Advertisement

Crickets become behaviourally more stable when raised under higher temperatures

  • Petri T. NiemeläEmail author
  • Peter Philip Niehoff
  • Clelia Gasparini
  • Niels J. Dingemanse
  • Cristina Tuni
Original Article

Abstract

Developmental plasticity “prepares” individuals to environments experienced during adulthood. Labile traits, such as behaviour, vary within and among individuals owing to (i) reversible plasticity and (ii) developmental plasticity and genetic make-up, respectively. Here, we test whether developmental environments affect the expression of both within- and among-individual variation in behaviour. In ectothermic species, low temperatures are associated with a reduced expression of behaviour and can also limit the expression of reversible plasticity and among-individual differentiation, for example, by restricting the expression of additive genetic variation. We focused on exploratory behaviour since activity-related behaviours are assumed to be temperature-dependent in ectotherms. Specifically, low temperatures can restrict the physiological machinery needed for movement. To test our predictions, we raised field crickets, Gryllus bimaculatus, at low versus high temperatures throughout ontogeny and compared behavioural means and variance components (i.e. among- and within-individual variances) across treatments. We also compared the coefficients of variation for each variance component across treatments to assess whether environmental effects on variance were independent of the effects on mean behaviour. As predicted, individuals raised at high temperatures were more explorative and exhibited more among- and within-individual variance compared to individuals raised at low temperatures. Interestingly, individuals raised under high temperatures became more stable in their behaviour when controlling for treatment effects on average behaviour. Our results show experimentally that different developmental temperatures trigger different amounts of behavioural “stability” in exploratory behaviour, thereby suggesting a key role for temperature in affecting the ecological processes associated with exploratory behaviour in ectothermic species.

Significance statement

Individuals differ relative to one another in their mean behavioural expression (i.e. “animal personality”), while, at the same time, individuals change their behaviour from one instance to the next (i.e. reversible plasticity). Expression of both above-mentioned components should strongly depend on environments experienced throughout ontogeny. Temperature represents a key environmental factor in most ectothermic animals as it directly affects the ability to express behaviour. Here, we experimentally test whether temperature experienced throughout ontogeny has permanent effects on the expression of individual differences and reversible plasticity in behaviour later in life. We show that individuals become more stable, i.e. express less reversible plasticity, in risky exploratory behaviour when raised under high temperatures. Our results thus suggest a key role for temperature in affecting the ecological processes associated with risky behaviours in ectothermic species.

Keywords

Behavioural variation Animal personality Individual variation Behavioural plasticity Coefficient of variation Developmental plasticity 

Introduction

The phenotypic expression of labile traits, such as behaviour, is hierarchically structured, varying, for example, among species, among populations, among genotypes and among individuals within populations and within individual among instances (Falconer and Mackay 1996; Lynch and Walsh 1998; Nussey et al. 2007; Dingemanse et al. 2010; Dingemanse and Dochtermann 2013; Westneat et al. 2015). Individual differences in mean behaviour (resulting in “among-individual” variation or “animal personality”) have been a major focus of behavioural ecological research. Reversible plasticity (resulting in “within-individual” variation) also plays a key role in ecological and evolutionary processes (Piersma and Drent 2003; Duckworth 2009; Stamps et al. 2012; Briffa 2013; Westneat et al. 2015). Theory predicts that plasticity has costs and limits that vary as a function of the environment (DeWitt et al. 1998; Auld et al. 2010), and thus, the expression of plasticity should strongly depend on the environment (although empirical testing of this prediction is lagging behind (Auld et al. 2010)). Moreover, permanent effects are expected to result from environmental influences occurring during sensitive developmental stages rather than after maturation (Nylin and Gotthard 1998; West-Eberhardt 2003; Abram et al. 2017). Experimental approaches are thus required to reveal how important the environment experienced throughout the juvenile period is in shaping the magnitude of reversible plasticity in behavioural expression in adulthood, compared to the environment experienced at adulthood. Researchers have been increasingly interested in the role of the environment in shaping the expression of above-mentioned variance components (Stamps et al. 2012; Careau et al. 2014; Niemelä and Dingemanse 2017; Royauté and Dochtermann 2017). Nevertheless, few experiments have tested how the environment experienced during juvenile versus adult life stages affects the expression of both individual differences and reversible plasticity in behaviour in concert.

Temperature represents a key environmental factor in most ectothermic animals as it directly affects the ability to express behaviour, with major consequences for life history and fitness (Nylin and Gotthard 1998; Abram et al. 2017). Changes in temperature from one moment to the next cause associated changes in behaviour (Abram et al. 2017), while exposure to a particular temperature early in life can also permanently affect behaviour expressed later in adulthood (Nylin and Gotthard 1998; Abram et al. 2017). Behaviours related to activity, such as exploration or boldness (Réale et al. 2007), are predicted to be particularly sensitive to environmental temperatures since low temperatures generally restrict the physiological machinery needed for movement (Abram et al. 2017). For example, fruit flies (Drosophila melanogaster) tend not to show flight activity below 15 °C because temperature restricts the mechanical power output of the flight muscles (Lehmann 1999). Moreover, female crickets (Gryllus firmus) show temperature-dependent phonotaxis towards male acoustic signals, while males show temperature-dependent acoustic signalling, due to effects of temperature on neurophysiology (Pires and Hoy 1992a, 1992b). Finally, in honey bees (Apis mellifera), individuals raised at relatively low temperatures are less likely to dance and show less cleaning behaviour, most likely because developmental temperature has effects on hormone metabolism (Becher et al. 2009). Interestingly, low temperatures can also negatively affect the expression of plasticity in behaviours related to activity by limiting the overall amount of activity that individuals can express (sensu Abram et al. 2017). Low temperatures can, finally, also limit the expression of individual differences in behaviour by negatively affecting the expression of additive genetic variation (Hoffmann and Merilä 1999). Such effects on the extent of among-individual differentiation might occur because restrictions on behavioural expression decrease among-family variation (Hoffmann and Merilä 1999).

We used an ectothermic insect, the field cricket Gryllus bimaculatus, to study how temperature (24 °C—low versus 28 °C—high) experienced (i) during development (i.e. nymphal stages) and (ii) adulthood (i.e. after maturation) affects exploratory behaviour. In field crickets, exploratory behaviour is assumed to represent a risky trait because movement is both energetically costly and increases the visibility to predators particularly in cases where animals are out of shelter (i.e. away from their burrow). This may explain why exploratory behaviour negatively affects longevity in this species group (sensu Niemelä et al. 2015, Niemelä et al. Unpublished data). We quantified treatment effects on mean exploratory behaviour, as well as effects on among- and within-individual variance components (Vi and Vr; individual differences and reversible plasticity, respectively). Finally, we compared coefficients of variation (both among individuals, CVi, and within individuals, CVr) across treatments to ascertain that the treatment effects on variance components were distinct from effects on the mean. Reversible plasticity (i.e. Vr) can be expressed because of two mutually non-exclusive reasons: (1) because individuals respond to moment-to-moment changes in environmental conditions or (2) because individuals are more or less stable in their behaviour in stable environments (Stamps et al. 2012; Briffa et al. 2013; Westneat et al. 2015). We focus here on the latter type of reversible plasticity, i.e. stability, by measuring treatment effects on exploratory behaviour in an unchanging environment. We expect the mean and the amount of both among- and within-individual variance in exploratory behaviour to be sensitive to developmental temperature: all are expected to increase in individuals raised under higher temperatures. Since developmental (compared to adult) environments are more likely to permanently affect behaviour later in life (Nylin and Gotthard 1998; Abram et al. 2017), we predict no effects of adult treatments on behaviour.

