Practice makes perfect? Experience boosts foraging performance in a ponerine ant

Practice makes perfect? 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Experience boosts foraging performance in a ponerine ant Maria Eduarda LIMA VIEIRA, Gabi Fernandes BIANCHI, Guilherme Rocha FIUZA, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7302949/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Understanding how internal mechanisms, social dynamics, and environmental factors interact to influence foraging behaviour is essential to unravel the complexity of decision-making in social insects. Despite extensive research on foraging behaviour in social insects, the mechanisms driving solitary foraging, particularly in Neotropical species, remain largely unexplored. This laboratory study evaluated how experience, colony size, and food distribution affect foraging activity, exploration, and success in a standardised situation in Pachycondyla striata . Experience was not experimentally controlled and depended on the ants' behavioural decisions. Additionally, we investigated whether behavioural traits associated with foraging are consistent across time and environmental contexts. Experience significantly increased foraging activity, exploration, and success. Foragers with prior successful experience were more likely to explore and achieve subsequent success. Workers’ activity was lower with dispersed food distribution. Colony-level differences were observed in activity, and intercolonial and interindividual differences emerged in foraging success, suggesting weak but consistent behavioural patterns across both levels of organisation. These findings emphasise the importance of experience and environmental modulation in ant foraging decisions. By demonstrating how internal and external factors influence task execution, this study advances our understanding of behavioural plasticity in social insects and suggests a strong effect of experience- and learning-based motivational processes on individual behaviour. This knowledge provides a framework to explore foraging strategies and colony dynamics in solitary foraging systems. activity exploration food distribution foraging success Pachycondyla solitary foraging Figures Figure 1 Figure 2 Significance Statement Foraging behaviour is an extremely important element of fitness, and understanding how individual behaviour influences task execution success in social species is an understudied subject. In this manuscript, we study how individual (e.g., experience) and environmental characteristics determine decision-making for foraging and foraging success. We apply an innovative protocol that leaves ants the possibility to participate in the experiments according to their motivation. Our main results show that experience is a key factor for foraging behaviour and performance in this species, with more experienced individuals being more active, exploratory, and successful. Specifically, a previous success experience increased the ants' motivation and resulted in more exploration and an increased probability of being successful again. These findings are novel in detailing the multifactorial effects influencing the foraging initiation and performance. We discuss the seldom mentioned motivational implications of these results. INTRODUCTION Social insects are known for their sophisticated division of labour, where most tasks are performed by specialised groups of sterile workers (reviewed in Beshers and Fewell 2001). This collective organisation allows the colony to supply all group needs efficiently without central control (Gordon 2007). In ants, foraging, i.e., searching for, finding and recovering food, is performed through solitary or collective strategies, the latter occurring through a varied number of forms (reviewed in Lanan 2014). In both solitary and collective foraging, individual characteristics play an important role in the activation, maintenance, and execution of successful foraging. For example, some ants have higher sensitivity to foraging-related stimuli, such as light or food, making them more likely to initiate foraging activities (Perez et al. 2013; Detrain and Prieur 2014). Besides, individuals and colonies can present consistent interindividual behavioural differences (across contexts and time) related to foraging performance, such as tendencies towards greater activity and exploration (Horna-Lowell et al. 2021). Additionally, differences can develop over time, with more experienced foragers demonstrating greater effort, success, and specialisation in this task (Dukas and Visscher 1994). For instance, the tendency to forage increases with repeated encounters with prey in the clonal ant Ooceraea biroi (Forel, 1907) (Ravary et al. 2007). Despite their relevance, individual characteristics have received less attention than collective information-sharing mechanisms such as pheromone trails (Czaczkes et al. 2015). Classic threshold models of division of labour, by looking mainly at which task will be done by which worker (Beshers and Fewell 2001), ignore motivational processes and how they can be modified both by immediate and experience-based elements (Lima Vieira et al. 2024). Motivational processes are internal neurophysiological and cognitive mechanisms that adjust an organism's behaviour based on internal states (e.g., hunger) and external cues (e.g., resource availability). They influence behaviour by modifying its frequency, intensity, and latency (Toates 1986; Hogan 2015, 2017). In foraging ants, motivational states may drive exploration, path fidelity, or recruitment efforts, reflecting an interplay between evolved behavioural repertoires and immediate ecological demands (Deneubourg and Goss 1989; Gordon 2019). Many of the studies investigating the mechanisms of ants’ foraging plasticity evaluate species that perform mass recruitment. A notable example is the sensitivity of workers to trail pheromone, or the propensity to deposit pheromone (e.g., Fewell et al. 1992; Breed et al. 1996; reviewed in Czaczkes et al. 2015). However, in solitary foraging species, trail pheromones are absent, and other parameters must influence the expression of behaviours associated with foraging. Most studies focus on the effect of spatial cognition on foraging performance (Reznikova 2020). In stark contrast, other aspects, such as non-spatial learning and experience and learning-based motivational processes, are less understood (Lima Vieira et al. 2024). In addition to individual intrinsic mechanisms, external factors (social or related to environmental conditions) also impact foraging motivation. In the colony, nutritional needs play an important role in modulating foraging efforts, with more populous colonies requiring more food resources (Beckers et al. 1989; Lima Vieira et al. 2024). Outside the colony, the indication of food presence or a greater quantity, quality and/or better food predictability can increase the individual motivation to forage (Schilman and Roces 2003; Schilman 2011; d’Ettorre et al. 2017). In the Argentine ant Linepithema humile (Mayr, 1868), for example, a pre-exposure to a trail pheromone component (used as reinforcement stimuli) can increase the propensity of ants to feed on the sucrose solution (Rossi et al. 2020). Environmental conditions and experience, including non-learning, social and environmental experience, contribute to variations in accumulated information and internal state modifications, leading to differences in workers' motivation and roles (e.g., Biesmeijer and de Vries 2001; Stroeymeyt et al. 2011; Grüter and Leadbeater 2014). However, how these variables impact foraging and, especially, to what extent they influence foraging performance is not fully understood. In particular, in hyper-diverse insect groups such as ants, we are in dire need of experiments to confirm the mechanisms behind how collective behaviours emerge from the behaviour of solitary foragers. Ants of the subfamily Ponerinae are good models to address this gap since they can perform solitary foraging but also recruit, emphasizing the role of foraging behaviour at both the individual and colonial levels. Thus, in this study, we investigate how internal factors (e.g., short, medium and long-term experience), social factors (e.g., colony size), and environmental factors (e.g., food distribution) influence the foraging activity, exploration rate, and performance of individuals in the ant Pachycondyla striata Smith, 1858. Our goal also was to assess the motivational processes through differences in ant experience and behaviour in a setup where ants could decide to engage or not in the foraging experiment. Additionally, we assess whether individual and colonial behavioural traits remain consistent over time and across different food distribution patterns. We hypothesise that individual foraging motivation and performance (i.e., foraging success) improve with greater experience, larger colony size, and when food is aggregated rather than dispersed. We also predict that individual foraging strategies will adapt to food distribution and that, rather than consistent differences, developmental differences will emerge at both individual and colony levels, with certain ants and colonies being more active, exploratory, and successful, reflecting a colony-level behavioural phenotype. METHODS Study subject and experimental area P. striata (Fig. 1 a) is a large ponerine ant (body size > 10 mm) widely distributed throughout South America. In Brazil, it is a common species in the Atlantic Forest from Bahia to Rio Grande do Sul (latitudinal range) and from Mato Grosso to Bahia (longitudinal range) (Marcineiro and Lattke 2024). Workers are generalist predators and scavengers feeding on a range of insect prey. They also consume plant material such as fruits and seeds (Medeiros and Oliveira 2009). They are considered one of the main seed-dispersing ants in the Atlantic Forest, cleaning and displacing seeds further than 10 m (Pizo and Oliveira 2001; Passos and Oliveira 2002, 2004; Magalhães et al. 2018). While foraging, the most commonly used strategy is solitary foraging, where the finding, capture, and food recovery occur independently by individual foragers without nestmate recruitment (Jaffe 1984; Silva et al. 2021). In addition to solitary foraging, the recruitment of nestmates by tandem running is also possible. During a tandem run, a worker with knowledge about a new food source or nesting site recruits a generally naïve worker. This contact is maintained through chemical and mechanical signals, which occur through constant contact between the follower's antennae and the leader's gaster (reviewed in Franklin 2014; Grüter et al. 2018; Silva et al. 2021). While tandem running was frequently observed during field collections of colonies, in the laboratory, workers never performed this recruitment strategy for food sources. For this reason, our results presented here refer exclusively to solitary foraging. We collected five P. striata colonies on the Universidade de São Paulo campus (-23.559540395733073, -46.724229128836186), São Paulo state, Brazil, between March and May 2023. The collection area corresponds to an Atlantic Forest largely affected by urbanisation and fragmentation (Kraus et al. 2005), where colonies of P. striata are common. All colonies were collected without queens or brood and we split the largest colony into two subcolonies (colony 6.1 and 6.2; colonies’ demography in Table S1 ) We maintained the colonies in the laboratory at stable conditions (temperature 25 ± 2°C, humidity 67 ± 3% and a 12-hour light/dark cycle) and provided them thrice weekly with Tenebrio molitor Linnaeus, 1758 larvae, a mix of apple and honey, and water ad libitum . The physical apparatus in which they were installed is an artificial plaster nest (50 cm x 50 cm x 50 cm) subdivided into six chambers and a foraging area (1 m x 1 m x 20 cm, hereafter external area) with expanded vermiculite as a substrate. All workers were marked with paint (Uni-paint PX20), and remarking was never necessary (Corbara et al. 1986). Colony identity was represented by a different colour mark on the ants’ petiole, and each ant had a distinct colour pattern in its thorax and gaster. This allowed individual identification of each forager. After at least 15 days of habituation, we submitted the colonies to the experimental protocol described below. Experimental Protocol We connected another foraging arena (1 m x 1 m x 20 cm, hereafter ‘experimental arena’) immediately adjacent to the external area (Fig. 1 b). The experimental arena is equipped with barriers to increase environmental complexity. These barriers were maintained at identical locations for all colonies for all the experiments. In addition, we placed 25 evenly distributed Petri dishes (60mm in diameter) where food would be available during the experiment. Petri dishes were buried in the substrate to avoid surface unevenness that would alter the ants' behaviour. Visual cues were present in the lab environment by the presence of a window, shelves on the walls and neon lights on the ceiling. As the colonies did not move during the experiments, these cues were identical throughout all the experiments. For the colonies to habituate to the new foraging area, we presented it five days before the beginning of the experiments. During this phase, the food location was alternated each feeding