Individuality across environmental context in Drosophila melanogaster

Animal species display enormous interindividual differences in behavior. The temporal consistency of these interindividual differences allows for defining these idiosyncrasies as animal individuality, also called animal temperament, behavioral syndrome, or if the entirety of trait differences between individuals is meant, even animal personality (Sih et al., 2004; Dall et al., 2004; Gosling, 2008; Réale et al., 2007). Animal individuality has been described for numerous species and behaviors, including: mouse exploration behavior (Freund et al., 2013), great tit feeding preferences (Partridge, 1976), octopus threat responses and feeding behavior (Mather and Anderson, 1993), pea aphid threat responses (Schuett et al., 2011), as well as vinegar fly handedness (Ayroles et al., 2015; Buchanan et al., 2015), olfaction (Honegger et al., 2020), phototaxis (Kain et al., 2012), and object orientation (Linneweber et al., 2020). The temporal consistency of individuality allows for discriminating it from short-term behavioral changes, like internal state variations. Furthermore, temporal consistency of behavior allows modifications by learning (Kahsai and Zars, 2011) and behavior-based natural selection (Lapiedra et al., 2018). Historically, the ‘nature vs. nurture’ debate (i.e. genome vs. environment) has shaped our understanding of the origins of behavioral individuality (Galton, 1875). Stochastic developmental processes are a more recently found factor (Honegger and de Bivort, 2018; Linneweber et al., 2020), providing an additional layer to gene-environmental interactions (Traynor and Singleton, 2010). Most experimental studies on animal individuality have focused on a single behavior set in a singular environment, yet commonly used definitions of animal individuality in predominantly field-based disciplines, such as behavioral ecology (Réale et al., 2007), and the definition of human personality entail behavioral consistency across situations (Mischel, 1968). Similar to humans, animals will never re-encounter the exact same situation under natural conditions, and animal personality can be an important factor in determining the ecological success of an animal and, as a consequence, its entire population (Takola and Schielzeth, 2022). It is, therefore, crucial to understand how an animal’s personality or individuality in specific traits interacts with variable environments. Despite decades of interest in animal individuality across situations, the experimental evidence remains insufficient, and the results are contradictory, with studies arguing for (Briffa et al., 2008; Herborn et al., 2010; Kralj-Fiser et al., 2007; Mowles et al., 2012) and against (Coleman and Wilson, 1998; D’Eath and Burn, 2002) individuality in animals across situations with many finding mixed results (Boissy and Bouissou, 1995; Spoolder et al., 1996; Werkhoven et al., 2021). Here, we sought to systematically investigate the consistency of individual behavioral differences across situations using laboratory-controlled variable environmental conditions.

There is ample evidence for the consistency of human personality and animal individuality over time (Costa and McCrae, 1988; Weisbuch et al., 2010). Intuitively, humans attribute consistent personalities to other humans (Ross, 1977). The consistency of human behavior across situations, including environmental context, nevertheless remains highly debated, with several studies arguing for (Costa and McCrae, 1988; Fleeson, 2001) and against it (Hartshorne and May, 1930; Mischel, 1968). Summarized as the ‘person-situation debate,’ psychologists have argued for over a century about the relationship between personality and the situational environment, placing this question at the core of personality psychology (Mischel, 1968). Animal models for behavioral consistency across situations provide an opportunity to address this question. In our study, the situation is the sum of all environmental features where a specific behavioral task is performed. The work presented here establishes a quantitative animal model to investigate this and related questions about behavioral consistency across environmental situations.

The vinegar fly Drosophila melanogaster provides an enormous toolset for studying individuality and its origins across multiple scales, from neurons to circuits to behavior. This comprises a rapidly unfolding connectome (Dorkenwald et al., 2024; Xu et al., 2020; Zheng et al., 2018), an extensive collection of cell type-specific, binary expression systems (Luan et al., 2006b; Pfeiffer et al., 2010), and a similarly extensive collection of tools for manipulating neuronal function, including neuronal silencers (Baines et al., 2001; Sweeney et al., 1995), activators (Luan et al., 2006a), and thermogenetic (Hamada et al., 2008) and optogenetic (Klapoetke et al., 2014; Pulver et al., 2009) effectors. Hence, the availability of tools and reagents for manipulating small, defined sets of neurons is no longer a major limitation in answering questions about individual behavioral differences. Instead, what has been missing is an integrative approach combining different high-throughput behavioral assays, which would allow for behavioral screening and classification of large quantities of individual flies for several different behavioral parameters across a variety of environmental contexts.

In recent years, mainly low-throughput, single-fly visual assays like the classical two-stripe Buridan assay (Bülthoff et al., 1982; Götz, 1980), arenas for tethered walking (Seelig et al., 2010) or virtual flight (Mathejczyk and Wernet, 2020) have been used to study behavioral individuality in vision-based tasks (Linneweber et al., 2020; Mathejczyk and Wernet, 2019). In Buridan’s paradigm, flies walk between two inaccessible visual targets (two vertical high-contrast stripes). The stripes are presented in a homogenously illuminated surrounding with no other visual or non-visual cues. Apart from work on individuality involving Dorsal Cluster Neurons (DCNs) (Hassan et al., 2000), also known as Lobula Columnar Neuron 14 (LC14) (Otsuna and Ito, 2006), Buridan’s paradigm has been used to show the importance of the central complex for normal walking speed (Strauss et al., 1992) and for studying photoreceptor function or development (Kiral et al., 2020; Strauss et al., 2001), and visual memory (Neuser et al., 2008). Other circular arenas have been frequently used to study locomotory activity without visual stimuli (Branson et al., 2009; Robie et al., 2017; Valente et al., 2007).

In contrast to these low-throughput assays, high-throughput assays for studying the behavioral responses of large amounts of animals have been used primarily for genetic screens. Examples include apparatuses to study phototaxis (Hirsch and Boudreau, 1958), geotaxis (Hirsch, 1959), olfactory learning (Jiang et al., 2016; Quinn et al., 1974), and circadian rhythms (Konopka and Benzer, 1971). The Ethoscope (Geissmann et al., 2017) and MARGO (Werkhoven et al., 2019) are recent additions to this repertoire that offer several additional high-throughput assays. Except for the Drosophila activity monitors (DAM, Trikinetiks), Ethoscope, and MARGO, these group assays cannot distinguish individual behavioral differences. Only the Ethoscope and MARGO provide some visual modules. Hence, progress towards understanding individuality in visually guided behaviors in a high-throughput fashion remains meager, with few exceptions (Ayroles et al., 2015; Buchanan et al., 2015; Kain et al., 2012).

To investigate the influence of the environment on the consistency of visually guided individuality and to systematically quantify individuality across many behavioral traits and their dependency on the environment in walking flies, we built a new multi-parameter assay called Indytrax. Indytrax is a compact and affordable Mathworks MATLAB-based modular assay and tracker for high-throughput individual ethological quantification. The assay includes swappable modules for multiple high-throughput Buridan paradigms, a Y-maze choice assay for studying the decision-making and locomotor handedness (Buchanan et al., 2015), and a simple setup to quantify locomotor activity in large groups of flies (Branson et al., 2009). We used this tracker to determine the relative contribution of various environmental features (temperature, visual cues, arena shape) to the consistency of individual behavior. Our results show that identically defined visual stimuli (stripe color, width, and length) do not necessarily evoke the same behavior in an individual when presented in different environments. Conversely, we find that specific individual behavioral traits (attention, exploration, and anxiety, categorized via multiple behavioral parameters) remain consistent even in different environments. Finally, using an LED-based virtual flight arena, we show that attention towards a stimulus remains consistent even across different modalities of locomotion (walking versus flying) when using the same Buridan-type visual stimulus. Hence, we find consistent behavioral traits across situations in our fly model, in analogy to what has been demonstrated for humans (Costa and McCrae, 1988; Fleeson, 2001). Altogether, our results provide the foundation for understanding the complex interaction between individuality and the environment, including the question of why individual behavior is more unpredictable in variable naturalistic conditions, much like the consistency across situations in human subjects (Mischel, 1968).

