Based on the fact that fish behaviour reflects individual differences in physiological needs, the prospect to use thermal preference as a mass‐screening paradigm is attractive. Different experimental screening methods for personality traits along a proactive–reactive continuum have being used with Nile tilapia as a model (Barreto et al . 2009 ; Barreto & Volpato 2011 ; Martins, Conceição & Schrama 2011a , b ; VeraCruz et al . 2011 ), with the disadvantage of them being intensive time‐consuming assays. In this study, we aimed to firstly assess whether final thermal preferendum played a pivotal role in determining the distribution pattern of Nile tilapia with different personalities and secondly to validate final thermal preferendum as a new physiological paradigm to screen for personality traits. In a final experiment, we further explored how a bacterial infection impacts upon the expression of behavioural fever in distinct animal personalities.

Recent progress addressing animal personalities (Overli et al . 2007 ; Huntingford et al . 2010 ; Silva et al . 2010 ; Martins et al . 2011 ; Castanheira et al . 2013a , b ; Rey et al . 2013 ; Herrera et al . 2014 ) has highlighted a central role for individual variation in ecological and environmental challenges (Sih, Bell & Johnson 2004 ; Reale et al . 2007 ) and as a tool for resolving variation (MacKenzie et al . 2009 ). Animal personality traits have been described as underlying tendencies that affect behaviour across contexts, that are stable over time and that vary across individuals (Reale et al . 2007 ; Dingemanse et al . 2010 ). Typically, animals categorized as ‘proactive’ (active coping or bold), ‘intermediate’ (more flexible individuals) or ‘reactive’ (passive coping or shy) show distinct differences in behavioural profiles for the same measured variable, such as risk taking and exploration, aggression or feeding (Rey et al . 2013 ; Castanheira et al . 2015 ). Among fish, there is scarce information linking personality traits and physiological profiles, particularly with respect to individual differences in adaptability, that is optimal conditions created for proactive individuals are likely to be different from those of reactive individuals (Rey, Digka & MacKenzie 2015 ) with ecological relevance. On the one hand, a proactive individual characterized by increased foraging activity may take more risks to attain more food (Finstad et al . 2007 ) and therefore is more prone to predation risks (Stamps 2007 ) or to being captured (Biro & Dingemanse 2009 ), suggesting higher internal energetic requirements with higher metabolic costs. In contrast, a reactive individual can be characterized by low levels of aggression, immobility and avoidance behaviour under aversive events (see review of Castanheira et al . 2015 ) that may reflect lower metabolic demands. Recently, Rey, Digka & MacKenzie ( 2015 ) using the zebra fish, Danio rerio, as a model showed that proactive and reactive animal personalities express different thermal preferendum and activity patterns when a thermal choice is available. Parallel research showed the effect of fluctuations in temperature on the personality of juvenile coral reef fish, reflected by behavioural variation among and within individuals (Biro, Beckmann & Stamps 2009 ).

Environmental temperature influences all aspects of an organism's physiology and behaviour, from reproduction to growth, and this dynamic interaction with the environment impacts upon fitness and survival. In mobile ectotherms, such as fish and insects, body temperature closely follows environmental temperature and can only be modified by behavioural means. This behavioural regulation occurs across different temporal scales including daily and seasonal cycles. This impacts upon the basal metabolic rates of ectotherms where small changes in environmental temperature may result in significant metabolic fluctuations (Clarke & Fraser 2004 ). For example, fish navigate thermal gradients to attain a preferred thermal optima to improve physiological and in some cases reproductive performance (Huey & Bennett 1987 ; Huey & Kingsolver 1989 ; Pawson, Pickett & Witthames 2000 ; Angilletta, Niewiarowski & Navas 2002 ). Recently, in Drosophila larvae thermosensory neurons were shown to provide the basis for thermotaxis coupled to environmental navigation in a fluctuating thermal environment highlighting the importance of thermal perception (Klein et al . 2014 ). Thermal optimum is defined as the temperature that maximizes physiological performance at an individual level. Under normal conditions if performance is linked to fitness, natural selection should favour a tight relationship between preferred body temperature and the preferred thermal optima by means of regulatory behaviour (Huey & Bennett 1987 ). Such behaviour can be seen as a thermoregulatory response with a positive trade‐off between energy demand and physiological requirements (Rey, Digka & MacKenzie 2015 ). The thermal optimum for an individual is defined as the final thermal preferendum (Fry 1947 ). Calculation of this preferendum is measured for each individual 24–96 h after exposure to an adequate thermal choice. This final temperature represents the interaction between environmental demand and an individual's capacity to respond to such demands. The final thermal preferendum calculated on an individual level has since been used as a mean temperature preference guide in fish (Rey, Digka & MacKenzie 2015 ).

