Journal of Animal Ecology 2014

doi: 10.1111/1365-2656.12210

Individual and sex-specific differences in intrinsic growth rate covary with consistent individual differences in behaviour Peter A. Biro1*, Bart Adriaenssens2† and Portia Sampson2 1

Centre for Integrative Ecology, School of Life and Environmental Science, Deakin University, 75 Pigdons Road, Geelong, Vic. 3216, Australia; and 2Evolution and Ecology Research Centre, University of New South Wales, Sydney, NSW 2032, Australia;

Summary 1. The evolutionary causes of consistent individual differences in behaviour are currently a source of debate. A recent hypothesis suggests that consistent individual differences in lifehistory productivity (growth and/or fecundity) may covary with behavioural traits that contribute to growth-mortality trade-offs, such as risk-proneness (boldness) and foraging activity (voraciousness). It remains unclear, however, to what extent individual behavioural and life-history profiles are set early in life, or are a more flexible result of specific environmental or developmental contexts that allow bold and active individuals to acquire more resources. 2. Longitudinal studies of individually housed animals under controlled conditions can shed light on this question. Since growth and behaviour can both vary within individuals (they are labile), studying between-individual correlations in behaviour and growth rate requires repeated scoring for both variables over an extended period of time. However, such a study has not yet been done. 3. Here, we repeatedly measured individual mass seven times each, boldness 40 times each and voracity eight times each during the first 4 months of life on 90 individually housed crayfish (Cherax destructor). Animals were fed ad libitum, generating a context where individuals can express their intrinsic growth rate (i.e. growth capacity), but in which bold and voracious behaviour is not necessary for high resource acquisition (crayfish can and do hoard food back to their burrow). 4. We show that individuals that were consistently bold over time during the day were also bolder at night, were more voracious and maintained higher growth rates over time than shy individuals. Independent of individual differences, we also observed that males were fastergrowing, bolder and more voracious than females. 5. Our findings imply that associations between bold behaviour and fast growth can occur in unlimited food contexts where there is no necessary link between bold behaviour and resource acquisition – offering support for the ‘personality–productivity’ hypothesis. We suggest future research should study links between consistent individual differences in behaviour and life history under a wider range of contexts, in order to shed light on the role of biotic and abiotic conditions in the strength, direction and stability of their covariance. Key-words: activity, boldness, crayfish, intrinsic growth rate, life history, personality, plasticity, temperament, trade-offs Introduction In recent years, numerous hypotheses have been proposed to explain why individuals might consistently differ in *Correspondence author. E-mail: [email protected] †Present address: Institute of Biodiversity, Animal Health and Comparative Medicine, College of Medical, Veterinary and Life Sciences, University of Glasgow, Graham Kerr Building, Glasgow, G12 8QQ, UK

behaviour. Many of these follow from the suggestion that behaviour is linked to individual differences in state (see discussion by Wolf & Weissing 2010). State variables, which may be linked to consistent levels of behaviour, include consistent levels of life-history productivity (growth and/or fecundity (Stamps 2007; Biro & Stamps 2008), metabolic rate (Careau et al. 2008, 2010; Biro & Stamps 2010) and immunocompetence (Barber & Dingemanse 2010; Kortet, Hedrick & Vainikka 2010). In

© 2014 The Authors. Journal of Animal Ecology © 2014 British Ecological Society

2 P. A. Biro, B. Adriaenssens & P. Sampson addition, positive or negative feedback mechanisms between state and behaviour are suggested to stabilize initial behavioural differences between individuals (Dall, Houston & McNamara 2004; Wolf et al. 2007). In particular, consistent individual differences in lifehistory productivity may be associated with consistent behavioural traits, provided that those behaviours contribute to the acquisition of food resources (Stamps 2007; Biro & Stamps 2008). In particular, behavioural traits of interest are those that contribute to growthmortality trade-offs, such as risk-proneness (boldness) and foraging activity (voraciousness). This ‘personalityproductivity’ hypothesis predicts positive correlations between production and behaviour when individuals follow a preferred or intrinsic rate of productivity (Stamps 2007; Biro & Stamps 2008). Motivational processes (e.g. hunger) are thought to play an important role in the association between behaviour and biomass production, particularly when both are linked together via energy metabolism (Careau et al. 2008; Biro & Stamps 2010). For example, earlier hunger sensation by individuals with a high metabolism may lead to higher risk-taking behaviour under food deprivation (Killen, Marras & McKenzie 2011; Quinn et al. 2012). This hypothesis is therefore equally useful for understanding life-history variation, as it is to understand behavioural variation. Indeed, a behavioural perspective has previously been shown to be important for understanding the mechanisms that affect life-history variation at the group level (Werner & Anholt 1993; Mangel & Stamps 2001; Biro, Post & Parkinson 2003; R
eale et al. 2010; Burton et al. 2011). Individual differences in growth rate and/or fecundity have been observed in a wide range of species and occur even when animals are housed individually and fed ad libitum, suggesting that production is an intrinsic individual attribute (see Arendt 1997; Stamps 2007; Biro & Stamps 2008). Such individual intrinsic life-history trajectories are shaped by genetic, maternal and developmental factors (Stamps 2007; Biro & Stamps 2008; Stamps & Groothuis 2010) and may be maintained by trade-offs between the costs and benefits associated with rapid production (Werner & Anholt 1993; Mangel & Stamps 2001). Costs can include, for example, reduced immunocompetence (Soler et al. 2003), increased oxidative damage (AlonsoAlvarez et al. 2007), increased risk of predation (Brodin & Johansson 2004) or risk of injury during competition (MacLean, Metcalfe & Mitchell 2000). However, the role of behaviour in these trade-offs need not be straightforward, because fast growth can be achieved by increasing both physiological efficiency and behavioural output (McPeek 2004; Thaler, McArt & Kaplan 2012). There is nevertheless some support for the idea that individual differences in productivity are linked with personality traits (reviewed by Biro & Stamps 2008). Because many of those reviewed studies were comparisons between strains or selected lines of animals rather than individuals