The goal of our study was to understand to what extent animal individuality is influenced by situational changes in the environment, i.e., how much of an animal’s individuality remains after one or more environmental features change. Based on our previous work on animal individuality (Linneweber et al., 2020), we started our analysis in the multiparametric two-stripe Buridan’s paradigm (Figures 1 for technical details) (Colomb et al., 2012; Linneweber et al., 2020). As expected, we measured interindividually variable, yet remarkably consistent behavioral responses, both on the individual (Figures 1b and 2a, Figure 2—figure supplement 2a) and mean group level (average of all individuals in the same situation, Figure 2—figure supplement 1a), when tested in the exact same situation with all environmental features kept the same. This was true for the five representative behavioral parameters (out of 20 parameters total, see Supplementary file 1 and Figure 4—figure supplement 3, the specific five parameters were chosen as they describe important information about the behavior and importantly can be measured across situations), defined in three behavioral traits: exploration (% of time walked, walking speed), attention (vector strength, angular velocity), and anxiety (centrophobicity). The exploration trait is characterized by parameters describing activity. Parameters related to attention reflect heightened responses to visual cues, but unlike commonly used metrics, such as angle or stripe deviations (Colomb et al., 2012; Linneweber et al., 2020), they can be calculated in the absence of visual cues. Lastly, the parameter centrophobicity, used as an indicator of the trait anxiety, is linked to the open-field test in mice, where the ratio of wall-to-open-field activity is frequently calculated as a measurement of anxiety (Carter and Shieh, 2015).

Figure 1 with 1 supplement see all

Download asset Open asset

Figure 2 with 2 supplements see all

Download asset Open asset

Modifying the visual stimulus within the Buridan arena (0, 1, and 2 stripes were tested, Figures 1c and 2b, Figure 2—figure supplements 1b and 2b and Figure 2—figure supplement 2b) significantly affected the mean group behavior across all three behavioral traits (Figure 2—figure supplement 1b). Especially, the means of the three parameters (% of time walked, vector strength, and centrophobicity) were strongly affected by the stimulus modification. Surprisingly, despite this change in group means, we observed only a minor effect on individuality when testing the same fly in different situations (Figures 1c and 2b, measured by a high Pearson correlation coefficient and a consistent rank within the distribution). The individuality for the traits exploration and anxiety persisted even upon modulation (variation of stripe number) and after removing visual cues from the arena. Only those parameters describing the fly’s attention towards visual cues showed an unsurprising reduction in behavioral consistency upon removal of the visual stimuli (Figure 2b). Altogether, these results demonstrate that individuality persisted after stimulus modifications within the same arena, even when the modifications resulted in altered group responses.

To further characterize the surprising relationship between individuality and group means upon situational changes, we modified the homogeneity of background illumination in the Buridan arena using two different diffuser materials (paper and acryl-based covers of fluorescent ring lights) and compared these conditions to a newly designed Buridan LED arena displaying the same visual stimulus (stripes of the same color, width, and height) (Figures 1d and 2c and Figure 2—figure supplements 1c and 2c). We measured no significant differences between the group means of the two diffuser conditions and only minor group-based changes compared to the LED Buridan (Figure 2—figure supplement 1c). We then tested the correlation of individual behavior across these conditions, as a measurement of behavioral consistency, expecting high correlations. Indeed, the different diffusers within the same arenas only had minor effects on the behavior of individual flies (Figure 1d). Conversely, the individual response changed dramatically when the same fly was tested again in the LED arena, albeit presenting the same stimulus (Figure 1d). A fly’s attention and anxiety parameters quantified within the original arena did not allow for any prediction for the LED arena (Figure 2c). Significant correlations between the LED arena and the original arenas were only measured for exploration parameters (Figure 2c). This unpredictability of individual fly behavior across situations was once again particularly surprising since group-based performances remained similar (Figure 2—figure supplement 1c). Finally, a detailed comparison of males and females revealed only quantitative but no qualitative differences across all parameters between the two sexes (Figure 2—figure supplements 1 and 2).

Based on the surprising result that individuality (Gosling, 2008) of visual orientation behavior is more strongly influenced by the arena type (despite producing very similar group responses) and, only to a lesser degree, dependent on the visual cue (despite strong differences in the group response), we set out to characterize the contribution of environmental situations to individuality even more quantitatively. We, therefore, built a multiplatform high-throughput assay (Indytrax) to quantitatively study the behavior of the same individual flies in different environmental situations (Figure 3, technical details: Figure 3—figure supplement 1).

Figure 3 with 2 supplements see all

Download asset Open asset

Using Indytrax, we compared the behavioral parameters of each fly in eight different environmental situations (Figure 4, Figure 4—figure supplement 1, Figure 4—figure supplement 2, Figure 4—figure supplement 3). The flies were tested in two independent arenas (Buridan and Y-maze) at two different temperatures (23°C and 32°C, Figure 3—figure supplement 2) and under both light and dark conditions. Our analysis showed that temperature significantly affected exploration parameters on the group mean level. As expected, most animals tested were more explorative at higher than at lower temperatures (reviewed in: Gibert et al., 2016; Figure 4—figure supplement 1). Despite these group changes, individuals kept their rank within the group, independent of the changed baseline activity. For example, fly #1 was the least explorative, fly #2 exploration performance ranked medium, and fly #3 the highest (Figure 4a). The graphical analysis of exploration parameters (Figure 4b) confirmed that most individuals maintain their rank in the distribution under different temperatures and light-dark conditions, whereas the individual distribution rank changed between the different arena types. This relationship was less pronounced for attention parameters. The detailed correlative analysis between individuals underscores these observations (Figure 4c, Figure 4—figure supplement 2 and full analysis Figure 4—figure supplement 3), containing also many parameters that can only be measured under specific situations (e.g. deviations from a stripe can only be measured under light conditions and not in the dark): Amongst the exploration parameters, the percentage of time walked significantly correlated between individuals (Pearson r=0.263–0.398) independently of temperature, illumination, and arena. Walking speed was highly correlated between individuals (Pearson r=0.81, 0.825) under temperature and illumination changes but uncorrelated in different arena types (Pearson r=0.091). Vector strength was the most correlated attention parameter. The individual correlations ranged from Pearson r=0.533 to –0197. Angular velocity significantly correlated with temperature and illumination (Pearson r=0.246, 0.354) but not in different arenas (Pearson r=0.087). The same is true for anxiety measured through centrophobicity (Pearson r=0.694, 0.740, 0.033). In summary, these data revealed that individuality measured in three traits is mostly unaffected by temperature and illumination but strongly affected by the arena type. Our analysis confirmed that even in situations with a large effect on the mean group behavior (like temperature), individual behavioral consistency of idiosyncrasies remained high, while conversely, other environmental situations like the arena type had little impact on the mean but a massive effect on individual behavioral consistency (Figure 4—figure supplement 1). Qualitatively similar results leading to the same conclusions were obtained for both sexes (Figure 4—figure supplement 1a, Figure 4—figure supplement 2a) and two different genotypes (Figure 4—figure supplement 1b, Figure 4—figure supplement 2b).