day between the external and the experimental arena. This allowed the ants to get familiar with the experimental arena, but the food location remained unpredictable. After the habituation phase, the experimental arena was only opened and available for forage during experimental trials, limiting the experience of the ants in this arena to the experimental period. During the experiments, the experimental foraging arena was filmed with a webcam (Logitech BRIO 4K Pro, resolution 3840 x 2160 px) positioned 1 m above its centre. To create two distinct food distributions, we offered food resources (adults of T. molitor ) in aggregated or dispersed regimes. Whole adults of the T. molitor beetle are slightly smaller than these ants, and therefore, each worker was able to transport one food item at a time. The dispersed distribution consisted of 10 out of 25 Petri dishes containing each one prey item. In the aggregated distribution, the 10 prey items were distributed in two patches, with each Petri dish containing five prey items. The treatment order was randomised using the website random.org . The locations where the food was placed were also randomised, but the presence of all food patches in the same line/row, close to the arena entrance or corners, or adjacent to others was not allowed. When any of these cases occurred, the locations were drawn again. The two distribution treatments were spaced 48 hours apart during each repetition. To quantify the effect of experience, we repeated each pair of treatments four times at 3-day intervals, alternating the treatment order. This results in eight experimental trials for each colony occurring over 25 days (colony x treatment x repetition: 6 x 2 x 4 = 48 trials in total; Fig. 1 c). Colonies were fed every 48 hours in the external area and thus were never starved. Each experimental trial began when the first forager entered the experimental arena. Two experimenters were present to place the food items and open/close the foraging arena. They also pre-registrated all ant identities, and whether they exited with or without prey. Behavioral observation itself was done by video recording through blind observers (more details in the following section). The duration of each trial depended on the foraging performance of the foragers (Fig. 1 d). If they were unsuccessful in collecting all items in one hour, the experiment trial ended at that moment. If they succeeded, we prolonged the experiment for an additional period equivalent to the feeding time (that is, how long the foragers took to collect all the food from the moment they discovered the first item). We implemented this additional observation time, which could last up to one hour, to investigate search behaviour after food depletion. As P. striata is a diurnal ant (Medeiros and Oliveira 2009), the experiments were restricted to the daytime period (9:00–16:00). During the experiments, the ants were not forced to enter the experimental arena or prevented from entering. This way, ants controlled their own activity and experience according to their motivation. This allowed us to evaluate differences in experience and how the interaction of internal and external factors modified experience. Behavioural Record and Foraging Success Three different observers coded the videos of the foraging experiments. Observers went through concordance sessions, analysing the same videos, until the behaviours they coded were at least 80% identically registered. The behaviour of all ants that entered the experimental arena was quantified through the focal animal sampling method (Altmann 1974) using the open-source software BORIS v.7.10.7 (Friard and Gamba 2016). We classified foraging trips as successful when the ant left the arena with food, unsuccessful when the ant did not, neutral when there was no food available, or incomplete when the experiment time ended before the end of the trip. We thus had a complete record of all the trips made by each ant in the experimental arena and their characteristics during the whole experimental period. To compare how interindividual differences could affect foraging success, we focused on foraging activity and exploration traits. Foraging activity was the number of consecutive foraging trips performed on the same day. This same measure was also considered experience, with more active ants necessarily having more short-term experience (further details in the section ‘Foraging Experience’ below). Foraging exploration rate was the number of different Petri dishes visited in the same trip divided by search duration. In unsuccessful, neutral, and incomplete trips, the search duration was equal to the entire trip duration, but for successful trips, the searching period ends after food discovery (i.e., in this case, we used latency to food discovery as search duration). Considering that a substantial number of trips (229/875) did not have at least one location explored, we also binomially compared trips with and without exploration. We also measured the latency until the ants explored the first Petri dish (be it loaded with food or not). For quantifying individual foraging success, the proportion of successful foraging trips was calculated over the sum of all successful and unsuccessful ones. For the foraging activity and exploration rate of individual foragers, we used all trip types. Since the crowdedness and supply conditions of the experimental arena were dynamic (e.g., over time, the tendency was for less food to be available and more foragers to be working at the same time), we quantified, at the beginning of each foraging trip, the number of food items still available and the number of simultaneous foragers. We did this to take into account that the foraging success was obviously linked to the number of prey available, and because other foragers could have some interference on the focal worker's behaviour (social experience). These two variables were included in all models. Foraging Experience To investigate how experience could affect foraging patterns, we analysed our variables using three different time scales: short-term experience, medium-term experience and long-term experience. For short-term experience, we considered the total number of trips made by the ant during a single trial. This number was incremented after each of the ant’s trips. For medium-term experience, we only considered ants that were active in both trials of the same repetition (Fig. 1 c), summing the total number of trips made in the previous trial (48 hours earlier). Long-term experience was calculated as the number of trials in which the ant participated throughout the experiment, regardless of the number of trips taken in each. All types of trips—successful, unsuccessful, and neutral—were considered in all experience time scales. Since we provided open access to the experimental arena during each trial, ants freely participated and performed multiple trips over the repetitions spread over 25 days. Because of the motivation difference, this led to differences in accumulated experience for the three studied timescales, which in turn modified motivation and behaviour. Further, we specifically distinguished positive experiences (i.e., previous successful trips) as this could serve as positive reinforcement and lead to increased ants’ motivation. We used these repeated measures (quantity and valence of the experience) to investigate inter-individual differences in foraging success, activity, and exploration. That is, whether some workers or colonies were consistently more successful, active, and exploratory than others. Colony Size and Presence of Brood To verify how colony demography affects foraging behaviour, we quantified the number of workers, eggs, larvae and pupae in each colony after each repetition. Since brood was absent from the colonies during most repetitions, we consider this variable as anecdotal but included it in the model for control purposes. Old gynes that lost their wings and behaved as workers were also considered workers. In addition, for activity models, we quantified the number of active foragers for each experimental trial, i.e. the number of ants seen at least once in the experimental arena. To analyse foraging success and exploration, we instead used the number of foragers simultaneously present in the foraging arena when an ant entered the experimental arena. Statistical Analysis We performed all statistical tests and figures using the open-source software R version 4.1.0 (R Core Team 2021). How foraging activity relates to food distribution, experience and colony size was tested using two generalised linear mixed-effects models (GLMM) with Poisson distribution. In both, foraging activity (discrete value) was the response variable. In one model, medium-term experience (covariate; number of trips in the previous trial of the same repetition) was the fixed predictor variable, while in the other, long-term experience (covariate; number of trials attended by the ant). We had to perform two distinct activity models since the individuals who acquired medium-term experience constitute a subset of the total number of individuals who participated in the experiments. In both models, food distribution (factor with two levels, aggregated or dispersed), the number of larvae (covariate), and the number of active foragers (covariate) were included as fixed effects. Observer, colony, and ant identities were included as random intercept factors. There were no indications of overdispersion in both models. To assess how individuals changed their exploration rate in different food distributions, colony sizes, and with experience, we used a linear mixed-effects model (LMM) with a Gaussian distribution. Therefore, the exploration rate (log[x] transformed) was used as the response variable. Food distribution (factor with 2 levels, aggregated or dispersed), long-term experience, same-day experience, number of larvae, number of simultaneous foragers, and number of prey available (all covariates) were included as fixed effects. Again, observer, colony, and ant identities were random factors. Similarly, we used trips with and without exploration (binomial) and latency to start exploration (log[x] transformed), as response variables in two other models, using the same covariates and random factors as described above. For the ‘exploration rate model’ and ‘latency to explore model’ we checked for normal distribution of model residuals and homogeneity of variances and used the Wald chi-square type III test to obtain p-values. In the ‘binomial exploration’ one we performed GLMM for binomial distribution with a logit link. We checked for overdispersion and collinearity in this case. To test whether the probability of being successful in foraging changed with food distribution, experience, and colony size, we performed a GLMM for binomial distribution with a logit link. Foraging success (0/1) on each foraging trip was used as a binomial response variable. The fixed effects included were exploration rate (covariate), food distribution (factor with 2 levels), previous successful experience (covariate; factor with 2 levels, 0/1) long-term experience (covariate), same-day experience (covariate; number of trips performed in a single trial), number of larvae (covariate), number of simultaneous foragers (covariate), and number of prey available (covariate). We included observer, colony, and ant identities as random (intercept) factors. The model was checked for overdispersion and collinearity. Foraging success, exploration, and activity models were later used in a repeatability test with 1000 permutations to verify inter-individual and inter-colonial differences. We chose the number of active/simultaneous foragers and larvae as the most accurate measure of colonial and brood size since the other variables measured (number of workers, pupae, eggs, total number of foragers, etc.) were correlated to them at some level. We used the R package lme4 (Bates et al. 2015) for all LMMs and GLMMs and rptR (Stoffel et al. 2017) for repeatability models. In all models, no variables showed (multi)collinearities, since variance inflation factors (VIF) were always lower than 2.11 (Faraway 2006). The packages dplyr (Wickham et al. 2023a) and tidyr (Wickham et al. 2023b) were used for data manipulation, performance (Lüdecke et al. 2021) to check for (and to exclude) overdispersion of the models and multi-collinearity of the predictor variables, and sjPlot (Lüdecke 2023) and ggplot2 (Wickham 2016) for data visualisation. Ethical Note This study was submitted to the responsible Brazilian authority Sistema de Autorização e Informação em Biodiversidade (SISBIO) of the Instituto Chico Mendes de Conservação da Biodiversidade (ICMBio), and approved by permission nº 47615. Because our experimental animal is an invertebrate, according to the Brazilian law Arouca, nº 11.794/2008, there was no need for submission to Comissão de Ética no Uso de Animais (CEUA/USP). We also considered the recommendations made in the Guidelines for the Use of Animals (ASAB Ethical Committee/ABS Animal Care Committee 2025). RESULTS A total of 875 foraging trips of 211 P. striata foragers were observed during the experiments, representing over 50 hours of observation of the videos. The duration of foraging trips was 10.73 ± 9.92 minutes (mean ± SD; min = 2 s, max = 69.81 min). Considering all trips completed where food