Methods

Test animals and experimental designs were identical to a study investigating the effect of temperature on sperm traits (Gasparini et al. 2018). Subjects were the F2 generation of a wild-derived laboratory population, collected from a tomato field of approximately 2500 m2 near Capalbio, Italy (42° 42′ 46.7′ N 11° 33′ 99.3′ E) in 2015. Wild-captured and F1 generation individuals were housed at the Ludwig-Maximilians University of Munich (Germany), where they were kept as outbred stock populations under standard conditions at 26 °C (± 0.5), 65% (± 0.5) humidity and 14:10 light:dark photoperiod. They were raised in large tanks (35 × 27 × 20 cm3, approx. 30–40 individuals each) equipped with gravel, egg carton shelters and water vials and were fed ad libitum with dry bird food (Aleckwa Delikat, Germany) and fresh slices of apples.

Experimental treatments

Prior to the start of the experiment, we added several small plastic cups containing moist soil to the stock populations to provide females a substrate for egg laying. Cups were removed after 1 week, and any eggs laid were left to hatch in small tanks equipped with egg carton shelters. Nymphs were randomly selected and assigned to their respective treatments 3 days after they had hatched. At this stage, each cricket was randomly assigned to one of the two groups. In the first group, temperature treatments were given throughout ontogeny (hereafter “developmental treatment”), and in the second group, temperature treatments were given after maturation (hereafter “adult treatment”) (Fig. 1). Addition of the adult treatment allowed us to ensure that the potential effects of temperature on behaviour caused by the developmental treatment were due to temperatures experienced throughout ontogeny not just shortly after maturation.
Fig. 1

The experimental setup. In each of the two developmental treatments, crickets were raised to maturation and maintained after maturation in either 24 °C (LD treatment: low developmental temperature) or 28 °C (HD treatment: high developmental temperature). In the two adult treatments, all crickets were raised to maturation at 26 °C and moved after maturation to either 24 °C (LA treatment: low adult temperature) or to 28 °C (HA treatment: high adult temperature). Individuals from all treatments were tested for exploration three times at 26 °C: 10, 14 and 18 days after maturation. Grey solid line indicates division of juvenile treatments at the age of 3 days after hatching from the egg. The grey dashed line indicates maturation and the division of adult treatments on the day of maturation

We created two developmental treatment groups, deviating ± 2 °C from the temperature in which the cricket population had been maintained for two generations (26 °C): individuals were raised either at 24 °C ± 0.5 (n = 70) or 28 °C ± 0.5 (n = 70), corresponding to low (LD) and high (HD) developmental temperature treatments, respectively (Fig. 1), in 65% humidity and 14:10 light:dark photoperiod. The range of our treatment temperatures is well within the range where activity-related behaviours are typically studied in crickets (Doherty 1985; Hedrick et al. 2002; Lachenicht et al. 2010; Santostefano et al. 2017a), and the temperature difference between treatments is similar to previous field cricket studies (Hedrick et al. 2002; Bégin et al. 2004). Moreover, the range of treatment temperatures is within the natural range of temperature experienced by this species in the wild during the breeding season (17–31 °C; Gasparini et al. 2018). Thus, we believe that the selected temperatures would most likely not induce a novelty (“stress”) effect (Charmantier and Garant 2005); the predicted reduction in average exploration and its variance components in cold temperatures should be due to negative effects of cold temperatures on trait expression in ectotherms (Nylin and Gotthard 1998; Abram et al. 2017). To obtain the desired temperature for each treatment, all animals were placed in a climate room set at 24 °C (temperature of the low treatment). The high (developmental) treatment was obtained by placing tanks on top (but not in direct contact) of 20-W Thermo Mats (Lucky Reptile, Germany). The effectiveness of the temperature treatments were confirmed through a series of temperature measurements within each treatment group (Gasparini et al. 2018).

We created two adult temperature treatment groups: individuals were all raised at 26 °C (i.e. in same conditions as the stock population) but randomly assigned to either low (n = 50, 24 °C) or high (n = 50, 28 °C) temperatures following their final moult to adulthood, corresponding to low (LA) and high (HA) adult temperature treatments, respectively (Fig. 1). Temperature conditions were obtained as for the developmental treatments (described above). All individuals were kept in their respective treatment temperatures until their natural death.

We hence created four treatment groups in total, consisting of individuals experiencing either (1) low or (2) high temperatures throughout ontogeny and individuals experiencing either (3) low or (4) high temperatures only after becoming sexually mature (Fig. 1). For each treatment, crickets were sexed (approximately at their second-third larval instar) and only males were selected for the experiment (sample sizes above) and placed into individual containers (10 × 10 × 9 cm3) in their respective treatment temperatures. Isolation ensured that social experience, which has been shown to be an important factor in the expression of exploratory behaviour in our model species (Santostefano et al. 2017b), do not affect the expression of measured behaviour. Each individual container had a flow through a plastic-netted lid that prevented escape but allowed air circulation. Containers also included an artificial, half-cylindrical shelter (6 × 3.5 × 2 cm3), a petri dish (with a diameter of 3.5 cm) with food and another identical petri dish with water held within a cotton-plugged vial. Food and water were replaced twice a week, and animals were checked every 2–3 days for moult to control for male age during behavioural trials.

Exploration measurements

Exploratory behaviour was assayed following a protocol detailed in Santostefano et al. (2016) between 0800 and 1500 h. All individuals were tested for their exploratory behaviour repeatedly as adults at the same intermediate temperature (26 °C; Fig. 1). In this way, any differences between the treatments can be attributed to permanent treatment effects rather than the environmental condition experienced during the test. Exploratory behaviour was recorded for eight individuals simultaneously using eight arenas located on two shelves (a bottom and upper shelf, with four arenas (10 × 10 cm2) on each shelf; for an illustration of the setup, see Figure 2 in Santostefano et al. 2016). Briefly, at the onset of the exploration test, each individual was moved (inside its own housing container) to a separate climate room (temperature 26 °C). In this room, each cricket was moved (inside its own shelter) from its housing container to one of the eight test arenas. Exploratory behaviour, defined as the distance moved in the arena, was then video recorded for 15 min using a fixed video camera located on top of each of the two shelves allowing automated data recording without disturbing the study subjects. Videos were analysed using EthoVision version 11.0 (Noldus, the Netherlands). This software enables tracking of isolated individuals and extracts the spatial coordinates for each video frame, allowing calculation of the total distance moved (in centimetres) in the novel environment, used as a proxy for “exploratory behaviour” (following Réale et al. 2007; see also Santostefano et al. 2016; Santostefano et al. 2017a; Santostefano et al. 2017b). Immediately after each behavioural trial, focal crickets were weighed to the nearest 0.01 g using a digital scale (KERN, PKT) to obtain a measure of body mass and returned to their housing containers in the allotted treatment within the experimental room. Exploratory behaviour was measured three times per individual: 10, 14 and 18 days after adult eclosion. Our sample sizes were (number of individuals (repeats/individual): high developmental treatment 59 (2.9), low developmental treatment 59 (2.7), high adult treatment 42 (2.8) and low adult treatment 46 (2.6). The sample sizes deviate from the sample sizes at the beginning of the treatments due to natural mortality.