Figure 4 with 3 supplements see all

Download asset Open asset

Next, we drastically modified the behavioral situation while keeping the visual stimulus as constant as possible. We, therefore, decided to investigate the influence of a fly’s modality of locomotion (walking versus flying) on individuality. To do this, we designed a new virtual flight arena for tethered flight utilizing the exact same LED panels as were used in the LED Buridan arena (Figure 5a-c, Figure 5—figure supplement 1 for technical details), to maximize the similarity of the visual context. This flight simulator tracked a fly’s heading in real-time in response to dynamic or static panoramic visual stimuli with high resolution (1.4° pixel size). This allowed us to quantitatively compare the visual decisions of both walking and flying flies in response to standard optomotor and Buridan-like stimuli (Figure 5d–e). We first tested individuality in flying flies over time under the same and a set of modified visual stimuli (Figure 6, Figure 6—figure supplement 1 and Figure 6—figure supplement 2). Each fly was tested over two consecutive days under three different visual conditions (dark stripes on a bright background with either 50% or 100% contrast, and an inverted Buridan with bright stripes on a dark background and 100% contrast) (Figure 6a). Overall, the flies showed some individuality in visual attention across days and contrast. This was also true for the inverted Buridan stimulus, in which the flies oriented themselves toward the dark areas, similar to previous results (Han et al., 2021). To allow a quantitative comparison across behavioral traits between walking and flight behavior, we derived an adapted set of parameters for exploration and attention quantification (exploration parameters: # of pauses, Absolute angular velocity; attention parameters: Vector strength, Angular velocity, and Median heading axial, Figure 6b). The resulting analyses revealed individuality in flying flies (Figure 6c). Only the inversion of the Buridan stimulus resulted in variable angles towards the black background on different days. However, it is remarkable that on a given day, each fly selected an arbitrary heading that was kept for the entire experimental length (Figure 6a). Again, the quantitative analysis between males and females did not reveal qualitative differences (Figure 6—figure supplement 2).

Figure 5 with 1 supplement see all

Download asset Open asset

Figure 6 with 2 supplements see all

Download asset Open asset

After confirming that individuality also existed in flying flies, we tested how different locomotion modalities affected a given animal’s behavioral individuality. Behavioral parameters of both walking and flying flies were quantified in response to the same panoramic LED screen displaying an identical two-stripe Buridan stimulus (Figure 7a, Figure 7—figure supplement 1 and Figure 7—figure supplement 2). We expected one of three possible outcomes: (1) There is no consistency of individual behavioral traits across flying and walking. (2) Only some behavioral traits are similar across flying and walking. 3. The fly’s individuality remains consistent across the same visual cue, independent of the locomotive modality. Subsequent quantitative analysis indeed revealed a significant correlation for angular velocity between flying and walking flies (Figure 7b), indicating that a fly’s attention towards a visual object is, to some degree, preserved independent of the locomotor modality. In contrast, we found no statistically significant correlations for any of the exploration parameters between flying and walking. Finally, a more detailed analysis revealed mostly comparable results for male and female data (Figure 7—figure supplements 1 and 2).

Figure 7 with 2 supplements see all

Download asset Open asset

The data presented here reveal a hierarchical influence of different parameters within the environmental context on individuality (Figure 8): First, we conclude that time (at least within the frame of days) has a negligible influence on individual behavioral responses (the basis of the definition of animal individuality in a single situation), since we found consistency of individual behavioral traits and parameters over time in both locomotor modalities, walking and flying. Second, temperature had the second weakest impact on individuality (although only tested for walking behavior). This contrasts with the dramatically altered group mean responses, a difference that can be explained by scaled individual responses in which individuals keep the rank in the group. Third, the nature of the visual input appears to influence the individual orientation responses but has little impact on the exploration parameters. Finally, we identified the most critical situational environmental features that determine individual responses as both the arena type and the locomotor modality (walking vs. flying). Therefore, we conclude that the individuality of visually guided behaviors persists specifically in some traits across situations, but the environmental context plays a prominent role in defining individual behavioral responses.

Figure 8

Download asset Open asset

To verify the results of our multiple correlative analyses, we took advantage of a generalized linear model (GLM, Figure 9). We first analyzed the effect of time, temperature, vision, and arena on the mean group responses analogous to Figure 2—figure supplement 1, Figure 4—figure supplement 1, Figure 6—figure supplement 1, Figure 7—figure supplement 1 (Figure 9a). Our reanalysis of the walking data confirmed the temperature dependency of activity parameters (Figure 4—figure supplement 1). It also revealed effects on the group means by vision and arena, particularly for walking speed. The orientation parameter vector strength was similarly affected by temperature, vision, and arena (see also Figure 2—figure supplement 1, Figure 4—figure supplement 1), while the position parameter centrophobicity was most strongly affected by the arena (see also Figure 2—figure supplement 1, Figure 4—figure supplement 1). For flight parameters, the reanalysis of mean data revealed a strong effect of vision on the orientation parameter vector strength and the heading parameter median heading axial (see also Figure 6—figure supplement 1). Our reanalysis, therefore, confirmed our previous analysis of mean group data.

Figure 9

Download asset Open asset

The next step of our reanalysis was to compare the rank changes of our flies across situations analogous to the correlation analysis we had done (Figures 2, 4, 6 and 7). Our reanalysis revealed significant changes in activity parameters and the orientation parameter vector strength. In these parameters, the effect on the rank of each individual increases from time over temperature, vision, and arena to the behavioral state. Confirming the data we found in Figures 2, 4, 6—8. Also, the lack of a significant effect of the behavioral state for angular velocity confirms our correlation analysis of Figures 7 and 8.

Furthermore, we generated a GLM with ridge regression to show the effect of changing environmental situations on individual rank differences (Figure 9c). First, we converted the individual mean values for every behavioral trait to within-group ranks (0–100). We then used the GLM to predict the average change in rank for a fly when altering a single environmental context. Each bar in Figure 9c represents the predicted influence of changing a certain environmental parameter on the persistence of each behavioral trait, where larger values indicate a larger influence on rank persistence and smaller values indicate less influence of the respective environmental context on rank persistence. Due to the response variable being in rank points, the predicted rank shift due to changing the respective environmental context is the height of a bar in Figure 9c added to the indicated intercept values. Based on our correlative analysis, we predicted a spectrum of context dependencies of individual behavioral consistency, where behavioral state > arena > vision > temperature > time in their ability to alter the persistence of individuality. The output of the GLM analysis supports that hypothesis, since calculated coefficients also indicate such a spectrum from rather stabilizing (e.g. time, temperature) to rather disruptive (arena, behavioral state). Our analysis shows that the effect was statistically significant for most environmental parameters.

For the activity parameters, time, temperature, and vision significantly stabilized individual phenotypical rank. In contrast, the arena and behavioral state significantly had a negative effect on rank consistency. For the orientation parameter vector strength, we could fully replicate our previous findings that time had the strongest positive effect on consistency, followed by temperature. Vision had no significant impact. In contrast, the arena had a small negative effect on behavioral consistency, while the behavioral state had a strong negative effect. In the second orientation parameter (angular velocity), a significant destabilizing effect was only found for the behavioral state. For the positional parameter (centrophobicity), only vision had a significant effect on behavioral consistency.

Furthermore, as an additional step in our reanalysis, a hierarchical linear mixed-model analysis confirmed the findings of the previous analyses while providing a more complete and accessible overview of the influence of different environmental contexts on behavioral traits and the individual consistency (repeatability) of these traits across environmental contexts. For walking as well as flight, the majority of behavioral traits showed significant repeatability across different contexts with ICCs on the order of about 0.1–0.6 for walking and 0.2–0.4 for flight (Figure 10, see also Supplementary file 3). When analyzing walking data, the highest ICCs were measured for activity and orientation-related traits, indicating that flies tend to keep their relative rank for these traits across environmental contexts. In contrast, centrophobicity as a measure for anxiety showed much lower repeatability across different contexts (ICC near 0). The pattern of repeatability was similar for flight traits, with high repeatability for activity traits and low repeatability for the exploratory trait (heading). Both vector strength and the heading showed only low ICCs across different contexts during flight, since the inversion of contrast had a very strong effect on the flies’ orientational choices (see Figure 6), reducing rank consistency in these traits. Although the flight assays showed slightly lower ICCs overall, possibly reflecting greater trial-to-trial noise, our key finding that specific individual behavioral traits are consistent across different environmental contexts was robust across behavioral modalities (walking vs flight). Group-level behavior was shifted by environmental manipulations (e.g. increasing the temperature led to an increase in mean activity in walking and flight assays). However, individual differences were not erased by these shifts. E.g., in each context, flies that were relatively less active than the average in one environmental context remained so in other environmental contexts, therefore, preserving rank order. Most environmental context effects appeared as shifts in the intercept (group means) rather than changes in the rank order of individual differences.