Statistics were performed usingstatistics v19.0, R(R Development Core Team, Vienna, Austria) andrismv6.0 (San Diego, CA, USA) for Windows. The assumptions of normality and homoscedasticity were confirmed by analysis of the residuals, with particular attention paid to homogeneity of variances between personality groups (Cleasby & Nakagawa). Arcsin or log+ 1) transformation was applied in the case of non‐normally distributed variables. DAIand DAIlatencies were collapsed into principal component scores prior to clustering. The same procedure was taken with the behaviours measured from NR test. Fish were then segregated in three groups using two distinct forms of unsupervised clustering (‐means clustering and hierarchical clustering using Ward's criterion), which displayed full consistency. Pearson correlation was used to verify the repeatability and consistency of individual DAI latencies between both DAI runs. A one‐waywas used to verify differences between the mean PC1 DAI values for each of the generated clusters, and theFisher LSD test was calculated for specific significant differences. A global principal component analysis (i.e. using all raw behavioural parameters measured from VF, NE, DAI and NR tests) was used to assess the quality of the separated groups using DAI latencies cut‐offs determined during the preliminary test. Using the same variables, Pearson correlation was performed to assess how correlated were the different behaviours measured. To examine the differences between behavioural phenotypes, one‐waywith Fisher LSDwas used for each of the behavioural paradigms (NE, VF, DAI, DAIand NR). For this purpose, behaviours taken during DAI test were collapsed into first component scores using principal components analysis (PCA) for both events. The same approach was used for the behaviours measured from restraining test. The correlation matrix was used to check multicollinearity after varimax rotation. Kaiser–Meyer–Olkin (KMO) test for sample adequacy was always > 0·5 and the Bartlett's test of sphericity was significant for all tests. A two‐waywas used to analyse latencies and transitions, after log transformation, as dependent variables with temperature and personality as fixed factors. Both acute and final thermal preference for each of the personality groups was calculated as follows:wherewas the number of fish in the chamber,the total number of fish in the tank andthe mean temperature of the chamber. Significant differences between thermal preferences of the different personality groups were checked afterwards through Kruskal–Wallis test since this remained non‐normal after transformation. For pairwise differences, a Mann–Whitney‐test was used. GLM mixed model was fitted with number of fish as dependent variable and independent variables (and higher order interactions) were selected for inclusion in the model through a backward stepwise likelihood ratio method and examination of the Wald χstatistic, to reach the minimum suitable model with the lowest Akaike Information Criterion (AIC) value. Leverage statistics and residual analysis were used to test the validity of model assumptions. The Wald χwas used to test for a significant effect of the temperature or chamber (this last in the case of controls under restriction conditions) and personality on the distribution of the fish over the experimental aquaria, estimated by their coefficients β (negative binomial distribution parameter).‐test or Mann–Whitney‐test was used for pairwise comparisons between thermal preference of control groups of both gradients (normal or non‐normal data, respectively) and Wilcoxon rank test for comparisons between thermal preference after 2 and 4 days post‐infection or dpi (non‐normal data). Statistical significance was taken at< 0·05. The results are expressed as mean ± standard error of the mean (SE).

