Exploration and Aggregation of Differently Sized Camponotus japonicus Worker Ants

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Jablonski, Sang-im Lee This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7915365/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 18 Apr, 2026 Read the published version in Insectes Sociaux → Version 1 posted 5 You are reading this latest preprint version Abstract Ants, as eusocial insects, rely on the division of labor shaped by factors such as age and body size, facilitating colony-level behavioral flexibility under novel conditions. In this study, we examined how body size is related to exploratory and aggregation behaviors, along with the role of inactivity, in foragers of the polymorphic ant Camponotus japonicus exposed to a novel environment. Minor, media, and major workers were tested separately in a round arena, with behaviors filmed for an hour. While exploratory activity declined with time, it did not differ significantly among size classes. In contrast, aggregation behavior was size-dependent: larger workers aggregated more and formed bigger clusters than smaller workers, and aggregation generally increased in later acclimation phases. However, the timing of aggregation did not vary across size classes. The proportion of time inactive and the spatial distribution of inactive ants remained consistent across size classes, though overall inactivity increased over time. Furthermore, the presence of inactive ants within clusters consistently reduced the movement speed of other ants within the cluster, irrespective of their size classes. These findings suggest that behavioral variations in C. japonicus may reflect functional specialization across worker sizes, promoting collective resilience when colonies encounter unfamiliar environments. exploration aggregation inactivity size class Camponotus japonicus Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Ants, as eusocial insects, exhibit a highly organized division of labor, where individuals perform specialized tasks that contribute to and sustain the colony’s complex social structure (Gordon, 1996 ; Hölldobler & Wilson, 1990 ; Oster & Wilson, 1978 ). Task allocation in worker ants is influenced by a range of intrinsic and extrinsic factors (Wills et al., 2018 ), with age, body size, or a combination of both playing a pivotal role in determining individual roles within the colony (Wilson, 1985 ). Among these factors, size polyethism (i.e. task allocation based on their body size) has been reported in many ant species (Wilson, 1978 , 1979 , 1980 , 1986 ). In polymorphic species, size polyethism may enhance colony efficiency by enabling task specialization according to morphological variation (Oster & Wilson, 1978 ; Wills et al., 2018 ). For instance, in leafcutter ants ( Atta spp.), major workers engage in colony defense and process food, while medium-sized workers (medias) forage, and minor workers protect the leaf fragments from parasitic phorids (Evison & Ratnieks, 2007 ; Feener & Brown, 1993 ; Hart & Ratnieks, 2001 ). Similarly, in Pheidole spp., the big-headed ants, majors primarily function as soldiers or food processors, whereas minors undertake brood care and foraging (Lillico-Ouachour & Abouheif, 2017 ; Wilson, 1976 , 1984 ). In Camponotus spp., which exhibit continuous polymorphism, majors are specialists in nest defense or act as storage vessels, while minors perform a broad range of tasks, including caring for broods and foraging (Hölldobler & Wilson, 1990 ; Lee, 1998 ). Other examples include turtle ants, Cephalotes spp., where large-headed soldiers block nest entrances to prevent intrusion (Creighton & Geegg, 1954 ; De Andrade & Urbani, 1999 ). For a comprehensive review of size polyethism in polymorphic ants, see Wills et al. ( 2018 ). Beyond task specialization, size polyethism may influence how individuals explore and respond to novel environments, with potential consequences for colony efficiency, by conducting different operations simultaneously (Oster & Wilson, 1978 ). Exploratory behavior is a critical component of survival, especially under environmental challenges, allowing colonies to assess risks and locate resources (Page et al., 2018 ). For example, in the Argentine ants ( Linepithema humile ), foragers encountering novel conditions exhibit behavioral heterogeneity, enabling effective risk assessment and resource discovery (Madrzyk & Pinter-Wollman, 2023 ). Additionally, groups with a higher proportion of exploratory individuals exhibit faster and more accurate collective nest selection (Hui & Pinter-Wollman, 2014 ). In Lasius niger , exploratory behavior correlates with foraging success (Pasquier & Grüter, 2016 ). Studies on polymorphic species further highlight variation in exploration: in Pheidole pallidula , minors primarily engage in food exploitation in foraging areas, while majors do not actively search (Detrain & Pasteels, 1991 ). Similarly, Camponotus yamaokai majors display lower exploratory tendencies than minors when introduced to an unfamiliar environment (Sakai et al., 2024 ). While the function of exploratory behavior has been widely studied across multiple species, its relationship with polymorphism remains poorly understood. Another crucial aspect of social organization is aggregation behavior, which enhances colony survival by facilitating interactions among nestmates (Deneubourg et al., 2002 ; Jeanson et al., 2012 ; Parrish & Edelstein-Keshet, 1999 ). In eusocial insects, spatial organization often arises from differential clustering based on age polyethism, where younger workers tend to remain near the brood while older workers position themselves closer to nest entrances (Mersch et al., 2013 ; Oster & Wilson, 1978 ; Seeley, 1982 ). However, the extent to which worker size influences aggregation patterns remains largely unexplored. Some studies have reported size-related differences in aggregation, such as in Pheidole pallidula , where majors exhibit a greater tendency to form a cluster compared to minors in a novel environment (Sempo et al., 2006 ). In contrast, no size-dependent effects on aggregation have been suggested in Atta sexdens rubropilosa or Solenopsis interrupta (Depickère, Ramírez Ávila, et al., 2008 ). Given that these species all belong to the same subfamily Myrmicinae, further investigations across different subfamilies are necessary to assess whether these patterns are broadly conserved (Ward, 2014 ). The mechanisms underlying aggregation behavior remain poorly studied. While direct physical contact and chemical cues contribute to cluster formation in P. pallidula (Sempo et al., 2006 ), the role of inactive ants, often representing around 40% of the colony (Charbonneau et al., 2017 ), has been largely overlooked. Inactive ants often remain near the nest center, a location that may be perceived as safe by nestmates (Charbonneau et al., 2017 ). Moreover, majors in P. pallidula are known to exhibit generally higher inactivity levels than minors (Sempo & Detrain, 2004 ). Since major workers both aggregate better and exhibit lower activity, inactivity may drive size-dependent aggregation. Despite the substantial proportion of inactive ants within many ant colonies (Cole, 1986 ; Dornhaus et al., 2009 ; Herbers, 1983 ; Mirenda & Vinson, 1981 ; Retana & Cerda, 1991 ), their functional role remains obscure. Addressing this knowledge gap requires a closer examination of the relationship between inactivity and aggregation behavior in polymorphic species. In addition to spatial and morphological factors, the temporal dynamics can play a critical role in shaping ant behavioral organization, operating across multiple timescales from circadian rhythms to short-term activity phases (Cole, 1991 ; Doering, 2021 ; Eban-Rothschild & Bloch, 2012 ; Fuchikawa, 2023 ). These patterns emerge through interactions between endogenous physiological cycles and external stimuli such as resource distribution or colony-level interactions (Doering, 2021 ; Willmer & Stone, 2004 ). Experimental evidence from Crematogaster scutellaris demonstrates this temporal flexibility: brood-tenders maintained stable clusters (~ 30% worker participation) while foragers showed increasing aggregation up to 60%, with the overall cluster numbers diminishing over time in novel environments (Depickère, Fresneau, et al., 2008). Therefore, analyzing behavioral responses across different time intervals can provide important insights into the temporal organization of ant societies and the mechanisms underlying collective behavior. In this study, we investigate Camponotus japonicus , a species from the subfamily Formicinae (Ward, 2007 , 2014 ), which is characterized by a broad range of worker sizes (Lee, 1998 ; Wills et al., 2018 ). Studies on Camponotus often distinguish media workers from minors and majors based on their distinct morphological differences (Sharma et al., 2004 ; Tross et al., 2022 ). In C. japonicus , minor workers primarily engage in foraging, which demands high levels of activity, while majors and medias take part in both foraging and defense, a task that generally requires relatively less activity. Their behavioral roles tend to vary with body size (Lee, 1998 ). We measured exploratory and aggregation behaviors across minors, medias, and majors when they were introduced to a novel environment. Specifically, we tested predictions from four hypotheses: (H1) exploratory behavior varies by size classes, with minors exploring the most, followed by medias, and then majors; (H2) aggregation behavior differs among size classes, with larger workers aggregating more than smaller workers as observed in other species; (H3) inactivity levels differ among size classes, with majors being the most inactive and minors the least; (H4) there is a correlation between inactivity and aggregation behavior, with inactive ants slowing down other individuals within the same cluster than active ants, with effects being the largest in majors and the least in minors. We also examined if each of these hypotheses held across time. By investigating these hypotheses, our study aims to contribute to the understanding of size-dependent behavioral variations in response to novel environments in polymorphic ants. Materials and Methods Study subject Japanese carpenter ants, Camponotus japonicus , were collected from seven field colonies (C1-C7) between late May to early July 2022 at Seoul National University Gwanak campus in South Korea. Only foragers outside the nest were collected to minimize the effect of age polyethism. Ants were classified into three size classes, with the largest as majors (> 10.5 mm), the smallest as minors (< 9 mm), and those in between as medias (9-10.5 mm) due to their distinct appearance (see supplementary material part S1). For each of the three size classes 16 ants were collected, resulting in 48 individuals per colony. Experimental design The experiment was conducted on the same day as the capture in a