Integrating behaviour with life history: boldness of the field cricket, Gryllus integer, during ontogeny

Authors

  • Petri T. Niemelä,

    Corresponding author
    1. Department of Biology, University of Oulu, P.O. Box 3000, FI 90014 Oulu, Finland
    2. Department of Biology, University of Eastern Finland, P.O. Box 111, FI 8101 Joensuu, Finland
      Corresponding author. E-mail: petri.niemela@oulu.fi
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  • Anssi Vainikka,

    1. Department of Biology, University of Oulu, P.O. Box 3000, FI 90014 Oulu, Finland
    2. Department of Biology, University of Eastern Finland, P.O. Box 111, FI 8101 Joensuu, Finland
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  • Ann V. Hedrick,

    1. Department of Neurobiology, Physiology & Behavior and Animal Behavior, University of California Davis, Davis, California 95616, USA
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  • Raine Kortet

    1. Department of Biology, University of Eastern Finland, P.O. Box 111, FI 8101 Joensuu, Finland
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Corresponding author. E-mail: petri.niemela@oulu.fi

Summary

1. According to a recent hypothesis, personality traits should form integrative pace-of-life syndromes with life-history traits. Potential life-history traits that explain personality variation are immune defence and growth rate.

2. We studied whether boldness, measured as hiding behaviour, is repeatable during ontogeny in the field cricket, Gryllus integer, and if it relates to the efficiency of immune function (i.e. the capacity to encapsulate a nylon implant), growth rate, developmental time and size as an adult.

3. Hiding behaviour was rank-order repeatable, and in general, juveniles were bolder than adults. Individuals that were cautious at early juvenile stages had higher encapsulation responses late in life compared with bold individuals. Most clearly, fast-growing individuals matured early and invested little in immune defence compared with their slower-growing conspecifics, i.e. showed patterns of a ‘grow fast, die young’ life-history strategy.

4. Our results may arise from a trade-off between immunity-dependent survival and bold behaviour. Trade-offs between investment in survival and behaviour could account for the maintenance of variation in personality traits by favouring certain combinations of behavioural and life-history strategies (i.e. pace-of-life-syndromes).

Introduction

Owing to their direct links to fitness, life-history strategies are among the most intensively studied subjects in evolutionary ecology (Stearns 1992; Roff 2002). In addition to life-history variables such as age at maturity and growth rate, various individually consistent behavioural tendencies directly influence fitness contingent upon the environment confronted. For example, the individual tendency to display antipredator behaviour directly determines an individual’s fitness under varying perceived and realized risks of predation (Sih, Kats & Maurer 2003; McPeek 2004; Lind & Cresswell 2005). Individually consistent behavioural tendencies are often referred to as behavioural syndromes or animal personalities (Réale et al. 2007). Behavioural syndromes are defined as individual behavioural differences which are correlated either across time (e.g. boldness before and after maturation) or between contexts (e.g. aggression in mating and in foraging contexts) (Stamps & Groothuis 2010). Personality differences are found, for example, in mating, exploration, migration, feeding and antipredator behaviours (Sih, Kats & Maurer 2003; Sih, Bell & Johnson 2004; Dingemanse et al. 2007; Réale et al. 2007; Wilson et al. 2010; Vainikka et al. 2011).

