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Frontiers in Ecology and Evolution ·

Volume 14 - 2026 | https://doi.org/10.3389/fevo.2026.1906000

Repeated changes in resource distribution mitigate inter-individual inequality of food acquisition in a captive group of large-billed crows

  • Yasuyuki Niki1*corresponding author
  • Mayu Takano1
  • Ei-Ichi Izawa1*corresponding author
  1. Department of Psychology, Keio University, Tokyo, Japan
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Abstract

Animal group foraging produces inequality in food acquisition among individuals due to variation in social characteristics such as dominance rank. External environmental factors, particularly the spatial distribution of food, can modulate the influence of these social features on individual foraging success. However, the effects of repeated changes in food distribution on social foraging inequality remain largely unexplored. Here, we investigated how such repeated environmental changes affect inequality in food acquisition in a captive group of large-billed crows ( Corvus macrorhynchos ). By repeatedly reallocating food quantity across three fixed feeding sites, we quantified changes in inequality using the Gini coefficient. In the short term, food acquisition became more unequal immediately after each change but recovered in subsequent sessions. In contrast, inequality declined over repeated reallocations, accompanied by an increase in group-level foraging efficiency. At the individual level, food acquisition was associated with sex, whereas the expected effect of dominance rank was not detected. These results provide experimental evidence that repeated changes in the spatial distribution of food were associated with a reduction in inequality in food acquisition within foraging groups, highlighting the importance of temporal environmental dynamics in resource distribution for social foraging.

Keywords

1 Introduction

Inequality is a defining feature of animal societies, including humans (Mattison et al., 2016; Smith et al., 2023; Strauss and Shizuka, 2022). While group living benefits individuals by efficiently detecting potential predators (Blumstein et al., 1999; Bertram, 1980) and finding food resources (Snijders et al., 2021), it also entails costs through intensified competition for limited resources. Competition over habitat, mating opportunities, and food leads to unequal resource acquisition among group members, driven by variation in social attributes and phenotypic traits such as kinship, age, sex, and dominance rank (de Waal and Tyack, 2003).

In group-foraging species, inequality in food acquisition arises because individuals compete for resources that are spatially and temporally heterogeneous and finite (Focardi and Pecchioli, 2005; Sansom et al., 2008). Dominance rank, based on an individual’s resource-holding potential, is a key factor contributing to such inequality (Drews, 1993; Parker, 1974). Individual variations in resource-holding potential influence priority of access to resources, leading to the formation of dyadic dominant-subordinate relationships and dominance rankings within a group.

Both theoretical and empirical studies have examined how dominance influences food acquisition in group-foraging contexts. Game-theoretical models predict that differences in competitive ability can drive producer-scrounger dynamics, whereby less competitive individuals discover food patches (producers) that are subsequently exploited by more competitive individuals (scroungers) (Barta and Giraldeau, 1998; Giraldeau and Caraco, 2000). Empirical studies across diverse taxa support this framework. For instance, in group-foraging capuchin monkeys (Cebus apella), subordinates initially benefit from discovering new food sources. However, this advantage is often transitory as dominants subsequently monopolize them upon arrival (di Bitetti and Janson, 2001). Such dominance-based tactics can generate inequality in food gain, with dominants typically securing greater overall intake than subordinates. Similar patterns have been observed in several group-foraging bird species, including geese (Branta leucopsis; Stahl et al., 2001) and corvids such as ravens (Corvus corax; Bugnyar and Kotrschal, 2002), northwestern crows (Corvus caurinus; Ha and Ha, 2003), Mexican jays (Aphelocoma ultramarina; Jablonski et al., 2007).

Extrinsic environmental factors, particularly the spatial distribution of food resources, also influence inequality by modulating dominance effects (Brown, 1964; Grant, 1993). When resources are spatially clumped, competition intensifies because access becomes limited, allowing dominant individuals to exclude subordinates and secure disproportionate gains. In contrast, when food is widely dispersed, monopolization becomes more difficult, reducing the advantages of dominants. Such effects have been reported in chacma baboons, Papio ursinus (Marshall et al., 2015) and ruddy turnstones, Arenaria interpres (Vahl et al., 2005), where differences in foraging success between dominant and subordinate individuals depend on the spatial distribution of food resources. Temporal variation in resource availability may also influence these dynamics. For example, an experimental study in convict cichlids (Cichlasoma nigrofasciatum) showed that dominant individuals gained disproportionate access to food when the spatial arrangement of patch rewards remained stable across trials, approximating an ideal despotic distribution (Grand and Grant, 1994). This study highlights how competitive outcomes can depend on the temporal structure of resource distribution under controlled conditions.