circular indoor arena, 56 cm in diameter. The arena was surrounded by polyvinyl chloride (PVC) walls coated with canola oil to prevent the ants from climbing. A sheet of gray paper was placed on the arena floor and replaced before each recording to eliminate residual pheromones. To prevent prior experience from influencing behavior, each size class was tested only once per colony. Before each recording session, 16 ants of the same size class were placed in a plastic container. The container’s top was left open, and the wall was coated with synthetic grease (Super Lube® Multi-Purpose Synthetic Grease with Syncolon® (PTFE)) to prevent escape. They were stabilized for one hour in the container before being introduced into the arena. Due to spatial constraints, only one size class could be observed at a time per colony (referred to as the variable “recording” in statistical analyses), and the order of recordings was randomly assigned. We video-recorded ants from above for one hour per experiment. Video digitization & editing The x- and y-coordinates of the center of each of the 16 ants in each experiment were tracked by a customized program we wrote in NetLogo (Wilensky, 1999 ). The body size of each size class was measured from the tip of the head to the tip of the gaster using ImageJ. Since the ants could occlude each other in the video, manual inspection corrected any misidentifications. Data were extracted every second starting from one minute after the ants were introduced into the arena until the end of the experiment (1 hour). Variables extracted from data Exploratory Behavior Exploratory behavior was quantified using Simpson’s evenness index (0–1) (Heip et al., 1998 ; Simpson, 1949 ; Smith & Wilson, 1996 ). The experimental arena was divided into 72 spatial slices radiating outward from the center, and the number of individual occurrences per second for each ant was recorded in each slice. The proportion of occurrences ( \(\:{p}_{i}\) ) in each slice relative to the total occurrences was calculated using Eq. 1 to obtain the circular distribution of occurrences on the arena. Simpson's dominance index (λ) was then calculated to measure the concentration of occurrences across slices (Eq. 2 ), while spatial slice richness (S) was defined as the total number of slices with recorded occurrences (72). The exploration index (E) was calculated as Simpson’s evenness index (e.g., the ratio of the reciprocal of Simpson’s dominance index to spatial slice richness) (Eq. 3 ). Values of exploration approaching 1 indicate high exploratory behavior, while values closer to 0 indicate low exploratory behavior. $$\:{p}_{i}=\:\frac{Occurrences\:in\:slice\:i}{Total\:occurrences}$$ 1 $$\:\lambda\:=\:{\sum\:}_{i=1}^{S}{p}_{i}^{2}$$ 2 $$\:E=\:\frac{1/\lambda\:}{S}$$ 3 Aggregation Behavior Ants were considered to be in the same cluster if the distance between their center coordinates was less than twice their mean body size. Clusters were then defined as groups of ants connected under this rule: if one ant was close enough to a second, and that second was close enough to a third, then all three were counted as part of the same cluster, even if the first and third were not directly within the threshold of each other. Cluster size was defined as the number of ants within a cluster. To investigate aggregation behavior across size classes, three variables were measured: 1. Aggregated fraction was calculated as the ratio of the total number of ants in clusters to the total number of ants in the arena for each second per experimental recording (Eq. 4). Aggregated fraction = \(\:\frac{Total\:number\:of\:ants\:in\:clusters}{Total\:number\:of\:ants\:(=16)}\) (4) 2. Maximum cluster size was defined as the number of ants in the largest cluster. Both variables were measured every second for the full duration of each experiment, and the mean value of these measurements over a given time window (see below) was used in the analysis. 3. Frequency of cluster transitions was measured as the sum of cluster entries and exits throughout the experiment per individual. Temporal and Spatial Variation of Inactive Ants Throughout the experiment,many ants remained motionless with their heads on the ground for extended periods. We defined inactivity as immobility lasting at least 30 seconds (see supplementary material part S2 for details on definition). 1. Inactivity proportion was defined as the ratio of total inactive counts over the experimental time (1 hour) of each recording per individual. 2. Circular standard deviation was measured using the same 72 spatial slices as in the exploratory behavior analysis. The circ.disp function from the CircStats R package was used. Effect of Inactive Ants on Movement in Clusters The effect of inactive ants on the movement of ants in clusters was analyzed by measuring the mean speed of individual ants over the next second (mm/s). The speed was measured for all ants within clusters and categorized by size class and the in-cluster presence of inactive ants at the current second. Statistical analysis All statistical analyses were performed using linear mixed-effects models in R (v4.2.2; Posit team, 2025 ) with the lme4 package (Bates et al., 2015 ). Significance levels (α = 0.05) were applied for hypothesis testing, and p-values were calculated using the lmerTest package (Kuznetsova et al., 2017 ). Figures were generated with the ggplot2 and dotwhisker package (Wickham, 2016 ). The models included ‘Size class’ and ‘Time phase’ (one hour was divided into 20-minute intervals: early, mid, or late phase) as fixed effects. We compared the goodness of fit of the models using AIC values among the base model, which includes size class, the model with time phase as an additive effect, and the model containing the interaction between the size class and time phase (refer to Table 1 ). ‘Colony’ and ‘Recording’ were included as random effects for models 1, 4, and 5 assessing exploratory behavior, transition of cluster entries and exits, and proportion of inactive ants as they were measured once per ant. For aggregation behavior (models 2–3) and spatial variation of inactive ants (model 6), which was measured at the colony level, only ‘Colony’ was included as a random effect. The model 7 analyzing the effect of inactive ants on movement included ‘Colony,’ ‘Recording,’ and individual ‘Ant ID’ as a random effect to account for repeated measures within ants. Significant values are in bold in the column “p-value”. Table 1 Model output from linear mixed-effects analyses testing the study’s hypotheses. Each model includes fixed effects of Size class (Major as reference, Media, Minor) and Time phase (Early as reference, Mid, Late), with a random factor for Colony and, where appropriate, Recording or Individual. Where indicated (rows marked with +), the reference level for Size class is Media, allowing direct comparison between Minor and Media classes. Prediction Model Estimate ± Std. error t-value p-value 1. Minors would explore the most, followed by medias, and then majors. 1. logit(Exploration) ~ Size class + Time phase + (1|Colony / Recording) Media: -0.110 ± 0.201 Minor: 0.088 ± 0.201 Minor + : 0.198 ± 0.201 Time_Mid: -0.113 ± 0.128 Time_Late: -0.805 ± 0.128 0.644 -0.548 0.987 -0.884 -6.296 0.525 0.594 0.343 0.377 < 0.001 2. Majors would aggregate the most, followed by medias, and then minors. 2. logit(Mean Aggregated Fraction) ~ Size class + Time phase + (1|Colony) Media: -0.178 ± 0.067 Minor: -0.417 ± 0.067 Minor + : -0.239 ± 0.067 Time_Mid: 0.030 ± 0.067 Time_Late: 0.204 ± 0.067 -2.652 -6.225 -3.572 0.452 3.053 0.011 < 0.001 < 0.001 0.653 0.004 3. log(Mean Maximum Cluster Size) ~ Size class + (1|Colony) Media: -0.096 ± 0.063 Minor: -0.236 ± 0.063 Minor + : -0.139 ± 0.063 -1.522 -3.723 -2.200 0.134 < 0.001 0.032 4. log(Transitions) ~ Size class + Time phase + (1|Colony / Recording) Media: 0.060 ± 0.110 Minor: 0.002 ± 0.110 Minor + : -0.062 ± 0.110 Time_Mid: -0.121 ± 0.029 Time_Late: -0.385 ± 0.029 0.551 -0.017 -0.568 -4.090 -13.060 0.592 0.987 0.580 < 0.001 < 0.001 3. Majors would be the most inactive, followed by medias, and then minors. 5. log(Inactivity Proportion + 0.000001) ~ Size class + Time phase + (1|Colony / Recording) Media: 0.254 ± 0.586 Minor: -0.147 ± 0.586 Minor + : -0.400 ± 0.586 Time_Mid: 1.360 ± 0.308 Time_Late: 2.966 ± 0.308 0.433 -0.250 -0.683 4.408 9.618 0.670 0.805 0.503 < 0.001 < 0.001 6. log(Circular Standard Deviation) ~ Size class + (1|Colony) Media: -0.061 ± 0.055 Minor: -0.063 ± 0.055 Minor + : -0.002 ± 0.055 -1.119 -1.149 -0.031 0.268 0.255 0.975 4. Inactive ants would slow down the movement of other ants in the same cluster, and its effect would differ by size class 7. log(Mean speed over the next second of mobile ants) ~ Presence of inactive ants within the cluster * Size class + Time phase + (1|Colony / Recording / AntID) Cluster with inactive ants: -0.177 ± 0.025 Media: 0.098 ± 0.046 Minor: 0.006 ± 0.046 Minor + : -0.104 ± 0.046 Time_Mid: -0.020 ± 0.017 Time_Late: -0.139 ± 0.017 Cluster with inactive ants:Media: -0.025 ± 0.035 Cluster with inactive ants:Minor: -0.030 ± 0.035 Cluster with inactive ants:Minor + : -0.005 ± 0.035 -7.145 2.151 -0.130 -2.284 -1.152 -7.972 -0.715 -0.851 -1.140 < 0.001 < 0.001 0.047 0.036 0.250 < 0.001 0.475 0.395 0.888 Results Model selection When we compared the AIC values of the models, we found that the additive effect of time phase significantly improved model fit across most cases. Conversely, models incorporating interactions between time phase and size class did not demonstrate significant improvements over their respective additive counterparts (see supplementary material part S3 for full model comparison results). For maximum cluster size and circular standard deviation of inactive ants, however, time phase (additive or interactive) did not significantly improve model fit, leading to the selection of models containing only size class as an explanatory variable. Final model specifications for each response variable are detailed in Table 1. Exploratory behavior Contrary to our first hypothesis, exploratory behavior did not differ consistently across size classes when analyzing the full dataset (Figure 1). Neither medias nor minors differed significantly from majors in exploration index, and no significant difference existed between medias and minors. Their exploratory behavior decreased significantly in the late phase compared to the early phase. These results suggest similar exploration levels across size classes, as detailed in Table 1 (Model 1). Aggregation behavior In contrast to exploratory behavior, aggregation patterns varied by the size classes (Table 1: Model 2; Figure 2a). Majors exhibited the highest aggregated fraction, followed by medias, and then minors, with significant differences between all pairs. While aggregated fraction remained stable in early and mid phases, it increased significantly in the late phase. Similarly, mean maximum cluster size differed significantly between majors