Statistical methods

Estimating differences in mean behaviour

We used general linear mixed effects models to study whether the treatments differed in mean exploratory behaviour. For the model, all the data was pooled and treatment (four treatments: HD, LD, HA, LA; categorical) fitted as a fixed effect. Further, individual identity was fitted as a random effect to control for pseudo-replication. To control for potential spatiotemporal biases in parameter estimates caused by the experimental setup, we also included within-individual test sequence (test sequence of behavioural trials within individual; mean-centred covariate), batch (test batch, consisting of a group of eight individuals tested on the same day; maximum three (juveniles) or maximum four (adults) levels/test day, categorical), shelf (upper or lower shelf in a test rack; two levels, categorical), arena (arena location identity within shelf (1–4); four levels, categorical), date (days since the beginning of the experiment, within-treatment mean-centred covariate) and time of day (minutes since 6 am, mean-centred covariate) as fixed effects. The differences between the two developmental temperature treatments, and the differences between the two adult temperature treatments, were estimated by comparing least squares means (adjusted means) using the lsmeans package (Lenth 2015) in statistical environment R (R core team 2014).

Estimating variance and coefficients of variation: univariate mixed effects models

To quantify whether the effects of temperature treatment on level of reversible plasticity and among-individual differentiation (i.e. within- and among-individual variances, respectively) were distinct from the effects on the mean behaviour, we analysed treatment effects on the coefficient of variation (abbreviated as CV: Houle 1992; Hansen et al. 2011). The coefficient of variation represents a mean-standardised variance (Houle 1992; Hansen et al. 2011), which breaks down any statistical association between mean and variance (Taylor’s law: Taylor 1961). Estimating CV allows us to (1) assess whether treatment effects on variance components were distinct from treatment effects on the mean and (2) estimate the amount of variance per unit trait value.

We used a separate univariate mixed effects model to estimate the among- and within-individual variances and coefficients of among- and within-individual variances for each of the four treatment groups separately. In all cases, random intercepts were fitted for individual identity; this enabled us to partition the phenotypic variance into variance attributable to individual identity and variance attributable to within-individual residual (Dingemanse and Dochtermann 2013). We used the same fixed effect structure as explained above, though treatment was not included (as a different model was fitted for each treatment group). The posterior distribution for coefficient of variation was estimated by dividing the square root of the focal posterior distribution of the focal variance component (i.e. among- and within-individual variances) with the focal posterior distribution of the intercept (i.e. mean trait value) (Houle 1992; Hansen et al. 2011), both derived from the same univariate model (which thus controlled for potentially biasing effects of the experimental setup). We then extracted the modes, with respective credible intervals 95% (Cls), for each focal variance component and coefficient of variation from their respective posterior distributions. Statistical significance was based on 95% credible intervals (Cls): estimates with Cls not overlapping towards zero were interpreted as statistically significant in the frequentist’s sense.

Comparing variance components and coefficients of variation across treatments

To compare variance components and coefficients of variation for each variance component (i.e. between the two developmental temperature treatments, and between the two adult temperature treatments), we used the posterior distributions of each above-mentioned component derived from the univariate models described above. When comparing the variance components, or coefficient of variation across the two focal treatments, the focal posterior distribution of the lower temperature treatment was subtracted from the posterior distribution of the high temperature treatment. The outcome of such procedure, termed here the ∆ posterior distribution, is the posterior distribution of the difference between the two treatments in a focal parameter estimate, which one can use to extract a point estimate (and associated 95% credible interval, 95% CI) for the difference between treatments. As above, ∆-estimates with Cls not overlapping zero were interpreted as statistically significant (Montiglio and Royauté 2014; Royauté et al. 2015; Royauté and Dochtermann 2017).

All analyses assumed a Gaussian error distribution; visual inspection of residuals confirmed that this error distribution was appropriate. All statistical models were fitted using the MCMCglmm R-package, with inverse-gamma priors (Hadfield 2010), in the statistical environment R version 3.1.3 (R core team 2014). We ran 3,300,000 iterations per model, from which we discarded the initial 300,000 (burn-in). Each iteration chain was sampled at an interval of 1000 iterations, which resulted in a low autocorrelation among samples (autocorrelation always < |0.05|).

Data

The data are available from the corresponding author upon request.

Results

Differences in mean exploratory behaviour

Individuals in the high developmental temperature treatment were more exploratory compared to the low developmental temperature treatment (mean (95% CI); HD 448.58 cm/15 min (367.81; 529.36), LD 250.73 cm/15 min (166.30; 335.43)). Individuals in the high adult temperature treatment did not differ in their mean exploratory behaviour compared to individuals in the low adult temperature treatment (mean (95% CI); HA 400.99 cm/15 min (313.70; 488.29), LA 273.39 cm/15 min (187.66; 359.12)). One potential explanation for treatment differences in behaviour is body size as treatment may affect body size and because larger individuals might cover more distance per unit time (Whitman 2009). Treatments did not differ in their body mass (F1,3 = 1.84, P = 0.14), indicating that indirect effects acting through body mass (as indication of body size) did not explain our findings.

Differences in variance components of exploratory behaviour

The high (versus low) developmental temperature treatment resulted in increased among-individual variance in exploratory behaviour while the adult treatment groups did not differ in among-individual variance (Tables 1 and 2). In other words, individuals differed more from each other in average behaviour if they had experienced higher developmental temperatures while such a pattern was absent in the adult temperature treatments.
Table 1

Sources of variation in exploration for (a) developmental treatment and (b) adult treatment; we present fixed (β) and random (σ2) parameters and coefficients of variation (CV), with their 95% credible interval (CI), derived from the univariate mixed effects models

Higher temperatures, both during development and adulthood, resulted in increased within-individual variance (Tables 1 and 2). Thus, repeated observations of the same individual differed more from each other for individuals that had experienced high versus low temperatures, and this effect did not vary with the age at which the treatment was applied.
Table 2

∆-estimates for among- and within-individual variance (σ2i and σ2r, respectively) and coefficient of variation (CVi and CVr, respectively) with 95% credible intervals (CI). Statistically significant differences are expressed in italics style font. ∆-estimates are calculated by subtracting the focal posterior distribution of the lower temperature treatment from the posterior distribution of the high temperature treatment

 

∆ (95% CI)

σ2i (× 103)

  Juvenile treatment

65.16 (28.43, 106.55)

  Adult treatment

42.83 (− 2.88, 96.04)

σ2r (× 103)

  Juvenile treatment

21.14 (5.21, 39.26)

  Adult treatment

37.81 (15.10, 65.11)

CVi

  Juvenile treatment

0.09 (− 0.107, 0.303)

  Adult treatment

0.02 (− 0.262, 0.320)

CVr

  Juvenile treatment

− 0.19 (− 0.303, − 0.082)

  Adult treatment

− 0.01 (− 0.161, 0.144)

Differences in coefficient of variation of exploratory behaviour

Neither developmental nor adult temperature treatment groups differed in coefficient of variation at the among-individual level (CVi) (Table 2). This means that even though the among-individual variance was higher in high temperature developmental treatment (see above), it did not differ more than expected based on the treatment effect on average behaviour.