within a population (e.g. Wirth-Dzieciolowska & Czuminska 2000; Brodin & Johansson 2004; Biro et al. 2006; Mondal, Rajkhowa & Prakash 2006; Walsh et al. 2006; Huff et al. 2007), it was suggested that future tests of this hypothesis should repeatedly measure both production and behaviour over some extended time interval, across a sample of individuals from a single population (Biro & Stamps 2008). Repeated measures are necessary to account for within-individual variation in behaviour and growth that can reduce statistical power and underestimate correlations between labile traits (Adolph & Hardin 2007). Several recent studies estimated correlations between production and behaviour within populations, but have either not found any relationship (Edenbrow & Croft 2011; Heg, Sch€ urch & Rothenberger 2011; Riebli et al. 2011; Niemel€ a et al. 2012; Nyqvist et al. 2012), or have found negative links between boldness and scores of biomass production (Wilson, Godin & Ward 2010; Adriaenssens & Johnsson 2011). These studies did not, however, repeatedly measure production and behaviour together over an extended period of time, precluding a strong test of the productivity–personality hypothesis. In addition, we should not a priori assume consistency in either growth or behaviour for several reasons. First, individual rank-order of growth rates can change over time (e.g. Baras & Lucas 2010), or across some contextual gradient, leading to situations where a single measure of growth would not covary with behaviour. Similarly, rank-order differences in behaviour may also change over time, or across some contextual gradient (reviewed by Mathot et al. 2012). And finally, even in the presence of individual mean-level differences, behaviour is highly labile and most behavioural variation occurs within individuals (Bell, Hankison & Laskowski 2009). Therefore, in order to fully understand links between growth and behaviour, repeated measures of both traits should be taken during an interval where food abundance and growth-related conditions (i.e. temperature) are known (see also Discussion). In this study, we tested the ‘productivity-personality’ hypothesis (Stamps 2007; Biro & Stamps 2008) at the within- and between-individual level by repeatedly measuring the growth, boldness and voracity of 90 individually housed crayfish over 4 months. They were consistently fed ad libitum to ensure that differences in growth rate could be attributed to intrinsic differences; by contrast, constant but restricted rations would leave individuals with high intrinsic growth rate potential (and high appetite) hungry, while slow growers may be satiated, because increased levels of behaviour cannot lead to increased intake rate. We then quantified the repeatability of, and covariance between, growth and behavioural traits using univariate and multivariate mixed models; we predicted a positive across-individual correlation between boldness, voracity and intrinsic growth rate.

© 2014 The Authors. Journal of Animal Ecology © 2014 British Ecological Society, Journal of Animal Ecology