and minors (Table 1: Model 3; Figure 2b), with majors forming the largest clusters and minors the smallest. Medias showed intermediate cluster sizes, differing significantly from minors but not majors. Time phase had no effect on maximum cluster size. Cluster entry/exit frequency did not vary by size class (Table 1: Model 4; Figure 2c). No significant differences were detected between size classes, including between medias and minors. However, transitions declined significantly over time, reaching their lowest frequency in the late phase. Temporal and spatial variation of inactive ants Inactivity proportion did not differ across size classes (Table 1: Model 5; Figure 3a). Inactive time was comparable between all size classes, though inactivity increased significantly over time. Conversely, the spatial distribution of inactive ants showed no significant variation by size class or time phase (Table 1: Model 6; Figure 3b). Effect of inactive ants Despite uniform inactivity levels, the presence of inactive ants within clusters significantly reduced movement speed of nearby individuals (Table 1: Model 7; Figure 4). Speed was also lower in the late phase compared to the early phase, with no difference between mid and early phases. No significant interactions between inactive ants and size class were detected, indicating consistent slowing effects across all workers. Discussion Size-independent Exploratory Behavior Our study found that Camponotus japonicus foragers exhibit similar exploratory behavior across size classes when they encounter novel environments. This size independence was consistent over time. In all size classes, exploratory activity declined significantly in mid and late phases, which suggests an acclimation process of the colony with little size-specific variations. This may be attributed to the relatively weak task specialization in this species compared to others with more pronounced role differentiation (Lee, 1998 ). In contrast to Pheidole species, where larger workers typically explore less (Detrain & Pasteels, 1991 ), C. japonicus majors engage in both nest defense and foraging. The temporal decline in exploration across all size classes may indicate a shared assessment strategy for novel environments, prioritizing initial investigation followed by reduced investment as familiarity increases. Size-dependent Aggregation Behavior On the other hand, aggregation behavior in Camponotus japonicus foragers was size-dependent, with larger ants forming bigger clusters and remaining within clusters for longer durations than smaller individuals. The significant increase in aggregated fraction in the late phase might show a temporal shift toward collective safety in numbers due to the prolonged exposure to the novel environment. This pattern is consistent with previous findings in Pheidole pallidula , where majors demonstrated a greater tendency to aggregate than minors (Sempo et al., 2006 ). One possible explanation is that majors possess a higher response threshold to minor disturbances, thereby reducing their risk of injury or mortality (Detrain & Pasteels, 1991 , 1992 ). The higher production and maintenance costs associated with majors, compared to smaller workers (Wilson, 1980 ), may promote aggregation as a protective strategy when navigating unfamiliar environments. Polyethism, including trophallaxis, may also contribute to aggregation behavior, with polymorphic workers engaging differently based on their roles (Bonavita-Cougourdan & Morel, 1985 ). Furthermore, majors in both P. pallidula and C. japonicus may serve as repletes, who store food in their crop for the colony and stay inside the nest, further reinforcing their propensity to aggregate (Lachaud et al., 1992 ; Lee, 1998 ). However, our findings contrast with studies on other species exhibiting continuous polymorphism, such as Atta sexdens rubropilosa and Solenopsis interrupta , in which no significant size-dependent differences in aggregation behavior were observed (Depickère, Ramírez Ávila, et al., 2008 ). This discrepancy may result from species-specific differences in worker roles and methodological differences in data collection. For example, A. sexdens rubropilosa majors function primarily as forager-excavators rather than defenders, unlike the role differentiation observed in Pheidole and Camponotus species. Additionally, previous studies often collected minors from nest interiors and majors from foraging areas (Depickère, Ramírez Ávila, et al., 2008 ), potentially influencing interpretations of behavioral outcomes. Properties and Effects of Inactive Ants Our study found that the proportion and spatial distribution of inactive ants were not correlated with body size, consistent with findings in other species (Charbonneau et al., 2017 ). However, inactivity proportion increased significantly over time, indicating a colony-level shift toward energy conservation as the novel environment became familiar. Inactive ants are known to be repletes in other ant species (Charbonneau et al., 2017 ). Our result suggests that such roles are not necessarily restricted to larger ants, but may also be fulfilled by smaller individuals (Børgesen, 2000 ). Since inactive ants tend to remain near the nest center (Charbonneau et al., 2017 ), their presence may serve as a signal of safety for all size classes. The exact mechanisms through which inactive ants influence the behavior of others remain unclear, although chemical cues may play a role, as demonstrated in other social organisms, including bees (Cameron, 1981 ; Gilbert et al., 2001 ), naked mole rats (Judd & Sherman, 1996 ), bark beetles (Deneubourg et al., 1990 ), and social amoebae (Marée & Hogeweg, 2001 ). Future Directions In this study, we examined whether foragers of different size classes exhibit behavioral differences when introduced to a novel environment. However, our experimental design did not include mixed-size groups. Future research should investigate aggregation dynamics in mixed-size groups, which would better reflect natural conditions. Additionally, exploring the potential role of chemical cues emitted by inactive ants would be a valuable next step. Further studies should also consider the influence of abiotic factors, competition, and resource availability on exploratory and aggregation behaviors, as these environmental variables are known to significantly affect ant behavior (Wills et al., 2018 ). Long-term observations across days or weeks could clarify whether the observed temporal patterns represent short-term adjustments or stable behavioral strategies. Although tracking individuals in wild colonies remains challenging, it is essential for developing a comprehensive understanding of size-related behavioral differences in polymorphic workers. Such investigations could yield insights into how these behaviors interact with the division of labor within colonies. Finally, understanding the social mechanisms underlying aggregation behavior may provide broader insights into the evolution of the division of labor in eusocial species. This knowledge can be applied across disciplines, including swarm robotics and sociology, contributing to a deeper understanding of collective behavior. Declarations Conflict of interest The authors declare that there are no conflicting interests. Funding This work was supported by the National Convergence Research of Scientific Challenges through the National Research Foundation of Korea funded by Ministry of Science and ICT (2021M3F7A1017476), Basic Science Research Program through the National Research Foundation of Korea funded by the Ministry of Education in 2020 (2020R1I1A1A01073669), and DGIST Start-up Fund Program (2023010191) of the Ministry of Science, ICT and Future Planning of Korea. This work was supported by the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea (BK21 FOUR). Author contributions H.J. devised the idea; H.J and W.S. designed the methodology, collected data, analyzed the data; P.G.J. and S.L. provided supervision and conceptual guidance; H.J. led the writing of the manuscript, but all authors contributed to writing the manuscript via critical feedback on analyses and the manuscript. Acknowledgements We thank Haeun Cho, Jinseok Park, Jonghyun Park, and Dr. Eun Ju Lee for their helpful discussions and constructive suggestions on earlier versions of the manuscript. We appreciate the Korea Safety Health Environment Foundation for the award of Graduate School Student Scholarship. We are also grateful to Dr. Noa Pinter-Wollman for generously providing laboratory space during the preparation of this work. References Bates, D., Mächler, M., Bolker, B., & Walker, S. (2015). Fitting Linear Mixed-Effects Models Using lme4. Journal of Statistical Software , 67 (1), 1–48. https://doi.org/10.18637/jss.v067.i01 Bonavita-Cougourdan, A., & Morel, L. (1985). 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Caste and division of labor in Erebomyrma, a genus of dimorphic ants (Hymenoptera: Formicidae: Myrmicinae). Insectes Sociaux , 33 (1), 59–69. Supplementary Files SupplementaryMaterialsHyeinJo.docx Cite Share Download PDF Status: Published Journal Publication published 18 Apr, 2026 Read the published version in Insectes Sociaux → Version 1 posted Editorial decision: Major Revisions Needed 26 Nov, 2025 Reviewers agreed at journal 28 Oct, 2025 Reviewers invited by journal 28 Oct, 2025 Editor assigned by journal 24 Oct, 2025 First submitted to journal 22 Oct, 2025 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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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-7915365","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":536180541,"identity":"bb7e5ccf-e687-433e-9053-e84a49e4dad5","order_by":0,"name":"Hyein Jo","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAElEQVRIiWNgGAWjYBACAwbGxgMMDGwM/OyNzQ8+AEXY2AlraQBrkew5fMxwBkgLM0EtDAwHwIwbaQnSPCAWIS3m7IcbDnxs45MzuJFjYGzza5s8HzMD44ePObi1WPYkNhyc2cZmLHnmjcHj3L7bhm3MDMySM7fhcdiBxIbDvG1siX3Hgbbk9txmBGphY+bFp+X8w4bDf4FaGg7kGEhb9ty2J6zlBtAWRqCWCSeA3mf4cTuRCC0PGw72nAP6BRTIvQ23k9uYGZvx++V8+sMHP8qOyYGj8sef27bz25sPfviIRwsYMLIdgzLawGQDAfUg8KcGxiBC8SgYBaNgFIw4AAAIJlynhJUyBgAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0009-0007-2954-7308","institution":"Seoul National University","correspondingAuthor":true,"prefix":"","firstName":"Hyein","middleName":"","lastName":"Jo","suffix":""},{"id":536180542,"identity":"1ed9f291-a2f4-4633-8a3a-cff79fa9a437","order_by":1,"name":"Woncheol Song","email":"","orcid":"","institution":"Seoul National University","correspondingAuthor":false,"prefix":"","firstName":"Woncheol","middleName":"","lastName":"Song","suffix":""},{"id":536180543,"identity":"fbeace97-c225-46e8-86e1-64f2a4a461e0","order_by":2,"name":"Piotr G. 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1","display":"","copyAsset":false,"role":"figure","size":120389,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eExploration behavior across size classes and time phases.