, SAS Institute, 2003). The relationship between the predation rate, excluding the killed unconsumed prey and the initial density of available prey, was investigated by fitting functional response curves. The shape of the curve was determined by logistic regression of the proportion of prey consumed as a function of the available prey density. The polynomial function from Juliano (1993) was used: Ne No ¼ expðp 0 þ P 1 No þ P 2 No2 þ P 3 No3Þ 1 þ expðpo þ P 1 No þ P 2 No2 þ P 3 No3Þ where Ne is the number of prey consumed, No is the initial prey number available, and P 0, P 1, P 2, and P 3 are the intercept, linear, quadratic and cubic coefficients, respectively estimated using the method of maximum likelihood. Estimates of the parameters Po top 3 were obtained by applying logistic regression. A linear term (P 1 ) that was not significantly different from 0 indicates a Type I functional response, while a significant negative value indicates a Type II response and a significant positive value indicates a Type III response. Each term was different from zero if its confidence interval did not include zero. Once the functional response type was determined, iterative non-linear least squares regression was used to fit the disc equation (Holling, 1959) after the transformation described by Livdahl and Stiven (1983). This transformation removes the statistical problems related to the transformation of Royama (1971) and Rogers (1972) and increases the explanation degree of the independent variable in the regression. This equation is: 1 ¼ 1 N a atno þ T h T where N a is the number of prey attacked, N is the initial prey density and T is the time (24 h) that the predator was free to forage. The parameter a is the attack rate and the parameter T h is the time ð1þ ð2þ

3 A.A. Fantinou et al. / Biological Control 47 (2008) required to handle a prey individual. Analyses were conducted with the SPSS statistical package (SPSS Inc., 2004). 3. Results 3.1. Total prey mortality Although partial prey consumption was not observed, killed and unconsumed preys were recorded. Total prey mortality (number of consumed plus non-consumed prey) was generally significantly higher as the temperature and prey density increased. Prey mortality was inversely related to the prey size increase (Table 1). Analyses showed that total prey mortality was significantly affected by the interaction between temperature and prey size (F 6,648 = 5.83, P < 0.001). This was mainly because the ratio of mortality at 30 C relative to that at lower temperatures was much higher for large prey than it was for smaller prey Consumptive mortality and functional response The results of the logistic regression showed that M. pygmaeus exhibited a Holling s Type II functional response for consumptive prey mortality for all of the prey instars at all temperatures (Fig. 1). This is because the linear term of Eq. (1) was negative and significantly different from zero (Table 2). The significant negative linear term along with a non-significant quadratic term offers adequate evidence that the functional responses were Type II (Trexler et al., 1988). The values of the functional response parameters are shown in Table 3. Increased temperature and prey size resulted in higher attack rates, whereas handling time was longer with larger prey Non-consumptive prey mortality The number of unconsumed prey was affected by the temperature and prey density (F 2,648 = 18.95, P < 0.001; F 6,648 = 5.82, P < 0.001, respectively). Larger and a greater number of prey were killed without being consumed at the lowest temperature relative to higher temperatures (Table 4). The interaction between temperature and prey size was significant (F 6,648 = 4.71, P < 