Individual-specific growth and behaviour

Materials and methods We used the common Australian freshwater crayfish (Cherax destructor), known locally as the yabby, as our model species. This decapod crustacean is native to eastern Australia, lives in still and flowing freshwater and spends most daylight hours sheltering in burrows dug into the bottom. This behaviour is thought to reduce exposure to visually hunting predators that are responsible for a large proportion of mortality in natural crayfish populations (Farrell & Leonard 2001). This species further exhibits highly variable growth trajectories in response to environmental conditions (Austin et al. 1997) and between genetic strains (Jerry et al. 2005). Like other indeterminate growers, growth rate is a major determinant of fitness in crayfish, with fast growth resulting in reduced mortality (e.g. to cannibalism) and high fecundity (Jerry et al. 2005). Six egg-bearing female yabbies of unknown age and similar size were sourced from Aquablue hatchery in Tamworth, New South Wales. Individuals originated from naturally sustaining pond populations at the hatchery and were harvested using commercial yabby traps. Upon arrival in the laboratory, each female was placed into an opaque plastic holding tank (20 L, 35 9 60 cm). A single piece of 14 9 5 cm diameter white PVC pipe was placed at one end of the container as refuge for the female, and many large pieces of gravel filling up half of the container to provide shelter for newborn yabbies. Animals were kept in a constant temperature room that maintained water temperatures between 17
6 and 20
9 °C, with a 12:12 h light/dark cycle and a single light on from 5
30 pm to 5
30 am to provide low-level background lighting at night. Throughout the experiments, all tap water was treated with ‘Hardness up’ and water purifier (Aquasonic Pty Ltd., Wauchope, NSW, Australia), sea salt, and aerated prior to use. Resulting water chemistry was 450 p.p.m. salinity, pH 7
8, dissolved oxygen 9
5 mg L 1 and 180 p.p.m. hardness. Females were fed six large feed pellets twice daily to standardize female condition and to discourage cannibalism when young were eventually released from her abdomen (nutritional content: 22% protein, 5% fibre, 0
4% salt, 1
2% calcium; ingredients: cereal grains, vegetable protein, animal protein fishmeal, edible oil, salt, vitamins and minerals). Tanks were checked each day for newly released young. Females started releasing offspring from their brood chamber after about 2 weeks in captivity, and all six began releasing young within 4 days of each other. At the third day of ‘birthing’, females had released the majority of offspring and all released young were removed and placed into a new aquarium. Once released from the female abdomen, they resemble tiny adults. To avoid behavioural sampling bias when sampling young (Biro & Dingemanse 2009), we gently stirred the water containing offspring of each clutch in order to suspend all individuals into the water column and then netted out 15 random individuals per family, giving 90 animals in total.

individual housing and feeding Each newborn yabby was placed into its own home tank (20 9 11 9 10 cm, filled with 0
8 L water, see above), which were visually isolated from one another with black corrugated plastic (same height as tanks), on April 4. Yabbies were between 0
6 and 0
7 cm in length, and about 0
015 g at this time. Each tank contained one PVC pipe section for shelter (3 cm length,

3

1
5 cm diameter) placed at one end of the tank that had sand covering half of the tank bottom in order to create a clear contrast between a sheltered and open half. Tanks were placed randomly along a single laboratory bench, arranged as four rows of 24 aquaria. Animals were fed ad libitum throughout the entire experiment, whereby the amount of food given was continuously adjusted to ensure excess food was always present. Food was placed in the open area of the tank, furthest from the shelter tube. Additional details of housing and feeding are found in the ESM.

scoring growth Yabbies were weighed ( 0
001 g) at the onset of the experiment (April 6–9), and every 3 weeks thereafter for 125 day (seven times each). Yabbies were gently netted and dabbed dry with kim-wipes to remove excess water prior to weighing while positioned on a petri dish. In total, this procedure took no more than 1 min after which the yabbies were immediately returned to the home tanks. Duration of these procedures was equal across individuals. One set of behavioural observations was conducted the day after being measured, whereas all other observations were conducted at least 3 day after.

scoring boldness Animals were left to acclimate until April 21 before the first observations were made to avoid individual differences in acclimation patterns from introducing unwanted variance into our data (see Biro 2012). Boldness was assayed by repeatedly scoring the position of the individual during a series of point (scan) observations and thus represents a measure of exposure to visually hunting predators. On each scan of a group of aquaria, yabbies were given a score reflecting the relative risk of their position: zero for hiding within the refuge, one for fully emerged from the refuge but still in the sheltered half of the aquarium, and two when positioned in the open half of the aquaria where they were least camouflaged. Each trial would involve scoring each animals position, in a pre-determined order, one by one, and then repeating this sequence for a total of 11 scan samples each (protocol 1). Scan samples were timed for consistency so that each animal’s position was observed once every 5 min. We then calculated an individual sheltering score by summing all 11 scores across the trial, yielding a total value where larger values represent reduced use of the burrow and sandy areas that provide concealment. We interpreted time spent outside the burrow to be a measure of the willingness of yabbies to accept risk. Reduced shelter use is therefore hereafter referred to as greater ‘boldness’ (Reale et al. 2007). Because yabbies can and do hoard food back to their burrows, our measure of boldness is not necessarily linked to feeding, and individuals spending much of their time outside shelter were rarely observed feeding. Since darkness may protect yabbies from day-active and visually hunting predators, we further assume activity outside shelter during daytime to be more risky than at night for this predominantly nocturnal species. As a result, we expect higher ‘boldness’ scores at night even in the absence of predators or predator cues. By late May, juveniles were large enough to permit reliable night observations. These observations were identical to these described above and started at least 30 min after the main lights turned off. From April 21 through June, each yabby was