was available, the ants achieved a foraging efficiency of 41.9%. In other words, in less than half of the trips, individual foragers were able to locate and remove a prey item from the experimental arena. Foraging activity Foragers presented an activity level (number of trips/trial) of 2.03 ± 1.36 (mean ± SD; min = 1, max = 8). Most of the time, individuals had a relatively low level of activity, performing just one (25%) or two (25%) trips in a trial. When facing an environment with dispersed food items, ants had lower activity (GLMM; N = 431 individuals/day, Z = -2.099, p = 0.036; Figure S1 a and S2). However, the higher the ant participation over the eight trials (i.e. long-term experience), the more active ants were (Z = 2.452, p = 0.014; Fig. 2 a). Medium-term experience (N = 84 individuals that were active in paired trials; Figure S1 b) did not influence activity (Z = 1.451, p = 0.147) and neither did the number of larvae (medium-term experience: N = 84, Z = -0.111, p = 0.912, long-term experience: N = 431, Z = -1.247, p = 0.212) and number of active foragers (medium-term experience: Z = -0.523, p = 0.601; long-term experience: Z = -0.473, p = 0.636) for both activity models (Figure S1 ). Colonies presented a low effect of an intercolonial difference in their foraging activity pattern (Repeatability test; R = 0.02, N = 6, p = 0.004). This means that colonies had consistent activity between the two different treatments and over 25 days, even if this correlation is weak. The same is not true for individuals who did not show any consistent interindividual differences over time and between conditions (R = 0.033, N = 211, p = 0.18). Foraging exploration Individuals explored the experimental foraging arena by visiting regularly distributed Petri dishes that could contain food or not. On a considerable number of foraging trips (229 out of 875), there was no exploration of the potential food patches. In the other trips, the ants explored one to ten different potential food locations. The exploration rate was 0.303 ± 0.518/minute (mean ± SD; min = 0.018, max = 9.607). Experience was the main factor that modulated exploration during foraging (Fig. 2 b and S3): ants that had a previous successful experience in that trial had a greater chance of exploring on the next trips (LMM; N = 875 foraging trips; Z = 4.706, p < 0.001; Fig. 2 b and S3a). A higher exploitation rate could also be attributed to individuals with previous successful experience (N = 646; X² = 7.345, df = 1, p = 0.007; Figure S3b and S4b) and short-term experience (meaning multiples foraging trips in a trial, X² = 7.253, df = 1, p = 0.007; Figure S3b and S4c). When there were multiple simultaneous foragers, individuals tended to carry out trips without exploration (Z = -2.890, p = 0.004; Figure S3a and S4a). Besides, the presence of larvae (X² = 18.261, df = 1, p < 0.001; Figure S3c and S4d) and multiple prey (X² = 77.906, df = 1, p < 0.001; Figure S3c and S4e) in the arena decreased the latency to start exploring, while the short-term experience (X² = 14.582, df = 1, p < 0.001; Figure S3c and S4f) and simultaneous foragers increased it (X² = 53.680, df = 1, p < 0.001; Figure S3c and S5h). We found no interindividual (Repeatability test; R = 0, N = 211, p = 1) or intercolonial (R = 0.016, N = 6, p = 0.058) consistent differences in the exploration rate. Foraging success In 41.9% of solitary foraging trips, ants found and transported a food item out of the experimental arena towards the nest. Even when exposed to a high-quality food patch containing five food items (i.e., aggregated food distribution), they never used tandem running to recruit nestmates to the food. We observed theft, cooperative transport and fragmentation of food by nestmates who met by chance, without communication and recruitment. Some ants also ingested the food before transporting it. Again, experience played a key role in predicting patterns of individual foraging success. Ants that had a previous successful experience in the same trial have 2.56 times more chance of having another success (GLMM; N = 618 foraging trips, Z = 2.998, p = 0.003; Fig. 2 c and S5), and long-term experience also significantly increased performance (Z = 2.212, p = 0.027; Fig. 2 d and S5). Exploration rate (Z = 4.093, p < 0.001; Figure S5 and S6a), but not activity level (Z = 1.067, p = 0.286), increased the likelihood of foraging success, which was also higher with more available food (Z = 3.590, p < 0.001; Figure S5 and S6b). Food distribution (Z = 0.371, p = 0.711), the number of larvae (Z = 0.109, p = 0.913) and simultaneous foragers (Z = -0.551, p = 0.582) had no significant influence on foraging success. We found a weak but significant effect of intercolonial and interindividual differences on individual foraging success. That is, in P. striata , we can find individuals (Repeatability test; R = 0.056, N = 211, p = 0.029) and colonies (R = 0.084, N = 6, p = 0.001) that are consistently better at foraging than others. DISCUSSION This study experimentally evaluated how internal mechanisms (e.g., experience, motivation) and environmental factors (e.g., colony demographics, food distribution) affect individual foraging in the ant P. striata . In addition, our experiments also did not constrain ant participation, which was studied as a measure of motivation. We found that experience is one of the key factors for foraging behaviour and performance in this species, with more experienced individuals being more active, exploratory, and successful. Specifically, a previous success experience increased the ants' motivation and resulted in more exploration and an increased probability of being successful again. Furthermore, foragers exhibit plasticity by altering their behaviour in response to environmental factors, such as being less active with dispersed food and less exploratory with multiple simultaneous foragers. On the other hand, they explore sooner with more prey available. Finally, we found intercolonial differences in foraging activity and success rate and interindividual differences only in the latter. These results illustrate that the decision to initiate foraging is complex and multifactorial. Above all, previous experience is a determining factor for the execution and performance of solitary foraging. In our study, P. striata foragers were more active with higher long-term experience. Previous success and higher same-day experience were associated with a higher exploration rate. More experienced individuals may become more active and exploratory through some non-exclusive mechanisms. First, age polyethism may lead older (and probably more experienced) individuals to focus on foraging and abandon other tasks (reviewed in Beshers & Fewell 2001). Second, behavioural specialisation can lead individuals who have already foraged to begin foraging again (Robinson et al. 2009). Lastly, knowledgeable individuals also improve their navigational, sensory, and locomotion abilities (e.g., von Frisch 1967; Harrison et al. 1989; Collett and Collett 2002), increasing route efficiency and potentially allowing more trips per trial. In social insects, experience can arise through the acquisition of private or social information (Grüter and Czaczkes 2019). An interesting example of the effect of experience and learning-based experience on ant foraging can be seen in Myrmica rubra (Linnaeus, 1758), where workers must individually learn to follow pheromone trails. Still, such learning can be enhanced by the presence of older, experienced nestmates (Cammaerts 2013). In our case, since all trips involved only solitary foraging, it is possible to determine that the source of expertise is mostly individual. Social information transmission was limited to contacts in the nest with returning foragers or in the foraging arena between foragers active at the same time. In both cases, contacts can provide cues to foragers and food availability, but not the precise location of a food source. While the positive impact of experience on foraging is not new, this study is significant because it shows these effects can last for long periods (up to 25 days), rare in insect studies, and in a ponerine ant species never before tested in lab conditions. However, it is important to consider that the experience effect found in our study may also be, at least partially, an age effect since the colonies studied here were brought from the field, and the age of the individuals is unknown. The experience effect is often confused with age, as it tends to correlate at some level – gaining experience takes time. Thus, controlling for this and finding both young and experienced foragers is virtually impossible since foraging in ants is highly age-dependent (Franklin et al. 2012; Lima Vieira et al. 2024). On the other hand, in many studies, recruitment, search behaviour and other foraging aspects appeared to depend on experience, rather than age (e.g., Fourcassié and Traniello 1994; Tripet and Nonacs 2004; Ravary et al. 2007; Franklin et al. 2012). Future studies should compare the role of activity in similar-age workers, as is done, for example, in clonal ponerine ants (Hartmann et al. 2020) or experimentally manipulate the foraging experience of known-age individuals of P. striata . We found that a higher success rate was associated with more long-term experience, previous success and greater exploration in P. striata workers. Thus, more experienced ants are not only more active and exploratory but also more successful than less experienced workers in our experimental setup. There is evidence suggesting that through experience, ants improve their task performance, recovering food more efficiently, either by increasing the food quantity or decreasing trip duration or food localisation. Informed ants perform better navigation, transport (Stroeymeyt et al. 2011), tandem running (Franklin et al. 2012; Glaser and Grüter 2018), pheromone trail recruitment (Cammaerts 2013), seed-harvesting techniques (Johnson 1991) and prey capture (Dejean 1985). These anterior experiences, especially those of positive valence, can constitute a positive reinforcement and therefore increase ulterior appetitive motivation (or motivation for food) as a consequence (Baracchi et al. 2017; Lima Vieira et al. 2024). Positive valence of experience underlies learning processes (e.g., associative, operant conditioning) widely studied in insects such as fruit flies and honey bees, and more recently, ants (e.g., Schilman and Roces 2003; Schilman 2011; Sadler and Nieh 2011; Plowright 2017; Metz et al. 2018; Czaczkes 2022). Furthermore, increased motivation in many cases can culminate in a positive state of arousal that makes individuals more responsive to encountering food, especially that of lower quality (Perez et al. 2013; d’Ettorre et al. 2017). Alternatively, neutral stimuli can also be learned in repeated trips in the same environment, through latent learning (Poissonier et al. 2023; Clement et al. 2024). In our study, it is not possible to verify that foragers learned specific regularities of the environment since food conditions were always unpredictable and there was no prior training with positive reinforcement. It is also not possible to say exactly how fast and for how long the information about previous routes is stored. However, our protocol was decisive in demonstrating in an experimental situation that simulates the conditions found in nature that individuals are (1) capable of dedicating themselves to foraging for long periods (both during a trial and across trials), (2) show increased motivation with their past success and (3) acquiring better exploration skills that effectively improve their foraging efficiency. In our protocol, activity decreased with dispersed food. We propose that the random and dispersed food distribution reduced the ants' motivation to continue searching for food on other trips. Workers tend to be more persistent when the sources are more persistent and/or renewed regularly. Otherwise, while foraging for highly unpredictable sources, they tend to give up rapidly (Fourcassié and Traniello 1994). In this sense, our aggregated food distribution was more renewable and thus allowed consecutive trips, which did not happen when the food was dispersed. Indeed, foragers of many solitary foraging species tend to return to previously rewarding sites to search for food (e.g., Fresneau 1985; Fourcassié and Traniello 1993; Wehner et al. 2004; Tanner 2009; Azevedo et al. 2014). Although it has not been tested formally in P. striata , field experiments show that they come back repeatedly to the same food source, suggesting the learning of foraging trips in the wild (Medeiros and Oliveira 2009; Silva et al. 2021). Besides, we also observed that the number of active foragers in the colony did not affect individual activity in P. striata . This can, at least initially, suggest that individual foraging activity is little affected by the colony's nutritional needs. Thus, an increase in foraging effort related to larger nutritional needs can be achieved by increasing the number of foragers rather than by increasing individual effort. Exploration rates also varied with social factors such as colony size and external factors such as the number of prey. We observed that trips without exploration were more common when there were multiple simultaneous foragers. Foragers likely detected the presence