An isolate of Streptococcus iniae recovered from natural infection in Tilapia was used for the bacterial challenge and had been identified using traditional bacterial methods. The isolate was recovered from frozen stocks onto tryptone soya agar (TSA, Oxoid, UK) plates, incubated for 48 h at 28 °C and identity confirmed using Gram, oxidase, motility, O/F methods (Frerichs & Millar 1993 ) and biochemical profile (API Strep; Biomérieux, Basingstoke, Hampshire, UK). The isolate was passaged through a single fish prior to performing the challenge studies to enhance virulence after long‐term storage and the bacterium recovered from the kidney. The challenge inoculum came from a 48‐h growth of a single S. iniae colony grown in 45 mL of Tryptic Soy Broth at 28 °C, which was centrifuged at 2000 g for 15 min at 4 °C and the cell pellet resuspended in sterile physiological saline (0·85% NaCl) to give an optical density of 1. The bacterial concentration was adjusted using sterile saline to give the challenge inoculate. The virulence and adjusted concentrations of bacteria to the weight of the fish were previously validated (Featherstone et al . 2015 ). Viable colony counts were performed using the Miles and Misra method (Miles, Misra & Irwin 1938 ) to check the bacterial concentration, and each fish was exposed to 0·1 mL of the bacterial suspension by intraperitoneal injection (i.p.). Homogeneous groups of eight Tilapia of proactive and reactive individuals were anesthetized with benzocaine and the bacteria administered by i.p. injection. A control group of naïve fish was used and injected with PBS and was subjected to T CH conditions. Test groups are described in Table 1 . Animals were monitored on a daily basis for 5 days and checked for morbidity/mortality and clinical signs of disease (Shoemaker, Klesius & Evans 2001 ). The distribution of the fish during the first hour of the first day after deployment and the first 8 h of the following days was video‐recorded (7 am–3 pm; for 25 s, every 15 min; n = 33 events).

Different aspects of fish behaviour were analysed to assess the different personality traits. During the first hour named here as acute thermal period (each min, n = 60 events), observations were centred upon (i) distribution of fish in the preference chamber for determination of acute thermal preference, (ii) individual latencies to exit from initial chamber to side chambers and (iii) the number of transitions between chambers for each group. For the last 8 h of the test, named here as late thermal period (7 am–3 pm; n = 33 events), the distribution of fish was recorded for 25 s, every 15 min and analysed ‘a posteriori’ for determination of the final thermal preference. The sequence of the trials was alternate between the personality groups to reduce experimental bias.

Eight groups ( n = 8) and three control groups of naïve fish randomly selected from stocking tanks ( n = 88) with mean weight of 19·23 ± 3·21 g were used to validate thermal preference as a physiological screening proxy without previous behavioural screening. Thermal preference assessment was performed in the same tanks following the same protocol as above with a refined gradient profile of 25·95 ± 0·2 to 33·59 ± 0·3 °C, with 1·91 ± 0·38 °C between chambers. After 48 h, fish were transferred to round tanks (same as previously used with the same water recirculation system) and housed as they were distributed in the different chambers in the T CH tank. All individuals were then subjected to the same battery of behaviour tests described above and moved afterwards to the holding tanks as established groups with homogeneous personality traits and left to recover for 12 days.

Thermal preference was assessed in a custom‐built multichamber tank (adapted from Rey, Digka & MacKenzie 2015 ) (Fig. S1) firstly under constant temperature and then under a continuous thermal gradient allowing the temperature preference of each group to be recorded over a 48‐h time period. The dimension of the tank was adapted to the size of the fish, 126 L (140 × 30 × 30 cm), and divided with six transparent glass screens to create seven equal interconnected chambers. Each screen had a hole at the centre (10 cm diameter; 20 cm from the bottom) to allow connection between chambers and support ease of movement of the fish. Mechanical filters were placed in the five central chambers and the bottom gravel covered. Two custom‐built multichamber tanks were used under different conditions: (i) thermal gradient ( T CH ) and (ii) thermal restriction ( T R ). Three video cameras (Linksys ® webcams) provided continuous monitoring of the tank chambers with automatic recording (fisheye software, UAB; see Rey, Digka & MacKenzie 2015 for details). Thermal gradient was first tested and optimized (Fig. S2) to ranges between 20·92 ± 0·04 °C (chamber 1) and 33·08 ± 0·08 °C (Chamber 5), with a mean difference in temperature of 3·04 ± 0·10 °C between each chamber. Extreme lateral chambers were operated as cooling (mean temperature 16 ± 0·02 °C), by means of a cooler system and a pump, and heating chambers (mean temperature 42 ± 0·02 °C), by means of a water bath and a pump. Individuals were prevented from entering those chambers by covering the hole in the screens. Thermal restriction conditions were established with the water at rearing temperature 26·58 ± 0·33 °C for all the tank chambers. During each test, dissolved oxygen was recorded at the beginning and at the end of each trial (Handy Polarisl OxyGuard ® International, Farum, Denmark). The temperature from each chamber was continuously recorded in the centre of each compartment at the inner wall of the swimming channel for each 15 min with Thermochron iButton (Maxim integrated ® , Rio Robles, San Jose, CA, USA). Three groups of fish ( n = 8) were used for each personality trait ( n = 24) to test for thermal preference. Six groups of naïve fish randomly selected from the stock tanks were used as control groups ( n = 48). Three of those groups were tested under T R conditions and another three under T CH conditions. Fish were settled in the middle chamber (26·58 ± 0·33 and 26·71 ± 0·03 °C, respectively) with the holes covered until all the fish were deployed and covers then immediately removed. Fish were left for 48 h and filming began immediately. Water was completely replaced between group tests to guarantee equal conditions throughout the trials.