© 2014 The Authors. Journal of Animal Ecology © 2014 British Ecological Society, Journal of Animal Ecology

4 P. A. Biro, B. Adriaenssens & P. Sampson observed 13 times each during the day (between 13:30 and 17:00 h), and nine times each at night (18:30–20:00). We increased the number of scans comprising each boldness trial from 11 to 30 per animal when assaying boldness during July (protocol 2). This was done in order to improve the precision of the estimates, but was substantially more time-consuming, requiring 2 days to observe all yabbies instead of one. On day one, the first 45 yabbies were observed during the day (first two rows), and the same individuals again that same night; day two this was repeated for the remainder (back two rows; each scan was timed to take 3 min). Doing observations by rows was due to concern that they were becoming increasingly aware of the observers movement, which could disturb those in the front rows while observing the back rows. Across 10 days in early July, we carried out 5 days time and five night observations per animal. In the latter half of July, we gathered four morning (08:30–10:30) observations in addition to four observations made during normal afternoon sampling. In total, 13 daytime and five night observations were taken for each individual during July (see Fig. 2 for distribution of these particular samples).

scoring voracity Each individual was scored eight times for the latency to feed following disturbance (June 7 and 14; July 4 and 8, 11; Aug 5, 9 and 13). Individuals were not fed fresh food that day, and any uneaten food was removed at least 10 min before the trial using a net. A trial began by placing three defrosted bloodworms in the open area at the furthermost end from the shelter using a small pipette. The pipette was then used to give one swirl of the water to spread the scent of the worms, which resulted in yabbies retreating to their burrow, or the sandy half. We then timed their latency to reach the food. For individuals that did not touch the food during the first 5 min, we performed point checks at 1-min intervals until food was missing and/or the yabby had reached the food. A maximum duration of 7200 s (2 h) was given to individuals that did not eat or reach the food within this time frame. Low latency values were assumed to reflect greater voracity. Food was introduced to the tanks in blocks of 12 (four deep 9 three tanks wide), working from left to right down the laboratory bench. This allowed us get latencies for all but the rare individual with a very long latency in the first block. Next, the second block was started, while a second observer watched the first block for any individual that had still not emerged after the initial 5-min interval after introducing the food. Thus, a new block was started every 7–10 min. This systematic approach working across the rows of tanks minimized any disturbances to the animals. Because there was fine-scale spatial and temporal variation in temperature among tanks, and because ectotherm behaviour can be highly sensitive to these variations (Biro, Beckmann & Stamps 2010; Pruitt, Demes & Dittrich-Reed 2011), we measured water temperature in each aquarium following a set of observations (both boldness and voracity assays) each day (see also Fig. 2).

assessment of intrinsic growth rates across individuals using mixed models We modelled growth as individual increases in mass over time using a linear mixed model: mass = b0 + b1(day) + b2(sex). Data

were linearized via a log-transformation of mass and day. We tested whether individuals differed in initial mass (the intercept, b0) and growth rate (the slope, b1) by specifying each parameter as random. See below for details of mixed model analyses, implementation and interpretation.

assessment of behavioural types using univariate mixed models Sets of boldness trials using Protocol 1, and Protocol 2, and voracity trials provided us with a means to characterize individual behavioural types (BTs), which we could in turn relate to one another, and to growth rate (details in ESM). Behavioural data were analysed separately for these three sets of trials according to their longitudinal nature (repeated observations over time and across day vs. night situations) using general linear mixed effects models (see Singer & Willett 2003; West et al. 2010). Three mixed model analyses were done: one for the early set of boldness trials using 11 scans per individual (thus values range from 0 to 33,), a second for subsequent boldness using 30 scans per individual (values from 0 to 90), and a third for latency to feed measures (time from 0 to 7200 s). The linear model took the form: behaviour = b0 + b1(observation day) + b2(day vs. night) + b3(temperature) + b4(sex). All voracity trials were conducted during the day and so time of day was not a predictor in that model. We began with a fully saturated model containing all fixed factors. In order to test for individual differences in initial boldness and initial voracity, we specified the intercept as a random effect. To test for individual differences in diurnal behaviour, individual differences in patterns of behaviour across days (e.g. Rodr
ıguez-Prieto, Mart
ın & Fern
andez-Juricic 2010; Stamps, Briffa & Biro 2012), and individual differences in behavioural response to temperature (Biro, Beckmann & Stamps 2010), we specified each effect as random slope. Similarly, we also tested for a family effect with respect to these variables (see also ESM). Specifying an effect as random fits a parameter describing the population mean, and a variance parameter describing variation across individuals for that parameter (see Singer & Willett 2003; West et al. 2010). Additional details are found in the ESM. Estimating correlations between predicted mean values of behaviours with growth rate is necessarily a two-step process, whereas multivariate analyses avoid this problem. However, univariate mixed models revealed very complex patterns of across-individual variation in behaviour (from 1 to 3 random slope effects and significant covariances), and these effects also differed between traits (see Results). When we next conducted multivariate mixed model analysis to explore across-individual correlations between daytime boldness, night-time boldness, voracity and growth rate, the model would not converge with even one random slope effect present, which we attributed to the highly unbalanced nature of the data set. Therefore, we fit a ‘simple’ random intercepts-only multivariate model, in addition to our univariate results. For this analysis, we used data that were collected from July onwards (protocol 2), as it contained the fewest random slope effects as indicated from the univariate results. Thus, only three mass measurements entered into the model (or two growth rate estimates). All four variables were standardized to mean of zero and sd of 1, and date was left centred (mean centring date had no effect on results); a separate residual variance was fit for each trait. A residual covariance