The coefficient of within-individual variance (CVr) was higher in low, compared to high, developmental temperature treatments but did not differ between adult temperature treatments (Δσ2r in Table 2). In other words, individuals raised in the low developmental temperature treatment showed greater amount of reversible plasticity (of unknown origin) when corrected for treatment effects on average behaviour.

Experimental artefacts

A potential explanation for our finding of treatment effects on CVr is that one treatment was more variable in temperature. In other words, manipulations of average temperature might unintentionally also result in a greater variance in temperature. If such treatment artefacts would exist (and explain effects on CVr), we would expect that the treatment having the largest CVr in exploratory behaviour (i.e. the low temperature treatment) should also have the largest coefficient of within-treatment variance in temperature. To test this, we analysed the temperature data collected from the high and low temperature treatments by using 12-programmed temperature loggers evenly distributed across the treatments (3468 temperature measurements; iButton, Maxim Integrated, USA). We found very small treatment differences in variability in temperature, with treatment differences that were opposite to expectations: the coefficient of within-treatment variance in temperature was slightly larger for the high versus low temperature treatment (CVhigh_r (95% CI) = 0.0093 (0.0089; 0.0096); CVlow_r (95% CI) = 0.0067 (0.0064; 0.0069)). The coefficient of among-temperature logger variation did not differ across temperature treatments (CVhigh_i (95% CI) = 0.059 (0.018; 0.126); CVlow_i (95% CI) = 0.0179 (0.006; 0.039). This implies that the reported results did not represent experimental artefacts.

Discussion

We showed that average levels of exploratory behaviour, as well as amounts of among-individual differences and levels of reversible plasticity, were higher when individuals were raised at high temperatures; these findings matched our expectations. However, levels of mean-corrected reversible plasticity were higher in individuals raised under low temperature while mean-corrected individual differences did not differ across treatments. Thus, treatment effects on level of reversible plasticity and individual differences might partly have been caused by the treatment effects on the mean. To understand the environmental determinants of individual differences in behavioural expression, studies have manipulated environmental factors experienced during ontogeny, such as the amount of resources (Tremmel and Müller 2013; DiRienzo and Montiglio 2016; Han and Dingemanse 2017; Royauté and Dochtermann 2017), or the social environment (McGhee and Travis 2011; Naguib et al. 2011; Niemelä et al. 2012). By contrast, research on the role of the environment experienced during the juvenile period in shaping levels of reversible plasticity has primarily been descriptive and/or has focused on adult individuals’ real-time behavioural expression within the manipulated environment (e.g. Sinn et al. 2008; Dingemanse et al. 2012; Stamps et al. 2012; Biro and Adriaenssens 2013; Briffa et al. 2013, but see Laskowski and Pruitt 2014; Han and Dingemanse 2017). Our study provides the first experimental evidence for developmental temperature affecting the level of reversible plasticity (i.e. behavioural stability) and magnitude of individual differentiation in behaviour later in life.

Ecological implications of treatment effects on the mean, variance and coefficient of variation

Since a high temperature increased average trait values, level of individual differentiation and level of reversible plasticity, developmental temperatures can have far reaching ecological and evolutionary consequences on the evolution of exploratory activity in this ectothermic species. Warmer developmental temperatures caused individuals to be, on average, more explorative, which may potentially affect the exploitation of prey, vulnerability to predation or patterns of migration (e.g. habitat expansion, potential to find high-quality food patches, etc) (Lima and Dill 1990; Lima 1998; Fraser et al. 2001; Chapman et al. 2011). This means that in years with relatively high average temperatures, cricket populations might be more vulnerable to predation or migrate over longer distances. Moreover, developmental temperature can affect population dynamics and the multispecies community structure through its effects on expression of absolute individual differences (Bolnick et al. 2011; Dall et al. 2012; Wolf and Weissing 2012). This is because individual differences in behavioural expression within populations can generate variation in the strength and identity of ecological interactions, spilling over trophic levels (Bolnick et al. 2011; Dall et al. 2012). Nevertheless, since CVi did not differ across treatments, our results also indicate that the ecological implications caused by the effects of developmental temperatures on average behaviour versus level of individual differentiation might not be biologically distinct.

The ability to express reversible plasticity is crucial for species to cope with environmental change (Tuomainen and Candolin 2011; Snell-Rood 2013). For example, the ability to plastically adjust behaviour and seasonal timing of reproduction in response to climate change helps great tits (Parus major) to cope with global environmental change (Vedder et al. 2013). Our results show that high developmental temperatures induce higher absolute amounts of reversible plasticity in behaviour. Assuming that our result also reflects the general ability to respond plastically to other environmental gradients, the ecological impact of environmental change caused by globally increasing temperatures may be buffered by the increased reversible plasticity caused by high developmental temperatures. However, since the mean-corrected level of reversible plasticity was higher in low temperatures (i.e. level of reversible plasticity per unit trait value), our results also indicate that this type of buffering mechanism will come with limitations. Nevertheless, environmental temperatures during development can be seen as a key factor affecting multiple ecological processes in crickets, and possibly in other ectotherms, due to the effects of temperature on many key aspects of the behavioural phenotype.

Mean-corrected reversible plasticity in exploration

Individuals expressed higher levels of reversible plasticity when raised in low temperatures when correcting for effects on the mean. Even though crickets were measured for their exploratory behaviour in a seemingly stable, unchanging environment, reversible plasticity in behaviour might have been expressed towards unmeasured environmental factors  that temporally changed across repeated observations. However, such variation in any unmeasured environmental variable should not cause our treatment effects on reversible plasticity since all the unmeasured spatial or temporal variation in the experimental setup was statistically controlled for (i.e. time of day, date, batch, sequence, test arena, shelf). Thus, we conclude that our estimate of reversible plasticity represents stability/predictability in behavioural expression (Stamps et al. 2012; Briffa 2013; Cleasby et al. 2015; Westneat et al. 2015). In this case, crickets are most likely not responding to any external environmental cue, but individuals are simply less stable (per unit of a trait) across observations when raised in low temperatures. One potential explanation for our results is that crickets are responding more to moment-to-moment variation in intrinsic “states” in low temperature developmental treatment (sensu Dammhahn et al. 2018), causing higher mean-corrected reversible plasticity. Indeed, moment-to-moment changes in “intrinsic” states are correlated with moment-to-moment changes in behavioural expression (Niemelä and Dingemanse 2018). Nevertheless, trait expression after maturation is extremely sensitive to developmental temperatures in invertebrates (Nylin and Gotthard 1998; Abram et al. 2017). Thus, differences in reversible plasticity across treatments might be caused by developmental restrictions on the proximate mechanisms allowing the expression of reversible plasticity. Indeed, individuals reached adulthood faster in high (versus low) temperatures (Gasparini et al. 2018), which may set limits to the development of the physiological machinery enhancing the expression of reversible plasticity in behaviour. However, further research is needed to verify this explanation.