Figure 10

Download asset Open asset

In summary, using three independent mathematical approaches, we have established a hierarchy of different environmental situations on behavioral consistency. This data demonstrates that temporal stability is more prevalent than consistency across situations in animals. Like humans, animals also have significant consistency across situations, but similar to humans, different situations evoke different behavioral responses, masking some measurable behavioral consistency.

Our work presented here focused on the question of whether the combination of consistent behavioral parameters ranging across different traits and, therefore, defining an animal’s individuality are invariable across situations or whether they are re-shaped by different environmental contexts. This question is closely related to the questions in the human ‘person-situation debate‘ (Mischel, 1968). Until now, in the neurosciences, individuality across situations has been largely ignored since many commonly used definitions of animal individuality only refer to the repeatability of behavior over time in a single environmental situation (Gosling, 2008). The contrary is true for mainly field and observation-based sciences, such as ethology and behavioral ecology that have since decades acquired observations for (Boissy and Bouissou, 1995; Spoolder et al., 1996) or against (Coleman and Wilson, 1998; D'Eath & Burn, 2002) behavioral consistency across situations, with some of the most convincing work probably found in hermit crabs (Briffa et al., 2008; Mowles et al., 2012), blue tits (Herborn et al., 2010), and geese (Kralj-Fiser et al., 2007). Our work establishes animal individuality across situations under several tightly controlled laboratory conditions, revealing that animal individuality shares remarkable similarities to what has been described in human personality research. In agreement with Vansteelandt and Van Mechelen, 2004 our results are in line with the concept of the personality triad, emphasizing the interplay of individual, situation, and behavior (in humans: person, situation, and behavior) (Vansteelandt and Van Mechelen, 2004).

Our results confirm that under the same environmental situation and stimulus, animals produce inter-individually variable, but extremely consistent behavioral responses when being retested (Bell et al., 2009). Therefore, consistent and individually variable behaviors likely result from individual brain properties that persist over long timescales (in this paper, days as shown in previous papers, months Linneweber et al., 2020), contrasting random behavioral variations like sudden internal state variations. Hence, when presented with the same stimulus in the same situation, each individual responds variably but repetitively, pointing towards a deterministic and hardwired basis of individual properties (Buchanan et al., 2015; Linneweber et al., 2020).

The variability of the situational environmental context makes individual animal behavior in the wild much less predictable than that under laboratory conditions (Briffa and Greenaway, 2011). In opposition to the vast human literature on the ‘person-situation debate’ (Diener and Larsen, 1984; Fleeson, 2001; Mischel, 1968; Vansteelandt and Van Mechelen, 2004; Weisbuch et al., 2010), the importance of environmental context in shaping individual animal behavior remains descriptive (Kralj-Fiser et al., 2007) and experimentally largely unexplored, with few exceptions (Briffa et al., 2008; Mowles et al., 2012; Versace et al., 2020; Werkhoven et al., 2021). Here, we closed this gap in knowledge by quantitatively demonstrating how situational environmental and locomotor context changes individual behavior and individuality in animals. Our results demonstrate that situational environmental context has a pronounced effect on individual behavioral responses, be it an alternative stimulus representation, a different arena, or another locomotive modality.

Similar to the human literature (Mischel, 1968), the correlation coefficients are lower across situations than in the same situation or environmental context. From this, we can conclude that under more natural conditions with varying contexts, the consistency of individual traits measured by behavioral parameters will be reduced but remain measurable; therefore, for each situational context, the brain computes the appropriate behavior. This is like the situation-behavior profile of the individual in the personality psychology literature (Mischel and Shoda, 1995; Mischel and Shoda, 1998). Each time the individual is placed in the same situational context, the same behavior will be evoked; in a different context, a modified behavior. But despite these modifications, the individual brain properties computing the response remain the same. In line with lower but measurable behavioral consistency across situations, a vast literature demonstrates the importance of animal individual differences for ecological communities under natural conditions (reviewed in: Sih et al., 2012; Takola and Schielzeth, 2022).

Also, our reanalysis using a hierarchical linear mixed model shows that individuality is consistent but not uniform across behavioral contexts. The repeatability of behavioral traits across environmental contexts was trait-dependent: some behaviors (e.g. edge preference/centrophobicity) exhibited lower intraclass correlation coefficients (ICCs), whereas others (e.g. general locomotor drive and sustained orientation) showed high ICCs. In a multi-context experimental design, such heterogeneity in ICCs is expected and can provide insights into the underlying mechanisms. Behaviors with high ICCs are likely influenced by stable, fly-specific factors (such as developmental or genetic differences), whereas traits with low ICCs may be more sensitive to the immediate environmental context. Importantly, low ICC values do not contradict the existence of individuality; rather, they suggest that inter-individual variation contributes differently to behavior depending on the environmental conditions.

Our quantitative analysis of the effect of environmental and locomotor context on behavioral consistency revealed a hierarchy. As expected from animal and human studies, the time frame of days had little effect on the behavioral outcome (Bell et al., 2009; Buchanan et al., 2015; Linneweber et al., 2020; Mischel, 1968; Pantoja et al., 2016; Ross, 1977; Schuett et al., 2011). The hierarchy revealed by our experiments argues for differences in the relevance of environmental features for the traits we measured, which aligns with Mischel’s strong or weak situations (Mischel, 1973). Therefore, it is unsurprising that exploration can be best measured across situations within experiments of walking flies, and attention towards a stimulus can be measured across movement modalities. Our quantitative analysis demonstrates that situational context can be the most significant factor in determining the individual behavioral outcome, even in a per-design visual assay. This argues that individual behavior is determined by the entirety of the situation, consisting of both the stimulus and the environmental context. For each situation, each individual manifests its individual behavior.

In the future, it will be important to investigate which individual neurophysiological responses change when tested under exactly the same stimulus, but different situations. A systematic analysis of relevant circuit elements for different behaviors will allow for determining the exact neuronal substrates underlying individual behavioral computations within a fly’s or other animals' brains. This future research on animal models will provide important new insight into the ‘person-situation debate‘.

On a more practical level, the importance of the situational environmental context for shaping individual behavioral outcomes is also very important when comparing the results of different research groups when using similar behavioral paradigms. In many instances, it is impossible to replicate all components of a specific assay, let alone the conditions of the experimental room. Our results show that such details substantially impact individual behavioral responses and contribute to errors in replicating the results from another lab (Crabbe et al., 1999; Lithgow et al., 2017). Only meticulous and detailed reporting of as many environmental features as possible will hopefully alleviate some of these problems in the future. Also, in terms of statistics, using well-established approaches can be an advantage. The hierarchical mixed-model analysis provides a more complete and accessible overview of the findings in our study, making it more comparable to previous findings in behavioral ecology (Dingemanse and Dochtermann, 2013).

In conclusion, our work confirms that animals, including flies, have, like humans, individuality across situations. Similar to humans, animal individuality across situations is also marked by lower correlation coefficients than repetitions of a single situation. The individual is consistent across situations, producing individualistic behavior through an interaction with the situation. This involves a hierarchy of influence of different environmental features on the predictability of behavior across situations.

All experimental data are available here: https://doi.org/10.5281/zenodo.18342691. The code for the analyses presented in this paper is openly accessible at https://github.com/LinneweberLab/Mathejczyk_2024_eLife_Individuality (copy archived at LinneweberLab, 2026).