According to a recent hypothesis, personality differences may couple with certain life-history characteristics, because certain general combinations of life history and behaviour are superior to others (Wolf et al. 2007; Dingemanse & Wolf 2010; Réale et al. 2010). For example, bold behaviour should yield better fitness when combined with early rather than late maturation, as higher mortality of bold individuals would make them unsuccessful in ever reaching the age required for late maturation. Integrative pace-of-life syndromes (POLS) (hereafter POLS) have been described along a fast-slow life-style continuum (Réale et al. 2010). POLS arise mainly from the general growth-mortality trade-off (Stamps 2007). At the slow end of the continuum, individuals are expected to have long life span, low aggressiveness and boldness, slow metabolism, efficient immune responses and long developmental times compared with individuals in the fast end of the continuum (Réale et al. 2010). In this framework, consistency in behaviours is assumed to arise from the inherent consistency of life-history strategies over an individual’s life span (Stamps 2007). Variation in the POLS is expected to be maintained because different combinations of life history and behavioural variables may yield equal expected life-time fitness (Biro et al. 2006; Réale et al. 2010; Wolf & Weissing 2010). Physiologically, POLSs can be based on genetic or hormonal pleiotropy so that certain genes impact both the expression of life-history traits and behaviour (e.g. Sih, Bell & Johnson 2004; Réale et al. 2007; Stamps & Groothuis 2010).

In addition to predation, parasitism generates significant mortality and other fitness costs (Barber & Dingemanse 2010; Kortet, Hedrick & Vainikka 2010). The strength of immune defence, necessary for fighting parasites and pathogens, is correlated with several life-history variables (e.g. Rolff & Siva-Jothy 2003; Ahtiainen et al. 2004; Rantala & Roff 2005). For example, body size and developmental time are associated with immune defence (Rantala & Roff 2005). Recent arguments also suggest that the efficiency of immune defence should be associated with personality (Barber & Dingemanse 2010; Kortet, Hedrick & Vainikka 2010). As building up and maintaining efficient immune defence is costly (Boots & Begon 1993; Kraaijeveld & Godfray 1997; Owens & Wilson 1999; McKean et al. 2008), bold individuals should be more capable of acquiring energy needed for an efficient immune defence system than cautious individuals (Kortet, Hedrick & Vainikka 2010). However, it is not known how the allocation of energy to immune function changes with pace-of-life, as rapidly developing, bold individuals could trade immunity for fast growth.

There are several factors that affect the payoff of certain behaviours during ontogeny. Sexual maturation is one of the most important life-history transitions (Bernardo 1993; Hodin 2006; Telang, Frame & Brown 2007). Because of the energy requirements of reproduction, sexual maturation causes a decrease or total stop in the growth rate of most organisms (Perrin & Sibly 1993). Additionally, it is likely that environmental changes in predation risk or niche shifts associated with growth and maturation alter an animal’s optimal behaviour (c.f. Sih, Kats & Maurer 2003; McPeek 2004; Cressler, King & Werner 2010). For example, in Gryllus integer and related species, adult males produce conspicuous courting song (Hedrick 2000; Zuk, Rotenberry & Tinghitella 2006) which is predicted to expose adults to more risky situations and induce shifts towards higher cautiousness in adults as compared to juveniles. Therefore, maturation is expected to alter the value of different behaviours, so that mature individuals should be more risk-averse than juveniles (Clark 1994; Dangles et al. 2007).

Here, we use the field cricket G. integer (Fig. 1) to investigate the stability of hiding behaviour in a potentially dangerous environment as a measure of boldness, across ontogeny. We study how boldness relates to life history by measuring four life-history variables: (i) growth rate, (ii) strength of the encapsulation response as a proxy for the strength of immune function, (iii) size as an adult (mass, length and pronotum width) and (iv) maturation time. In addition, we examine whether males and females show divergent patterns. In accordance with the POLS hypothesis (Réale et al. 2010), we predict that fast growing, early maturing individuals should trade-off their survival by being bold and by investing less in immune function than slow-growing, late-maturing individuals. We also predict that adults should be more cautious compared with juveniles because of their conspicuous mating behaviour.

Figure 1.

 Field cricket (Gryllus integer) (Photograph by Petri Niemelä).