\u003c/strong\u003e Each violin shows the distribution of values per group, with internal boxplots representing the median and interquartile range. Individual observations are shown as jittered points. Size classes are distinguished by color: Major (pink), Media (orange), and Minor (blue). Statistical results are shown in Table 1 (Model 1).\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7915365/v1/e44aa0681a10a0568769cc1d.png"},{"id":95382658,"identity":"c49977d3-6ce9-4de6-a494-e43fb00d7e45","added_by":"auto","created_at":"2025-11-07 12:10:53","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":56751,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAggregation behavior across size classes and time phases.\u003c/strong\u003e Graphs depict model-estimated (a) aggregated fraction, (b) maximum cluster size, and (c) number of cluster transitions are shown for each size class (Major, Media, Minor) across three time phases (Early, Mid, Late). Points indicate fixed-effect predicted means, and error bars represent 95% confidence intervals. All values are back-transformed from logit or log scale, where applicable. Statistical results for transformed variables are provided in Table 1 (Models 2-4). In Figure 2c, Major and Minor estimates closely overlap.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7915365/v1/6ca0c33ce9aebe8c37f95a37.png"},{"id":95382659,"identity":"ed9a5b17-2219-4d39-991a-bf66def42f23","added_by":"auto","created_at":"2025-11-07 12:10:53","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":49330,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRelationship between inactivity and size class.\u003c/strong\u003e Graphs depict model-estimated (a) inactivity proportion and (b) spatial distribution of inactive ants (circular standard deviation) across size classes and time phases. Refer to Figure 2 for detailed descriptions. Statistical results are shown in Table 1 (Models 5-6). In Figure 3b, Media and Minor estimates closely overlap.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7915365/v1/276154540e6870870fdd237f.png"},{"id":95382661,"identity":"291d0ea7-b800-4052-9812-92787eb3d807","added_by":"auto","created_at":"2025-11-07 12:10:53","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":97143,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEffect of inactive ants on the speed of ants across size classes and time phases. \u003c/strong\u003eEach violin shows the distribution of observed speeds of mobile ants for a given group, with embedded boxplots representing the median and interquartile range. Colors indicate whether inactive ants were absent (purple) or present (cyan) in the cluster. Median values are additionally highlighted by colored dots (purple for “absent,” cyan for “present”), and medians within each condition are connected by lines across time phases (Early, Mid, Late) within the same size class (Major, Media, Minor). A red dotted line marks the 20 mm/s threshold for reference. Statistical results are reported in Table 1 (Model 7).\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7915365/v1/ec7087da23e70542d4803be7.png"},{"id":107350702,"identity":"c4ab1785-d23a-4ec2-ab74-e0bbc77ac79a","added_by":"auto","created_at":"2026-04-20 16:00:30","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":938862,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7915365/v1/fef75d20-41d3-4cb5-b92b-1184d0175049.pdf"},{"id":95382674,"identity":"71e5ad61-a665-4827-94d2-72c004e82e67","added_by":"auto","created_at":"2025-11-07 12:10:53","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":2874509,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterialsHyeinJo.docx","url":"https://assets-eu.researchsquare.com/files/rs-7915365/v1/89b8648a80159983d1d4347d.docx"}],"financialInterests":"","formattedTitle":"Exploration and Aggregation of Differently Sized Camponotus japonicus Worker Ants","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAnts, as eusocial insects, exhibit a highly organized division of labor, where individuals perform specialized tasks that contribute to and sustain the colony\u0026rsquo;s complex social structure (Gordon, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e1996\u003c/span\u003e; H\u0026ouml;lldobler \u0026amp; Wilson, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e1990\u003c/span\u003e; Oster \u0026amp; Wilson, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e1978\u003c/span\u003e). Task allocation in worker ants is influenced by a range of intrinsic and extrinsic factors (Wills et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), with age, body size, or a combination of both playing a pivotal role in determining individual roles within the colony (Wilson, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e1985\u003c/span\u003e). Among these factors, size polyethism (i.e. task allocation based on their body size) has been reported in many ant species (Wilson, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e1978\u003c/span\u003e, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e1979\u003c/span\u003e, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e1980\u003c/span\u003e, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e1986\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn polymorphic species, size polyethism may enhance colony efficiency by enabling task specialization according to morphological variation (Oster \u0026amp; Wilson, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e1978\u003c/span\u003e; Wills et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). For instance, in leafcutter ants (\u003cem\u003eAtta\u003c/em\u003e spp.), major workers engage in colony defense and process food, while medium-sized workers (medias) forage, and minor workers protect the leaf fragments from parasitic phorids (Evison \u0026amp; Ratnieks, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Feener \u0026amp; Brown, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e1993\u003c/span\u003e; Hart \u0026amp; Ratnieks, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). Similarly, in \u003cem\u003ePheidole\u003c/em\u003e spp., the big-headed ants, majors primarily function as soldiers or food processors, whereas minors undertake brood care and foraging (Lillico-Ouachour \u0026amp; Abouheif, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Wilson, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e1976\u003c/span\u003e, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e1984\u003c/span\u003e). In \u003cem\u003eCamponotus\u003c/em\u003e spp., which exhibit continuous polymorphism, majors are specialists in nest defense or act as storage vessels, while minors perform a broad range of tasks, including caring for broods and foraging (H\u0026ouml;lldobler \u0026amp; Wilson, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e1990\u003c/span\u003e; Lee, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e1998\u003c/span\u003e). Other examples include turtle ants, \u003cem\u003eCephalotes\u003c/em\u003e spp., where large-headed soldiers block nest entrances to prevent intrusion (Creighton \u0026amp; Geegg, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e1954\u003c/span\u003e; De Andrade \u0026amp; Urbani, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e1999\u003c/span\u003e). For a comprehensive review of size polyethism in polymorphic ants, see Wills et al. (\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eBeyond task specialization, size polyethism may influence how individuals explore and respond to novel environments, with potential consequences for colony efficiency, by conducting different operations simultaneously (Oster \u0026amp; Wilson, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e1978\u003c/span\u003e). Exploratory behavior is a critical component of survival, especially under environmental challenges, allowing colonies to assess risks and locate resources (Page et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). For example, in the Argentine ants (\u003cem\u003eLinepithema humile\u003c/em\u003e), foragers encountering novel conditions exhibit behavioral heterogeneity, enabling effective risk assessment and resource discovery (Madrzyk \u0026amp; Pinter-Wollman, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Additionally, groups with a higher proportion of exploratory individuals exhibit faster and more accurate collective nest selection (Hui \u0026amp; Pinter-Wollman, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). In \u003cem\u003eLasius niger\u003c/em\u003e, exploratory behavior correlates with foraging success (Pasquier \u0026amp; Gr\u0026uuml;ter, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Studies on polymorphic species further highlight variation in exploration: in \u003cem\u003ePheidole pallidula\u003c/em\u003e, minors primarily engage in food exploitation in foraging areas, while majors do not actively search (Detrain \u0026amp; Pasteels, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e1991\u003c/span\u003e). Similarly, \u003cem\u003eCamponotus yamaokai\u003c/em\u003e majors display lower exploratory tendencies than minors when introduced to an unfamiliar environment (Sakai et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). While the function of exploratory behavior has been widely studied across multiple species, its relationship with polymorphism remains poorly understood.