1. 1. Thomas FM
   2. Cara K
   3. Muhammad AH
   4. Florian F
   5. Tydings M
   6. Mathias W
   7. Gerit L

   (2026) Zenodo

   Individuality across environmental context in Drosophila melanogaster.

   https://doi.org/10.5281/zenodo.18342691

1. 1. Ayroles JF
   2. Buchanan SM
   3. O’Leary C
   4. Skutt-Kakaria K
   5. Grenier JK
   6. Clark AG
   7. Hartl DL
   8. de Bivort BL

   (2015) Behavioral idiosyncrasy reveals genetic control of phenotypic variability

   PNAS 112:6706–6711.

   https://doi.org/10.1073/pnas.1503830112

   • Google Scholar
2. 1. Baines RA
   2. Uhler JP
   3. Thompson A
   4. Sweeney ST
   5. Bate M

   (2001) Altered Electrical Properties in

   The Journal of Neuroscience 21:1523–1531.

   https://doi.org/10.1523/JNEUROSCI.21-05-01523.2001

   • Google Scholar
3. 1. Bell AM
   2. Hankison SJ
   3. Laskowski KL

   (2009) The repeatability of behaviour: a meta-analysis

   Animal Behaviour 77:771–783.

   https://doi.org/10.1016/j.anbehav.2008.12.022

   • PubMed
   • Google Scholar
4. 1. Berens P

   (2009) CircStat: a matlab toolbox for circular statistics

   Journal of Statistical Software 31:21.

   https://doi.org/10.18637/jss.v031.i10

   • Google Scholar
5. 1. Boissy A
   2. Bouissou MF

   (1995) Assessment of individual differences in behavioural reactions of heifers exposed to various fear-eliciting situations

   Applied Animal Behaviour Science 46:17–31.

   https://doi.org/10.1016/0168-1591(95)00633-8

   • Google Scholar
6. 1. Branson K
   2. Robie AA
   3. Bender J
   4. Perona P
   5. Dickinson MH

   (2009) High-throughput ethomics in large groups of Drosophila

   Nature Methods 6:451–457.

   https://doi.org/10.1038/nmeth.1328

   • PubMed
   • Google Scholar
7. 1. Briffa M
   2. Rundle SD
   3. Fryer A

   (2008) Comparing the strength of behavioural plasticity and consistency across situations: animal personalities in the hermit crab Pagurus bernhardus

   Proceedings Biological Sciences 275:1305–1311.

   https://doi.org/10.1098/rspb.2008.0025

   • PubMed
   • Google Scholar
8. 1. Briffa M
   2. Greenaway J

   (2011) High in situ repeatability of behaviour indicates animal personality in the beadlet anemone Actinia equina (Cnidaria)

   PLOS ONE 6:e21963.

   https://doi.org/10.1371/journal.pone.0021963

   • PubMed
   • Google Scholar
9. 1. Buchanan SM
   2. Kain JS
   3. de Bivort BL

   (2015) Neuronal control of locomotor handedness in Drosophila

   PNAS 112:6700–6705.

   https://doi.org/10.1073/pnas.1500804112

   • PubMed
   • Google Scholar
10. 1. Bülthoff H
    2. Götz KG
    3. Herre M

    (1982) Recurrent inversion of visual orientation in the walking fly,Drosophila melanogaster

    Journal of Comparative Physiology 148:471–481.

    https://doi.org/10.1007/BF00619785

    • Google Scholar
11. Book

    1. Carter M
    2. Shieh J

    (2015) Chapter 2 - animal behavior

    In: Carter M, Shieh J, editors. Guide to Research Techniques in Neuroscience. Academic Press. pp. 39–71.

    https://doi.org/10.1016/B978-0-12-800511-8.00002-2

    • Google Scholar
12. 1. Coleman K
    2. Wilson DS

    (1998) Shyness and boldness in pumpkinseed sunfish: individual differences are context-specific

    Animal Behaviour 56:927–936.

    https://doi.org/10.1006/anbe.1998.0852

    • PubMed
    • Google Scholar
13. 1. Colomb J
    2. Reiter L
    3. Blaszkiewicz J
    4. Wessnitzer J
    5. Brembs B

    (2012) Open source tracking and analysis of adult Drosophila locomotion in Buridan’s paradigm with and without visual targets

    PLOS ONE 7:e42247.

    https://doi.org/10.1371/journal.pone.0042247

    • PubMed
    • Google Scholar
14. 1. Costa PT
    2. McCrae RR

    (1988) Personality in adulthood: a six-year longitudinal study of self-reports and spouse ratings on the NEO Personality Inventory

    Journal of Personality and Social Psychology 54:853–863.

    https://doi.org/10.1037//0022-3514.54.5.853

    • PubMed
    • Google Scholar
15. 1. Crabbe JC
    2. Wahlsten D
    3. Dudek BC

    (1999) Genetics of mouse behavior: interactions with laboratory environment

    Science 284:1670–1672.

    https://doi.org/10.1126/science.284.5420.1670

    • PubMed
    • Google Scholar
16. 1. Dall SRX
    2. Houston AI
    3. McNamara JM

    (2004) The behavioural ecology of personality: consistent individual differences from an adaptive perspective

    Ecology Letters 7:734–739.

    https://doi.org/10.1111/j.1461-0248.2004.00618.x

    • Google Scholar
17. 1. D’Eath R
    2. Burn C

    (2002) Individual differences in behaviour: A test of “coping style” does not predict resident-intruder aggressiveness in pigs

    Behaviour 139:1175–1194.

    https://doi.org/10.1163/15685390260437326

    • Google Scholar
18. 1. Diener E
    2. Larsen RJ

    (1984) Temporal stability and cross-situational consistency of affective, behavioral, and cognitive responses

    Journal of Personality and Social Psychology 47:871–883.

    https://doi.org/10.1037//0022-3514.47.4.871

    • PubMed
    • Google Scholar
19. 1. Dingemanse NJ
    2. Dochtermann NA

    (2013) Quantifying individual variation in behaviour: mixed-effect modelling approaches

    The Journal of Animal Ecology 82:39–54.

    https://doi.org/10.1111/1365-2656.12013

    • PubMed
    • Google Scholar
20. 1. Dorkenwald S
    2. Matsliah A
    3. Sterling AR
    4. Schlegel P
    5. Yu SC
    6. McKellar CE
    7. Lin A
    8. Costa M
    9. Eichler K
    10. Yin Y
    11. Silversmith W
    12. Schneider-Mizell C
    13. Jordan CS
    14. Brittain D
    15. Halageri A
    16. Kuehner K
    17. Ogedengbe O
    18. Morey R
    19. Gager J
    20. Kruk K
    21. Perlman E
    22. Yang R
    23. Deutsch D
    24. Bland D
    25. Sorek M
    26. Lu R
    27. Macrina T
    28. Lee K
    29. Bae JA
    30. Mu S
    31. Nehoran B
    32. Mitchell E
    33. Popovych S
    34. Wu J
    35. Jia Z
    36. Castro MA
    37. Kemnitz N
    38. Ih D
    39. Bates AS
    40. Eckstein N
    41. Funke J
    42. Collman F
    43. Bock DD
    44. Jefferis G
    45. Seung HS
    46. Murthy M
    47. FlyWire Consortium

    (2024) Neuronal wiring diagram of an adult brain

    Nature 634:124–138.

    https://doi.org/10.1038/s41586-024-07558-y

    • PubMed
    • Google Scholar
21. 1. Fleeson W

    (2001)

    Toward a structure- and process-integrated view of personality: traits as density distribution of states

    Journal of Personality and Social Psychology 80:1011–1027.

    • PubMed
    • Google Scholar
22. 1. Freund J
    2. Brandmaier AM
    3. Lewejohann L
    4. Kirste I
    5. Kritzler M
    6. Krüger A
    7. Sachser N
    8. Lindenberger U
    9. Kempermann G

    (2013) Emergence of individuality in genetically identical mice

    Science 340:756–759.

    https://doi.org/10.1126/science.1235294

    • PubMed
    • Google Scholar
23. 1. Galton F

    (1875)

    The history of twins, as a criterion of the relative powers of nature and nurture

    Fraser’s Magazine 12:566–576.