Mean-corrected individual differences in exploration

The amount of individual differentiation in exploratory behaviour was higher in the treatment where crickets were raised in high temperature. This effect was absent in the adult treatments. However, the observed individual differences in exploratory behaviour across developmental treatments disappeared when individual differences were controlled for effects on the mean trait value. This indicates the presence of a mean-variance relationship in the expression of individual differences in the measured behaviour (Taylor 1961). Thus, the same biological mechanisms might be underpinning both the mean trait expression and the expression of individual differences. Since this (potentially biological) linkage between the two, any treatment effects on individual differences in behavioural expression (e.g. “personality”) are best studied in the future by reporting also the treatment effects on the coefficient of among-individual variation, i.e. the mean-standardised individual variance.

Treatment effects on mean exploration

Research focusing on animal personality, i.e. repeatable individual differences in behaviour, has mainly focused on comparing mean behavioural differences across environmental treatments/groups (Table 3). Differences in mean behaviour between treatments can provide important insights into the potential mechanisms generating individual differences in behaviour within populations. Indeed, spatial or temporal differences in experience between individuals have been suggested as one of the main mechanisms generating individual differences in behavioural expression (Stamps and Groothuis 2010). Our finding that  treatments differed in their mean exploratory behaviour, shows that differences in temperature experienced throughout ontogeny can act as a mechanism that generates individual differences in behaviour within populations (i.e. animal personality). Spatiotemporal differences in the ecological and environmental factors within a population are the rule rather than the exception in the wild (Pickett and Cadenasso 1995; Campbell and Norman 1998; Bolnick et al. 2011). For example, individuals born in different years or at different times in the season are likely to experience different mean temperatures throughout their ontogeny, generating individual differences in mean trait value (i.e. animal personality) in behavioural expression later in life.
Table 3

Summary of papers studying the effects of developmental plasticity on individual level behavioural expression after (or near before) maturation. Here, we provide study identity, species, treatment and the reported estimate: mean trait value (M), phenotypic, among- or within-individual variance (VpVi, Vr, respectively), individual repeatability (Ri) or phenotypic and individual or residual level correlation (CORp,i,r, respectively). Studies were extracted from the Web of Science (June 2017) by using search terms (“animal personality” AND “development”) + (“animal personality” AND “ontogeny”) + (“behavio* syndrome” AND “development”) + (“behavio* syndrome” AND “ontogeny”)

Study

Species

Treatment

Parameter estimate

Butler et al. (2012)

Mallard (Anas platyrhynchos)

Immune challenge

M

Guenther et al. (2014)

Brazilian guinea pig (Cavia aperea)

Photoperiod

M

Gracceva et al. (2011)

Brown rat (Rattus norvegicus)

Sex ratio

M

Naguib et al. (2011)

Great tit (Parus major)

Social

M

DiRienzo et al. (2012)

Western trilling cricket (Gryllus integer)

Exposure to conspecific song

M

Liedtke et al. (2015)

Jumping spider (Marpissa muscosa)

Environmental enrichment + Social

M

Ruuskanen and Laaksonen (2010)

Pied flycatcher (Ficedula hypoleuca)

Hormone treatment

M

McGhee and Travis (2011)

Bluefin killifish (Lucania goodei)

Food + social

M

Tremmel and Müller (2013)

Mustard leaf beetle (Phaedon cochleariae)

Food

M

Niemelä et al. (2012)

Western trilling cricket (Gryllus integer)

Population density

M, CORp

Bengston et al. (2014)

Basal tarantula (Brachypelma smithi)

Environmental enrichment

M, CORp

D’Amore et al. (2015)

Swordtail fish (Xiphophorus multilineatus)

Food of mothers + social

M, CORp

DiRienzo et al. (2015)

Western trilling cricket (Gryllus integer)

Bacterial exposure

M, Ri

DiRienzo and Montiglio (2016)

Western black widow spider (Latrodectus hesperus)

Food

M, Ri

Urszán et al. (2015)

The agile frog (Rana dalmatina)

Social and predation

M, Ri, CORi,p

Royauté and Dochtermann (2017)

House cricket (Acheta domesticus)

Food

M, Vi,r, Ri, CORi,r

Careau et al. (2014)

Zebra finch (Taeniopygia guttata)

Food

M, Vi,r,p, Ri

Usage of the coefficient of variation in behavioural ecology

To increase biological interpretability in research focusing on the individual differences or reversible plasticity in any labile trait, we recommend (i) to refrain from using simple trait group mean or repeatability estimates when comparing treatments or groups and (ii) to report the actual variance components and mean-standardised variance components, i.e. the coefficient of variation. In this way, the biology behind the simple group mean expression in behaviour or its repeatability, mainly reported in the literature in the context of this study (Table 3), becomes more interpretable. To date, we know of no empirical studies focusing on individual differences in behaviour (a.k.a. animal personality) that report coefficient of variation (search from the Web of Science (28 March 2019) by using search term "animal personality” AND “coefficient of variation”). The coefficient of variation is not a flawless estimate given that the scaling with the mean trait value is meaningful only for data measured on a ratio or log-interval scale (Hansen et al. 2011). Fortunately, behavioural traits are often measured in these scales: e.g. continuous time or distance variables with meaningful zero-points. We, nevertheless, recommend quantifying behaviour (or any labile trait) in a scale that allows the estimation of above-mentioned parameters (Hansen et al. 2011; Houle et al. 2011).

Conclusions

Our study implies that developmental temperature has multiple effects on the expression of behaviour, as it affects both mean behaviour and its underlying variance components. We therefore encourage researchers to use appropriate comprehensive statistical and methodological approaches when aiming at understanding how developmental environments “prepare” animals to their future environments.

Notes

Acknowledgements

We thank two anonymous referees for constructive comments for the manuscript.

Funding information

P.T.N. was funded by Deutsche Forschungsgemeinschaft (DFG grant no. NI 1539/1-1); CT was funded by the BioNa Junior Scientist Award of the Ludwig-Maximilians University; C.G. was funded by a Research Collaboration Award from UWA and an ARC DECRA (DE150101625) fellowship; and P.N. and N.J.D were funded by the Ludwig-Maximilians University.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no competing interests.