Initial trials, using O. niloticus (wild‐type, n = 54), were performed to establish the distribution of behavioural phenotypes and behavioural consistency over time. Subsequent experiments using a population of O. niloticus (homogold, n = 350) were then carried out due to availability of stocks. The consistency of behavioural responses between these two populations was analysed using duration of appetite inhibition (DAI) data (see below) and is shown in Fig. S2 (Supporting information). Prior to screening, individuals were sorted for size and weight and extremes discarded. Selected animals with mean weight of 8·18 ± 1·55 g were moved to three smaller holding tanks, at high density in order to avoid aggression: 20‐L white rounded tanks on a recirculation system (37 cm diameter × 28 cm height, with a drainage tube in the centre 3 cm diameter and 20 cm height) and acclimated for 3 weeks prior to behavioural tests ( n = 96/tank; N = 288). A total of 94 animals with mean weight of 10·43 ± 1·33 g were individually tested for DAI (DAI latency) distribution and net restraining (NR) behaviour ( n = 94) in parallel tanks to those they were held in (VeraCruz et al . ( 2011 ). To test for behavioural consistency, the DAI latency measure test was repeated 24 h after the first feeding event. After the analysis, the distribution regarding the DAI latency was determined and used as a baseline to discriminate the population by personality and establish the corresponding cut‐off DAI latencies. Animals were not individually tagged in our experiments, and behavioural screening was individually performed before designation of personality.

Nile tilapia ( Oreochromis niloticus ; wild‐type and homogold strains) were obtained at the Institute of Aquaculture, University of Stirling, UK. Prior to the experiments, these fish had been reared together, each family, in the same tanks under normal stocking conditions. Fish were kept in a 500‐L fibreglass tank within a RAS system with a continuous water flow. Animals were reared under a 12‐h/12‐h light–dark cycle, and the mean water temperature was of 26·8 ± 1·5 °C. Aeration was supplied through an air stone, and fish were fed with a commercial diet (Skretting ® Trout Standard Expanded) twice a day.

Using a previously established S. iniae infection model, we tested the behavioural fever response (under T CH conditions) of proactive, reactive and randomly selected individuals using an intraperitoneal route of infection (Table 1 , Fig. 5 ). Throughout the 5 days of bacterial challenge, no abnormal swimming behaviour was observed in any experimental group. Control, i.p. injected with PBS 0·1%, individuals did not present any mortality throughout the experimentation. In the proactive group, mortalities were observed with three dead fish at 2 days post‐infection, and in reactive fish, a single mortality was recorded at 1 day post‐infection. In the first hour of acclimation to the T CH conditions, differences in thermal preference were found between the groups tested (proactive T = 27·95 ± 0·05 °C; reactive T = 29·17± 0·18 °C; control T = 31·94 ± 0·58 °C; Kruskal–Wallis χ 2 (2,179) = 129·6, P < 0·001) with both personality groups showing a decrease in acute thermal preference. At 24 h post‐infection, a behavioural fever response was evident for both groups with reactive individuals showing a significantly altered temperature preference (proactive T = 31·75 ± 0·25 °C; reactive T = 32·94 ± 0·06 °C; Mann–Whitney, U ‐test = 43·50; P < 0·001). Over the next 48 h, both groups showed a decrease in thermal preference with no significant difference between thermal preferences for the personality groups and the control group (proactive T = 30·63 ± 0·23 °C; reactive T = 30·88 ± 0·28 °C; control T = 30·73 ± 0·24 °C; Kruskal–Wallis χ 2 (2,98) = 1·800, P = 0·407). However, at 4–5 days thermal preference returned to the previously measured final thermal preference of each group (Fig. 5 : proactive: Wilcoxon rank test: Z = −3·510; P < 0·001; T = 31·13 ± 0·10 °C; reactive: Z = −4·621; P < 0·001; T = 30·03 ± 0·12 °C; control: Z = −5·012; P < 0·001; T = 29·36 ± 0·16 °C).