0.001). This was attributed to the more frequent non-consumptive mortality occurring with larger prey when compared to smaller prey as the temperature increased (Tables 4 and 5). Non-consumptive mortality was also affected by the interaction between temperature and prey density (F 12,648 = 3.26, P < 0.001). At the lowest temperature, the numbers of non-consumed items were similar among the various prey densities. At the middle temperature range, they were lower than at the low densities. At high temperatures, the numbers of non-consumed prey were relatively low, but there was a remarkable peak at the density of 24 individuals (Tables 4 and 5). The interaction between density and prey size (F 18,648 = 2.55, P < 0.001) clearly indicates that non-consumptive prey mortality was affected by prey density (Tables 4 and 5). The mortality of the 1st and 2nd instars was very similar throughout the various prey densities. Interestingly, at densities of 16 and over, considerably more unconsumed killed prey of the 3rd and 4th instars were found. In particular, a significantly higher number of unconsumed prey was recorded when 4th instar nymphs were offered to the predator at the density of 24 individuals Biomass consumption The average total biomass consumption obtained by each predator in a dish was found to be affected by temperature, prey size and density (Fig. 2). Consumption increased significantly with these factors between densities of 24 and 32 individuals. A significant interaction was recorded between temperature and prey size (F 6,648 = 8.88, P < 0.001). This was mainly attributable to the much higher amount of biomass of the 3rd and 4th instar when consumed at 30 C versus lower temperatures (Fig. 2). Temperature and density (F 12,648 = 2.91, P< 0.001) were also related to biomass consumption since smaller differences among temperatures were found at higher prey densities. The interaction between prey size and density (F 18, 648 = 2.31, P < 0.001) shows that there was an increase in consumed biomass of each instar until a density of 24 was reached, after which, biomass consumption decreased after the 3rd instar (Fig. 2). Table 1 Total prey mortality (mean ± SE) by the predator M. pygmaeus when feeding on all instar nymphs of aphid M. persicae at different densities and temperatures Temperature Prey density Prey instar First Second Third Fourth 20 C ± 0.3 Ad 3.5 ± 0.2 Ae 2.9 ± 0.3 Ad 2.6 ± 0.2 Ae ± 0.3 Acd 4.8 ± 0.5 Ade 4.9 ± 0.4 Acd 4.2 ± 0.5 Ade ± 0.8 ABc 7.6 ± 0.9 ABcd 9.0 ± 0.6 Bbc 4.6 ± 0.7 Ade ± 0.8 Abc 10.7 ± 1.2 ABbc 7.4 ± 0.9 Bbc 7.3 ± 0.9 Bbc ± 0.7 Aab 12.3 ± 0.9 ABab 8.3 ± 1.0 BCbc 6.5 ± 0.9 BCbcd ± 0.7 Aa 15.6 ± 1.4 ABa 14.7 ± 1.2 ABa 11.2 ± 0.8 Ba ± 1.1 Aa 15.7 ± 1.4 Aa 10.6 ± 0.8 Bab 8.1 ± 0.6 Bbc 25 C ± 0.2 Ad 3.4 ± 0.3 Ad 2.4 ± 0.4 Ad 2.6 ± 0.3 Ac ± 0.3 Ac 6.8 ± 0.3 Ac 5.4 ± 0.3 Ac 3.7 ± 0.6 Abc ± 0.4 Abc 9.2 ± 0.5 Abc 7.6 ± 0.7 Ac 6.7 ± 0.4 Aab ± 0.4 Aab 13.8 ± 0.5 Aab 11.6 ± 0.7 Aab 7.0 ± 0.9 Bab ± 0.8 Aa 17.2 ± 0.5 Aa 9.2 ± 0.8 Bb 7.3 ± 0.6 Ba ± 0.6 Aa 16.8 ± 0.7 Aa 13.1 ± 1.1 Ba 8.6 ± 0.7 Ba ± 0.7 Aa 16.7 ± 1.2 Aa 11.6 ± 1.2 Bab 9.4 ± 0.4 Ba 30 C ± 0.0 Ae 3.9 ± 0.1 Ae 4.0 ± 0.0 Ad 3.4 ± 0.2 Ac ± 0.3 Ade 6.7 ± 0.7 Ade 6.9 ± 0.4 Acd 6.3 ± 0.7 Abc ± 0.3 Acd 10.4 ± 0.7 Acd 10.2 ± 0.6 Abc 9.7 ± 0.5 Aab ± 0.9 Abc 13.6 ± 0.5 Abc 11.7 ± 0.5 ABab 11.1 ± 0.6 Ba ± 0.9 Aab 16.5 ± 0.8 Aab 15.8 ± 1.3 ABa 10.9 ± 0.7 Ba ± 0.9 Aa 19.2 ± 1.0 Aa 15.9 ± 1.0 ABa 13.5 ± 1.6 Ba ± 1.1 Aab 16.8 ± 1.1 Aab 11.8 ± 0.8 Bab 10.7 ± 1.0 Ba Values followed by the same lower case letters were not significantly different among prey densities within each temperature. Values followed by the same upper case letters were not significantly different among instars for a given prey density and temperature.