\u003c/p\u003e\u003cp\u003eAnother crucial aspect of social organization is aggregation behavior, which enhances colony survival by facilitating interactions among nestmates (Deneubourg et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Jeanson et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Parrish \u0026amp; Edelstein-Keshet, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e1999\u003c/span\u003e). In eusocial insects, spatial organization often arises from differential clustering based on age polyethism, where younger workers tend to remain near the brood while older workers position themselves closer to nest entrances (Mersch et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Oster \u0026amp; Wilson, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e1978\u003c/span\u003e; Seeley, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e1982\u003c/span\u003e). However, the extent to which worker size influences aggregation patterns remains largely unexplored. Some studies have reported size-related differences in aggregation, such as in \u003cem\u003ePheidole pallidula\u003c/em\u003e, where majors exhibit a greater tendency to form a cluster compared to minors in a novel environment (Sempo et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). In contrast, no size-dependent effects on aggregation have been suggested in \u003cem\u003eAtta sexdens rubropilosa\u003c/em\u003e or \u003cem\u003eSolenopsis interrupta\u003c/em\u003e (Depick\u0026egrave;re, Ram\u0026iacute;rez \u0026Aacute;vila, et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Given that these species all belong to the same subfamily Myrmicinae, further investigations across different subfamilies are necessary to assess whether these patterns are broadly conserved (Ward, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe mechanisms underlying aggregation behavior remain poorly studied. While direct physical contact and chemical cues contribute to cluster formation in \u003cem\u003eP. pallidula\u003c/em\u003e (Sempo et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2006\u003c/span\u003e), the role of inactive ants, often representing around 40% of the colony (Charbonneau et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), has been largely overlooked. Inactive ants often remain near the nest center, a location that may be perceived as safe by nestmates (Charbonneau et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Moreover, majors in \u003cem\u003eP. pallidula\u003c/em\u003e are known to exhibit generally higher inactivity levels than minors (Sempo \u0026amp; Detrain, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). Since major workers both aggregate better and exhibit lower activity, inactivity may drive size-dependent aggregation. Despite the substantial proportion of inactive ants within many ant colonies (Cole, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e1986\u003c/span\u003e; Dornhaus et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Herbers, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e1983\u003c/span\u003e; Mirenda \u0026amp; Vinson, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e1981\u003c/span\u003e; Retana \u0026amp; Cerda, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e1991\u003c/span\u003e), their functional role remains obscure. Addressing this knowledge gap requires a closer examination of the relationship between inactivity and aggregation behavior in polymorphic species.\u003c/p\u003e\u003cp\u003eIn addition to spatial and morphological factors, the temporal dynamics can play a critical role in shaping ant behavioral organization, operating across multiple timescales from circadian rhythms to short-term activity phases (Cole, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e1991\u003c/span\u003e; Doering, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Eban-Rothschild \u0026amp; Bloch, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Fuchikawa, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). These patterns emerge through interactions between endogenous physiological cycles and external stimuli such as resource distribution or colony-level interactions (Doering, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Willmer \u0026amp; Stone, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). Experimental evidence from \u003cem\u003eCrematogaster scutellaris\u003c/em\u003e demonstrates this temporal flexibility: brood-tenders maintained stable clusters (~\u0026thinsp;30% worker participation) while foragers showed increasing aggregation up to 60%, with the overall cluster numbers diminishing over time in novel environments (Depick\u0026egrave;re, Fresneau, et al., 2008). Therefore, analyzing behavioral responses across different time intervals can provide important insights into the temporal organization of ant societies and the mechanisms underlying collective behavior.\u003c/p\u003e\u003cp\u003eIn this study, we investigate \u003cem\u003eCamponotus japonicus\u003c/em\u003e, a species from the subfamily Formicinae (Ward, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2007\u003c/span\u003e, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), which is characterized by a broad range of worker sizes (Lee, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e1998\u003c/span\u003e; Wills et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Studies on \u003cem\u003eCamponotus\u003c/em\u003e often distinguish media workers from minors and majors based on their distinct morphological differences (Sharma et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Tross et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In \u003cem\u003eC. japonicus\u003c/em\u003e, minor workers primarily engage in foraging, which demands high levels of activity, while majors and medias take part in both foraging and defense, a task that generally requires relatively less activity. Their behavioral roles tend to vary with body size (Lee, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e1998\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eWe measured exploratory and aggregation behaviors across minors, medias, and majors when they were introduced to a novel environment. Specifically, we tested predictions from four hypotheses: (H1) exploratory behavior varies by size classes, with minors exploring the most, followed by medias, and then majors; (H2) aggregation behavior differs among size classes, with larger workers aggregating more than smaller workers as observed in other species; (H3) inactivity levels differ among size classes, with majors being the most inactive and minors the least; (H4) there is a correlation between inactivity and aggregation behavior, with inactive ants slowing down other individuals within the same cluster than active ants, with effects being the largest in majors and the least in minors. We also examined if each of these hypotheses held across time. By investigating these hypotheses, our study aims to contribute to the understanding of size-dependent behavioral variations in response to novel environments in polymorphic ants.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003eStudy subject\u003c/h2\u003e\n \u003cp\u003eJapanese carpenter ants, \u003cem\u003eCamponotus japonicus\u003c/em\u003e, were collected from seven field colonies (C1-C7) between late May to early July 2022 at Seoul National University Gwanak campus in South Korea. Only foragers outside the nest were collected to minimize the effect of age polyethism. Ants were classified into three size classes, with the largest as majors (\u0026gt;\u0026thinsp;10.5 mm), the smallest as minors (\u0026lt;\u0026thinsp;9 mm), and those in between as medias (9-10.5 mm) due to their distinct appearance (see supplementary material part S1). For each of the three size classes 16 ants were collected, resulting in 48 individuals per colony.\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eExperimental design\u003c/h3\u003e\n\u003cp\u003eThe experiment was conducted on the same day as the capture in a circular indoor arena, 56 cm in diameter. The arena was surrounded by polyvinyl chloride (PVC) walls coated with canola oil to prevent the ants from climbing. A sheet of gray paper was placed on the arena floor and replaced before each recording to eliminate residual pheromones. To prevent prior experience from influencing behavior, each size class was tested only once per colony. Before each recording session, 16 ants of the same size class were placed in a plastic container. The container\u0026rsquo;s top was left open, and the wall was coated with synthetic grease (Super Lube\u0026reg; Multi-Purpose Synthetic Grease with Syncolon\u0026reg; (PTFE)) to prevent escape. They were stabilized for one hour in the container before being introduced into the arena. Due to spatial constraints, only one size class could be observed at a time per colony (referred to as the variable \u0026ldquo;recording\u0026rdquo; in statistical analyses), and the order of recordings was randomly assigned. We video-recorded ants from above for one hour per experiment.\u003c/p\u003e\n\u003ch3\u003eVideo digitization \u0026amp; editing\u003c/h3\u003e\n\u003cp\u003eThe x- and y-coordinates of the center of each of the 16 ants in each experiment were tracked by a customized program we wrote in NetLogo (Wilensky, \u003cspan class=\"CitationRef\"\u003e1999\u003c/span\u003e). The body size of each size class was measured from the tip of the head to the tip of the gaster using ImageJ. Since the ants could occlude each other in the video, manual inspection corrected any misidentifications. Data were extracted every second starting from one minute after the ants were introduced into the arena until the end of the experiment (1 hour).\u003c/p\u003e\n\u003ch3\u003eVariables extracted from data\u003c/h3\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n \u003ch2\u003eExploratory Behavior\u003c/h2\u003e\n \u003cp\u003eExploratory behavior was quantified using Simpson\u0026rsquo;s evenness index (0\u0026ndash;1) (Heip et al., \u003cspan class=\"CitationRef\"\u003e1998\u003c/span\u003e; Simpson, \u003cspan class=\"CitationRef\"\u003e1949\u003c/span\u003e; Smith \u0026amp; Wilson, \u003cspan class=\"CitationRef\"\u003e1996\u003c/span\u003e). The experimental arena was divided into 72 spatial slices radiating outward from the center, and the number of individual occurrences per second for each ant was recorded in each slice. The proportion of occurrences (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{p}_{i}\\)\u003c/span\u003e\u003c/span\u003e) in each slice relative to the total occurrences was calculated using Eq.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e to obtain the circular distribution of occurrences on the arena. Simpson\u0026apos;s dominance index (\u0026lambda;) was then calculated to measure the concentration of occurrences across slices (Eq.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e), while spatial slice richness (S) was defined as the total number of slices with recorded occurrences (72). The \u003cstrong\u003eexploration index\u003c/strong\u003e (E) was calculated as Simpson\u0026rsquo;s evenness index (e.g., the ratio of the reciprocal of Simpson\u0026rsquo;s dominance index to spatial slice richness) (Eq.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). Values of exploration approaching 1 indicate high exploratory behavior, while values closer to 0 indicate low exploratory behavior.\u003c/p\u003e\n \u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e$$\\:{p}_{i}=\\:\\frac{Occurrences\\:in\\:slice\\:i}{Total\\:occurrences}$$\u003c/div\u003e\n \u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e$$\\:\\lambda\\:=\\:{\\sum\\:}_{i=1}^{S}{p}_{i}^{2}$$\u003c/div\u003e\n \u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Equ3\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equ3\" name=\"EquationSource\"\u003e$$\\:E=\\:\\frac{1/\\lambda\\:}{S}$$\u003c/div\u003e\n \u003cdiv class=\"EquationNumber\"\u003e3\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cem\u003eAggregation Behavior\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003eAnts were considered to be in the same cluster if the distance between their center coordinates was less than twice their mean body size. Clusters were then defined as groups of ants connected under this rule: if one ant was close enough to a second, and that second was close enough to a third, then all three were counted as part of the same cluster, even if the first and third were not directly within the threshold of each other. Cluster size was defined as the number of ants within a cluster. To investigate aggregation behavior across size classes, three variables were measured:\u003c/p\u003e\u003cspan\u003e\n \u003cp\u003e\u003cstrong\u003e1. Aggregated fraction\u003c/strong\u003e was calculated as the ratio of the total number of ants in clusters to the total number of ants in the arena for each second per experimental recording (Eq. 4).