Without any previous behavioural screening, eight groups of naïve individuals ( n = 64) were tested under T CH conditions. The population distribution in the last 8 h over 48‐h test was significantly higher for chamber 4 as previously observed ( T = 31·19 ± 0·07 °C) (Wald χ 2 (4 , 1277) = 26·781; P < 0·001) (Fig. 4 a). A posteriori behavioural screening of this population identified that 70% of the fish found in 31·19 ± 0·07 °C–33·59 ± 0·10 °C exhibited proactive traits. Intermediate and reactive individuals were identified in chambers with temperatures ranging between 27·80 ± 0·07 °C and 29·74 ± 0·05 °C (Fig. 4 b). In parallel, three control groups with eight naïve fish were tested in the T R environment ( T = 26·58 ± 0·35 °C) to evaluate the possibility of chamber preference independent of temperature. A homogeneous distribution was observed throughout the tank (Wald χ 2 (1 , 475) = 65·933 P > 0·05) with no significant differences in preference.

For different personalities, distributions across chambers under T CH were not significantly different (Wald χ 2 (2 , 2698) = 16·996, P = 0·590), however significantly different acute thermal preference were observed (Kruskal–Wallis χ 2 (2,539) = 7·675, P = 0·022). This is shown as the mean of the individuals at each time point ( n = 60) for each chamber temperature (eqn 1 ). It is worth noting that this latter difference is strongly influenced by the thermal preference found for intermediates ( T = 30·28 ± 0·18 °C) that was lower than the measured proactive preference ( T = 31·10 ± 0·15 °C; Mann–Whitney, U ‐test = −2·579, P = 0·01). No differences were found between intermediate and reactive groups ( T = 30·88 ± 0·15 °C; Mann–Whitney, U ‐test = −1·854, P = 0·06) or between proactive and reactive groups (Mann–Whitney, U ‐test = −1·225, P = 0·22). Latency to exit and number of chamber transitions did not reveal any significant differences ( F 2,33 = 0·357; P = 0·702; F 2,33 = 0·142; P = 0·868) during the first hour of acclimation to the thermal gradient.

Changes in acute thermal preference for each group during the first hour were assessed by counting the number of fish in each chamber at each minute ( n = 60 events). The provision of thermal choice ( T CH ) induced an immediate and significant change highlighting preference for the warmest chambers (chamber 4: T = 29·86 ± 0·02 °C and chamber 5: 33·08 ± 0·02 °C) in comparison with T R conditions (Wald χ 2 (4 , 1497) = 148·447 P < 0·001; interaction Chamber × Condition). Distribution under thermal restriction ( T R ) condition was chamber dependent (Wald χ 2 (4 , 897) = 142·93, P < 0·001) for all groups tested, indicating that in the absence of a gradient fish remain in the chamber they are introduced into. Comparing T R and T CH control groups, latency to exit and number of transitions between chambers show that fish became more exploratory when in a thermal gradient, with lower latency for chamber transitions and a higher number of transitions between chambers ( F 1,3 = 9·092, P < 0·001 and F 1,3 = 193·727, P = 0·02, respectively).