4 190 A.A. Fantinou et al. / Biological Control 47 (2008) Fig. 1. Functional response curves of M. pygmaeus when feeding on all instar nymphs of M. persicae at different temperatures. 4. Discussion 4.1. Consumptive prey mortality Macrolophus pygmaeus showed a Holling s Type II asymptotic curve in all experimental settings (Fig. 1). The Logistic Regression Analysis is appropriate, because it allows discriminating readily between Type II and Type III in determining functional response. However, at relatively low prey densities it is difficult to distinguish between Type II and III functional responses. Although we tested quite low prey densities the experimentation with even lower might be more elucidatory to distinguish between the two types. The Type II of functional response is common among predatory heteropterans (i.e., Foglar et al., 1990; Montserrat et al., 2000). A Type II functional response relies on a constant rate of attack on each prey throughout prey densities. As a result, M. pygmaeus might remain effective at low prey densities. Thus, its phytophagous habits do not seem to prevent prey consumption, at least to a level specified by the response curve. Macrolophus pygmaeus showed a higher predation rate and preference for smaller prey instars (Table 1, Fig. 1). However, when the predator fed on larger prey it gained a higher amount of biomass (Fig. 2). According to the optimal foraging theory, a predator should select prey that maximizes the net rate or the profitability of food intake (the net quantity of food extracted per unit of handling time) (Krebs, 1977). Large prey might be less profitable since it often results in increased handling time and mortality risk (Pastorok, 1981; Sabelis, 1992). On the other hand, smaller prey is consumed by the predator in higher numbers to result in a sufficient amount of biomass consumption. Thus, time limitations may pose an upper threshold on energy gain (see, e.g., Roger et al., 2000). Macrolophus pygmaeus appears to apply this latter predation pattern in cases where the prey consists exclusively of small M. persicae instars. The data from the current study also reveal that the prey mortality exhibited by the predator was not solely determined by the prey identities. At 30 C, a much higher predation rate and an accordingly higher amount of biomass was gained from the larger prey compared to those observed at lower temperatures (Table 1, Figs. 1 and 2). The reasons for this shift in the predator s behaviour when foraging at the higher temperature may be related to increased metabolic rates and behavioural changes of the predator

5 A.A. Fantinou et al. / Biological Control 47 (2008) Table 2 Maximum likelihood estimates from logistic regression of the proportion of Myzus persicae instars eaten by Macrolophus pygmaeus as a function of initial prey density Temperature Instar Parameter Estimate SE 20 C First Intercept Linear Second Intercept Linear Quadratic Cubic Third Intercept Linear Fourth Intercept Linear Quadratic Intercept C First Intercept Linear Quadratic Second Intercept Linear Third Intercept Linear Fourth Intercept Linear C First Intercept Linear Second Intercept Linear Third Intercept Linear Fourth Intercept Linear Table 4 Non consumptive prey mortality (mean ± SE) of the predator M. pygmaeus when feeding on all instar nymphs of aphid M. persicae at different densities and temperatures Temperature Prey density Prey instar First Second Third Fourth 20 C ± 0.1 Aa 0.3 ± 0.2 Aa 0.2 ± 0.2 