Materials and methods

Study Animals

We used crickets from the 4th laboratory generation of a laboratory stock originating from a wild population (Davis, California, USA). Crickets were maintained at the Experimental Unit of University of Oulu, Finland under a constant 12:12 h light–dark cycle, at 27 ± 1 °C with ad libitum food [fish and reindeer pellets (Rehuraisio OY, poron herkku) as well as fresh cabbage] and ad libitum water. Individuals used in the experiments were derived from the bulk laboratory stock (population size more than 2000 individuals) as larvae/nymphs and reared individually in covered plastic containers (length 128 mm × width 98 mm × height 73 mm). For ventilation, there was a hole (3·2 mm in diameter) covered with plastic netting in the lid of the container. The containers were also equipped with a shelter made of cardboard. All individuals were physically, but not acoustically, isolated to ensure virginity.

Behavioural Trials

Methods of behavioural testing to assess the boldness of individual crickets were as described previously in Hedrick (2000) and used thereafter in multiple experiments (e.g. Hedrick & Kortet 2006; Kortet & Hedrick 2007; Kortet, Rantala & Hedrick 2007). Behavioural trials were conducted in a sound-proof, temperature-controlled dark room (27 ± 1 °C), in which the experimental setting was composed of a computer, a desk and three separate experimental arenas (length 188 mm × width 188 mm × height 112 mm). As Gryllus spp. likely cannot see long (red) wavelengths properly (Briscoe & Chittka 2001), and therefore dim red light mimics dark conditions, we used dim red light (25 W red incandescent bulb) to minimize potential disturbance by the observer. Unfamiliar lighting may also have intensified the perceived level of novelty in the environment. Each arena was placed in a polystyrene box having 15 mm thick walls (length 282 mm × width 270 mm × height 207 mm) for acoustic shielding (although males did not sing during the trials).

In behavioural trials, the latency to become active (LTA) (i.e. time when cricket started to move its body inside an opaque tube) and latency to emerge from a shelter (LTE; i.e. time when the cricket’s entire body was out of the tube) (hereafter LTA and LTE, respectively) were recorded for each individual repeatedly. LTA, LTE and body mass were measured for juveniles at the age of 2 weeks (LTA1, LTE1: = 99), 4 weeks (LTA2, LTE2: = 85), 6 weeks (LTA3, LTE3: = 78) and as adults (LTA4, LTE4; 1 week after maturation: (= 58 (26 males, 32 females), average age at maturation = 13·5 weeks). The sample sizes decreased over time because of natural mortality. We did not have data about total life span of the adult life of our crickets, but the maximum was approximately 2 months. Dead individuals were compared with those that survived for every measurement time (2 weeks old, 4 weeks old and 6 weeks old) in all the available variables in order to study whether differential mortality could have a biasing effect in any further analysis. However, the only difference we found between dead and surviving individuals was in body mass, which in turn also caused differences in growth rates at the age of four and 6 weeks (for body mass, T-test: t = 3·015, df = 81, P = 0·003, t = 2·410, df = 75, = 0·018; for growth rate, T-test: t = 2·403, df = 81, = 0·019, t = 3·220, df = 75, P = 0·002, respectively). This indicates that lighter individuals could have been sick or simply did not manage to use the provided food sources properly. Dead individuals were not used in the final analysis.

At the beginning of each trial, the focal cricket was placed in a clean experimental tube directly from the housing box and then transferred immediately to the centre of the experimental arena. After a 2-min acclimation period, during which the tube was situated vertically in the arena, the tube was carefully laid down lengthwise in the arena and a plexiglass cover was set over the top of the arena to cover it, attenuate sounds from outside, and prevent crickets from escaping. Each trial lasted for 600 s, as former experiments had shown that if a cricket does not emerge within 600 s, it often hides for a much longer period (Hedrick & Kortet 2006). All of the crickets achieving maximum time were given a maximum boldness score, 600 s. Proportions of individuals that did not came out were LTA1 and LTE1 = 7·1% and 19·3%; LTA2 and LTE2 = 9·5% and 17·8%; LTA3 and LTE3 = 3·8% and 17·9% and LTA4 and LTE4 = 15·5% and 43·0%. During these trials, the parameters LTA and LTE were recorded using custom time recording software ‘AV Bio-Statistics 4.9’ (http://personal.inet.fi/koti/ansvain/avbs/). We used experimental tubes of three different sizes according to the age of the crickets (2 and 4 weeks old: 19 mm long, 8 mm in diameter; 6 weeks old: 35 mm long, 14 mm in diameter; adults: 77 mm long, 23 mm in diameter, opaque plastic tubes with a small glass base). After the trials, crickets were weighed to the nearest 0·001 g. Length of the individuals was measured from the tip of the forehead to the end of the abdomen to the nearest 0·01 mm. The width of the pronotum was measured from the widest section of the pronotum to the nearest 0·01 mm for adult crickets. All experiments were conducted between 08:00 and 13:00.