of more foragers by chemical cues or direct social contacts and adjusted their exploratory behaviour to avoid locations where other nestmates were already exploring, thus momentarily decreasing their exploration. We also found differences in the latency to begin exploration, with individuals taking less time to explore with multiple prey and taking longer with more short-term experience and simultaneous foragers. With more prey available for capture, foragers probably detected it quicker by crowding at the food source or volatile chemical compounds emitted by dead arthropods (Fourcassié and Traniello 1994; Buehlmann et al. 2014) and consequently began exploring earlier. Notwithstanding, short-term experience delays the beginning of exploration because ants likely tend to avoid previously visited plates on consecutive routes, and new sites decreases in number. These results suggest a possible role of memory in individual decisions about last visited sites. We found intercolonial differences in activity but not in exploration level. This means that colonies showed consistency, even if low, in their activity pattern over 25 days. Similarly, Pogonomyrmex barbatus (Smith, 1858) colonies exhibit variations in scout rates and the number of foragers departing the nest, with these intercolonial differences remaining consistent each year (Gordon et al. 2011, 2013). Interindividual differences, often called personality traits when they occur, are thought to be important drivers of both motivation and task execution. In social insects, they could also influence division of labour and other colony characteristics (Jandt et al. 2013). Thus, colonies that display distinct collective behavioural responses consistently across time and contexts are described as possessing personality traits (Stamps 2016). We found no individual differences in activity or exploration, but we found a weak but significant effect of intercolonial and interindividual differences on individual foraging success. ‘Activity’ and ‘exploration’ are classically considered personality traits in non-human animals (Réale et al. 2007). The ways of measuring personality traits in the literature are not standardised, and large differences between the definitions of 'activity' and ‘exploration’ can be found (reviewed in Carter et al. 2013). It is important to remember that workers modified their behaviour over 25 days, some becoming more active and others more exploratory as they gained different levels of experience and success. Twenty-five days is a long period of time when looking at the typical experiments testing individual personality (Jardim et al. 2025). In long-lived ponerine ants tested in semi-natural conditions such as ours, where ants were free to access the arena, the lack of repeatability can also come from the crucial importance of experience and success we showed in the expression of the very flexible behaviour that is foraging. Perhaps in more controlled conditions, repeatability will show higher significant values (Jardim et al. 2025), and it seems that testing ants in different environments, which are poorer or richer in terms of stimuli, could give us very relevant information about the stability of interindividual differences. Indeed, putting experience and development back into the study of interindividual differences would help better understand the plasticity of behavioural responses in a more integrative way (Stamps 2016). Interestingly, our data also suggest that consistent intercolonial differences can be detected without interindividual differences and stresses the fact that what makes colony personality could not only be the result of individual personalities but also of colonial idiosyncratic properties themselves and how they interact with individual plasticity (Pinter-Wollman 2012). Behavioural plasticity and personality traits should thus be studied in a more inclusive and mechanistic way across contexts to improve our understanding of ant behaviour in an ecologically relevant way. In conclusion, our study reveals the pivotal role of experience in shaping individual foraging behaviours and motivation in P. striata . Long-term experience enhances activity, exploration, and foraging success, highlighting the importance of cumulative experience and learning in task performance. Social and environmental factors such as food distribution and colony demographics further influence behavioural adjustments, showcasing the phenotypic plasticity of these ants. While intercolonial differences in activity and success rates suggest a form of collective "personality," individual variations in success suggest the presence of more efficient foragers. These findings provide valuable insights into the intricate interplay between internal mechanisms, environmental and social contexts in driving self-organised tasks in social insect societies. Details of extrinsic and intrinsic mechanisms regulating P. striata foraging behaviour have just begun to be addressed, and many aspects remain to be explored. For example, it is still unknown how memory of past experience is formed, consolidated and retrieved, the limits of appetitive learning and the effect of age on P. striata workers. Motivational processes were important in our study of solitary foragers to understand behaviour. This kind of study should be expanded to other species of ants, including trail-laying foraging species. Our study also confirms that experience-based processes greatly influence foraging behaviour, and their influence on collective foraging behaviour and division of labour should also be investigated more thoroughly. Declarations Declaration of interest None declared. Funding statement Maria Eduarda Lima Vieira received a funding grant from the Brazilian Science Ministry (Conselho Nacional de Desenvolvimento Científico e Tecnológico, CNPq), MCTI/CNPq/Universal PQ 311790/2021-8 and Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES), CAPES/PRInt 88887.916823/2023-00. Nicolas Châline received funding from CAPES, process CAPES/PRInt 88887.915491/2023-00. Guilherme Rocha Fiuza and Gabi Fernandes Bianchi received funding from Universidade de São Paulo, PUB 2023-83-1/2273. Author Contribution Maria Eduarda Lima Vieira: Conceptualisation, Data curation, Formal analysis, Investigation, Methodology, Project administration, Visualisation, Writing - original draft, Writing - review & editing. Gabi Fernandes Bianchi: Data curation, Methodology, Writing - review & editing. Guilherme Rocha Fiuza: Methodology, Writing - review & editing. Nicolas Châline: Supervision, Conceptualisation, Data curation, Funding acquisition, Investigation, Methodology, Project administration, Resources, Validation, Writing - original draft, Writing - review & editing. Acknowledgement We acknowledge Camila Gesicki Boggiani, Maria Eduarda Martins Bertanha, and Stefani Bonassi Maschia for their assistance in collecting the colonies. We thank Monise Menezes for letting us use the ‘Figure 1a’ photo. We also thank Stéphane Chameron and Jerry Hogan for their useful contributions and insights on the manuscript. 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Harvard University Press, Cambridge Wehner R, Meier C, Zollikofer C (2004) The ontogeny of foraging behaviour in desert ants, Cataglyphis bicolor . Ecol Entomol 29:240–250. http://dx.doi.org/10.1111/j.0307-6946.2004.00591.x Wickham H (2016) ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York Wickham H, François R, Henry L, et al (2023a) dplyr: A Grammar of Data Manipulation (R package version 1.1.4). Wickham H, Vaughan D, Girlich M (2023b) tidyr: Tidy Messy Data (R package version 1.3.0). Additional Declarations No competing interests reported. Supplementary Files SupplementaryMaterialNaturwiss.pdf Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7302949","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":504274263,"identity":"a121e437-e6bd-4dac-b389-d72db8d4e622","order_by":0,"name":"Maria Eduarda LIMA VIEIRA","email":"","orcid":"","institution":"Universidade de São Paulo","correspondingAuthor":false,"prefix":"","firstName":"Maria","middleName":"Eduarda LIMA","lastName":"VIEIRA","suffix":""},{"id":504274264,"identity":"03efe80e-1b36-43a6-a752-5e5660ad6f8b","order_by":1,"name":"Gabi Fernandes BIANCHI","email":"","orcid":"","institution":"Universidade de São Paulo","correspondingAuthor":false,"prefix":"","firstName":"Gabi","middleName":"Fernandes","lastName":"BIANCHI","suffix":""},{"id":504274265,"identity":"2845535c-5266-49f0-83fc-de7278f3e99d","order_by":2,"name":"Guilherme Rocha FIUZA","email":"","orcid":"","institution":"Universidade de São Paulo","correspondingAuthor":false,"prefix":"","firstName":"Guilherme","middleName":"Rocha","lastName":"FIUZA","suffix":""},{"id":504274266,"identity":"b8f47ff8-1e98-4fbb-b0bb-fa01fa0876bc","order_by":3,"name":"Nicolas CHÂLINE","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAsElEQVRIiWNgGAWjYFACHgaGBwYMDPwgNmMDsVoSgFokG0jTAqQMDhCrxZz97MEPCQV35IxvpD97wLjjHmEtlj15yRIJBs+MzW7kmBswnikmrMXgBo8BUMvhxG03ctgkGNsSiNJi/AOopX7zjPRnRGsxA9mSALTLjEgtZ3LMLIBaDGeceWNukHiGGC3Hzxjf+PDnsDx/OzDEPu4gQgsyYGMgUQNIyygYBaNgFIwCbAAA4N46KK1Rq7YAAAAASUVORK5CYII=","orcid":"","institution":"Universidade de São Paulo","correspondingAuthor":true,"prefix":"","firstName":"Nicolas","middleName":"","lastName":"CHÂLINE","suffix":""}],"badges":[],"createdAt":"2025-08-05 17:08:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7302949/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7302949/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":90204132,"identity":"b4276240-71c1-428b-84d8-7aa934a92e42","added_by":"auto","created_at":"2025-08-29 20:22:13","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":512814,"visible":true,"origin":"","legend":"\u003cp\u003eWorker of the ant \u003cem\u003eP. striata\u003c/em\u003e and summary of the experimental protocol. (a) Two foragers of the ant \u003cem\u003eP. striata\u003c/em\u003e hunting a \u003cem\u003eT. molitor\u003c/em\u003e larva. Photo credit: Monise Menezes. (b) Physical apparatus of the experimental setup, with an artificial nest and two foraging areas, the first is the usual external area of the colony, and the second is the experimental arena, where the food was distributed in an aggregated or dispersed way. (c) experimental timeline with eight trials over 25 days in four repetitions spaced three days apart. (d) trial duration depending on the ants' performance, and whether or not there was food depletion.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7302949/v1/4561e5e6145f07831d63c59b.jpeg"},{"id":90204022,"identity":"4f227742-7445-43f3-8b5d-b86097e44558","added_by":"auto","created_at":"2025-08-29 20:14:13","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":150418,"visible":true,"origin":"","legend":"\u003cp\u003ePredicted effects (with 95% confidence intervals) of experience on the foraging behaviour of \u003cem\u003eP. striata\u003c/em\u003e. Grey dots represent raw data, with high opacity representing overlapping dots. (a) effect of the long-term experience (i.e., number of trials the ant previously participated in) on foraging activity. (b) effect of a past success experience on that day on the propensity to explore in the current trip (N ‘no’ = 633, N ‘yes’ = 242). (c) effect of past success on the propensity to be successful again (N ‘no’ = 449, N ‘yes’ = 169) and (d) the effect of long-term experience on foraging success.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7302949/v1/93017920428fc1fbac57b7f5.png"},{"id":93803294,"identity":"f02d7be8-c07a-4c4b-9b64-6c866a9b12a8","added_by":"auto","created_at":"2025-10-17 17:31:41","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1207189,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7302949/v1/aef12e47-3475-4f2d-8d8f-fa721638f360.pdf"},{"id":90204133,"identity":"2ab213bf-8804-4ec2-8abb-66676806cbd2","added_by":"auto","created_at":"2025-08-29 20:22:13","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":1171120,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterialNaturwiss.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7302949/v1/4c86a0714e164ac6d8024d69.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Practice makes perfect? Experience boosts foraging performance in a ponerine ant","fulltext":[{"header":"Significance Statement","content":"\u003cp\u003eForaging behaviour is an extremely important element of fitness, and understanding how individual behaviour influences task execution success in social species is an understudied subject. In this manuscript, we study how individual (e.g., experience) and environmental characteristics determine decision-making for foraging and foraging success. We apply an innovative protocol that leaves ants the possibility to participate in the experiments according to their motivation. Our main results show that experience is a key factor for foraging behaviour and performance in this species, with more experienced individuals being more active, exploratory, and successful. Specifically, a previous success experience increased the ants\u0026apos; motivation and resulted in more exploration and an increased probability of being successful again. These findings are novel in detailing the multifactorial effects influencing the foraging initiation and performance. We discuss the seldom mentioned motivational implications of these results.