© 2014 The Authors. Journal of Animal Ecology © 2014 British Ecological Society, Journal of Animal Ecology

Individual-specific growth and behaviour was not possible to fit as these variables were not (closely) matched in time.

Results do individuals consistently differ in growth rate? (n = 602) Neither individuals (random intercept, variance = 0, P = 1
0), nor families (random intercept, P > 0
08) differed in initial mass and these random effects were removed from the model. On average, yabbies grew considerably over the 125 days experiment (in mass, F1,412 = 4342, bday = 1
37, P < 0
0001; Fig. 1). Females were somewhat slower-growing than males (F1,195 = 4
4, bfemale = 0
17, P = 0
038). Individuals consistently and substantially differed in their rate of mass gain over an extended period of time (Fig. 1; random slope effect, P < 0
0001), but growth did not differ across families (random slope, P = 0
18). Based upon the slope (0
0119) and residual (0
1978) variances, we evaluated repeatability of growth rate at various points in time that overlapped with our behavioural measures: at day 10 (R = 0
85), day 35 (R = 0
98) and day 90 (R = 0
99; (see formula in Biro et al. 2013; for an example where a similar calculation was used, but including two random slope effects instead of one). See ESM for why and how we must calculate situation-specific repeatability when individuals differ in their responses across some gradient (see also Singer & Willett 2003; Martin et al. 2011).

Mass (g)

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5

do individuals consistently differ in behaviour? Boldness protocol 1 (Late April through June, n = 1805) Mean-level results. On average, there was a weak tendency for yabbies to become more bold across the first 72 days of observations (F1,951 = 20
1, P < 0
0001; bdate = 0
011, b0 = 0
31) and at night (F1,790 = 136, P < 0
0001; bnight = 1
03). This increase in boldness was less pronounced for night than daytime observations (date*time of day effect: F1,1632 = 13
1, P < 0
0005; bdate*time of day = 0
0062). Females were not only slowergrowing (above), but also somewhat more shy compared with males (F1,84 = 7
8, P < 0
004; bfemale = 0
249). Temperature had no effect on boldness (F1,1616 = 0
27, P = 0
60). Individual-level results. Families did not differ in their initial daytime boldness, nor in diurnal patterns of boldness (random intercept and slope effects, both P > 0
15). However, individuals differed in initial daytime boldness (random intercept effect, P < 0
001; variance = 0
268, SE = 0
081). In addition, individuals differed in the extent to which boldness changed across days (random slope effect, P < 0
0001; variance = 0
000080, SE = 0
000020) and differed in the degree to which boldness increased at night (random slope effect, P < 0
01; variance = 0
0697, SE = 0
0297). Responses to temperature variation were, however, similar across individuals (random slope effect, P = 0
488). Individual intercepts, and slopes with respect to day vs. night, were negatively correlated (covis = 0
0992, SE = 0
043, P < 0
03), indicating that boldness of daytime-active individuals was more consistent across day-night contexts. There was no covariance between individual intercepts, and slopes with respect to changes in boldness across days (covis = 0
00012, SE = 0
0009, P = 0
89), or between individual slopes (covss = 0
00076, SE = 0
00056, P = 0
18). Based on the (co-)variances (above) and residual variance (0
5833, SE = 0
02), repeatability during daytime observations was R = 0
19 when evaluated at day 1, R = 0
25 on day 36, and R = 0
44 on day 72; when evaluated at night, R = 0
20, R = 0
21 and R = 0
38 on days 1, 36 and 72. Again, see ESM for details on how this was calculated.