The two behavioural variables we studied differ from each other. LTA indicates the duration of freezing (an immobile state which is easily reversed by disturbing the animal), a common antipredator mechanism used by several taxa (Chelini, Willemart & Hebets 2009). After becoming active (starting to move their body), crickets begin to move carefully inside the tube and investigate their surroundings thoroughly. When reaching the open end of the tube, crickets often behave quite cautiously and do not emerge from the tube until they have spent a long time scanning the novel environment. Some crickets may turn back when reaching the open end and do not emerge until several re-scannings of the open end. Therefore, LTE is defined here as measurement of boldness in an exploratory context. To ensure the independence of the behavioural variables, we subtracted the values of LTA from the values of LTE (hereafter LTE). However, if the value for LTE was 600 s (maximum value), we gave that individual a value of 600 s.

Encapsulation Response

Insect immunity is characterized by an inducible expression of a large array of antimicrobial peptides and the constitutive melanization–encapsulation response (e.g. Siva-Jothy, Moret & Rolff 2005; Schulenburg et al. 2009). The encapsulation response against a novel antigen such as a nylon monofilament has been widely used to estimate the strength of immune function in insects (Gillespie, Kanost & Trenczek 1997; Rantala & Roff 2005 and references therein). The ability to encapsulate abiotic material is related to the ability to encapsulate multicellular pathogens such as fungi, nematodes and parasitoids (Gillespie, Kanost & Trenczek 1997). In the encapsulation response, haemocytes aggregate and form a capsule around the novel object or parasite. A cascade of biochemical reactions leads to the deposition of melanin and hardening of the capsule (Gillespie, Kanost & Trenczek 1997).

We placed a 2-mm-long implant made of 0·16 mm fishing line (Stroft GTM, Reinfeld, Germany), knotted at one end, inside a CO2-anesthetized cricket’s abdomen (between the second and third segments). To increase the encapsulation area, the implant line had been roughened throughout with P400 sand paper. The cricket’s immune system was allowed to react to the implant for 24 h. Encapsulation measurements were always performed 1 week after maturation, right after the behavioural tests and weighing. After removal, the implants were frozen at −20 °C until later analysis in which the implants were photographed from three different angles under a light microscope with an attached digital camera (approximately 20× magnification). The pictures were then analysed using the open-source image-j (http://rsbweb.nih.gov/ij/) program for the grey values of reflected light from the implants. Average grey values of the measured implants minus the grey value of a clear implant were used to measure the strength of encapsulation. Thus, high values for the encapsulation response indicate dark implants, i.e. a strong immune response (see e.g. Rantala & Kortet 2004).

Statistical Methods

As the causal relationships between personality and life-history variables are not yet clear, we chose a correlative approach (Réale et al. 2010). Because the stability of individual differences is more important than absolute repeatability of behaviours in a personality perspective (Dingemanse & Wolf 2010; Gyuris et al. 2010), we used Kendall`s coefficient of concordance (W) to measure the rank-order consistency of individual differences in boldness (Gyuris et al. 2010). To reduce the number of correlated variables, we used principal component analysis (PCA). However, we used PCA on behavioural but not on life-history variables, as different life-history traits are likely to have different associations with behaviour, and by using combined variables, it would become difficult to examine which life-history variables are the most important ones in explaining personality variation.