\u003c/p\u003e"},{"header":"INTRODUCTION","content":"\u003cp\u003eSocial insects are known for their sophisticated division of labour, where most tasks are performed by specialised groups of sterile workers (reviewed in Beshers and Fewell 2001). This collective organisation allows the colony to supply all group needs efficiently without central control (Gordon 2007). In ants, foraging, i.e., searching for, finding and recovering food, is performed through solitary or collective strategies, the latter occurring through a varied number of forms (reviewed in Lanan 2014). In both solitary and collective foraging, individual characteristics play an important role in the activation, maintenance, and execution of successful foraging. For example, some ants have higher sensitivity to foraging-related stimuli, such as light or food, making them more likely to initiate foraging activities (Perez et al. 2013; Detrain and Prieur 2014). Besides, individuals and colonies can present consistent interindividual behavioural differences (across contexts and time) related to foraging performance, such as tendencies towards greater activity and exploration (Horna-Lowell et al. 2021). Additionally, differences can develop over time, with more experienced foragers demonstrating greater effort, success, and specialisation in this task (Dukas and Visscher 1994). For instance, the tendency to forage increases with repeated encounters with prey in the clonal ant \u003cem\u003eOoceraea biroi\u003c/em\u003e (Forel, 1907) (Ravary et al. 2007).\u003c/p\u003e\u003cp\u003eDespite their relevance, individual characteristics have received less attention than collective information-sharing mechanisms such as pheromone trails (Czaczkes et al. 2015). Classic threshold models of division of labour, by looking mainly at which task will be done by which worker (Beshers and Fewell 2001), ignore motivational processes and how they can be modified both by immediate and experience-based elements (Lima Vieira et al. 2024). Motivational processes are internal neurophysiological and cognitive mechanisms that adjust an organism's behaviour based on internal states (e.g., hunger) and external cues (e.g., resource availability). They influence behaviour by modifying its frequency, intensity, and latency (Toates 1986; Hogan 2015, 2017). In foraging ants, motivational states may drive exploration, path fidelity, or recruitment efforts, reflecting an interplay between evolved behavioural repertoires and immediate ecological demands (Deneubourg and Goss 1989; Gordon 2019). Many of the studies investigating the mechanisms of ants\u0026rsquo; foraging plasticity evaluate species that perform mass recruitment. A notable example is the sensitivity of workers to trail pheromone, or the propensity to deposit pheromone (e.g., Fewell et al. 1992; Breed et al. 1996; reviewed in Czaczkes et al. 2015). However, in solitary foraging species, trail pheromones are absent, and other parameters must influence the expression of behaviours associated with foraging. Most studies focus on the effect of spatial cognition on foraging performance (Reznikova 2020). In stark contrast, other aspects, such as non-spatial learning and experience and learning-based motivational processes, are less understood (Lima Vieira et al. 2024).\u003c/p\u003e\u003cp\u003eIn addition to individual intrinsic mechanisms, external factors (social or related to environmental conditions) also impact foraging motivation. In the colony, nutritional needs play an important role in modulating foraging efforts, with more populous colonies requiring more food resources (Beckers et al. 1989; Lima Vieira et al. 2024). Outside the colony, the indication of food presence or a greater quantity, quality and/or better food predictability can increase the individual motivation to forage (Schilman and Roces 2003; Schilman 2011; d\u0026rsquo;Ettorre et al. 2017). In the Argentine ant \u003cem\u003eLinepithema humile\u003c/em\u003e (Mayr, 1868), for example, a pre-exposure to a trail pheromone component (used as reinforcement stimuli) can increase the propensity of ants to feed on the sucrose solution (Rossi et al. 2020).\u003c/p\u003e\u003cp\u003eEnvironmental conditions and experience, including non-learning, social and environmental experience, contribute to variations in accumulated information and internal state modifications, leading to differences in workers' motivation and roles (e.g., Biesmeijer and de Vries 2001; Stroeymeyt et al. 2011; Gr\u0026uuml;ter and Leadbeater 2014). However, how these variables impact foraging and, especially, to what extent they influence foraging performance is not fully understood. In particular, in hyper-diverse insect groups such as ants, we are in dire need of experiments to confirm the mechanisms behind how collective behaviours emerge from the behaviour of solitary foragers. Ants of the subfamily Ponerinae are good models to address this gap since they can perform solitary foraging but also recruit, emphasizing the role of foraging behaviour at both the individual and colonial levels. Thus, in this study, we investigate how internal factors (e.g., short, medium and long-term experience), social factors (e.g., colony size), and environmental factors (e.g., food distribution) influence the foraging activity, exploration rate, and performance of individuals in the ant \u003cem\u003ePachycondyla striata\u003c/em\u003e Smith, 1858. Our goal also was to assess the motivational processes through differences in ant experience and behaviour in a setup where ants could decide to engage or not in the foraging experiment. Additionally, we assess whether individual and colonial behavioural traits remain consistent over time and across different food distribution patterns. We hypothesise that individual foraging motivation and performance (i.e., foraging success) improve with greater experience, larger colony size, and when food is aggregated rather than dispersed. We also predict that individual foraging strategies will adapt to food distribution and that, rather than consistent differences, developmental differences will emerge at both individual and colony levels, with certain ants and colonies being more active, exploratory, and successful, reflecting a colony-level behavioural phenotype.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cp\u003e\u003cb\u003eStudy subject and experimental area\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eP. striata\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea) is a large ponerine ant (body size\u0026thinsp;\u0026gt;\u0026thinsp;10 mm) widely distributed throughout South America. In Brazil, it is a common species in the Atlantic Forest from Bahia to Rio Grande do Sul (latitudinal range) and from Mato Grosso to Bahia (longitudinal range) (Marcineiro and Lattke 2024). Workers are generalist predators and scavengers feeding on a range of insect prey. They also consume plant material such as fruits and seeds (Medeiros and Oliveira 2009). They are considered one of the main seed-dispersing ants in the Atlantic Forest, cleaning and displacing seeds further than 10 m (Pizo and Oliveira 2001; Passos and Oliveira 2002, 2004; Magalh\u0026atilde;es et al. 2018). While foraging, the most commonly used strategy is solitary foraging, where the finding, capture, and food recovery occur independently by individual foragers without nestmate recruitment (Jaffe 1984; Silva et al. 2021). In addition to solitary foraging, the recruitment of nestmates by tandem running is also possible. During a tandem run, a worker with knowledge about a new food source or nesting site recruits a generally na\u0026iuml;ve worker. This contact is maintained through chemical and mechanical signals, which occur through constant contact between the follower's antennae and the leader's gaster (reviewed in Franklin 2014; Gr\u0026uuml;ter et al. 2018; Silva et al. 2021). While tandem running was frequently observed during field collections of colonies, in the laboratory, workers never performed this recruitment strategy for food sources. For this reason, our results presented here refer exclusively to solitary foraging.\u003c/p\u003e\u003cp\u003eWe collected five \u003cem\u003eP. striata\u003c/em\u003e colonies on the Universidade de S\u0026atilde;o Paulo campus (-23.559540395733073, -46.724229128836186), S\u0026atilde;o Paulo state, Brazil, between March and May 2023. The collection area corresponds to an Atlantic Forest largely affected by urbanisation and fragmentation (Kraus et al. 2005), where colonies of \u003cem\u003eP. striata\u003c/em\u003e are common. All colonies were collected without queens or brood and we split the largest colony into two subcolonies (colony 6.1 and 6.2; colonies\u0026rsquo; demography in Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e) We maintained the colonies in the laboratory at stable conditions (temperature 25\u0026thinsp;\u0026plusmn;\u0026thinsp;2\u0026deg;C, humidity 67\u0026thinsp;\u0026plusmn;\u0026thinsp;3% and a 12-hour light/dark cycle) and provided them thrice weekly with \u003cem\u003eTenebrio molitor\u003c/em\u003e Linnaeus, 1758 larvae, a mix of apple and honey, and water \u003cem\u003ead libitum\u003c/em\u003e. The physical apparatus in which they were installed is an artificial plaster nest (50 cm x 50 cm x 50 cm) subdivided into six chambers and a foraging area (1 m x 1 m x 20 cm, hereafter external area) with expanded vermiculite as a substrate. All workers were marked with paint (Uni-paint PX20), and remarking was never necessary (Corbara et al. 1986). Colony identity was represented by a different colour mark on the ants\u0026rsquo; petiole, and each ant had a distinct colour pattern in its thorax and gaster. This allowed individual identification of each forager. After at least 15 days of habituation, we submitted the colonies to the experimental protocol described below.\u003c/p\u003e\u003cp\u003e\u003cb\u003eExperimental Protocol\u003c/b\u003e\u003c/p\u003e\u003cp\u003eWe connected another foraging arena (1 m x 1 m x 20 cm, hereafter \u0026lsquo;experimental arena\u0026rsquo;) immediately adjacent to the external area (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb). The experimental arena is equipped with barriers to increase environmental complexity. These barriers were maintained at identical locations for all colonies for all the experiments. In addition, we placed 25 evenly distributed Petri dishes (60mm in diameter) where food would be available during the experiment. Petri dishes were buried in the substrate to avoid surface unevenness that would alter the ants' behaviour. Visual cues were present in the lab environment by the presence of a window, shelves on the walls and neon lights on the ceiling. As the colonies did not move during the experiments, these cues were identical throughout all the experiments. For the colonies to habituate to the new foraging area, we presented it five days before the beginning of the experiments. During this phase, the food location was alternated each feeding day between the external and the experimental arena. This allowed the ants to get familiar with the experimental arena, but the food location remained unpredictable. After the habituation phase, the experimental arena was only opened and available for forage during experimental trials, limiting the experience of the ants in this arena to the experimental period.\u003c/p\u003e\u003cp\u003eDuring the experiments, the experimental foraging arena was filmed with a webcam (Logitech BRIO 4K Pro, resolution 3840 x 2160 px) positioned 1 m above its centre. To create two distinct food distributions, we offered food resources (adults of \u003cem\u003eT. molitor\u003c/em\u003e) in aggregated or dispersed regimes. Whole adults of the \u003cem\u003eT. molitor\u003c/em\u003e beetle are slightly smaller than these ants, and therefore, each worker was able to transport one food item at a time. The dispersed distribution consisted of 10 out of 25 Petri dishes containing each one prey item. In the aggregated distribution, the 10 prey items were distributed in two patches, with each Petri dish containing five prey items. The treatment order was randomised using the website \u003cem\u003erandom.org\u003c/em\u003e. The locations where the food was placed were also randomised, but the presence of all food patches in the same line/row, close to the arena entrance or corners, or adjacent to others was not allowed. When any of these cases occurred, the locations were drawn again. The two distribution treatments were spaced 48 hours apart during each repetition. To quantify the effect of experience, we repeated each pair of treatments four times at 3-day intervals, alternating the treatment order. This results in eight experimental trials for each colony occurring over 25 days (colony x treatment x repetition: 6 x 2 x 4\u0026thinsp;=\u0026thinsp;48 trials in total; Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec). Colonies were fed every 48 hours in the external area and thus were never starved.