2

1

0 0

20

40

60

80

100

120

140

Day Fig. 1. Growth trajectories of the slowest-growing (final mass = 0
15 g; no fill), an intermediate (grey fill) and the fastestgrowing individuals (final mass 3
18 g; black fill), which represents a 21-fold range in growth rates. Mean initial mass at the outset of measures was 0
01 g when assessed after 5 days in their home tanks. Note the slightly outlying data point at day 100 for the fastest grower likely reflects an instance just prior to moulting.

Boldness protocol 2 (July, n = 1548) Mean-level results. On average, individuals maintained similar levels of boldness during July (days 75–90; F1,1358 = 2
6, P = 0
11), and the effect of time of day effect did not vary between days (day*time of day; F1,1258 = 0
28, P = 0
60). As expected, boldness was greater at night (F1,84
8 = 145
1, P < 0
0001; bnight = 0
957; b0 = 2
42) and increased with temperature (F1,76
2 = 74
6, P < 0
0001 btemp = 0
223). Females were, again, less bold than males (F1,83
9 = 5
9, P < 0
02; bfemale = 0
436).

© 2014 The Authors. Journal of Animal Ecology © 2014 British Ecological Society, Journal of Animal Ecology

6 P. A. Biro, B. Adriaenssens & P. Sampson Individual-level results. Families did not differ in average use of shelter, or in diurnal patterns of shelter use (random intercept and slope effects, both P > 0
13). However, individuals differed in daytime boldness (random intercept effect, P < 0
02; variance = 6
50, SE = 3
02), in the extent to which boldness increased at night (random slope effect, P < 0
001; variance = 0
266, SE = 0
085), and in their responses to temperature (random slope effect, P < 0
02; variance = 0
0159, SE = 0
0070). See Fig. 2 for an illustration of raw data for several individuals. Individuals did not differ in boldness trends across days (random slope, P > 0
4). Individuals that were more bold during initial daytime observations tended to (i) not increase boldness as much as at night (i.e. were somewhat less plastic and therefore were relatively more consistent across day-night situations (covis = 0
704, P = 0
089, SE = 0
41), and (ii) increase their boldness less with increases in temperature (covis = 0
293, P < 0
05, SE = 0
14). There was no correlation between individual slopes with respect to temperature, and slopes with respect to time of day (i.e. plasticity was not correlated across situations, covss = 0
018, SE = 0
018, P = 0
32). To illustrate the behavioural differ-

ences observed, we have plotted the data for several individuals exhibiting varying levels of boldness and different patterns of diurnal behaviour, in relation to temperature variation within this period (Fig. 2). Based on the (co-)variances (above) and residual variance (0
763), we assessed repeatability to be R = 0
46 during daytime samples and R = 0
49 at night, when evaluated at the average temperature (19 °C; range in July was 17–20
5 °C). Daytime repeatability at 17 °C was R = 0
44, and at 20
5 °C was R = 0
50. Consistency in boldness between protocols? Average-predicted levels of boldness for each individual estimated using protocol 1 (late April through June) were highly correlated with estimates from protocol 2 (July), whether during daytime or at night (Pearson correlations: rday = 0
75, rnight = 0
73, n = 86, both P < 0
0001; see Fig. 3a). Thus, boldness was consistent not only within the two time periods, but also consistent across the two periods, indicating that mean levels of boldness were maintained across nearly 4 months.

Fig. 2. Trajectories of boldness over time for a sample of six yabbies with contrasting patterns of diurnal and mean-level behaviour, from the July observations (Protocol 2). Solid symbols and joining lines represent night samples, open symbols and dotted lines join day samples. Because of significant temperature effects on behaviour, the inset graph shows patterns of fine-scale temperature variation over time in the 90 experimental home tanks. Note the increases in boldness with temperature in all the individuals depicted here, particularly during days 86–90. © 2014 The Authors. Journal of Animal Ecology © 2014 British Ecological Society, Journal of Animal Ecology

Individual-specific growth and behaviour

7

Voracity (June through August, n = 688) Mean-level results. On average, yabby voracity (measured during daytime only) decreased over the 68-day interval (F1,82
7 = 10
5, P < 0
002; bday = 0
0116; b0 = 9
97) and increased with temperature (F1,76
7 = 19
5, P < 0
0001; btemp = 0
230). There was some indication that females might also be less voracious (F1,84 = 3
5, P = 0
065; bfemale = 0
529).

boldness, in either analysis (all P > 0
20). Fast growers were also more bold during daytime and at night during April–June (rday = 0
44, rnight = 0
47, n = 86, both P < 0
0001), and again there was no effect of sex, either as a main effect or interaction with boldness, in either analysis (all P > 0
5). Growth rate was also correlated with voracity (r = 0
49, P < 0
0001).