References

  1. Abram PK, Boivin G, Moiroux J, Brodeur J (2017) Behavioural effects of temperature on ectothermic animals: unifying thermal physiology and behavioural plasticity. Biol Rev 92:1859–1876.  https://doi.org/10.1111/brv.12312 CrossRefPubMedGoogle Scholar
  2. Auld JR, Agrawal AA, Relyea RA (2010) Re-evaluating the costs and limits of adaptive phenotypic plasticity. Proc R Soc B Biol Sci 277:503–511.  https://doi.org/10.1098/rspb.2009.1355 CrossRefGoogle Scholar
  3. Becher MA, Scharpenberg H, Moritz RFA (2009) Pupal developmental temperature and behavioral specialization of honeybee workers (Apis mellifera L.). J Comp Physiol A 195:673–679.  https://doi.org/10.1007/s00359-009-0442-7 CrossRefGoogle Scholar
  4. Bégin M, Roff DA, Debat V (2004) The effect of temperature and wing morphology on quantitative genetic variation in the cricket Gryllus firmus, with an appendix examining the statistical properties of the Jackknife-MANOVA method of matrix comparison. J Evol Biol 17:1255–1267.  https://doi.org/10.1111/j.1420-9101.2004.00772.x CrossRefPubMedGoogle Scholar
  5. Bengston SE, Pruitt JN, Riechert SE (2014) Differences in environmental enrichment generate contrasting behavioural syndromes in a basal spider lineage. Anim Behav 93:105–110.  https://doi.org/10.1016/j.anbehav.2014.04.022 CrossRefGoogle Scholar
  6. Biro PA, Adriaenssens B (2013) Predictability as a personality trait: consistent differences in intraindividual behavioral variation. Am Nat 182:621–629.  https://doi.org/10.1086/673213 CrossRefPubMedGoogle Scholar
  7. Bolnick DI, Amarasekare P, Araújo MS, Bürger R, Levine JM, Novak M, Rudolf VHW, Schreiber SJ, Urban MC, Vasseur DA (2011) Why intraspecific trait variation matters in community ecology. Trends Ecol Evol 26:183–192CrossRefGoogle Scholar
  8. Briffa M (2013) Plastic proteans: reduced predictability in the face of predation risk in hermit crabs. Biol Lett 9:20130592–20130592.  https://doi.org/10.1098/rsbl.2013.0592 CrossRefPubMedPubMedCentralGoogle Scholar
  9. Briffa M, Bridger D, Biro PA (2013) How does temperature affect behaviour? Multilevel analysis of plasticity, personality and predictability in hermit crabs. Anim Behav 86:47–54CrossRefGoogle Scholar
  10. Butler MW, Toomey MB, McGraw KJ, Rowe M (2012) Ontogenetic immune challenges shape adult personality in mallard ducks. Proc R Soc B Biol Sci 279:326–333.  https://doi.org/10.1098/rspb.2011.0842 CrossRefGoogle Scholar
  11. Campbell GS, Norman JM (1998) An introduction to environmental biophysics. Springer, New YorkCrossRefGoogle Scholar
  12. Careau V, Buttemer WA, Buchanan KL (2014) Early-developmental stress, repeatability, and canalization in a suite of physiological and behavioral traits in female zebra finches. Integr Comp Biol 54:539–554.  https://doi.org/10.1093/icb/icu095 CrossRefPubMedGoogle Scholar
  13. Chapman BB, Hulthén K, Blomqvist DR, Hansson LA, Nilsson JÅ, Brodersen J, Anders Nilsson P, Skov C, Brönmark C (2011) To boldly go: individual differences in boldness influence migratory tendency. Ecol Lett 14:871–876.  https://doi.org/10.1111/j.1461-0248.2011.01648.x CrossRefPubMedGoogle Scholar
  14. Charmantier A, Garant D (2005) Environmental quality and evolutionary potential: lessons from wild populations. Proc R Soc B Biol Sci 272:1415–1425.  https://doi.org/10.1098/rspb.2005.3117 CrossRefGoogle Scholar
  15. Cleasby IR, Nakagawa S, Schielzeth H (2015) Quantifying the predictability of behaviour: statistical approaches for the study of between-individual variation in the within-individual variance. Methods Ecol Evol 6:27–37.  https://doi.org/10.1111/2041-210X.12281 CrossRefGoogle Scholar
  16. core team R (2014) R: a language and environment for statistical computing. R Found Stat Comput, ViennaGoogle Scholar
  17. D’Amore DM, Rios-Cardenas O, Morris MR (2015) Maternal investment influences development of behavioural syndrome in swordtail fish, Xiphophorus multilineatus. Anim Behav 103:147–151.  https://doi.org/10.1016/j.anbehav.2015.02.013 CrossRefGoogle Scholar
  18. Dall SRX, Bell AM, Bolnick DI, Ratnieks FLW (2012) An evolutionary ecology of individual differences. Ecol Lett 15:1189–1198.  https://doi.org/10.1111/j.1461-0248.2012.01846.x CrossRefPubMedPubMedCentralGoogle Scholar
  19. Dammhahn M, Dingemanse NJ, Niemelä PT, Réale D (2018) Pace-of-life syndromes: a framework for the adaptive integration of behaviour, physiology and life history. Behav Ecol Sociobiol 72:62.  https://doi.org/10.1007/s00265-018-2473-y CrossRefGoogle Scholar
  20. DeWitt TJ, Sih A, Wilson DS (1998) Costs and limits of phenotypic plasticity. Trends Ecol Evol 13:77–81.  https://doi.org/10.1016/S0169-5347(97)01274-3 CrossRefPubMedGoogle Scholar
  21. Dingemanse NJ, Dochtermann NA (2013) Quantifying individual variation in behaviour: mixed-effect modelling approaches. J Anim Ecol 82:39–54.  https://doi.org/10.1111/1365-2656.12013 CrossRefPubMedGoogle Scholar
  22. Dingemanse NJ, Bouwman KM, van de Pol M, van Overveld T, Patrick SC, Matthysen E, Quinn JL (2012) Variation in personality and behavioural plasticity across four populations of the great tit Parus major. J Anim Ecol 81:116–126.  https://doi.org/10.1111/j.1365-2656.2011.01877.x CrossRefPubMedGoogle Scholar
  23. DiRienzo N, Montiglio PO (2016) The contribution of developmental experience vs. condition to life history, trait variation and individual differences. J Anim Ecol 85:915–926CrossRefGoogle Scholar
  24. DiRienzo N, Pruitt JN, Hedrick AV (2012) Juvenile exposure to acoustic sexual signals from conspecifics alters growth trajectory and an adult personality trait. Anim Behav 84:861–868.  https://doi.org/10.1016/j.anbehav.2012.07.007 CrossRefGoogle Scholar
  25. DiRienzo N, Niemelä PT, Skog A et al (2015) Juvenile pathogen exposure affects the presence of personality in adult field crickets. Front Ecol Evol 3.  https://doi.org/10.3389/fevo.2015.00036
  26. Doherty J (1985) Temperature coupling and “trade-off” phenomena in the acoustic communication system of the cricket, Gryllus bimaculatus de Geer (Gryllidae). J Exp Biol 114:17–35Google Scholar