\u003c/p\u003e\u003cp\u003eEach experimental trial began when the first forager entered the experimental arena. Two experimenters were present to place the food items and open/close the foraging arena. They also pre-registrated all ant identities, and whether they exited with or without prey. Behavioral observation itself was done by video recording through blind observers (more details in the following section). The duration of each trial depended on the foraging performance of the foragers (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ed). If they were unsuccessful in collecting all items in one hour, the experiment trial ended at that moment. If they succeeded, we prolonged the experiment for an additional period equivalent to the feeding time (that is, how long the foragers took to collect all the food from the moment they discovered the first item). We implemented this additional observation time, which could last up to one hour, to investigate search behaviour after food depletion. As \u003cem\u003eP. striata\u003c/em\u003e is a diurnal ant (Medeiros and Oliveira 2009), the experiments were restricted to the daytime period (9:00\u0026ndash;16:00). During the experiments, the ants were not forced to enter the experimental arena or prevented from entering. This way, ants controlled their own activity and experience according to their motivation. This allowed us to evaluate differences in experience and how the interaction of internal and external factors modified experience.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eBehavioural Record and Foraging Success\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThree different observers coded the videos of the foraging experiments. Observers went through concordance sessions, analysing the same videos, until the behaviours they coded were at least 80% identically registered. The behaviour of all ants that entered the experimental arena was quantified through the focal animal sampling method (Altmann 1974) using the open-source software BORIS v.7.10.7 (Friard and Gamba 2016). We classified foraging trips as successful when the ant left the arena with food, unsuccessful when the ant did not, neutral when there was no food available, or incomplete when the experiment time ended before the end of the trip. We thus had a complete record of all the trips made by each ant in the experimental arena and their characteristics during the whole experimental period.\u003c/p\u003e\u003cp\u003eTo compare how interindividual differences could affect foraging success, we focused on foraging activity and exploration traits. Foraging activity was the number of consecutive foraging trips performed on the same day. This same measure was also considered experience, with more active ants necessarily having more short-term experience (further details in the section \u0026lsquo;Foraging Experience\u0026rsquo; below). Foraging exploration rate was the number of different Petri dishes visited in the same trip divided by search duration. In unsuccessful, neutral, and incomplete trips, the search duration was equal to the entire trip duration, but for successful trips, the searching period ends after food discovery (i.e., in this case, we used latency to food discovery as search duration). Considering that a substantial number of trips (229/875) did not have at least one location explored, we also binomially compared trips with and without exploration. We also measured the latency until the ants explored the first Petri dish (be it loaded with food or not). For quantifying individual foraging success, the proportion of successful foraging trips was calculated over the sum of all successful and unsuccessful ones. For the foraging activity and exploration rate of individual foragers, we used all trip types. Since the crowdedness and supply conditions of the experimental arena were dynamic (e.g., over time, the tendency was for less food to be available and more foragers to be working at the same time), we quantified, at the beginning of each foraging trip, the number of food items still available and the number of simultaneous foragers. We did this to take into account that the foraging success was obviously linked to the number of prey available, and because other foragers could have some interference on the focal worker's behaviour (social experience). These two variables were included in all models.\u003c/p\u003e\u003cp\u003e\u003cb\u003eForaging Experience\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo investigate how experience could affect foraging patterns, we analysed our variables using three different time scales: short-term experience, medium-term experience and long-term experience. For short-term experience, we considered the total number of trips made by the ant during a single trial. This number was incremented after each of the ant\u0026rsquo;s trips. For medium-term experience, we only considered ants that were active in both trials of the same repetition (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec), summing the total number of trips made in the previous trial (48 hours earlier). Long-term experience was calculated as the number of trials in which the ant participated throughout the experiment, regardless of the number of trips taken in each. All types of trips\u0026mdash;successful, unsuccessful, and neutral\u0026mdash;were considered in all experience time scales. Since we provided open access to the experimental arena during each trial, ants freely participated and performed multiple trips over the repetitions spread over 25 days. Because of the motivation difference, this led to differences in accumulated experience for the three studied timescales, which in turn modified motivation and behaviour. Further, we specifically distinguished positive experiences (i.e., previous successful trips) as this could serve as positive reinforcement and lead to increased ants\u0026rsquo; motivation. We used these repeated measures (quantity and valence of the experience) to investigate inter-individual differences in foraging success, activity, and exploration. That is, whether some workers or colonies were consistently more successful, active, and exploratory than others.\u003c/p\u003e\u003cp\u003e\u003cb\u003eColony Size and Presence of Brood\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo verify how colony demography affects foraging behaviour, we quantified the number of workers, eggs, larvae and pupae in each colony after each repetition. Since brood was absent from the colonies during most repetitions, we consider this variable as anecdotal but included it in the model for control purposes. Old gynes that lost their wings and behaved as workers were also considered workers. In addition, for activity models, we quantified the number of active foragers for each experimental trial, i.e. the number of ants seen at least once in the experimental arena. To analyse foraging success and exploration, we instead used the number of foragers simultaneously present in the foraging arena when an ant entered the experimental arena.\u003c/p\u003e\u003cdiv id=\"Sec2\" class=\"Section2\"\u003e\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\u003cp\u003eWe performed all statistical tests and figures using the open-source software R version 4.1.0 (R Core Team 2021).\u003c/p\u003e\u003cp\u003eHow foraging activity relates to food distribution, experience and colony size was tested using two generalised linear mixed-effects models (GLMM) with Poisson distribution. In both, foraging activity (discrete value) was the response variable. In one model, medium-term experience (covariate; number of trips in the previous trial of the same repetition) was the fixed predictor variable, while in the other, long-term experience (covariate; number of trials attended by the ant). We had to perform two distinct activity models since the individuals who acquired medium-term experience constitute a subset of the total number of individuals who participated in the experiments. In both models, food distribution (factor with two levels, aggregated or dispersed), the number of larvae (covariate), and the number of active foragers (covariate) were included as fixed effects. Observer, colony, and ant identities were included as random intercept factors. There were no indications of overdispersion in both models.\u003c/p\u003e\u003cp\u003eTo assess how individuals changed their exploration rate in different food distributions, colony sizes, and with experience, we used a linear mixed-effects model (LMM) with a Gaussian distribution. Therefore, the exploration rate (log[x] transformed) was used as the response variable. Food distribution (factor with 2 levels, aggregated or dispersed), long-term experience, same-day experience, number of larvae, number of simultaneous foragers, and number of prey available (all covariates) were included as fixed effects. Again, observer, colony, and ant identities were random factors. Similarly, we used trips with and without exploration (binomial) and latency to start exploration (log[x] transformed), as response variables in two other models, using the same covariates and random factors as described above. For the \u0026lsquo;exploration rate model\u0026rsquo; and \u0026lsquo;latency to explore model\u0026rsquo; we checked for normal distribution of model residuals and homogeneity of variances and used the Wald chi-square type III test to obtain p-values. In the \u0026lsquo;binomial exploration\u0026rsquo; one we performed GLMM for binomial distribution with a logit link. We checked for overdispersion and collinearity in this case.\u003c/p\u003e\u003cp\u003eTo test whether the probability of being successful in foraging changed with food distribution, experience, and colony size, we performed a GLMM for binomial distribution with a logit link. Foraging success (0/1) on each foraging trip was used as a binomial response variable. The fixed effects included were exploration rate (covariate), food distribution (factor with 2 levels), previous successful experience (covariate; factor with 2 levels, 0/1) long-term experience (covariate), same-day experience (covariate; number of trips performed in a single trial), number of larvae (covariate), number of simultaneous foragers (covariate), and number of prey available (covariate). We included observer, colony, and ant identities as random (intercept) factors. The model was checked for overdispersion and collinearity.\u003c/p\u003e\u003cp\u003eForaging success, exploration, and activity models were later used in a repeatability test with 1000 permutations to verify inter-individual and inter-colonial differences. We chose the number of active/simultaneous foragers and larvae as the most accurate measure of colonial and brood size since the other variables measured (number of workers, pupae, eggs, total number of foragers, etc.) were correlated to them at some level. We used the R package \u003cem\u003elme4\u003c/em\u003e (Bates et al. 2015) for all LMMs and GLMMs and \u003cem\u003erptR\u003c/em\u003e (Stoffel et al. 2017) for repeatability models. In all models, no variables showed (multi)collinearities, since variance inflation factors (VIF) were always lower than 2.11 (Faraway 2006). The packages \u003cem\u003edplyr\u003c/em\u003e (Wickham et al. 2023a) and \u003cem\u003etidyr\u003c/em\u003e (Wickham et al. 2023b) were used for data manipulation, \u003cem\u003eperformance\u003c/em\u003e (L\u0026uuml;decke et al. 2021) to check for (and to exclude) overdispersion of the models and multi-collinearity of the predictor variables, and \u003cem\u003esjPlot\u003c/em\u003e (L\u0026uuml;decke 2023) and \u003cem\u003eggplot2\u003c/em\u003e (Wickham 2016) for data visualisation.\u003c/p\u003e\u003cp\u003e\u003cb\u003eEthical Note\u003c/b\u003e\u003c/p\u003e\u003cp\u003e This study was submitted to the responsible Brazilian authority Sistema de Autoriza\u0026ccedil;\u0026atilde;o e Informa\u0026ccedil;\u0026atilde;o em Biodiversidade (SISBIO) of the Instituto Chico Mendes de Conserva\u0026ccedil;\u0026atilde;o da Biodiversidade (ICMBio), and approved by permission n\u0026ordm; 47615. Because our experimental animal is an invertebrate, according to the Brazilian law Arouca, n\u0026ordm; 11.794/2008, there was no need for submission to Comiss\u0026atilde;o de \u0026Eacute;tica no Uso de Animais (CEUA/USP). We also considered the recommendations made in the Guidelines for the Use of Animals (ASAB Ethical Committee/ABS Animal Care Committee 2025).\u003c/p\u003e\u003c/div\u003e"},{"header":"RESULTS","content":"\u003cp\u003eA total of 875 foraging trips of 211 \u003cem\u003eP. striata\u003c/em\u003e foragers were observed during the experiments, representing over 50 hours of observation of the videos. The duration of foraging trips was 10.73\u0026thinsp;\u0026plusmn;\u0026thinsp;9.92 minutes (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD; min\u0026thinsp;=\u0026thinsp;2 s, max\u0026thinsp;=\u0026thinsp;69.81 min). Considering all trips completed where food was available, the ants achieved a foraging efficiency of 41.9%. In other words, in less than half of the trips, individual foragers were able to locate and remove a prey item from the experimental arena.