    • Google Scholar
24. 1. Geissmann Q
    2. Garcia Rodriguez L
    3. Beckwith EJ
    4. French AS
    5. Jamasb AR
    6. Gilestro GF

    (2017) Ethoscopes: An open platform for high-throughput ethomics

    PLOS Biology 15:e2003026.

    https://doi.org/10.1371/journal.pbio.2003026

    • PubMed
    • Google Scholar
25. 1. Gibert JP
    2. Chelini MC
    3. Rosenthal MF
    4. DeLong JP

    (2016) Crossing regimes of temperature dependence in animal movement

    Global Change Biology 22:1722–1736.

    https://doi.org/10.1111/gcb.13245

    • PubMed
    • Google Scholar
26. 1. Gosling SD

    (2008) Personality in Non‐human Animals

    Social and Personality Psychology Compass 2:985–1001.

    https://doi.org/10.1111/j.1751-9004.2008.00087.x

    • Google Scholar
27. Book

    1. Götz KG

    (1980) Visual guidance in drosophila

    In: Siddiqi O, Babu P, Hall LM, Hall JC, editors. Development and Neurobiology of Drosophila. Springer. pp. 391–407.

    https://doi.org/10.1007/978-1-4684-7968-3_28

    • Google Scholar
28. 1. Hamada FN
    2. Rosenzweig M
    3. Kang K
    4. Pulver SR
    5. Ghezzi A
    6. Jegla TJ
    7. Garrity PA

    (2008) An internal thermal sensor controlling temperature preference in Drosophila

    Nature 454:217–220.

    https://doi.org/10.1038/nature07001

    • PubMed
    • Google Scholar
29. 1. Han R
    2. Wei TM
    3. Tseng SC
    4. Lo CC

    (2021) Characterizing approach behavior of Drosophila melanogaster in Buridan’s paradigm

    PLOS ONE 16:e0245990.

    https://doi.org/10.1371/journal.pone.0245990

    • PubMed
    • Google Scholar
30. Book

    1. Hartshorne H
    2. May MA

    (1930)

    Studies in the Nature of Character: Studies in Deceit

    McMillian.

    • Google Scholar
31. 1. Hassan BA
    2. Bermingham NA
    3. He Y
    4. Sun Y
    5. Jan YN
    6. Zoghbi HY
    7. Bellen HJ

    (2000) atonal regulates neurite arborization but does not act as a proneural gene in the Drosophila brain

    Neuron 25:549–561.

    https://doi.org/10.1016/s0896-6273(00)81059-4

    • PubMed
    • Google Scholar
32. 1. Herborn KA
    2. Macleod R
    3. Miles WTS
    4. Schofield ANB
    5. Alexander L
    6. Arnold KE

    (2010) Personality in captivity reflects personality in the wild

    Animal Behaviour 79:835–843.

    https://doi.org/10.1016/j.anbehav.2009.12.026

    • Google Scholar
33. 1. Hirsch J
    2. Boudreau JC

    (1958) Studies in experimental behavior genetics. I. The heritability of phototaxis in a population of Drosophila melanogaster

    Journal of Comparative and Physiological Psychology 51:647–651.

    https://doi.org/10.1037/h0039498

    • PubMed
    • Google Scholar
34. 1. Hirsch J

    (1959) Studies in experimental behavior genetics. II. Individual differences in geotaxis as a function of chromosome variations in synthesized Drosophila populations

    Journal of Comparative and Physiological Psychology 52:304–308.

    https://doi.org/10.1037/h0043498

    • PubMed
    • Google Scholar
35. 1. Honegger K
    2. de Bivort B

    (2018) Stochasticity, individuality and behavior

    Current Biology 28:R8–R12.

    https://doi.org/10.1016/j.cub.2017.11.058

    • PubMed
    • Google Scholar
36. 1. Honegger KS
    2. Smith MAY
    3. Churgin MA
    4. Turner GC
    5. de Bivort BL

    (2020) Idiosyncratic neural coding and neuromodulation of olfactory individuality in Drosophila

    PNAS 117:23292–23297.

    https://doi.org/10.1073/pnas.1901623116

    • Google Scholar
37. 1. Jiang H
    2. Hanna E
    3. Gatto CL
    4. Page TL
    5. Bhuva B
    6. Broadie K

    (2016) A fully automated Drosophila olfactory classical conditioning and testing system for behavioral learning and memory assessment

    Journal of Neuroscience Methods 261:62–74.

    https://doi.org/10.1016/j.jneumeth.2015.11.030

    • PubMed
    • Google Scholar
38. Book

    1. Kahsai L
    2. Zars T

    (2011) International review of neurobiology

    In: Atkinson N, editors. Learning and Memory in Drosophila: Behavior, Genetics, and Neural Systems. Academic Press. pp. 139–167.

    https://doi.org/10.1016/B978-0-12-387003-2.00006-9

    • Google Scholar
39. 1. Kain JS
    2. Stokes C
    3. de Bivort BL

    (2012) Phototactic personality in fruit flies and its suppression by serotonin and white

    PNAS 109:19834–19839.

    https://doi.org/10.1073/pnas.1211988109

    • Google Scholar
40. 1. Kiral FR
    2. Linneweber GA
    3. Mathejczyk T
    4. Georgiev SV
    5. Wernet MF
    6. Hassan BA
    7. von Kleist M
    8. Hiesinger PR

    (2020) Autophagy-dependent filopodial kinetics restrict synaptic partner choice during Drosophila brain wiring

    Nature Communications 11:1325.

    https://doi.org/10.1038/s41467-020-14781-4

    • PubMed
    • Google Scholar
41. 1. Klapoetke NC
    2. Murata Y
    3. Kim SS
    4. Pulver SR
    5. Birdsey-Benson A
    6. Cho YK
    7. Morimoto TK
    8. Chuong AS
    9. Carpenter EJ
    10. Tian Z
    11. Wang J
    12. Xie Y
    13. Yan Z
    14. Zhang Y
    15. Chow BY
    16. Surek B
    17. Melkonian M
    18. Jayaraman V
    19. Constantine-Paton M
    20. Wong GKS
    21. Boyden ES

    (2014) Independent optical excitation of distinct neural populations

    Nature Methods 11:338–346.

    https://doi.org/10.1038/nmeth.2836

    • PubMed
    • Google Scholar
42. 1. Konopka RJ
    2. Benzer S

    (1971) Clock mutants of Drosophila melanogaster

    PNAS 68:2112–2116.

    https://doi.org/10.1073/pnas.68.9.2112

    • Google Scholar
43. 1. Kralj-Fiser S
    2. Scheiber IBR
    3. Blejec A
    4. Moestl E
    5. Kotrschal K

    (2007) Individualities in a flock of free-roaming greylag geese: behavioral and physiological consistency over time and across situations

    Hormones and Behavior 51:239–248.

    https://doi.org/10.1016/j.yhbeh.2006.10.006

    • PubMed
    • Google Scholar
44. 1. Lapiedra O
    2. Schoener TW
    3. Leal M
    4. Losos JB
    5. Kolbe JJ

    (2018) Predator-driven natural selection on risk-taking behavior in anole lizards

    Science 360:1017–1020.

    https://doi.org/10.1126/science.aap9289

    • PubMed
    • Google Scholar
45. 1. Linneweber GA
    2. Andriatsilavo M
    3. Dutta SB
    4. Bengochea M
    5. Hellbruegge L
    6. Liu G
    7. Ejsmont RK
    8. Straw AD
    9. Wernet M
    10. Hiesinger PR
    11. Hassan BA

    (2020)

    A neurodevelopmental origin of behavioral individuality in the

    Science 367:1112–1119.