The first PCA was used to combine LTA and LTE, as these were strongly correlated in every measurement time (Pearson’s correlation coefficient: LTA1 and LTE1; = 0·829, N = 57, P < 0·001; LTA2 and LTE2; = 0·807, N = 57, P < 0·001; LTA3 and LTE3; = 0·859, N = 57, P < 0·001; LTA4 and LTE4; = 0·615, N = 57, P < 0·001). The resulting variable, ‘boldness’ was formed by first pooling all measurement occasions (Jolliffe 2002) (Hereafter; boldness1, boldness2, boldness3 and boldness4, respectively). The new variable ‘boldness’ included 90·5% of the total variance in the two original variables. The second PCA was used to create a new variable, ‘size as an adult’ (Jolliffe 2002). Body length and size of the pronotum, which were combined in this PCA were highly correlated in adult crickets (Rp = 0·795, = 55, P < 0·001). The size component (hereafter referred to as size as adult) explained 89·7% of the total variance of the two variables.

Specific growth rate from hatching to adulthood, μ, was calculated as

image(1)

where inline image and inline image are the mass as an adult and directly after hatching (in mg), respectively, and t2 and t1 are maturation time and time after hatching (here 0) in days. We used the same formula to determine the growth rates when comparing dead individuals to the survived ones (see earlier in the methods).

Spearman’s rank correlation analysis was used to study the associations of life history and behaviour (Tabachnick & Fidell 2001). In addition, repeated measures analysis of variance (RM-anova) was used to study differences in behaviour between different measurement times and between sexes so that behaviour in each testing occasion was set as a within-subject variable (altogether four occasions) and sex as a between-subject factor. To study sex differences in variables measured only once we used one-way anova. Behavioural data were Box–Cox transformed to meet the assumptions of normality (Box & Cox 1964). Dependent variables passed Levene`s test of equality of variances in GLM analyses. All tests were conducted using pasw Statistics 18 (SPSS Inc., Chicago, IL, USA) and AV Bio-Statistics (version 4.9.).

Results

Consistency of Boldness

Boldness was rank-order repeatable during ontogeny (Kendall’s = 0·337, = 56, = 0·042), but significantly higher in juvenile groups compared with adult groups (RM-anova, Wilk’s λ = 0·708, F1,55 = 7·258, < 0·001) (Fig. 2). Males and females did not differ in boldness (RM-anova: F1,55 = 0·022, = 0·883).

Figure 2.

 Mean boldness ± standard errors at different measurement times. High values of behavioural variables stand for longer hiding times and therefore lower boldness.

Sex Differences in Life-History Variables and Their Associations with Personality

There were no differences between males and females in the strength of the encapsulation response (one-way anova, F1,44 = 0·845, P = 0·363), maturation time (one-way anova, F1,56 = 0·105, P = 0·747) or growth rate (one-way anova, F1,44 = 0·158, P = 0·693). Only size as an adult was different between the sexes (one-way anova, F1,53 = 31·874, P < 0·001), as males were larger in weight than females (mean weights: 0·814 ± 0·09 g; 0·664 ± 0·115 g, respectively). Early juvenile boldness was positively correlated with the strength of the encapsulation response (Table 1). Because high values of behavioural variables stand for longer hiding times and therefore lower boldness, the strength of encapsulation response and boldness were indeed negatively associated. There was a positive correlation also between the strength of the encapsulation response and maturation time (Table 1). In addition, both maturation time and encapsulation response were negatively correlated with growth rate (Table 1). However, neither growth rate nor maturation time were correlated with boldness (Table 1).