  27. Duckworth RA (2009) The role of behavior in evolution: a search for mechanism. Evol Ecol 23:513–531.  https://doi.org/10.1007/s10682-008-9252-6 CrossRefGoogle Scholar
  28. Falconer DS, Mackay TFC (1996) Introduction to quantitative genetics, Ed 4. Longmans Green, Harlow, Essex, UKGoogle Scholar
  29. Fraser DF, Gilliam JF, Daley MJ, le AN, Skalski GT (2001) Explaining leptokurtic movement distributions: intrapopulation variation in boldness and exploration. Am Nat 158:124–135.  https://doi.org/10.1086/321307 CrossRefPubMedGoogle Scholar
  30. Gasparini G, Lu C, Dingemanse NJ, Tuni C (2018) Paternal-effects in a terrestrial ectotherm are temperature dependent but no evidence for adaptive effects. Funct Ecol 32:1011-1021.CrossRefGoogle Scholar
  31. Gracceva G, Koolhaas JM, Groothuis TGG (2011) Does the early social environment affect structure and consistency of personality in wild-type male’s rat? Dev Psychobiol 53:614–623.  https://doi.org/10.1002/dev.20586 CrossRefPubMedGoogle Scholar
  32. Guenther A, Finkemeier MA, Trillmich F (2014) The ontogeny of personality in the wild guinea pig. Anim Behav 90:131–139.  https://doi.org/10.1016/j.anbehav.2014.01.032 CrossRefGoogle Scholar
  33. Hadfield JD (2010) MCMC methods for multi-response generalized linear mixed models: the MCMCglmm R package. J Stat Softw 33:1–22.  https://doi.org/10.1002/ana.22635 CrossRefGoogle Scholar
  34. Han CS, Dingemanse NJ (2017) You are what you eat: diet shapes body composition, personality and behavioural stability. BMC Evol Biol 17:8.  https://doi.org/10.1186/s12862-016-0852-4 CrossRefPubMedPubMedCentralGoogle Scholar
  35. Hansen TF, Pélabon C, Houle D (2011) Heritability is not evolvability. Evol Biol 38:258–277.  https://doi.org/10.1007/s11692-011-9127-6 CrossRefGoogle Scholar
  36. Hedrick AV, Perez D, Lichti N, Yew J (2002) Temperature preferences of male field crickets (Gryllus integer) alter their mating calls. J Comp Physiol A 188:799–805.  https://doi.org/10.1007/s00359-002-0368-9 CrossRefGoogle Scholar
  37. Hoffmann AA, Merilä J (1999) Heritable variation and evolution under favourable and unfavourable conditions. Trends Ecol Evol 14:96–101.  https://doi.org/10.1016/S0169-5347(99)01595-5 CrossRefPubMedGoogle Scholar
  38. Houle D (1992) Comparing evolvability and variability of quantitative traits. Genetics 130:195–204 doi: citeulike-article-id:10041224 PubMedPubMedCentralGoogle Scholar
  39. Houle D, Pélabon C, Wagner GP, Hansen TF (2011) Measurement and meaning in biology. Q Rev Biol 86:3–34.  https://doi.org/10.1086/658408 CrossRefPubMedGoogle Scholar
  40. Lachenicht MW, Clusella-Trullas S, Boardman L, le Roux C, Terblanche JS (2010) Effects of acclimation temperature on thermal tolerance, locomotion performance and respiratory metabolism in Acheta domesticus L. (Orthoptera: Gryllidae). J Insect Physiol 56:822–830.  https://doi.org/10.1016/j.jinsphys.2010.02.010 CrossRefPubMedGoogle Scholar
  41. Laskowski KL, Pruitt JN (2014) Evidence of social niche construction: persistent and repeated social interactions generate stronger personalities in a social spider. Proc R Soc B Biol Sci 281:20133166–20133166.  https://doi.org/10.1098/rspb.2013.3166 CrossRefGoogle Scholar
  42. Lehmann FO (1999) Ambient temperature affects free-flight performance in the fruit fly Drosophila melanogaster. J Comp Physiol B 169:165–171.  https://doi.org/10.1007/s003600050207 CrossRefPubMedGoogle Scholar
  43. Lenth R (2016) lsmeans: least-squares means. R-package version 220-23. J. Stat. Software 69:1-33. doi: http://CRAN.R-project.org/package=lsmeans
  44. Liedtke J, Redekop D, Schneider JM, Schuett W (2015) Early environmental conditions shape personality types in a jumping spider. Front Ecol Evol 3.  https://doi.org/10.3389/fevo.2015.00134
  45. Lima SL (1998) Nonlethal effects in the ecology of predator-prey interactions. Bioscience 48:25–34.  https://doi.org/10.2307/1313225 CrossRefGoogle Scholar
  46. Lima SL, Dill LM (1990) Behavioral decisions made under the risk of predation: a review and prospectus. Can J Zool 68:619–640.  https://doi.org/10.1139/z90-092 CrossRefGoogle Scholar
  47. Lynch M, Walsh B (1998) Genetics and analysis of quantitative traits. Sinauer, Sunderland, MAGoogle Scholar
  48. McGhee KE, Travis J (2011) Early food and social environment affect certain behaviours but not female choice or male dominance in bluefin killifish. Anim Behav 82:139–147.  https://doi.org/10.1016/j.anbehav.2011.04.009 CrossRefGoogle Scholar
  49. Montiglio PO, Royauté R (2014) Contaminants as a neglected source of behavioural variation. Anim Behav 88:29–25.  https://doi.org/10.1016/j.anbehav.2013.11.018 CrossRefGoogle Scholar
  50. Naguib M, Flörcke C, Van Oers K (2011) Effects of social conditions during early development on stress response and personality traits in great tits (Parus major). Dev Psychobiol 53:592–600.  https://doi.org/10.1002/dev.20533 CrossRefPubMedGoogle Scholar
  51. Niemelä PT, Dingemanse NJ (2017) Individual versus pseudo-repeatability in behaviour: lessons from translocation experiments in a wild insect. J Anim Ecol 86:1033–1043.  https://doi.org/10.1111/1365-2656.12688 CrossRefPubMedGoogle Scholar
  52. Niemelä PT, Dingemanse NJ (2018) Meta-analysis reveals weak associations between intrinsic state and personality. Proc R Soc B Biol Sci 285:20172823.  https://doi.org/10.1098/rspb.2017.2823 CrossRefGoogle Scholar
  53. Niemelä PT, Vainikka A, Lahdenperä S, Kortet R (2012) Nymphal density, behavioral development, and life history in a field cricket. Behav Ecol Sociobiol 66:645–652.  https://doi.org/10.1007/s00265-011-1312-1 CrossRefGoogle Scholar
  54. Niemelä PT, Lattenkamp EZ, Dingemanse NJ (2015) Personality-related survival and sampling bias in wild cricket nymphs. Behav Ecol 26:936–946.  https://doi.org/10.1093/beheco/arv036 CrossRefGoogle Scholar