    • Google Scholar
46. Software

    1. LinneweberLab

    (2026) Mathejczyk_2024_eLife_Individuality, version swh:1:rev:9fe421a271e152aaf58f17086c5d8fa341e96cdb

    Software Heritage.

    https://archive.softwareheritage.org/swh:1:dir:efd3e5344b2813174a7fcce0d4b824057b4d42ca;origin=https://github.com/LinneweberLab/Mathejczyk_2024_eLife_Individuality;visit=swh:1:snp:c636adc34ea487994164ac82317a561176c33f56;anchor=swh:1:rev:9fe421a271e152aaf58f17086c5d8fa341e96cdb
47. 1. Lithgow GJ
    2. Driscoll M
    3. Phillips P

    (2017) A long journey to reproducible results

    Nature 548:387–388.

    https://doi.org/10.1038/548387a

    • PubMed
    • Google Scholar
48. 1. Luan H
    2. Lemon WC
    3. Peabody NC
    4. Pohl JB
    5. Zelensky PK
    6. Wang D
    7. Nitabach MN
    8. Holmes TC
    9. White BH

    (2006a) Functional dissection of a neuronal network required for cuticle tanning and wing expansion in Drosophila

    The Journal of Neuroscience 26:573–584.

    https://doi.org/10.1523/JNEUROSCI.3916-05.2006

    • PubMed
    • Google Scholar
49. 1. Luan H
    2. Peabody NC
    3. Vinson CR
    4. White BH

    (2006b) Refined spatial manipulation of neuronal function by combinatorial restriction of transgene expression

    Neuron 52:425–436.

    https://doi.org/10.1016/j.neuron.2006.08.028

    • PubMed
    • Google Scholar
50. 1. Mathejczyk TF
    2. Wernet MF

    (2019) Heading choices of flying Drosophila under changing angles of polarized light

    Scientific Reports 9:16773.

    https://doi.org/10.1038/s41598-019-53330-y

    • PubMed
    • Google Scholar
51. 1. Mathejczyk TF
    2. Wernet MF

    (2020) Modular assays for the quantitative study of visually guided navigation in both flying and walking flies

    Journal of Neuroscience Methods 340:108747.

    https://doi.org/10.1016/j.jneumeth.2020.108747

    • PubMed
    • Google Scholar
52. 1. Mather JA
    2. Anderson RC

    (1993) Personalities of octopuses (Octopus rubescens)

    Journal of Comparative Psychology 107:336–340.

    https://doi.org/10.1037//0735-7036.107.3.336

    • Google Scholar
53. Book

    1. Mischel W

    (1968)

    Personality and Assessment

    Wiley.

    • Google Scholar
54. 1. Mischel W

    (1973) Toward a cognitive social learning reconceptualization of personality

    Psychological Review 80:252–283.

    https://doi.org/10.1037/h0035002

    • PubMed
    • Google Scholar
55. 1. Mischel W
    2. Shoda Y

    (1995) A cognitive-affective system theory of personality: reconceptualizing situations, dispositions, dynamics, and invariance in personality structure

    Psychological Review 102:246–268.

    https://doi.org/10.1037/0033-295x.102.2.246

    • PubMed
    • Google Scholar
56. 1. Mischel W
    2. Shoda Y

    (1998) Reconciling processing dynamics and personality dispositions

    Annual Review of Psychology 49:229–258.

    https://doi.org/10.1146/annurev.psych.49.1.229

    • PubMed
    • Google Scholar
57. 1. Mowles SL
    2. Cotton PA
    3. Briffa M

    (2012) Consistent crustaceans: the identification of stable behavioural syndromes in hermit crabs

    Behavioral Ecology and Sociobiology 66:1087–1094.

    https://doi.org/10.1007/s00265-012-1359-7

    • Google Scholar
58. 1. Neuser K
    2. Triphan T
    3. Mronz M
    4. Poeck B
    5. Strauss R

    (2008) Analysis of a spatial orientation memory in Drosophila

    Nature 453:1244–1247.

    https://doi.org/10.1038/nature07003

    • PubMed
    • Google Scholar
59. 1. Otsuna H
    2. Ito K

    (2006) Systematic analysis of the visual projection neurons of Drosophila melanogaster. I. Lobula-specific pathways

    The Journal of Comparative Neurology 497:928–958.

    https://doi.org/10.1002/cne.21015

    • PubMed
    • Google Scholar
60. 1. Pantoja C
    2. Hoagland A
    3. Carroll EC
    4. Karalis V
    5. Conner A
    6. Isacoff EY

    (2016) Neuromodulatory regulation of behavioral individuality in zebrafish

    Neuron 91:587–601.

    https://doi.org/10.1016/j.neuron.2016.06.016

    • PubMed
    • Google Scholar
61. 1. Partridge L

    (1976) Individual differences in feeding efficiencies and feeding preferences of captive great tits

    Animal Behaviour 24:230–240.

    https://doi.org/10.1016/S0003-3472(76)80119-4

    • Google Scholar
62. 1. Pfeiffer BD
    2. Ngo TTB
    3. Hibbard KL
    4. Murphy C
    5. Jenett A
    6. Truman JW
    7. Rubin GM

    (2010) Refinement of tools for targeted gene expression in Drosophila

    Genetics 186:735–755.

    https://doi.org/10.1534/genetics.110.119917

    • PubMed
    • Google Scholar
63. 1. Pulver SR
    2. Pashkovski SL
    3. Hornstein NJ
    4. Garrity PA
    5. Griffith LC

    (2009) Temporal dynamics of neuronal activation by Channelrhodopsin-2 and TRPA1 determine behavioral output in Drosophila larvae

    Journal of Neurophysiology 101:3075–3088.

    https://doi.org/10.1152/jn.00071.2009

    • PubMed
    • Google Scholar
64. 1. Quinn WG
    2. Harris WA
    3. Benzer S

    (1974) Conditioned behavior in Drosophila melanogaster

    PNAS 71:708–712.

    https://doi.org/10.1073/pnas.71.3.708

    • Google Scholar
65. 1. Réale D
    2. Reader SM
    3. Sol D
    4. McDougall PT
    5. Dingemanse NJ

    (2007) Integrating animal temperament within ecology and evolution

    Biological Reviews of the Cambridge Philosophical Society 82:291–318.

    https://doi.org/10.1111/j.1469-185X.2007.00010.x

    • PubMed
    • Google Scholar
66. 1. Robie AA
    2. Hirokawa J
    3. Edwards AW
    4. Umayam LA
    5. Lee A
    6. Phillips ML
    7. Card GM
    8. Korff W
    9. Rubin GM
    10. Simpson JH
    11. Reiser MB
    12. Branson K

    (2017) Mapping the neural substrates of behavior

    Cell 170:393–406.

    https://doi.org/10.1016/j.cell.2017.06.032

    • Google Scholar
67. Book

    1. Ross L

    (1977) Advances in experimental social psychology

    In: Berkowitz L, editors. The Intuitive Psychologist And His Shortcomings: Distortions in the Attribution Process. Academic Press. pp. 173–220.

    https://doi.org/10.1016/S0065-2601(08)60357-3

    • Google Scholar
68. 1. Schuett W
    2. Dall SRX
    3. Baeumer J
    4. Kloesener MH
    5. Nakagawa S
    6. Beinlich F
    7. Eggers T

    (2011) Personality variation in a clonal insect: the pea aphid, Acyrthosiphon pisum

    Developmental Psychobiology 53:631–640.

    https://doi.org/10.1002/dev.20538

    • PubMed
    • Google Scholar
69. 1. Seelig JD
    2. Chiappe ME
    3. Lott GK
    4. Dutta A
    5. Osborne JE
    6. Reiser MB
    7. Jayaraman V

    (2010) Two-photon calcium imaging from head-fixed Drosophila during optomotor walking behavior

    Nature Methods 7:535–540.

    https://doi.org/10.1038/nmeth.1468

    • PubMed
    • Google Scholar
70. 1. Sih A
    2. Bell AM
    3. Johnson JC
    4. Ziemba RE

    (2004) Behavioral syndromes: an intergrative overiew

    The Quarterly Review of Biology 79:241–277.

    https://doi.org/10.1086/422893

    • PubMed
    • Google Scholar
71. 1. Sih A
    2. Cote J
    3. Evans M
    4. Fogarty S
    5. Pruitt J

    (2012) Ecological implications of behavioural syndromes

    Ecology Letters 15:278–289.

    https://doi.org/10.1111/j.1461-0248.2011.01731.x

    • PubMed
    • Google Scholar
72. 1. Spoolder HAM
    2. Burbidge JA
    3. Lawrence AB
    4. Simmins PH
    5. Edwards SA