Group clustering with fish from the same batch was used to define the cut‐offs of DAI latencies as shown (Fig. S2). PC1 of DAI latencies explained 88% of DAI latency variance, while the PC1 of the restraining test explained 62% of the restraining test variance, with 75% of the overall behavioural variation being retained in these two components. Fish tested from different batch and strain showed parallel cut‐offs for DAI latency (Fig. S2). The consistency over time and across behavioural paradigms of behavioural responses is shown in Table 2 and illustrated in Fig. S2. Interestingly, mostly of the variables were significantly correlated with DAI latency corroborating the use of this variable to separate the groups. Principal component analysis collapsing all behavioural parameters measured is represented in Fig. 1 . The groups exhibited, ‘driven’ by DAI latency reveal that this behaviour is a robust predictor of distinctive personality groups, as PC1 explained 34% of such grouping. Behavioural variation unsurprisingly was significant across situations. Nevertheless, 50% of the grouping was explained by the two first dimensions of the PCA showing that personality is well represented in the behavioural phenotypes. Individual variation of each personality trait regarding each of the behavioural paradigms tested is shown in Table S1. Three groups were generated using determined DAI latencies cut‐offs (proactive n = 30; intermediate n = 27; reactive n = 26) identifying a balanced population of personality traits based on swimming activity, VF, repeated DAI events and the three variables taken from the restraining test. One of the clusters (which we categorized as proactive individuals) took less time to restart feeding (one‐way anova; post hoc Fisher LSD P = 0·04; P < 0·001) and had a lower number of opercular beats after being deployed in the new environment ( post hoc Fisher LSD P = 0·02; P < 0·001) when compared with the other two clusters (intermediate and reactive, respectively). When compared with reactive individuals, proactive fish showed increased escape behaviour ( post hoc Fisher LSD P < 0·001), increased swimming and exhibited lower levels of thigmotaxis ( post hoc Fisher LSD P = 0·02). Differences between personality groups under the different behavioural tests are shown in Fig. 2 . No differences in initial body weight, coefficient of variation of initial body weights and total biomass were observed between individuals ( F 2,71 = 1·215; P = 0·303).

Discussion

The linkage between thermal preference and animal personality is a relatively new research paradigm. To the best of our knowledge, few studies have used similar approaches to assess personality in fish (Killen 2014; Rey, Digka & MacKenzie 2015) or other vertebrate species. In Tilapia housed in a freely accessible thermal gradient, spanning approximately 12 °C, proactive individuals showed preference for higher temperatures, as compared with reactive individuals. Testing was carried out by both pre‐screening the fish for animal personality prior to thermal preference testing and vice versa. To mimic natural conditions, fish should be capable to express their full behavioural repertoire under artificial environments. Giving control or some sense or control can provide to fish the opportunity to activate proper coping mechanisms and minimize the effect of a punishment avoidance situation (normally the effect of standard behavioural paradigms to screen fish), for example by escaping, avoiding, moving or defend themselves against it. This allowed us to accurately validate the potential of thermal preference as an indicator of animal personality in mobile aquatic ectotherms. Additional experiments using bacterial infection further highlighted the importance of thermal choice at an individual level. Our results are of significant importance to understand the adaptive meaning of animal personality regarding ecological performance within a population.

Driven by the statement of Bell et al. (2009) in which biologically meaningful variability is conditioned by consistency of individual patterns, we demonstrate the repeatability of individual patterns over time (DAI 1 and DAI 2 ) and across different situations. Our results show divergent personality traits within our population with proactive individuals being characterized by a faster FI recovery after transfer into a NE, lower breathing frequency and being more prone to escape from restraining, as compared with reactive individuals. The existence of ‘intermediate’ individuals is common when working with domestic species due to low environmental challenge (Boersma 2011). Despite the extensive range of behavioural methodologies used, different suites of personality traits have been observed in many animal species (Reale et al. 2007; Briffa, Bridger & Biro 2013; Montiglio et al. 2014) and multiple fish species including the olive flounder, Paralichthys olivaceus (Rupia et al. 2016), Mulloway Argyrossomus japonicas (Raoult et al. 2012), Gilthead Seabream Sparus aurata (Herrera et al. 2014), Senegalense sole Solea senegalensis (Castanheira et al. 2011; Martins et al. 2011), European sea bass Dicentrarchus labrax (Killen et al. 2012) and zebra fish D. rerio (Rey, Digka & MacKenzie 2015). In the case of Tilapia, the strong correlations between DAI and personality highlight the usefulness of DAI as a screening tool for boldness. Our results also support the use of VF as an indicator of behavioural–physiological traits by showing that VF is a good tool for predicting feeding behaviour in the same context. The same positive correlation between VF and DAI was also found by Barreto & Volpato (2011). In summary, such behavioural responses have also been described in other fish species (Overli et al. 2002; MacKenzie et al. 2009; Silva et al. 2010; Martins et al. 2011) corroborating the idea that by choosing and tailoring behavioural tests different behavioural profiles can be accurately identified.