  55. Nussey DH, Wilson AJ, Brommer JE (2007) The evolutionary ecology of individual phenotypic plasticity in wild populations. J Evol Biol 20:831–844.  https://doi.org/10.1111/j.1420-9101.2007.01300.x CrossRefPubMedGoogle Scholar
  56. Nylin S, Gotthard K (1998) Plasticity in life-history traits. Annu Rev Entomol 43:63–83.  https://doi.org/10.1146/annurev.ento.43.1.63 CrossRefPubMedGoogle Scholar
  57. Pickett STA, Cadenasso ML (1995) Landscape ecology: spatial heterogeneity in ecological systems. Science 80(269):331–334.  https://doi.org/10.1126/science.269.5222.331 CrossRefGoogle Scholar
  58. Piersma T, Drent J (2003) Phenotypic flexibility and the evolution of organismal design. Trends Ecol Evol 18:228–233.  https://doi.org/10.1016/S0169-5347(03)00036-3 CrossRefGoogle Scholar
  59. Pires A, Hoy RR (1992a) Temperature coupling in cricket acoustic communication - I. Field and laboratory studies of temperature effects on calling song production and recognition in Gryllus firmus. J Comp Physiol A 171:69–78.  https://doi.org/10.1007/BF00195962 CrossRefPubMedGoogle Scholar
  60. Pires A, Hoy RR (1992b) Temperature coupling in cricket acoustic communication - II. Localization of temperature effects on song production and recognition networks in Gryllus firmus. J Comp Physiol A 171:79–92.  https://doi.org/10.1007/BF00195963 CrossRefPubMedGoogle Scholar
  61. Réale D, Reader SM, Sol D, McDougall PT, Dingemanse NJ (2007) Integrating animal temperament within ecology and evolution. Biol Rev 82:291–318.  https://doi.org/10.1111/j.1469-185X.2007.00010.x CrossRefPubMedGoogle Scholar
  62. Royauté R, Dochtermann NA (2017) When the mean no longer matters: developmental diet affects behavioral variation but not population averages in the house cricket (Acheta domesticus). Behav Ecol 28:337–345.  https://doi.org/10.1093/beheco/arw164 CrossRefGoogle Scholar
  63. Royauté R, Buddle CM, Vincent C (2015) Under the influence: sublethal exposure to an insecticide affects personality expression in a jumping spider. Funct Ecol 29:962–970.  https://doi.org/10.1111/1365-2435.12413 CrossRefGoogle Scholar
  64. Ruuskanen S, Laaksonen T (2010) Yolk hormones have sex-specific long-term effects on behavior in the pied flycatcher (Ficedula hypoleuca). Horm Behav 57:119–127.  https://doi.org/10.1016/j.yhbeh.2009.09.017 CrossRefPubMedGoogle Scholar
  65. Santostefano F, Wilson AJ, Araya-Ajoy YI, Dingemanse NJ (2016) Interacting with the enemy: indirect effects of personality on conspecific aggression in crickets. Behav Ecol 27:1235–1246.  https://doi.org/10.1093/beheco/arw037 CrossRefGoogle Scholar
  66. Santostefano F, Wilson AJ, Niemelä PT, Dingemanse NJ (2017a) Behavioural mediators of genetic life-history trade-offs in field crickets. Proc R Soc B Biol Sci 284:20171567.Google Scholar
  67. Santostefano F, Wilson AJ, Niemelä PT, Dingemanse NJ (2017b) Indirect genetic effects: a key component of the genetic architecture of behaviour. Sci Rep 7:10235.  https://doi.org/10.1038/s41598-017-08258-6 CrossRefPubMedPubMedCentralGoogle Scholar
  68. Sinn DL, Gosling SD, Moltschaniwskyj NA (2008) Development of shy/bold behaviour in squid: context-specific phenotypes associated with developmental plasticity. Anim Behav 75:433–442CrossRefGoogle Scholar
  69. Snell-Rood EC (2013) An overview of the evolutionary causes and consequences of behavioural plasticity. Anim Behav 85:1004–1011.  https://doi.org/10.1016/j.anbehav.2012.12.031 CrossRefGoogle Scholar
  70. Stamps J, Groothuis TGG (2010) The development of animal personality: relevance, concepts and perspectives. Biol Rev 85:301–325.  https://doi.org/10.1111/j.1469-185X.2009.00103.x CrossRefPubMedGoogle Scholar
  71. Stamps JA, Briffa M, Biro PA (2012) Unpredictable animals: individual differences in intraindividual variability (IIV). Anim Behav 83:1325–1334.  https://doi.org/10.1016/j.anbehav.2012.02.017 CrossRefGoogle Scholar
  72. Taylor LR (1961) Aggregation, variance and the mean. Nature 189:732–735.  https://doi.org/10.1038/189732a0 CrossRefGoogle Scholar
  73. Tremmel M, Müller C (2013) Insect personality depends on environmental conditions. Behav Ecol 24:386–392.  https://doi.org/10.1093/beheco/ars175 CrossRefGoogle Scholar
  74. Tuomainen U, Candolin U (2011) Behavioural responses to human-induced environmental change. Biol Rev 86:640–657.  https://doi.org/10.1111/j.1469-185X.2010.00164.x CrossRefPubMedGoogle Scholar
  75. Urszán TJ, Garamszegi LZ, Nagy G, Hettyey A, Török J, Herczeg G (2015) No personality without experience? A test on Rana dalmatina tadpoles. Ecol Evol 5(24):5847–5856.  https://doi.org/10.1002/ece3.1804 CrossRefPubMedPubMedCentralGoogle Scholar
  76. Vedder O, Bouwhuis S, Sheldon BC (2013) Quantitative assessment of the importance of phenotypic plasticity in adaptation to climate change in wild bird populations. PLoS Biol 11.  https://doi.org/10.1371/journal.pbio.1001605 CrossRefGoogle Scholar
  77. West-Eberhardt MJ (2003) Developmental plasticity and evolution. Oxford University Press, New YorkGoogle Scholar
  78. Westneat DF, Wright J, Dingemanse NJ (2015) The biology hidden inside residual within-individual phenotypic variation. Biol Rev 90:729–743.  https://doi.org/10.1111/brv.12131 CrossRefPubMedGoogle Scholar
  79. Whitman DW (2009) The significance of body size in the orthoptera: a review. J. Orthop. Res. 17:117–134.  https://doi.org/10.1665/1082-6467-17.2.117 CrossRefGoogle Scholar
  80. Wolf M, Weissing FJ (2012) Animal personalities: consequences for ecology and evolution. Trends Ecol Evol 27:452–461CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Petri T. Niemelä
    • 1
    Email author
  • Peter Philip Niehoff
    • 1
  • Clelia Gasparini
    • 2
    • 3
  • Niels J. Dingemanse
    • 1
  • Cristina Tuni
    • 1
  1. 1.Behavioural Ecology, Department of Biology IILudwig-Maximilians University of MunichMunichGermany
  2. 2.Centre for Evolutionary Biology, School of Biological SciencesUniversity of Western AustraliaCrawleyAustralia
  3. 3.Department of BiologyUniversity of PadovaPadovaItaly

Personalised recommendations