Table 1.   Results of Spearman’s rank correlation coefficient (ρ), P-values and sample sizes (a) between boldness in different measurement times and life-history traits (Sample size was n = 46 in all encapsulation correlations and n = 57 in all growth rate and maturation time correlations) and (b) between adult size and boldness for both sexes
(a)
TraitEncapsulationGrowth rateMaturation time
ρPρPρP
Boldness10·3110·036*0·0950·9200·0090·948
Boldness2−0·0420·7810·0540·6170·0680·617
Boldness30·0640·6750·0480·722−0·160·236
Boldness40·0670·657−0·1500·266−0·0330·807
Encapsulation××××××
Growth rate0·2960·046*××××
MT0·3940·007*0·758<0·001*××
(b)
TraitSexAdult size
ρPn
  1. MT, maturation time.

  2. Boldness1, boldness2, boldness3 and boldness4 refers to boldness after 2, 4, 6 weeks and as adult, respectively.

  3. Significant correlations are marked with asterisks and presented as bold type.

Boldness1−0·1430·52622
0·0750·68432
Boldness20·1960·38122
−0·1370·45532
Boldness30·0460·8422
−0·0630·73132
Boldness40·1000·68522
0·1520·40532
Encapsulation0·3930·09619
0·290·14227
Growth rate0·4040·06222
0·1510·40133
MT−0·2660·23122
−0·1390·43933

Discussion

Our study indicates that individual rank-order differences in boldness are sustained through ontogeny. In addition, the present data demonstrates a negative association between early boldness and the strength of the encapsulation response, but not the expected positive correlation between growth rate and boldness behaviour. Our results provide the very first documented evidence on the association between a juvenile personality trait that is consistent over ontogeny and adult immunocompetence. In general, our results are in line with the POLS hypothesis but do not provide strong support for it (Réale et al. 2010). The concept of POLS is mainly supported by the finding that in accordance with our prediction, early maturing, fast-growing individuals elicited weaker encapsulation responses than late maturing, slow-growing individuals. In addition, our data shows that personality may change considerably at maturation but still remain rank-order repeatable among individuals.

We observed sex differences only in adult size. This is surprising, because measures of immunity, for example, are often reported to differ between the sexes (Kurtz & Sauer 2001; Rolff 2002; McKean & Nunney 2005). Because there were no overall sex differences in the behaviours or in life-history variables, we pooled the sexes for most of the subsequent analyses. By doing this, we lost the ability to examine whether the relationships between personality and life-history variables would differ between the sexes. However, examination of all correlations within sexes separately would have introduced a need to correct analyses for multiple testing and significantly reduced the statistical power of correlation analyses by also reducing sampling sizes. Moreover, there are no theoretical foundations why and how sexes should differ in the composition of their POLS, although some differences might be expected based on Bateman’s principle (Bateman 1948; Rolff 2002). Further, correlations between adult size (the only variable that differed between the sexes) and boldness were not significant in either sex. Sex differences in the configurations of POLS are clearly a subject for future work.

Pace-of-life syndromes are based on growth-mortality trade-offs that can maintain different syndrome structures, which yield equal fitness (Stamps 2007). At a mechanistic level, a fast life style often involves a fast growth rate, early maturation and a short life span, and a slow life style involves a slow growth rate, late maturation and a long life span (Réale et al. 2010). Given that bold and active behaviour increases energy intake rates (see Biro & Stamps 2008; Kortet, Hedrick & Vainikka 2010) and that immune function is condition- and energy-dependent (Kraaijeveld & Godfray 1997; Zuk & Stoehr 2002; Freitak, Ots & Horak 2003; Schmid-Hempel 2005; Houston et al. 2007), the result that bold individuals possessed a poor immune response may seem counter-intuitive. However, bold individuals did not either grow at a faster pace than shy individuals as would have been expected (Stamps 2007). In the literature, both positive and negative associations, or no association at all have been found between growth rate/developmental time and boldness (e.g. Laakkonen & Hirvonen 2007; Biro & Post 2008; Heg, Schurch & Rothenberger 2011), which suggest that growth rate and boldness are not as tightly coupled as suggested (Adriaenssens & Johnsson 2008). Therefore, the association between boldness and immunity as well is likely highly dependent on the environment and resource availability (Koolhaas 2008). Ad libitum food conditions, as used in our study, may have balanced the differences between individuals in their capacity to increase their food-intake rates with behavioural boldness. This might have prevented us from observing a positive association between boldness and growth rate or development time, and consequently between boldness and immunity. In addition, immune functions may also increase in efficiency if individuals trade growth for survival. Therefore, a slow life style should adaptively involve simultaneous high immune defence, slow growth and low boldness, as they all potentially increase survival. Consequently, slow-growing, shy individuals may be predisposed to invest in survival at the cost of growth even in ad libitum food conditions.