Individual-level results. Individuals did not differ substantially in voracity on the first trial (random intercept effect, P = 0
13). However, individuals differed in the rate at which voracity changed across days (random slope effect, P < 0
0015, variance = 0
000446, SE = 0
000148) and in their responses to temperature (random slope effect, P < 0
0001, variance = 0
00582, SE = 0
00132). Individuals whose voracity changed substantially across days were less responsive to temperature (covss = 0
00090, SE = 0
000375, P < 0
02). On the basis of the (co-)variances and residual variance (2
055, SE = 0
128), we estimated repeatability of voracity as being R = 0
50 on day 1 and R = 0
47 on day 68 when evaluated at 19 °C.

multivariate analysis of trait correlations Mean-level results As expected, females were less bold during daytime (bfemale = 0
46) and at night (bfemale = 0
41), took longer to feed (bfemale = 0
21) and grew more slowly (bfemale = 0
41) than males (F4,2146 = 3
2, P < 0
015). On average, traits did vary somewhat over time (F4,2146 = 67
1, P < 0
0001), whereby daytime boldness increased (b = 0
036, SE = 0
0045), night-time boldness decreased slightly (b = 0
018, SE = 0
010), latency to feed did not vary (b = 0
0010, SE = 0
0012) and of course mass increased over time (b = 0
025, SE = 0
0017).

Correlation between voracity and boldness?

Individual-level results

Individuals that were more voracious during daytime were also bolder during daytime, both when using protocol 1 data (April through June, r = 0
70) or protocol 2 data (July, r = 0
79, n = 86, both P < 0
0001; Fig. 3b); there was no effect of sex in either analysis, either as a main effect or interaction (all P > 0
2).

There were significant correlations across individuals among all of the traits (all random intercept effects, covariances and residual variances were significant, all P < 0
0001; see ESM for detailed results). Daytime (r = 0
72; Fig. 5) and night-time boldness (r = 0
85) were both strongly correlated with mass differences, as was voracity (r = 0
60). Recall there was no significant variance in initial mass across individuals at the start of the experiment. Using growth rate as a trait, instead of mass, yielded very similar results (see ESM). In other words, bolder and more voracious individuals were fastergrowing and achieved larger size, and these results were highly congruent with the univariate results. Day and night-time boldness were strongly correlated (r = 0
87),

are fast-growing individuals more bold and voracious? Fast-growing individuals tended to be more bold during daytime and at night during July (rday = 0
53, rnight = 0
58, n = 86, both P < 0
0001; Fig. 4); there was no effect of sex, either as a main effect or interaction with

Boldness score July

4

(a)

(b)

3 2 1 0 0

1

Boldness score May - June

2

4

5

6

7

8

9

Latency to feed

Fig. 3. (a) Consistency of daytime boldness across sampling protocols (July: Protocol 2 compared with May–June: Protocol 1). Note that higher scores in July do not reflect greater boldness (see Results) but rather a greater number of scan samples in the summed score for each yabby. (b) Boldness in relation to voracity (latency to feed) during daytime sampling All boldness scores are average-predicted values derived from mixed models on ln(x + 1) transformed data, after accounting for fixed and random effects (see Materials and methods). © 2014 The Authors. Journal of Animal Ecology © 2014 British Ecological Society, Journal of Animal Ecology

8 P. A. Biro, B. Adriaenssens & P. Sampson

Growth rate (slope)

1·1

1·0

0·9

0·8

0·7 0

1

2

3

4

Boldness score Fig. 4. Growth rate (slopes from log–log growth model) in relation to average-predicted levels of boldness during July (determined from univariate mixed models that accounts for two random slope effects and fixed effect covariates).

Fig. 5. Covariance between boldness and growth rate as determined from a multivariate mixed model. Data are dipicted as the individual deviations from the mean-level effect, and error bars indicate SE’s for the individual BLUP estimates.

and voracity (latency) was also correlated with day (r = 0
70) and night boldness (r = 0
78; see ESM for detailed output of all random effects parameters, including SE’s and P-values).