    (1996) Individual behavioural differences in pigs: intra-and inter-test consistency

    Applied Animal Behaviour Science 49:185–198.

    https://doi.org/10.1016/0168-1591(96)01033-7

    • Google Scholar
73. 1. Strauss R
    2. Hanesch U
    3. Kinkelin M
    4. Wolf R
    5. Heisenberg M

    (1992) No-bridge of Drosophila melanogaster: portrait of a structural brain mutant of the central complex

    Journal of Neurogenetics 8:125–155.

    https://doi.org/10.3109/01677069209083444

    • PubMed
    • Google Scholar
74. 1. Strauss R
    2. Renner M
    3. Götz K

    (2001) Task-specific association of photoreceptor systems and steering parameters in Drosophila

    Journal of Comparative Physiology A 187:617–632.

    https://doi.org/10.1007/s003590100234

    • Google Scholar
75. 1. Sweeney ST
    2. Broadie K
    3. Keane J
    4. Niemann H
    5. O’Kane CJ

    (1995) Targeted expression of tetanus toxin light chain in Drosophila specifically eliminates synaptic transmission and causes behavioral defects

    Neuron 14:341–351.

    https://doi.org/10.1016/0896-6273(95)90290-2

    • PubMed
    • Google Scholar
76. 1. Takola E
    2. Schielzeth H

    (2022) Hutchinson’s ecological niche for individuals

    Biology & Philosophy 37:849-y.

    https://doi.org/10.1007/s10539-022-09849-y

    • Google Scholar
77. 1. Traynor BJ
    2. Singleton AB

    (2010) Nature versus nurture: death of a dogma, and the road ahead

    Neuron 68:196–200.

    https://doi.org/10.1016/j.neuron.2010.10.002

    • PubMed
    • Google Scholar
78. 1. Valente D
    2. Golani I
    3. Mitra PP

    (2007) Analysis of the trajectory of Drosophila melanogaster in a circular open field arena

    PLOS ONE 2:e1083.

    https://doi.org/10.1371/journal.pone.0001083

    • PubMed
    • Google Scholar
79. 1. Vansteelandt K
    2. Van Mechelen I

    (2004) The personality triad in balance: Multidimensional individual differences in situation–behavior profiles

    Journal of Research in Personality 38:367–393.

    https://doi.org/10.1016/j.jrp.2003.08.001

    • Google Scholar
80. 1. Versace E
    2. Caffini M
    3. Werkhoven Z
    4. de Bivort BL

    (2020) Individual, but not population asymmetries, are modulated by social environment and genotype in Drosophila melanogaster

    Scientific Reports 10:4480.

    https://doi.org/10.1038/s41598-020-61410-7

    • PubMed
    • Google Scholar
81. 1. Warren TL
    2. Weir PT
    3. Dickinson MH

    (2018) Flying Drosophila melanogaster maintain arbitrary but stable headings relative to the angle of polarized light

    The Journal of Experimental Biology 221:jeb177550.

    https://doi.org/10.1242/jeb.177550

    • PubMed
    • Google Scholar
82. 1. Weisbuch M
    2. Slepian ML
    3. Clarke A
    4. Ambady N
    5. Veenstra-Vanderweele J

    (2010) Behavioral stability across time and situations: Nonverbal versus verbal consistency

    Journal of Nonverbal Behavior 34:43–56.

    https://doi.org/10.1007/s10919-009-0079-9

    • PubMed
    • Google Scholar
83. 1. Werkhoven Z
    2. Rohrsen C
    3. Qin C
    4. Brembs B
    5. de Bivort B

    (2019) MARGO (Massively Automated Real-time GUI for Object-tracking), a platform for high-throughput ethology

    PLOS ONE 14:e0224243.

    https://doi.org/10.1371/journal.pone.0224243

    • PubMed
    • Google Scholar
84. 1. Werkhoven Z
    2. Bravin A
    3. Skutt-Kakaria K
    4. Reimers P
    5. Pallares LF
    6. Ayroles J
    7. de Bivort BL

    (2021) The structure of behavioral variation within a genotype

    eLife 10:e64988.

    https://doi.org/10.7554/eLife.64988

    • PubMed
    • Google Scholar
85. Preprint

    1. Xu CS
    2. Januszewski M
    3. Lu Z
    4. Takemura SY
    5. Hayworth KJ
    6. Huang G
    7. Shinomiya K
    8. Maitin-Shepard J
    9. Ackerman D
    10. Berg S
    11. Blakely T
    12. Bogovic J
    13. Clements J
    14. Dolafi T
    15. Hubbard P
    16. Kainmueller D
    17. Katz W
    18. Kawase T
    19. Khairy KA
    20. Plaza SM

    (2020) A connectome of the adult drosophila central brain

    bioRxiv.

    https://doi.org/10.1101/2020.01.21.911859

    • Google Scholar
86. 1. Zheng Z
    2. Lauritzen JS
    3. Perlman E
    4. Robinson CG
    5. Nichols M
    6. Milkie D
    7. Torrens O
    8. Price J
    9. Fisher CB
    10. Sharifi N
    11. Calle-Schuler SA
    12. Kmecova L
    13. Ali IJ
    14. Karsh B
    15. Trautman ET
    16. Bogovic JA
    17. Hanslovsky P
    18. Jefferis G
    19. Kazhdan M
    20. Khairy K
    21. Saalfeld S
    22. Fetter RD
    23. Bock DD

    (2018) A complete electron microscopy volume of the brain of adult Drosophila melanogaster

    Cell 174:730–743.

    https://doi.org/10.1016/j.cell.2018.06.019

    • PubMed
    • Google Scholar

Author details

1. Thomas F Mathejczyk

   Institut für Biologie Abteilung Neurobiologie Fachbereich Biologie, Chemie und Pharmazie, Freie Universität Berlin, Berlin, Germany

   Contribution

   Conceptualization, Data curation, Formal analysis, Visualization

   Competing interests

   No competing interests declared

2. Cara Knief

   Institut für Biologie Abteilung Neurobiologie Fachbereich Biologie, Chemie und Pharmazie, Freie Universität Berlin, Berlin, Germany

   Contribution

   Data curation

   Competing interests

   No competing interests declared

3. Muhammad A Haidar

   Institut für Biologie Abteilung Neurobiologie Fachbereich Biologie, Chemie und Pharmazie, Freie Universität Berlin, Berlin, Germany

   Contribution

   Data curation

   Competing interests

   No competing interests declared

4. Florian Freitag

   Institut für Biologie Abteilung Neurobiologie Fachbereich Biologie, Chemie und Pharmazie, Freie Universität Berlin, Berlin, Germany

   Contribution

   Formal analysis

   Competing interests

   No competing interests declared

5. Tydings McClary

   Institut für Biologie Abteilung Neurobiologie Fachbereich Biologie, Chemie und Pharmazie, Freie Universität Berlin, Berlin, Germany

   Contribution

   Data curation

   Competing interests

   No competing interests declared

6. Mathias F Wernet

   Institut für Biologie Abteilung Neurobiologie Fachbereich Biologie, Chemie und Pharmazie, Freie Universität Berlin, Berlin, Germany

   Contribution

   Supervision, Funding acquisition, Writing – review and editing

   For correspondence

   mathias.wernet@fu-berlin.de

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-5233-2654

7. Gerit A Linneweber

   Institut für Biologie Abteilung Neurobiologie Fachbereich Biologie, Chemie und Pharmazie, Freie Universität Berlin, Berlin, Germany

   Contribution

   Conceptualization, Resources, Supervision, Funding acquisition, Writing – original draft, Project administration, Writing – review and editing

   For correspondence

   gerit.linneweber@fu-berlin.de

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-8393-6426

• 1,889

  views

• 103

  downloads

• 2

  citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.

Citations by DOI

• 1

  citation for Reviewed Preprint v2 https://doi.org/10.7554/eLife.98171.2

• 1

  citation for Reviewed Preprint v4 https://doi.org/10.7554/eLife.98171.4