Aa 0.2 ± 0.1 Aa ± 0.1 Aa 0.3 ± 0.2 Aa 0.6 ± 0.2 Aa 1.4 ± 0.3 Ba ± 0.2 Aa 0.5 ± 0.3 Aa 0.4 ± 0.2 Aa 0.7 ± 0.1 Aa ± 0.0 Aa 0.5 ± 0.3 Aa 0.4 ± 0.2 Aa 0.6 ± 0.1 Aa ± 0.0 Aa 0.3 ± 0.2 Aa 0.9 ± 0.3 Aa 0.9 ± 0.5 Aa ± 0.0 Aa 0.1 ± 0.1 Aa 0.2 ± 0.1 Aa 1.0 ± 0.2 Aa ± 0.0 Aa 0.4 ± 0.2 Aa 0.5 ± 0.2 Aa 0.4 ± 0.2 Aa 25 C ± 0.0 Aa 0.0 ± 0.0 Aa 0.0 ± 0.0 Aa 0.0 ± 0.0 Ac ± 0.0 Aa 0.0 ± 0.0 Aa 0.3 ± 0.1 Aa 0.1 ± 0.1 Ac ± 0.0 Aa 0.0 ± 0.0 Aa 0.9 ± 0.3 Aa 0.4 ± 0.1 Abc ± 0.1 Aa 0.0 ± 0.0 Aa 0.7 ± 0.2 ABa 1.3 ± 0.4 Bab ± 0.0 Aa 0.1 ± 0.1 Aa 1.0 ± 0.3 ABa 1.5 ± 0.3 Ba ± 0.0 Aa 0.2 ± 0.1 Aa 0.9 ± 0.4 ABa 1.4 ± 0.3 Bab ± 0.0 Aa 0.0 ± 0.0 Aa 0.6 ± 0.3 Aa 0.7 ± 0.2 Aabc 30 C ± 0.0 Aa 0.0 ± 0.0 Aa 0.1 ± 0.1 Aa 0.1 ± 0.1 Aa ± 0.0 Aa 0.2 ± 0.1 Aa 0.1 ± 0.1 Aa 0.1 ± 0.1 Aa ± 0.0 Aa 0.0 ± 0.0 Aa 0.0 ± 0.0 Aa 0.0 ± 0.0 Aa ± 0.0 Aa 0.0 ± 0.0 Aa 0.0 ± 0.0 Aa 0.3 ± 0.3 Aa ± 0.0 Aa 0.0 ± 0.0 Aa 0.3 ± 0.2 Aa 0.7 ± 0.3 Aa ± 0.1 Aa 0.2 ± 0.1 Aa 0.7 ± 0.3 Aa 1.8 ± 0.5 Bb ± 0.0 Aa 0.1 ± 0.1 Aa 0.1 ± 0.1 Aa 0.3 ± 0.2 Aa Values followed by the same lower case letters were not significantly different among prey densities within each temperature. Values followed by the same upper case letters were not significantly different among instars for a given prey density and temperature. Table 3 Mean (±SE) estimates of the attack constant [a(/24 h)] and handling time [T h /24 h] of the predator M. pygmaeus on instar nymphs of the aphid M. persicae at different densities and temperatures Temperature Instar Parameter Estimate SE t-value P 20 C First a <0.01 T h <0.01 Second a <0.01 T h <0.04 Third a <0.01 T h <0.02 Fourth a <0.01 T h < C First a <0.01 T h <0.01 Second a <0.01 T h <0.01 Third a <0.01 T h <0.01 Fourth a <0.01 T h < C First a <0.01 T h <0.01 Second a <0.01 T h <0.01 Third a <0.01 T h <0.01 Fourth a <0.01 T h <0.01 and/or prey, such as more successful predatory search or less effective defence by the prey Non-consumptive prey mortality This work proved that total prey mortality caused by M. pygmaeus was the outcome of consumed and unconsumed prey. Thus, in Table 5 Non-consumptive prey mortality (mean ± SE) of the predator M. pygmaeus when feeding on all instar nymphs of the aphid M. persicae as affected by as affected by the interactions of the factors prey instar, temperature and prey density (a) Instar Temperature 20 C 25 C 30 C First 0.1 ± 0.04 Ac 0.0 ± 0.0 Ab 0.1 ± 0.02 Ab Second 0.3 ± 0.1 Abc 0.1 ± 0.02 Ab 0.1 ± 0.03 Ab Third 0.5 ± 0.1 ABab 0.6 ± 0.1 Ba 0.2 ± 0.06 Aab Fourth 0.7 ± 0.1 Aa 0.8 ± 0.1 Aa 0.5 ± 0.1 Aa (b) Density Temperature 20 C 25 C 30 C ± 0.08 Aa 0.0 ± 0.0 Ab 0.1 ± 0.03 Aa ± 0.1 Aa 0.1 ± 0.05 Ab 0.1 ± 0.06 Aa ± 0.1 Aa 0.3 ± 0.1 Aa 0.0 ± 0.0 Aa ± 0.1 Aa 0.5 ± 0.1 Aa 0.1 ± 0.07 Aa ± 0.1 Aa 0.6 ± 0.1 Aa 0.3 ± 0.1 Aab ± 0.1 Aa 0.6 ± 0.2 Aa 0.7 ± 0.2 Ab ± 0.1 Aa 0.3 ± 0.1 Aa 0.1 ± 0.06 Aa (c) Density Instar First Second Third Fourth ± 0.04 Aa 0.1 ± 0.06 Aa 0.1 ± 0.07 Ab 0.1 ± 0.05 Ad ± 0.03 Aa 0.2 ± 0.07 Aa 0.3 ± 0.09 Ab 0.5 ± 0.1 Abcd ± 0.06 Aa 0.2 ± 0.09 Aa 0.4 ± 0.1 Aab 0.4 ± 0.1 Abcd ± 0.0 Aa 0.2 ± 0.01 ABa 0.4 ± 0.1 ABab 0.7 ± 0.2 Bbc ± 0.0 Ca 0.1 ± 0.1 ACa 0.7 ± 0.2 ABa 1.0 ± 0.2 Bab ± 0.04 Aa 0.2 ± 0.07 Aa 0.6 ± 0.2 Aab 1.4 ± 0.2 Ba ± 0.0 Aa 0.2 ± 0.07 Aa 0.4 ± 0.1 Aab 0.5 ± 0.1 Abcd Values followed by the same lower case letters were not significantly within a column and values followed by the same upper case letters were not significantly different within a row. the applied context, this behaviour would enhance the regulatory effect of M. pygmaeus in biological control.