\u003c/p\u003e\n \u003c/span\u003e\n \u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003eAggregated fraction = \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{Total\\:number\\:of\\:ants\\:in\\:clusters}{Total\\:number\\:of\\:ants\\:(=16)}\\)\u003c/span\u003e\u003c/span\u003e (4)\u003c/p\u003e\n \u003c/div\u003e\u003cspan\u003e\n \u003cp\u003e\u003cstrong\u003e2. Maximum cluster size\u003c/strong\u003e was defined as the number of ants in the largest cluster.\u003c/p\u003e\n \u003c/span\u003e\n \u003cp\u003eBoth variables were measured every second for the full duration of each experiment, and the mean value of these measurements over a given time window (see below) was used in the analysis.\u003c/p\u003e\u003cspan\u003e\n \u003cp\u003e\u003cstrong\u003e3. Frequency of cluster transitions\u003c/strong\u003e was measured as the sum of cluster entries and exits throughout the experiment per individual.\u003c/p\u003e\n \u003c/span\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003eTemporal and Spatial Variation of Inactive Ants\u003c/h2\u003e\n \u003cp\u003eThroughout the experiment,many ants remained motionless with their heads on the ground for extended periods. We defined inactivity as immobility lasting at least 30 seconds (see supplementary material part S2 for details on definition).\u003c/p\u003e\u003cspan\u003e\n \u003cp\u003e\u003cstrong\u003e1. Inactivity proportion\u003c/strong\u003e was defined as the ratio of total inactive counts over the experimental time (1 hour) of each recording per individual.\u003c/p\u003e\n \u003c/span\u003e\u003cspan\u003e\n \u003cp\u003e\u003cstrong\u003e2. Circular standard deviation\u003c/strong\u003e was measured using the same 72 spatial slices as in the exploratory behavior analysis. The \u003cem\u003ecirc.disp\u003c/em\u003e function from the \u003cem\u003eCircStats\u003c/em\u003e R package was used.\u003c/p\u003e\n \u003c/span\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n \u003ch2\u003eEffect of Inactive Ants on Movement in Clusters\u003c/h2\u003e\n \u003cp\u003eThe effect of inactive ants on the movement of ants in clusters was analyzed by measuring the \u003cstrong\u003emean speed of individual ants over the next second\u003c/strong\u003e (mm/s). The speed was measured for all ants within clusters and categorized by size class and the in-cluster presence of inactive ants at the current second.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003eStatistical analysis\u003c/h2\u003e\n \u003cp\u003eAll statistical analyses were performed using linear mixed-effects models in R (v4.2.2; Posit team, \u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e) with the \u003cem\u003elme4\u003c/em\u003e package (Bates et al., \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e). Significance levels (\u0026alpha;\u0026thinsp;=\u0026thinsp;0.05) were applied for hypothesis testing, and p-values were calculated using the \u003cem\u003elmerTest\u003c/em\u003e package (Kuznetsova et al., \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e). Figures were generated with the \u003cem\u003eggplot2\u003c/em\u003e and \u003cem\u003edotwhisker\u003c/em\u003e package (Wickham, \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eThe models included \u0026lsquo;Size class\u0026rsquo; and \u0026lsquo;Time phase\u0026rsquo; (one hour was divided into 20-minute intervals: early, mid, or late phase) as fixed effects. We compared the goodness of fit of the models using AIC values among the base model, which includes size class, the model with time phase as an additive effect, and the model containing the interaction between the size class and time phase (refer to Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). \u0026lsquo;Colony\u0026rsquo; and \u0026lsquo;Recording\u0026rsquo; were included as random effects for models 1, 4, and 5 assessing exploratory behavior, transition of cluster entries and exits, and proportion of inactive ants as they were measured once per ant. For aggregation behavior (models 2\u0026ndash;3) and spatial variation of inactive ants (model 6), which was measured at the colony level, only \u0026lsquo;Colony\u0026rsquo; was included as a random effect. The model 7 analyzing the effect of inactive ants on movement included \u0026lsquo;Colony,\u0026rsquo; \u0026lsquo;Recording,\u0026rsquo; and individual \u0026lsquo;Ant ID\u0026rsquo; as a random effect to account for repeated measures within ants. Significant values are in bold in the column \u0026ldquo;p-value\u0026rdquo;.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eModel output from linear mixed-effects analyses testing the study\u0026rsquo;s hypotheses. Each model includes fixed effects of Size class (Major as reference, Media, Minor) and Time phase (Early as reference, Mid, Late), with a random factor for Colony and, where appropriate, Recording or Individual. Where indicated (rows marked with +), the reference level for Size class is Media, allowing direct comparison between Minor and Media classes.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePrediction\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" style=\"width: 21.2793%;\"\u003e\n \u003cp\u003eModel\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" style=\"width: 16.2939%;\"\u003e\n \u003cp\u003eEstimate\u0026thinsp;\u0026plusmn;\u0026thinsp;Std. error\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" style=\"width: 13.043%;\"\u003e\n \u003cp\u003et-value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" style=\"width: 17.6419%;\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1. Minors would explore the most, followed by medias, and then majors.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 21.2793%;\"\u003e\n \u003cp\u003e1. logit(Exploration)\u0026thinsp;~\u0026thinsp;Size class\u0026thinsp;+\u0026thinsp;Time phase + (1|Colony / Recording)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 16.2939%;\"\u003e\n \u003cp\u003eMedia: -0.110\u0026thinsp;\u0026plusmn;\u0026thinsp;0.201\u003c/p\u003e\n \u003cp\u003eMinor: 0.088\u0026thinsp;\u0026plusmn;\u0026thinsp;0.201\u003c/p\u003e\n \u003cp\u003eMinor\u003csup\u003e+\u003c/sup\u003e: 0.198\u0026thinsp;\u0026plusmn;\u0026thinsp;0.201\u003c/p\u003e\n \u003cp\u003eTime_Mid: -0.113\u0026thinsp;\u0026plusmn;\u0026thinsp;0.128\u003c/p\u003e\n \u003cp\u003eTime_Late: -0.805\u0026thinsp;\u0026plusmn;\u0026thinsp;0.128\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 13.043%;\"\u003e\n \u003cp\u003e0.644\u003c/p\u003e\n \u003cp\u003e-0.548\u003c/p\u003e\n \u003cp\u003e0.987\u003c/p\u003e\n \u003cp\u003e-0.884\u003c/p\u003e\n \u003cp\u003e-6.296\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 17.6419%;\"\u003e\n \u003cp\u003e0.525\u003c/p\u003e\n \u003cp\u003e0.594\u003c/p\u003e\n \u003cp\u003e0.343\u003c/p\u003e\n \u003cp\u003e0.377\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003e2. Majors would aggregate the most, followed by medias, and then minors.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 21.2793%;\"\u003e\n \u003cp\u003e2. logit(Mean Aggregated Fraction)\u0026thinsp;~\u0026thinsp;Size class\u0026thinsp;+\u0026thinsp;Time phase + (1|Colony)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 16.2939%;\"\u003e\n \u003cp\u003eMedia: -0.178\u0026thinsp;\u0026plusmn;\u0026thinsp;0.067\u003c/p\u003e\n \u003cp\u003eMinor: -0.417\u0026thinsp;\u0026plusmn;\u0026thinsp;0.067\u003c/p\u003e\n \u003cp\u003eMinor\u003csup\u003e+\u003c/sup\u003e: -0.239\u0026thinsp;\u0026plusmn;\u0026thinsp;0.067\u003c/p\u003e\n \u003cp\u003eTime_Mid: 0.030\u0026thinsp;\u0026plusmn;\u0026thinsp;0.067\u003c/p\u003e\n \u003cp\u003eTime_Late: 0.204\u0026thinsp;\u0026plusmn;\u0026thinsp;0.067\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 13.043%;\"\u003e\n \u003cp\u003e-2.652\u003c/p\u003e\n \u003cp\u003e-6.225\u003c/p\u003e\n \u003cp\u003e-3.572\u003c/p\u003e\n \u003cp\u003e0.452\u003c/p\u003e\n \u003cp\u003e3.053\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 17.6419%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.011\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e0.653\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e0.004\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 21.2793%;\"\u003e\n \u003cp\u003e3. log(Mean Maximum Cluster Size)\u0026thinsp;~\u0026thinsp;Size class + (1|Colony)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 16.2939%;\"\u003e\n \u003cp\u003eMedia: -0.096\u0026thinsp;\u0026plusmn;\u0026thinsp;0.063\u003c/p\u003e\n \u003cp\u003eMinor: -0.236\u0026thinsp;\u0026plusmn;\u0026thinsp;0.063\u003c/p\u003e\n \u003cp\u003eMinor\u003csup\u003e+\u003c/sup\u003e: -0.139\u0026thinsp;\u0026plusmn;\u0026thinsp;0.063\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 13.043%;\"\u003e\n \u003cp\u003e-1.522\u003c/p\u003e\n \u003cp\u003e-3.723\u003c/p\u003e\n \u003cp\u003e-2.200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 17.6419%;\"\u003e\n \u003cp\u003e0.134\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e0.032\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 21.2793%;\"\u003e\n \u003cp\u003e4. log(Transitions)\u0026thinsp;~\u0026thinsp;Size class\u0026thinsp;+\u0026thinsp;Time phase + (1|Colony / Recording)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 16.2939%;\"\u003e\n \u003cp\u003eMedia: 0.060\u0026thinsp;\u0026plusmn;\u0026thinsp;0.110\u003c/p\u003e\n \u003cp\u003eMinor: 0.002\u0026thinsp;\u0026plusmn;\u0026thinsp;0.110\u003c/p\u003e\n \u003cp\u003eMinor\u003csup\u003e+\u003c/sup\u003e: -0.062\u0026thinsp;\u0026plusmn;\u0026thinsp;0.110\u003c/p\u003e\n \u003cp\u003eTime_Mid: -0.121\u0026thinsp;\u0026plusmn;\u0026thinsp;0.029\u003c/p\u003e\n \u003cp\u003eTime_Late: -0.385\u0026thinsp;\u0026plusmn;\u0026thinsp;0.029\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 13.043%;\"\u003e\n \u003cp\u003e0.551\u003c/p\u003e\n \u003cp\u003e-0.017\u003c/p\u003e\n \u003cp\u003e-0.568\u003c/p\u003e\n \u003cp\u003e-4.090\u003c/p\u003e\n \u003cp\u003e-13.060\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 17.6419%;\"\u003e\n \u003cp\u003e0.592\u003c/p\u003e\n \u003cp\u003e0.987\u003c/p\u003e\n \u003cp\u003e0.580\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e3. Majors would be the most inactive, followed by medias, and then minors.