Acute Thermal Period Fish exhibited significantly different distributions over T R and T CH in agreement with our predictions based around previous observations in zebra fish (Rey, Digka & MacKenzie 2015). Fish were able to see between chambers, and in the absence of an environmental motivation such as a thermal gradient (T R ), fish displayed strong shoaling behaviour. An environmental enrichment, such as the gravel on the bottom of the tank, might have made individuals inhibit threat‐sensitive behaviour such as exploration, risk taking or foraging and to reduce stress (Galhardo, Correia & Oliveira 2008). In contrast, under T CH conditions control fish distribution was strongly affected and the thermal gradient stimulated increased exploratory activity between chambers. Thus, the fish are able to detect environmental thermal variation that could be linked to how individual animals appraise such changes (Martins, Conceição & Schrama 2011b). Perception of positive environmental factors combined with the possibility to control phenotypically such environmental condition (e.g. by swimming through the experimental tank) is known to have a positive impact on animal welfare (Greiveldinger, Veissier & Boissy 2009). The change in swimming patterns and distribution over the first hour of test might be the result of a trade‐off between the costs and benefits associated with thermoregulation (Shine & Madsen 1996). Differences in swimming activity regarding exploration have previously been reported (Careau et al. 2008) as an intrinsic pattern of personality traits in several animal species (Reale et al. 2007). Several findings have shown that individual metabolic patterns may be related to different behavioural traits in animals (Biro & Stamps 2008; Careau et al. 2008; Herrera et al. 2014). This behavioural and physiological covariation has been previously hypothesised in which personality types mirror variation in metabolic patterns (Biro & Stamps 2010; Careau & Garland 2012; Metcalfe, Van Leeuwen & Killen 2016). Alternatively, the impact of the effects of stress on thermal preference due to emotional fever could also explain the change in fish distribution with naïve animals experiencing higher stress responses to the novel tank environment (Rey et al. 2015).

Temperature Preference Predicts Personality – Final Thermal Period As stated by Jobling (1981) if given enough time fish will congregate at the final thermal preferendum, which is deemed to correspond to the T optima at which fish growth fast. In Nile tilapia, the reported T optima is of 27–33 °C (Azaza, Dhraïef & Kraïem 2008; Azaza et al. 2010). Our results agree with this thermal range corresponding to the final thermal preferendum (Fry 1947). When fish were screened posteriorly for personality, our results confirmed an increased final thermal preference in proactive fish in comparison with both other groups tested. Therefore, thermal preference can be used as a physiological paradigm to screen fish for personality and provides an important insight into individual variation. Our results are in agreement with previous studies in zebra fish (Rey, Digka & MacKenzie 2015). Thus, the higher end of temperature preference for the species may reflect increased energetic requirements in proactive individuals with a higher metabolic engine. Interestingly, Blackmer et al. (2005) suggested that increased energy requirements in proactive individuals are compensated by swimming behaviour, that is displacement to warmer places, another component of the energetic repertoire. Such association between metabolic rate and behaviour leading to elevated energy expression in proactive individuals has been previously reported (Careau et al. 2008; Biro & Stamps 2010). Despite the apparently small difference between thermal preferences for each personality type (about 1 °C), it has a relevant ecological impact as metabolic rates from proactive individuals increase by > 10% (Clarke & Johnston 1999) when compared to reactive conspecifics. Thermal preference could be seen as a coadaptation of natural selection being partially explained as a trade‐off between inherited behavioural predispositions and physiological demands (Korte et al. 2005). Thus, in mobile ectotherms including fish, we propose that thermal preference is correlated to animal personality and understanding how individuals use thermal gradients will aid in our understanding of how individuals in a population optimize and adapt performance and fitness in different environmental scenarios.