Discussion Our study revealed that yabbies exhibit patterns of growth and behaviour that covaried with one another. To summarize, we observed consistent individual differences in (i) intrinsic growth rate, (ii) boldness, both within and across day-night situations and (iii) voracity. Most importantly, we observed (iv) that there were strong positive correlations between growth rate and boldness, and between growth rate and voracity. We believe our data

provide compelling support for the recent hypothesis suggesting that intrinsically productive individuals may exhibit consistently high levels of behaviours that, in nature, affect intake rate (Stamps 2007; Biro & Stamps 2008). In addition to individual-level differences, these results were also mirrored in the observed sex differences in these traits; male crayfish were faster-growing, more bold and more voracious than females. In summary, our study provides evidence for consistent life-history trajectories associated with consistent behavioural differences at both the individual and sex level. Individual differences in life history and behaviour were apparently set very early in life, suggesting this variation arose primarily from genetic or permanent environmental effects (see Jerry et al. 2005 demonstrating heritable growth rate in yabbies). Food in our experiment was ad libitum, and crayfish in our study were found to hoard food back to the burrow; thus, greater boldness was thus not required for high feeding rates. In fact, much of their time spent outside the shelter was spent being motionless, active and re-arranging sand in the tank. Given this, we might ask why we observed a strong correlation between boldness and productivity (e.g. Niemel€ a et al. 2012)? One possibility is that the correlation between growth and behaviour is under genetic control, as suggested by the relatively high phenotypic correlations we observed (Dochtermann 2011). We might even speculate that intrinsic growth rate differences may be a proximate driver of behavioural differences in yabbies, whereby fast growers are on average more hungry and that this motivates behaviour that increase feeding rates. This is consistent with studies showing that growth enhancement through artificial selection or genome manipulation results in enhanced expression of feeding and risk-taking behaviour (Johnsson & Abrahams 1991; Johnsson et al. 1996; Biro et al. 2006; Sundstr€ om & Devlin 2011). Individual growth rates in the present study were correlated with one score of feeding activity (voracity) and one score of risk-taking behaviour (boldness), and the two behavioural variables were also correlated with one another. This observation is in line with recent suggestions that individual differences in state variables (such as growth) may simultaneously affect a whole set of behaviours in different situations and lead to interindividual correlations among seemingly unrelated behaviours (personality or behavioural syndromes (Sih & Bell 2008; R
eale et al. 2010). An important implication of the productivity–personality hypothesis is that it provides a basis from which to make a priori predictions about how and why correlations between production and behaviour can vary within and across different contexts. It predicts positive correlations when individuals follow their preferred/intrinsic rate of productivity (Stamps 2007; Biro & Stamps 2008), and thus, we chose to feed our animals ad libitum. Environments in which individuals cannot maintain their preferred productivity, such as restricted feeding in the

© 2014 The Authors. Journal of Animal Ecology © 2014 British Ecological Society, Journal of Animal Ecology

Individual-specific growth and behaviour laboratory or very scarce and/or highly dispersed food in nature, may lead to non-positive correlations between productivity and consistent behaviour. Such conditions may be often met by animals under natural conditions (Adriaenssens & Johnsson 2009) and lead to context dependence of correlations between growth and behaviour (Heg, Sch€ urch & Rothenberger 2011; Riebli et al. 2011). In order to better understand how and why correlations between production and consistent behaviour might differ from a positive correlation, future work should therefore study associations within and across contexts that we expect will change the costs and benefits of different levels of productivity and behaviour (Killen, Marras & McKenzie 2011; Quinn et al. 2012). Although not the focus of this study, we also detected, and controlled for, quite complex patterns of individual behaviour over time, across day-night situations and with temperature variation. For instance, individuals differed in the extent of change in boldness across days, between daynight situations and in their responsiveness to temperature (i.e. individual differences in plasticity were detected in response to each of these variables). Employing an approach of repeated sampling within and across situations, and analysis by random regression, helped reveal this individual consistency within and across situations and over time. For example, individuals with stronger habituation responses in terms of voracity (change across repeated assays) were those that tended to respond relatively little to temperature variation (i.e. a negative slope– slope covariance). Although our estimates of repeatability were rather low using Protocol 1 (0
20–0
36), a greater number of scans used in Protocol 2 led to greater repeatability estimates (0
45–0
50), indicating that individuals were considerably more distinct from one another. These values are higher than is typically reported in the literature (Bell, Hankison & Laskowski 2009), and we attribute this to the large sample size (within and across individuals) that allowed us to detect and account for complex individual differences in behaviour and assess correlations at the between-individual level (Martin et al. 2011; Wolak, Fairbairn & Paulsen 2012). We note also that these repeatability estimates are not confounded by (inflated by) sexspecific differences in behaviour that we accounted for in our models. In conclusion, our study has demonstrated a clear association between productivity and consistent behaviour. Future studies should examine these correlations across contexts that affect the energetic costs and benefits associated with bold behaviour, in order to better understand the complex role of environmental conditions in the strength, direction and stability of such links.

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9

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Supporting Information Additional Supporting Information may be found in the online version of this article. Data S1. Details on housing and statistical analysis.

© 2014 The Authors. Journal of Animal Ecology © 2014 British Ecological Society, Journal of Animal Ecology