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 21.2793%;\"\u003e\n \u003cp\u003e5. log(Inactivity Proportion\u0026thinsp;+\u0026thinsp;0.000001)\u0026thinsp;~\u0026thinsp;Size class\u0026thinsp;+\u0026thinsp;Time phase + (1|Colony / Recording)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 16.2939%;\"\u003e\n \u003cp\u003eMedia: 0.254\u0026thinsp;\u0026plusmn;\u0026thinsp;0.586\u003c/p\u003e\n \u003cp\u003eMinor: -0.147\u0026thinsp;\u0026plusmn;\u0026thinsp;0.586\u003c/p\u003e\n \u003cp\u003eMinor\u003csup\u003e+\u003c/sup\u003e: -0.400\u0026thinsp;\u0026plusmn;\u0026thinsp;0.586\u003c/p\u003e\n \u003cp\u003eTime_Mid: 1.360\u0026thinsp;\u0026plusmn;\u0026thinsp;0.308\u003c/p\u003e\n \u003cp\u003eTime_Late: 2.966\u0026thinsp;\u0026plusmn;\u0026thinsp;0.308\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 13.043%;\"\u003e\n \u003cp\u003e0.433\u003c/p\u003e\n \u003cp\u003e-0.250\u003c/p\u003e\n \u003cp\u003e-0.683\u003c/p\u003e\n \u003cp\u003e4.408\u003c/p\u003e\n \u003cp\u003e9.618\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 17.6419%;\"\u003e\n \u003cp\u003e0.670\u003c/p\u003e\n \u003cp\u003e0.805\u003c/p\u003e\n \u003cp\u003e0.503\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 21.2793%;\"\u003e\n \u003cp\u003e6. log(Circular Standard Deviation)\u0026thinsp;~\u0026thinsp;Size class + (1|Colony)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 16.2939%;\"\u003e\n \u003cp\u003eMedia: -0.061\u0026thinsp;\u0026plusmn;\u0026thinsp;0.055\u003c/p\u003e\n \u003cp\u003eMinor: -0.063\u0026thinsp;\u0026plusmn;\u0026thinsp;0.055\u003c/p\u003e\n \u003cp\u003eMinor\u003csup\u003e+\u003c/sup\u003e: -0.002\u0026thinsp;\u0026plusmn;\u0026thinsp;0.055\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 13.043%;\"\u003e\n \u003cp\u003e-1.119\u003c/p\u003e\n \u003cp\u003e-1.149\u003c/p\u003e\n \u003cp\u003e-0.031\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 17.6419%;\"\u003e\n \u003cp\u003e0.268\u003c/p\u003e\n \u003cp\u003e0.255\u003c/p\u003e\n \u003cp\u003e0.975\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4. Inactive ants would slow down the movement of other ants in the same cluster, and its effect would differ by size class\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 21.2793%;\"\u003e\n \u003cp\u003e7. log(Mean speed over the next second of mobile ants)\u0026thinsp;~\u0026thinsp;Presence of inactive ants within the cluster * Size class\u0026thinsp;+\u0026thinsp;Time phase + (1|Colony / Recording / AntID)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 16.2939%;\"\u003e\n \u003cp\u003eCluster with inactive ants: -0.177\u0026thinsp;\u0026plusmn;\u0026thinsp;0.025\u003c/p\u003e\n \u003cp\u003eMedia: 0.098\u0026thinsp;\u0026plusmn;\u0026thinsp;0.046\u003c/p\u003e\n \u003cp\u003eMinor: 0.006\u0026thinsp;\u0026plusmn;\u0026thinsp;0.046\u003c/p\u003e\n \u003cp\u003eMinor\u003csup\u003e+\u003c/sup\u003e: -0.104\u0026thinsp;\u0026plusmn;\u0026thinsp;0.046\u003c/p\u003e\n \u003cp\u003eTime_Mid: -0.020\u0026thinsp;\u0026plusmn;\u0026thinsp;0.017\u003c/p\u003e\n \u003cp\u003eTime_Late: -0.139\u0026thinsp;\u0026plusmn;\u0026thinsp;0.017\u003c/p\u003e\n \u003cp\u003eCluster with inactive ants:Media: -0.025\u0026thinsp;\u0026plusmn;\u0026thinsp;0.035\u003c/p\u003e\n \u003cp\u003eCluster with inactive ants:Minor: -0.030\u0026thinsp;\u0026plusmn;\u0026thinsp;0.035\u003c/p\u003e\n \u003cp\u003eCluster with inactive ants:Minor\u003csup\u003e+\u003c/sup\u003e: -0.005\u0026thinsp;\u0026plusmn;\u0026thinsp;0.035\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 13.043%;\"\u003e\n \u003cp\u003e-7.145\u003c/p\u003e\n \u003cp\u003e2.151\u003c/p\u003e\n \u003cp\u003e-0.130\u003c/p\u003e\n \u003cp\u003e-2.284\u003c/p\u003e\n \u003cp\u003e-1.152\u003c/p\u003e\n \u003cp\u003e-7.972\u003c/p\u003e\n \u003cp\u003e-0.715\u003c/p\u003e\n \u003cp\u003e-0.851\u003c/p\u003e\n \u003cp\u003e-1.140\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 17.6419%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e0.047\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e0.036\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e0.250\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e0.475\u003c/p\u003e\n \u003cp\u003e0.395\u003c/p\u003e\n \u003cp\u003e0.888\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eModel selection\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWhen we compared the AIC values of the models, we found that the additive effect of time phase significantly improved model fit across most cases. Conversely, models incorporating interactions between time phase and size class did not demonstrate significant improvements over their respective additive counterparts (see supplementary material part S3 for full model comparison results). For maximum cluster size and circular standard deviation of inactive ants, however, time phase (additive or interactive) did not significantly improve model fit, leading to the selection of models containing only size class as an explanatory variable. Final model specifications for each response variable are detailed in Table 1.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eExploratory behavior\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eContrary to our first hypothesis, exploratory behavior did not differ consistently across size classes when analyzing the full dataset (Figure 1). Neither medias nor minors differed significantly from majors in exploration index, and no significant difference existed between medias and minors. Their exploratory behavior decreased significantly in the late phase compared to the early phase. These results suggest similar exploration levels across size classes, as detailed in Table 1 (Model 1).\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003e\u003cem\u003eAggregation behavior\u003c/em\u003e\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eIn contrast to exploratory behavior, aggregation patterns varied by the size classes (Table 1: Model 2; Figure 2a). Majors exhibited the highest aggregated fraction, followed by medias, and then minors, with significant differences between all pairs. While aggregated fraction remained stable in early and mid phases, it increased significantly in the late phase. Similarly, mean maximum cluster size differed significantly between majors and minors (Table 1: Model 3; Figure 2b), with majors forming the largest clusters and minors the smallest. Medias showed intermediate cluster sizes, differing significantly from minors but not majors. Time phase had no effect on maximum cluster size.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCluster entry/exit frequency did not vary by size class (Table 1: Model 4; Figure 2c). No significant differences were detected between size classes, including between medias and minors. However, transitions declined significantly over time, reaching their lowest frequency in the late phase.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eTemporal and spatial variation of inactive ants\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInactivity proportion did not differ across size classes (Table 1: Model 5; Figure 3a). Inactive time was comparable between all size classes, though inactivity increased significantly over time. Conversely, the spatial distribution of inactive ants showed no significant variation by size class or time phase (Table 1: Model 6; Figure 3b).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eEffect of inactive ants\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDespite uniform inactivity levels, the presence of inactive ants within clusters significantly reduced movement speed of nearby individuals (Table 1: Model 7; Figure 4). Speed was also lower in the late phase compared to the early phase, with no difference between mid and early phases. No significant interactions between inactive ants and size class were detected, indicating consistent slowing effects across all workers.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003eSize-independent Exploratory Behavior\u003c/h2\u003e\u003cp\u003eOur study found that \u003cem\u003eCamponotus japonicus\u003c/em\u003e foragers exhibit similar exploratory behavior across size classes when they encounter novel environments. This size independence was consistent over time. In all size classes, exploratory activity declined significantly in mid and late phases, which suggests an acclimation process of the colony with little size-specific variations. This may be attributed to the relatively weak task specialization in this species compared to others with more pronounced role differentiation (Lee, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e1998\u003c/span\u003e). In contrast to \u003cem\u003ePheidole\u003c/em\u003e species, where larger workers typically explore less (Detrain \u0026amp; Pasteels, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e1991\u003c/span\u003e), \u003cem\u003eC. japonicus\u003c/em\u003e majors engage in both nest defense and foraging. The temporal decline in exploration across all size classes may indicate a shared assessment strategy for novel environments, prioritizing initial investigation followed by reduced investment as familiarity increases.\u003c/p\u003e\u003cdiv id=\"Sec19\" class=\"Section3\"\u003e\u003ch2\u003eSize-dependent Aggregation Behavior\u003c/h2\u003e\u003cp\u003eOn the other hand, aggregation behavior in \u003cem\u003eCamponotus japonicus\u003c/em\u003e foragers was size-dependent, with larger ants forming bigger clusters and remaining within clusters for longer durations than smaller individuals. The significant increase in aggregated fraction in the late phase might show a temporal shift toward collective safety in numbers due to the prolonged exposure to the novel environment. This pattern is consistent with previous findings in \u003cem\u003ePheidole pallidula\u003c/em\u003e, where majors demonstrated a greater tendency to aggregate than minors (Sempo et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). One possible explanation is that majors possess a higher response threshold to minor disturbances, thereby reducing their risk of injury or mortality (Detrain \u0026amp; Pasteels, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e1991\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e1992\u003c/span\u003e). The higher production and maintenance costs associated with majors, compared to smaller workers (Wilson, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e1980\u003c/span\u003e), may promote aggregation as a protective strategy when navigating unfamiliar environments. Polyethism, including trophallaxis, may also contribute to aggregation behavior, with polymorphic workers engaging differently based on their roles (Bonavita-Cougourdan \u0026amp; Morel, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e1985\u003c/span\u003e). Furthermore, majors in both \u003cem\u003eP. pallidula\u003c/em\u003e and \u003cem\u003eC. japonicus\u003c/em\u003e may serve as repletes, who store food in their crop for the colony and stay inside the nest, further reinforcing their propensity to aggregate (Lachaud et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e1992\u003c/span\u003e; Lee, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e1998\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eHowever, our findings contrast with studies on other species exhibiting continuous polymorphism, such as \u003cem\u003eAtta sexdens rubropilosa\u003c/em\u003e and \u003cem\u003eSolenopsis interrupta\u003c/em\u003e, in which no significant size-dependent differences in aggregation behavior were observed (Depick\u0026egrave;re, Ram\u0026iacute;rez \u0026Aacute;vila, et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). This discrepancy may result from species-specific differences in worker roles and methodological differences in data collection. For example, \u003cem\u003eA. sexdens rubropilosa\u003c/em\u003e majors function primarily as forager-excavators rather than defenders, unlike the role differentiation observed in \u003cem\u003ePheidole\u003c/em\u003e and \u003cem\u003eCamponotus\u003c/em\u003e species. Additionally, previous studies often collected minors from nest interiors and majors from foraging areas (Depick\u0026egrave;re, Ram\u0026iacute;rez \u0026Aacute;vila, et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2008\u003c/span\u003e), potentially influencing interpretations of behavioral outcomes.