6 192 A.A. Fantinou et al. / Biological Control 47 (2008) Fig. 2. Biomass (mgr) consumed by M. pygmaeus when feeding on nymphs of M. persicae at different prey densities and temperatures. The main finding was that the predatory behaviour of killing without consumption was plastic depended on biotic and abiotic factors. Prey size proved to be positively related to the frequency of non-consumptive prey mortality (Table 4). This may be attributed to a predator s difficulties in seizing larger prey and an increase in handling time, and to the better defence of larger prey (Table 3). As a result, predators might face difficulties in capturing, extracting or digesting the body fluid of larger less suitable prey. Temperature negatively influenced the frequency of non-consumptive prey mortality (Tables 4, 5a). Lower temperatures were most likely related to predator reduced metabolic and mobility rates, and possibly involved in a decision of abandonment a killed, but for some reason not suitable prey (e.g increased prey handling time, particularly at the larger prey Table 3), more often compared to higher temperatures. This behaviour might also be enhanced when predator forages under a restricted arena such as a petri dish. The increase of prey availability was not correlated with nonconsumptive mortality enhancement. This was in contrast to the prediction that it should increase with prey density (Samu and Biro, 1993; Lang and Gsodl, 2003). Instead, non-consumptive mortality showed three patterns: at both low and the highest densities, non-consumptive mortality remained low, but it increased (significantly at some cases) at intermediate densities, particularly at larger instars (Tables 4, 5b, Fig. 2). The decline in the frequency of this behaviour may have been due to food-limitations at low prey densities, whereas the predator might selectively avoid less profitable prey at higher densities. However, at densities close to saturation, the predator might be confused by the relatively high number of prey, which was not sufficient to support saturation. As a consequence, the predator might be driven to try to feed on more prey. These patterns were consistent and provide further insight into the predictions that a forager should be more selective when more profitable prey is available (Krebs, 1977) or when the more profitable prey exceeds a threshold abundance (Abrams, 1990). However, direct observations by recording predator s behaviour would be essential to provide

7 A.A. Fantinou et al. / Biological Control 47 (2008) information on what factors actually determine the higher frequency of the unconsumed prey at prey densities close to saturation. Therefore, our results indicate that the non-consumptive prey mortality, which is dependent on temperature, was positively associated with a certain range of prey densities, whereas it was more frequent for the larger prey tested. In general, a predator s attempt to feed on more prey than it finally consume could be interpreted as an effort to optimize its profit by early rejection of less profitable prey when exploiting a patch of relatively high prey density. This may be an adaptive behavioural function because natural selection should favour individuals that maximize their net energy intake per unit of feeding time (Pyke et al., 1977). It could be proposed that predatory non-consumptive killing behaviour might add plasticity in feeding decisions. 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