Our results may suggest that immunity is surprisingly important in setting behavioural trajectories and that it also drives other associations between life history and behaviour. They also suggest that bold behaviours are not more likely associated with growth than shy behaviours in ad libitum conditions, and that the observable phenotypic correlations may instead reflect underlying genetic correlations (van Oers et al. 2005). If genetic correlations exist, the connections between life-history traits other than those of which are strongly condition dependent might not be easily decoupled by environmental factors, and could remain visible in ad libitum food laboratory conditions such as our experiment. Therefore, we argue that the correlation between early boldness and adult immunity may be a result of a predisposed life-style strategy, rather than the experimental conditions.

Individual rank-order differences in boldness were consistent across ontogeny, indicating that the latencies to recover from freezing and emerge from a shelter can be considered as measures of a temporally consistent personality trait. We had only one behavioural measurement time on the adult stage but based on our unpublished data, boldness is also rank-order repeatable after maturation (P. T. Niemelä, unpublished data). Our results indicate that rank orders in behaviours may be defined at very early stages of individual ontogeny and maintained thereafter through individual life span. Individual differences in behaviour may originate and be maintained because of individual-level variation in early state that is later reinforced via positive or negative feedback loops on between behaviour and individuals state (Luttbeg & Sih 2010).

Mature crickets were significantly more cautious than juveniles. Our results accord with those of Hedrick and Kortet (A. V. Hedrick and R. Kortet, unpublished data) that indicated that juvenile G. integer field crickets derived from wild collected mothers are bolder than adults. Because adults may compensate for conspicuous courting behaviour with longer hiding times (Hedrick 2000), a decrease in boldness at maturation seems adaptive. However, the decrease in boldness with sexual maturation may have other adaptive explanations too. For example, juveniles confront different predation pressures compared with adults because of migration or niche shifts and may depend on different strategies to avoid predators (Sih, Kats & Maurer 2003; McPeek 2004). In the natural habitat of G. integer, several predators including birds, spiders, bats and some small mammalian predators such as mice and rats forage on crickets. Thus, adults, because of their courting song and mating behaviour, may be more vulnerable to predation and parasitoids than juveniles (Hedrick 2000; Kortet & Hedrick 2004; Hedrick & Kortet 2006). Our results may also be explained by asset protection theory (Wolf et al. 2007). According to this theory, reproductive crickets should be more alert than juveniles because they have successfully gained costly resources that they invest in reproduction and can lose by becoming predated (Wolf et al. 2007; but see McElreath et al. 2007).

In conclusion, our results provide the first documented evidence on the association between the early expression of a personality trait and immune defence. Moreover, our data suggest that early-maturing individuals have lower immune defence against multicellular pathogens. These results are well, but not entirely, in line with the POLS approach to the integration of life history and behaviour. We encourage researchers to search for genetic correlations between personality and life-history traits to enable a better understanding of how behavioural traits are related to other traits, and how they evolve.

Acknowledgements

This research has been supported by the Academy of Finland (project 127398) and the National Science Foundation (IOS-0716332). We thank Nick DiRienzo, Arja Kaitala, Eija Hurme, Sami Kivelä Jukka Forsman, Indrikis Krams and two anonymous referees for very helpful comments. We thank also Anne Leonard and Markus Rantala, who helped us to establish the laboratory population used in this study. We would also like to thank the University of Oulu Zoo and its very helpful staff (P.M, J.M and S.I) for assistance in our work.

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