\u003c/p\u003e\u003cp\u003e\u003cb\u003eForaging activity\u003c/b\u003e\u003c/p\u003e\u003cp\u003eForagers presented an activity level (number of trips/trial) of 2.03\u0026thinsp;\u0026plusmn;\u0026thinsp;1.36 (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD; min\u0026thinsp;=\u0026thinsp;1, max\u0026thinsp;=\u0026thinsp;8). Most of the time, individuals had a relatively low level of activity, performing just one (25%) or two (25%) trips in a trial. When facing an environment with dispersed food items, ants had lower activity (GLMM; N\u0026thinsp;=\u0026thinsp;431 individuals/day, Z = -2.099, p\u0026thinsp;=\u0026thinsp;0.036; Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003ea and S2). However, the higher the ant participation over the eight trials (i.e. long-term experience), the more active ants were (Z\u0026thinsp;=\u0026thinsp;2.452, p\u0026thinsp;=\u0026thinsp;0.014; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea). Medium-term experience (N\u0026thinsp;=\u0026thinsp;84 individuals that were active in paired trials; Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eb) did not influence activity (Z\u0026thinsp;=\u0026thinsp;1.451, p\u0026thinsp;=\u0026thinsp;0.147) and neither did the number of larvae (medium-term experience: N\u0026thinsp;=\u0026thinsp;84, Z = -0.111, p\u0026thinsp;=\u0026thinsp;0.912, long-term experience: N\u0026thinsp;=\u0026thinsp;431, Z = -1.247, p\u0026thinsp;=\u0026thinsp;0.212) and number of active foragers (medium-term experience: Z = -0.523, p\u0026thinsp;=\u0026thinsp;0.601; long-term experience: Z = -0.473, p\u0026thinsp;=\u0026thinsp;0.636) for both activity models (Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Colonies presented a low effect of an intercolonial difference in their foraging activity pattern (Repeatability test; R\u0026thinsp;=\u0026thinsp;0.02, N\u0026thinsp;=\u0026thinsp;6, p\u0026thinsp;=\u0026thinsp;0.004). This means that colonies had consistent activity between the two different treatments and over 25 days, even if this correlation is weak. The same is not true for individuals who did not show any consistent interindividual differences over time and between conditions (R\u0026thinsp;=\u0026thinsp;0.033, N\u0026thinsp;=\u0026thinsp;211, p\u0026thinsp;=\u0026thinsp;0.18).\u003c/p\u003e\u003cp\u003e\u003cb\u003eForaging exploration\u003c/b\u003e\u003c/p\u003e\u003cp\u003eIndividuals explored the experimental foraging arena by visiting regularly distributed Petri dishes that could contain food or not. On a considerable number of foraging trips (229 out of 875), there was no exploration of the potential food patches. In the other trips, the ants explored one to ten different potential food locations. The exploration rate was 0.303\u0026thinsp;\u0026plusmn;\u0026thinsp;0.518/minute (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD; min\u0026thinsp;=\u0026thinsp;0.018, max\u0026thinsp;=\u0026thinsp;9.607). Experience was the main factor that modulated exploration during foraging (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb and S3): ants that had a previous successful experience in that trial had a greater chance of exploring on the next trips (LMM; N\u0026thinsp;=\u0026thinsp;875 foraging trips; Z\u0026thinsp;=\u0026thinsp;4.706, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb and S3a). A higher exploitation rate could also be attributed to individuals with previous successful experience (N\u0026thinsp;=\u0026thinsp;646; X\u0026sup2; = 7.345, df\u0026thinsp;=\u0026thinsp;1, p\u0026thinsp;=\u0026thinsp;0.007; Figure S3b and S4b) and short-term experience (meaning multiples foraging trips in a trial, X\u0026sup2; = 7.253, df\u0026thinsp;=\u0026thinsp;1, p\u0026thinsp;=\u0026thinsp;0.007; Figure S3b and S4c). When there were multiple simultaneous foragers, individuals tended to carry out trips without exploration (Z = -2.890, p\u0026thinsp;=\u0026thinsp;0.004; Figure S3a and S4a). Besides, the presence of larvae (X\u0026sup2; = 18.261, df\u0026thinsp;=\u0026thinsp;1, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Figure S3c and S4d) and multiple prey (X\u0026sup2; = 77.906, df\u0026thinsp;=\u0026thinsp;1, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Figure S3c and S4e) in the arena decreased the latency to start exploring, while the short-term experience (X\u0026sup2; = 14.582, df\u0026thinsp;=\u0026thinsp;1, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Figure S3c and S4f) and simultaneous foragers increased it (X\u0026sup2; = 53.680, df\u0026thinsp;=\u0026thinsp;1, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Figure S3c and S5h). We found no interindividual (Repeatability test; R\u0026thinsp;=\u0026thinsp;0, N\u0026thinsp;=\u0026thinsp;211, p\u0026thinsp;=\u0026thinsp;1) or intercolonial (R\u0026thinsp;=\u0026thinsp;0.016, N\u0026thinsp;=\u0026thinsp;6, p\u0026thinsp;=\u0026thinsp;0.058) consistent differences in the exploration rate.\u003c/p\u003e\u003cp\u003e\u003cb\u003eForaging success\u003c/b\u003e\u003c/p\u003e\u003cp\u003eIn 41.9% of solitary foraging trips, ants found and transported a food item out of the experimental arena towards the nest. Even when exposed to a high-quality food patch containing five food items (i.e., aggregated food distribution), they never used tandem running to recruit nestmates to the food. We observed theft, cooperative transport and fragmentation of food by nestmates who met by chance, without communication and recruitment. Some ants also ingested the food before transporting it.\u003c/p\u003e\u003cp\u003eAgain, experience played a key role in predicting patterns of individual foraging success. Ants that had a previous successful experience in the same trial have 2.56 times more chance of having another success (GLMM; N\u0026thinsp;=\u0026thinsp;618 foraging trips, Z\u0026thinsp;=\u0026thinsp;2.998, p\u0026thinsp;=\u0026thinsp;0.003; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec and S5), and long-term experience also significantly increased performance (Z\u0026thinsp;=\u0026thinsp;2.212, p\u0026thinsp;=\u0026thinsp;0.027; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ed and S5). Exploration rate (Z\u0026thinsp;=\u0026thinsp;4.093, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Figure S5 and S6a), but not activity level (Z\u0026thinsp;=\u0026thinsp;1.067, p\u0026thinsp;=\u0026thinsp;0.286), increased the likelihood of foraging success, which was also higher with more available food (Z\u0026thinsp;=\u0026thinsp;3.590, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Figure S5 and S6b). Food distribution (Z\u0026thinsp;=\u0026thinsp;0.371, p\u0026thinsp;=\u0026thinsp;0.711), the number of larvae (Z\u0026thinsp;=\u0026thinsp;0.109, p\u0026thinsp;=\u0026thinsp;0.913) and simultaneous foragers (Z = -0.551, p\u0026thinsp;=\u0026thinsp;0.582) had no significant influence on foraging success. We found a weak but significant effect of intercolonial and interindividual differences on individual foraging success. That is, in \u003cem\u003eP. striata\u003c/em\u003e, we can find individuals (Repeatability test; R\u0026thinsp;=\u0026thinsp;0.056, N\u0026thinsp;=\u0026thinsp;211, p\u0026thinsp;=\u0026thinsp;0.029) and colonies (R\u0026thinsp;=\u0026thinsp;0.084, N\u0026thinsp;=\u0026thinsp;6, p\u0026thinsp;=\u0026thinsp;0.001) that are consistently better at foraging than others.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThis study experimentally evaluated how internal mechanisms (e.g., experience, motivation) and environmental factors (e.g., colony demographics, food distribution) affect individual foraging in the ant \u003cem\u003eP. striata\u003c/em\u003e. In addition, our experiments also did not constrain ant participation, which was studied as a measure of motivation. We found that experience is one of the key factors for foraging behaviour and performance in this species, with more experienced individuals being more active, exploratory, and successful. Specifically, a previous success experience increased the ants' motivation and resulted in more exploration and an increased probability of being successful again. Furthermore, foragers exhibit plasticity by altering their behaviour in response to environmental factors, such as being less active with dispersed food and less exploratory with multiple simultaneous foragers. On the other hand, they explore sooner with more prey available. Finally, we found intercolonial differences in foraging activity and success rate and interindividual differences only in the latter. These results illustrate that the decision to initiate foraging is complex and multifactorial. Above all, previous experience is a determining factor for the execution and performance of solitary foraging.\u003c/p\u003e\u003cp\u003eIn our study, \u003cem\u003eP. striata\u003c/em\u003e foragers were more active with higher long-term experience. Previous success and higher same-day experience were associated with a higher exploration rate. More experienced individuals may become more active and exploratory through some non-exclusive mechanisms. First, age polyethism may lead older (and probably more experienced) individuals to focus on foraging and abandon other tasks (reviewed in Beshers \u0026amp; Fewell 2001). Second, behavioural specialisation can lead individuals who have already foraged to begin foraging again (Robinson et al. 2009). Lastly, knowledgeable individuals also improve their navigational, sensory, and locomotion abilities (e.g., von Frisch 1967; Harrison et al. 1989; Collett and Collett 2002), increasing route efficiency and potentially allowing more trips per trial. In social insects, experience can arise through the acquisition of private or social information (Gr\u0026uuml;ter and Czaczkes 2019). An interesting example of the effect of experience and learning-based experience on ant foraging can be seen in \u003cem\u003eMyrmica rubra\u003c/em\u003e (Linnaeus, 1758), where workers must individually learn to follow pheromone trails. Still, such learning can be enhanced by the presence of older, experienced nestmates (Cammaerts 2013). In our case, since all trips involved only solitary foraging, it is possible to determine that the source of expertise is mostly individual. Social information transmission was limited to contacts in the nest with returning foragers or in the foraging arena between foragers active at the same time. In both cases, contacts can provide cues to foragers and food availability, but not the precise location of a food source. While the positive impact of experience on foraging is not new, this study is significant because it shows these effects can last for long periods (up to 25 days), rare in insect studies, and in a ponerine ant species never before tested in lab conditions. However, it is important to consider that the experience effect found in our study may also be, at least partially, an age effect since the colonies studied here were brought from the field, and the age of the individuals is unknown. The experience effect is often confused with age, as it tends to correlate at some level \u0026ndash; gaining experience takes time. Thus, controlling for this and finding both young and experienced foragers is virtually impossible since foraging in ants is highly age-dependent (Franklin et al. 2012; Lima Vieira et al. 2024). On the other hand, in many studies, recruitment, search behaviour and other foraging aspects appeared to depend on experience, rather than age (e.g., Fourcassi\u0026eacute; and Traniello 1994; Tripet and Nonacs 2004; Ravary et al. 2007; Franklin et al. 2012). Future studies should compare the role of activity in similar-age workers, as is done, for example, in clonal ponerine ants (Hartmann et al. 2020) or experimentally manipulate the foraging experience of known-age individuals of \u003cem\u003eP. striata\u003c/em\u003e.\u003c/p\u003e\u003cp\u003eWe found that a higher success rate was associated with more long-term experience, previous success and greater exploration in \u003cem\u003eP. striata\u003c/em\u003e workers. Thus, more experienced ants are not only more active and exploratory but also more successful than less experienced workers in our experimental setup. There is evidence suggesting that through experience, ants improve their task performance, recovering food more efficiently, either by increasing the food quantity or decreasing trip duration or food localisation. Informed ants perform better navigation, transport (Stroeymeyt et al. 2011), tandem running (Franklin et al. 2012; Glaser and Gr\u0026uuml;ter 2018), pheromone trail recruitment (Cammaerts 2013), seed-harvesting techniques (Johnson 1991) and prey capture (Dejean 1985). These anterior experiences, especially those of positive valence, can constitute a positive reinforcement and therefore increase ulterior appetitive motivation (or motivation for food) as a consequence (Baracchi et al. 2017; Lima Vieira et al. 2024). Positive valence of experience underlies learning processes (e.g., associative, operant conditioning) widely studied in insects such as fruit flies and honey bees, and more recently, ants (e.g., Schilman and Roces 2003; Schilman 2011; Sadler and Nieh 2011; Plowright 2017; Metz et al. 2018; Czaczkes 2022). Furthermore, increased motivation in many cases can culminate in a positive state of arousal that makes individuals more responsive to encountering food, especially that of lower quality (Perez et al. 2013; d\u0026rsquo;Ettorre et al. 2017). Alternatively, neutral stimuli can also be learned in repeated trips in the same environment, through latent learning (Poissonier et al. 2023; Clement et al. 2024). In our study, it is not possible to verify that foragers learned specific regularities of the environment since food conditions were always unpredictable and there was no prior training with positive reinforcement. It is also not possible to say exactly how fast and for how long the information about previous routes is stored. However, our protocol was decisive in demonstrating in an experimental situation that simulates the conditions found in nature that individuals are (1) capable of dedicating themselves to foraging for long periods (both during a trial and across trials), (2) show increased motivation with their past success and (3) acquiring better exploration skills that effectively improve their foraging efficiency.