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003eProperties and Effects of Inactive Ants\u003c/h2\u003e\u003cp\u003eOur study found that the proportion and spatial distribution of inactive ants were not correlated with body size, consistent with findings in other species (Charbonneau et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). However, inactivity proportion increased significantly over time, indicating a colony-level shift toward energy conservation as the novel environment became familiar. Inactive ants are known to be repletes in other ant species (Charbonneau et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Our result suggests that such roles are not necessarily restricted to larger ants, but may also be fulfilled by smaller individuals (B\u0026oslash;rgesen, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). Since inactive ants tend to remain near the nest center (Charbonneau et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), their presence may serve as a signal of safety for all size classes. The exact mechanisms through which inactive ants influence the behavior of others remain unclear, although chemical cues may play a role, as demonstrated in other social organisms, including bees (Cameron, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e1981\u003c/span\u003e; Gilbert et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2001\u003c/span\u003e), naked mole rats (Judd \u0026amp; Sherman, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e1996\u003c/span\u003e), bark beetles (Deneubourg et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e1990\u003c/span\u003e), and social amoebae (Mar\u0026eacute;e \u0026amp; Hogeweg, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2001\u003c/span\u003e).\u003c/p\u003e\u003cdiv id=\"Sec21\" class=\"Section3\"\u003e\u003ch2\u003eFuture Directions\u003c/h2\u003e\u003cp\u003eIn this study, we examined whether foragers of different size classes exhibit behavioral differences when introduced to a novel environment. However, our experimental design did not include mixed-size groups. Future research should investigate aggregation dynamics in mixed-size groups, which would better reflect natural conditions. Additionally, exploring the potential role of chemical cues emitted by inactive ants would be a valuable next step. Further studies should also consider the influence of abiotic factors, competition, and resource availability on exploratory and aggregation behaviors, as these environmental variables are known to significantly affect ant behavior (Wills et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Long-term observations across days or weeks could clarify whether the observed temporal patterns represent short-term adjustments or stable behavioral strategies. Although tracking individuals in wild colonies remains challenging, it is essential for developing a comprehensive understanding of size-related behavioral differences in polymorphic workers. Such investigations could yield insights into how these behaviors interact with the division of labor within colonies. Finally, understanding the social mechanisms underlying aggregation behavior may provide broader insights into the evolution of the division of labor in eusocial species. This knowledge can be applied across disciplines, including swarm robotics and sociology, contributing to a deeper understanding of collective behavior.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003eConflict of interest\u003c/h2\u003e\u003cp\u003eThe authors declare that there are no conflicting interests.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003eThis work was supported by the National Convergence Research of Scientific Challenges through the National Research Foundation of Korea funded by Ministry of Science and ICT (2021M3F7A1017476), Basic Science Research Program through the National Research Foundation of Korea funded by the Ministry of Education in 2020 (2020R1I1A1A01073669), and DGIST Start-up Fund Program (2023010191) of the Ministry of Science, ICT and Future Planning of Korea. This work was supported by the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea (BK21 FOUR).\u003c/p\u003e\u003ch2\u003eAuthor contributions\u003c/h2\u003e\u003cp\u003eH.J. devised the idea; H.J and W.S. designed the methodology, collected data, analyzed the data; P.G.J. and S.L. provided supervision and conceptual guidance; H.J. led the writing of the manuscript, but all authors contributed to writing the manuscript via critical feedback on analyses and the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e\u003cp\u003eWe thank Haeun Cho, Jinseok Park, Jonghyun Park, and Dr. Eun Ju Lee for their helpful discussions and constructive suggestions on earlier versions of the manuscript. We appreciate the Korea Safety Health Environment Foundation for the award of Graduate School Student Scholarship. We are also grateful to Dr. Noa Pinter-Wollman for generously providing laboratory space during the preparation of this work.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBates, D., M\u0026auml;chler, M., Bolker, B., \u0026amp; Walker, S. (2015). Fitting Linear Mixed-Effects Models Using lme4. \u003cem\u003eJournal of Statistical Software\u003c/em\u003e, \u003cem\u003e67\u003c/em\u003e(1), 1\u0026ndash;48. https://doi.org/10.18637/jss.v067.i01 \u003c/li\u003e\n\u003cli\u003eBonavita-Cougourdan, A., \u0026amp; Morel, L. (1985). Polyethism in social interactions in ants. \u003cem\u003eBehavioural Processes\u003c/em\u003e, \u003cem\u003e11\u003c/em\u003e(4), 425\u0026ndash;433. \u003c/li\u003e\n\u003cli\u003eB\u0026oslash;rgesen, L. (2000). 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Caste and division of labor in Erebomyrma, a genus of dimorphic ants (Hymenoptera: Formicidae: Myrmicinae). \u003cem\u003eInsectes Sociaux\u003c/em\u003e, \u003cem\u003e33\u003c/em\u003e(1), 59\u0026ndash;69. \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"insectes-sociaux","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"inso","sideBox":"Learn more about [Insectes Sociaux](http://link.springer.com/journal/40)","snPcode":"40","submissionUrl":"https://www.editorialmanager.com/inso/default2.aspx","title":"Insectes Sociaux","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"exploration, aggregation, inactivity, size class, Camponotus japonicus","lastPublishedDoi":"10.21203/rs.3.rs-7915365/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7915365/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAnts, as eusocial insects, rely on the division of labor shaped by factors such as age and body size, facilitating colony-level behavioral flexibility under novel conditions. In this study, we examined how body size is related to exploratory and aggregation behaviors, along with the role of inactivity, in foragers of the polymorphic ant \u003cem\u003eCamponotus japonicus\u003c/em\u003e exposed to a novel environment. Minor, media, and major workers were tested separately in a round arena, with behaviors filmed for an hour. While exploratory activity declined with time, it did not differ significantly among size classes. In contrast, aggregation behavior was size-dependent: larger workers aggregated more and formed bigger clusters than smaller workers, and aggregation generally increased in later acclimation phases. However, the timing of aggregation did not vary across size classes. The proportion of time inactive and the spatial distribution of inactive ants remained consistent across size classes, though overall inactivity increased over time. Furthermore, the presence of inactive ants within clusters consistently reduced the movement speed of other ants within the cluster, irrespective of their size classes. These findings suggest that behavioral variations in \u003cem\u003eC. japonicus\u003c/em\u003e may reflect functional specialization across worker sizes, promoting collective resilience when colonies encounter unfamiliar environments.\u003c/p\u003e","manuscriptTitle":"Exploration and Aggregation of Differently Sized Camponotus japonicus Worker Ants","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-07 12:10:48","doi":"10.21203/rs.3.rs-7915365/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major Revisions Needed","date":"2025-11-26T06:32:15+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"","date":"2025-10-28T14:33:02+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-28T10:32:43+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-10-24T08:03:17+00:00","index":"","fulltext":""},{"type":"submitted","content":"Insectes Sociaux","date":"2025-10-22T14:05:27+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"insectes-sociaux","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"inso","sideBox":"Learn more about [Insectes Sociaux](http://link.springer.com/journal/40)","snPcode":"40","submissionUrl":"https://www.editorialmanager.com/inso/default2.aspx","title":"Insectes Sociaux","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"08182276-5c5c-4e71-b417-1b41b7d4cc3f","owner":[],"postedDate":"November 7th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-04-20T16:00:12+00:00","versionOfRecord":{"articleIdentity":"rs-7915365","link":"https://doi.org/10.1007/s00040-026-01093-x","journal":{"identity":"insectes-sociaux","isVorOnly":false,"title":"Insectes Sociaux"},"publishedOn":"2026-04-18 15:56:58","publishedOnDateReadable":"April 18th, 2026"},"versionCreatedAt":"2025-11-07 12:10:48","video":"","vorDoi":"10.1007/s00040-026-01093-x","vorDoiUrl":"https://doi.org/10.1007/s00040-026-01093-x","workflowStages":[]},"version":"v1","identity":"rs-7915365","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7915365","identity":"rs-7915365","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}