\u003c/p\u003e\u003cp\u003eIn our protocol, activity decreased with dispersed food. We propose that the random and dispersed food distribution reduced the ants' motivation to continue searching for food on other trips. Workers tend to be more persistent when the sources are more persistent and/or renewed regularly. Otherwise, while foraging for highly unpredictable sources, they tend to give up rapidly (Fourcassi\u0026eacute; and Traniello 1994). In this sense, our aggregated food distribution was more renewable and thus allowed consecutive trips, which did not happen when the food was dispersed. Indeed, foragers of many solitary foraging species tend to return to previously rewarding sites to search for food (e.g., Fresneau 1985; Fourcassi\u0026eacute; and Traniello 1993; Wehner et al. 2004; Tanner 2009; Azevedo et al. 2014). Although it has not been tested formally in \u003cem\u003eP. striata\u003c/em\u003e, field experiments show that they come back repeatedly to the same food source, suggesting the learning of foraging trips in the wild (Medeiros and Oliveira 2009; Silva et al. 2021). Besides, we also observed that the number of active foragers in the colony did not affect individual activity in \u003cem\u003eP. striata\u003c/em\u003e. This can, at least initially, suggest that individual foraging activity is little affected by the colony's nutritional needs. Thus, an increase in foraging effort related to larger nutritional needs can be achieved by increasing the number of foragers rather than by increasing individual effort.\u003c/p\u003e\u003cp\u003eExploration rates also varied with social factors such as colony size and external factors such as the number of prey. We observed that trips without exploration were more common when there were multiple simultaneous foragers. Foragers likely detected the presence of more foragers by chemical cues or direct social contacts and adjusted their exploratory behaviour to avoid locations where other nestmates were already exploring, thus momentarily decreasing their exploration. We also found differences in the latency to begin exploration, with individuals taking less time to explore with multiple prey and taking longer with more short-term experience and simultaneous foragers. With more prey available for capture, foragers probably detected it quicker by crowding at the food source or volatile chemical compounds emitted by dead arthropods (Fourcassi\u0026eacute; and Traniello 1994; Buehlmann et al. 2014) and consequently began exploring earlier. Notwithstanding, short-term experience delays the beginning of exploration because ants likely tend to avoid previously visited plates on consecutive routes, and new sites decreases in number. These results suggest a possible role of memory in individual decisions about last visited sites.\u003c/p\u003e\u003cp\u003eWe found intercolonial differences in activity but not in exploration level. This means that colonies showed consistency, even if low, in their activity pattern over 25 days. Similarly, \u003cem\u003ePogonomyrmex barbatus\u003c/em\u003e (Smith, 1858) colonies exhibit variations in scout rates and the number of foragers departing the nest, with these intercolonial differences remaining consistent each year (Gordon et al. 2011, 2013). Interindividual differences, often called personality traits when they occur, are thought to be important drivers of both motivation and task execution. In social insects, they could also influence division of labour and other colony characteristics (Jandt et al. 2013). Thus, colonies that display distinct collective behavioural responses consistently across time and contexts are described as possessing personality traits (Stamps 2016). We found no individual differences in activity or exploration, but we found a weak but significant effect of intercolonial and interindividual differences on individual foraging success. \u0026lsquo;Activity\u0026rsquo; and \u0026lsquo;exploration\u0026rsquo; are classically considered personality traits in non-human animals (R\u0026eacute;ale et al. 2007). The ways of measuring personality traits in the literature are not standardised, and large differences between the definitions of 'activity' and \u0026lsquo;exploration\u0026rsquo; can be found (reviewed in Carter et al. 2013). It is important to remember that workers modified their behaviour over 25 days, some becoming more active and others more exploratory as they gained different levels of experience and success. Twenty-five days is a long period of time when looking at the typical experiments testing individual personality (Jardim et al. 2025). In long-lived ponerine ants tested in semi-natural conditions such as ours, where ants were free to access the arena, the lack of repeatability can also come from the crucial importance of experience and success we showed in the expression of the very flexible behaviour that is foraging. Perhaps in more controlled conditions, repeatability will show higher significant values (Jardim et al. 2025), and it seems that testing ants in different environments, which are poorer or richer in terms of stimuli, could give us very relevant information about the stability of interindividual differences. Indeed, putting experience and development back into the study of interindividual differences would help better understand the plasticity of behavioural responses in a more integrative way (Stamps 2016). Interestingly, our data also suggest that consistent intercolonial differences can be detected without interindividual differences and stresses the fact that what makes colony personality could not only be the result of individual personalities but also of colonial idiosyncratic properties themselves and how they interact with individual plasticity (Pinter-Wollman 2012). Behavioural plasticity and personality traits should thus be studied in a more inclusive and mechanistic way across contexts to improve our understanding of ant behaviour in an ecologically relevant way.\u003c/p\u003e\u003cp\u003eIn conclusion, our study reveals the pivotal role of experience in shaping individual foraging behaviours and motivation in \u003cem\u003eP. striata\u003c/em\u003e. Long-term experience enhances activity, exploration, and foraging success, highlighting the importance of cumulative experience and learning in task performance. Social and environmental factors such as food distribution and colony demographics further influence behavioural adjustments, showcasing the phenotypic plasticity of these ants. While intercolonial differences in activity and success rates suggest a form of collective \"personality,\" individual variations in success suggest the presence of more efficient foragers. These findings provide valuable insights into the intricate interplay between internal mechanisms, environmental and social contexts in driving self-organised tasks in social insect societies. Details of extrinsic and intrinsic mechanisms regulating \u003cem\u003eP. striata\u003c/em\u003e foraging behaviour have just begun to be addressed, and many aspects remain to be explored. For example, it is still unknown how memory of past experience is formed, consolidated and retrieved, the limits of appetitive learning and the effect of age on \u003cem\u003eP. striata\u003c/em\u003e workers. Motivational processes were important in our study of solitary foragers to understand behaviour. This kind of study should be expanded to other species of ants, including trail-laying foraging species. Our study also confirms that experience-based processes greatly influence foraging behaviour, and their influence on collective foraging behaviour and division of labour should also be investigated more thoroughly.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003eDeclaration of interest\u003c/h2\u003e\u003cp\u003eNone declared.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding statement\u003c/h2\u003e\u003cp\u003eMaria Eduarda Lima Vieira received a funding grant from the Brazilian Science Ministry (Conselho Nacional de Desenvolvimento Cient\u0026iacute;fico e Tecnol\u0026oacute;gico, CNPq), MCTI/CNPq/Universal PQ 311790/2021-8 and Coordena\u0026ccedil;\u0026atilde;o de Aperfei\u0026ccedil;oamento de Pessoal de N\u0026iacute;vel Superior (CAPES), CAPES/PRInt 88887.916823/2023-00. Nicolas Ch\u0026acirc;line received funding from CAPES, process CAPES/PRInt 88887.915491/2023-00. Guilherme Rocha Fiuza and Gabi Fernandes Bianchi received funding from Universidade de S\u0026atilde;o Paulo, PUB 2023-83-1/2273.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eMaria Eduarda Lima Vieira: Conceptualisation, Data curation, Formal analysis, Investigation, Methodology, Project administration, Visualisation, Writing - original draft, Writing - review \u0026amp; editing. Gabi Fernandes Bianchi: Data curation, Methodology, Writing - review \u0026amp; editing. Guilherme Rocha Fiuza: Methodology, Writing - review \u0026amp; editing. Nicolas Ch\u0026acirc;line: Supervision, Conceptualisation, Data curation, Funding acquisition, Investigation, Methodology, Project administration, Resources, Validation, Writing - original draft, Writing - review \u0026amp; editing.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eWe acknowledge Camila Gesicki Boggiani, Maria Eduarda Martins Bertanha, and Stefani Bonassi Maschia for their assistance in collecting the colonies. We thank Monise Menezes for letting us use the \u0026lsquo;Figure 1a\u0026rsquo; photo. We also thank St\u0026eacute;phane Chameron and Jerry Hogan for their useful contributions and insights on the manuscript.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eData and R script are available in the Zenodo repository: https://doi.org/10.5281/zenodo.15337394\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAltmann J (1974) Observational study of behavior: sampling methods. 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Springer-Verlag New York\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWickham H, Fran\u0026ccedil;ois R, Henry L, et al (2023a) dplyr: A Grammar of Data Manipulation (R package version 1.1.4).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWickham H, Vaughan D, Girlich M (2023b) tidyr: Tidy Messy Data (R package version 1.3.0).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"activity, exploration, food distribution, foraging success, Pachycondyla, solitary foraging","lastPublishedDoi":"10.21203/rs.3.rs-7302949/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7302949/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eUnderstanding how internal mechanisms, social dynamics, and environmental factors interact to influence foraging behaviour is essential to unravel the complexity of decision-making in social insects. Despite extensive research on foraging behaviour in social insects, the mechanisms driving solitary foraging, particularly in Neotropical species, remain largely unexplored. This laboratory study evaluated how experience, colony size, and food distribution affect foraging activity, exploration, and success in a standardised situation in \u003cem\u003ePachycondyla striata\u003c/em\u003e. Experience was not experimentally controlled and depended on the ants' behavioural decisions. Additionally, we investigated whether behavioural traits associated with foraging are consistent across time and environmental contexts. Experience significantly increased foraging activity, exploration, and success. Foragers with prior successful experience were more likely to explore and achieve subsequent success. Workers\u0026rsquo; activity was lower with dispersed food distribution. Colony-level differences were observed in activity, and intercolonial and interindividual differences emerged in foraging success, suggesting weak but consistent behavioural patterns across both levels of organisation. These findings emphasise the importance of experience and environmental modulation in ant foraging decisions. By demonstrating how internal and external factors influence task execution, this study advances our understanding of behavioural plasticity in social insects and suggests a strong effect of experience- and learning-based motivational processes on individual behaviour. This knowledge provides a framework to explore foraging strategies and colony dynamics in solitary foraging systems.\u003c/p\u003e","manuscriptTitle":"Practice makes perfect? Experience boosts foraging performance in a ponerine ant","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-29 20:14:08","doi":"10.21203/rs.3.rs-7302949/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"a37f2e82-3034-4739-acf7-681829fc9a9c","owner":[],"postedDate":"August 29th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-01-07T10:11:55+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-29 20:14:08","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7302949","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7302949","identity":"rs-7302949","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}