However, such temporal structure in resource distribution in natural systems is rarely static and often unfolds through continuous or repeated changes over time. Natural foraging environments are therefore inherently dynamic and often influenced by ongoing temporal processes. Seasonal changes can systematically alter the type, abundance, and spatial distribution of food resources by influencing primary production and availability (Pettorelli et al., 2005; Yang and Rudolf, 2010). At shorter time scales, resource landscapes are further modified through exploitation: repeated foraging reduces local resource availability, prompting individuals to shift to alternative patches. Previously depleted patches may subsequently recover when foraging pressure is reduced, generating cycles of depletion and renewal that continually reshape resource distributions (Stephens and Krebs, 1986; Sutherland, 1995). Such temporally structured dynamics can redistribute opportunities for access and competition among group members, potentially altering the costs and benefits of dominance-based foraging strategies. When profitable patches change over time, dominant individuals may be unable to consistently monopolize resource-rich locations, whereas subordinates may gain access to newly favorable opportunities. These processes can therefore modify, and in some cases reduce inequality in resource acquisition within groups. Moreover, repeated environmental changes over extended time scales may produce cumulative effects on access to resources and the distribution of foraging gains.

Despite these considerations, most experimental studies of social foraging have examined responses to experimentally structured resource conditions over relatively short time scales. Current understanding of inequality in social foraging remains largely based on short-term observations in relatively stable environments. It remains unclear how repeated, longer-term environmental changes influence inequality within social groups, and whether dominance-associated disparities persist, weaken, or are redistributed under such dynamic conditions.

Corvids, particularly crows and ravens, are well suited for addressing this question due to their well-documented group-foraging behavior (Boucherie et al., 2022; Heinrich and Marzluff, 1991) and reliance on dominance-based interactions and producer–scrounger tactics (Bugnyar and Kotrschal, 2002; Ha and Ha, 2003). Large-billed crows (Corvus macrorhynchos) are especially suitable for experimental investigation, as they form stable, linear dominance hierarchies in captivity, with males typically dominant over females (Izawa and Watanabe, 2008; Miyazawa et al., 2020; Ode et al., 2015). Importantly, unpaired individuals are non-territorial and forage in flexible groups that range over large areas, often spanning tens of square kilometers (Izawa, 2011). Under such conditions, access to food resources is not tied to fixed locations or defended territories, but instead depends on movement, encounter dynamics, and social interactions within the group. These characteristics make large-billed crows a useful model for examining how temporally structured changes in resource distribution influence inequality in social foraging systems where resources are not monopolized through territorial defense.

In this study, we investigated how repeated, structured changes in the spatial allocation of food resources across feeding sites affect inequality in resource acquisition and foraging performance within a captive group of large-billed crows. We established three feeding sites that differed in food availability and repeatedly altered which location provided rich, intermediate, or no food. In the present study, we use the term “environmental change” specifically to refer to repeated changes in the spatial distribution of food resources among feeding sites, while holding total food abundance and spatial arrangement constant. This design therefore provided a simplified model of temporally structured variation in resource distribution, including the depletion of profitable patches and the recovery of previously less profitable ones. The total amount of food in each trial was held constant throughout the experiment, allowing us to single out the effect of changing spatial allocation from overall abundance.

We assessed the effects of these repeated food-distribution changes at both group and individual levels. At the group level, we quantified how inequality in food acquisition changed over time using the Gini coefficient, alongside measures of foraging efficiency. At the individual level, we tested whether cumulative food acquisition across repeated environmental shifts was associated with dominance rank or sex. Together, these analyses allow us to evaluate how ongoing temporal dynamics in resource distribution interact with social structure to shape inequality within group-living animals.

2 Materials and methods

2.1 Animals

We used 10 juvenile large-billed crows (5 males and 5 females), aged 2 years and weighing 640–820 g. Sex was identified by DNA analysis of blood samples. The birds were captured as free-floater yearlings in the Yokohama and Kamagaya areas between October and December 2022 under authorization from Japan’s Ministry of the Environment (license no. 041454056). No individuals were paired during the experimental period. The captured birds were group-housed in an outdoor aviary measuring 5 × 10 × 3 m. The aviary had sufficient wooden perches for all the birds to perch simultaneously, and tubs of water were supplied for drinking and bathing. Slight food deprivation, specifically no food 1 h before the start of daily sessions, was used to encourage the crows to forage in the experimental setting. Except during deprivation periods and experimental trials, food (dried dog food, eggs, and vitamin and mineral supplements) was always available in each compartment.

2.2 Ethical statement

Ethical approval and relevant permissions for the experimental protocols, including slight food deprivation, and housing conditions, were obtained from the Animal Care and Use Committee of Keio University (No. A2022-348). The study adhered to the Japanese National Regulations for Animal Welfare and the Guidelines for Proper Conduct of Animal Experiments in Japan.

2.3 Dominance rank assessment

2.3.1 Observations of agonistic interactions

Before starting the group foraging experiment, the dominance ranks of individuals in the group were established using social interaction data collected through daily observations from April to June 2023. Daily video observations were conducted using two omnidirectional video cameras (PIXPRO sp360–4 K; Eastman Kodak Co., Rochester, NY, USA), which were placed at the center of the aviary ceiling to capture the entire space, including perches and floors, and to allow collection of all social interactions. Video recordings were conducted for 30 min, twice daily, from 8:00–9:00 and 14:00–15:00. A researcher (M. T.) used the behavior coding software BORIS version 7 (Friard and Gamba, 2016) to code agonistic interactions offline from video-recorded data. Dominance rank was determined using a total of 8.5 h of video data. We defined an agonistic interaction as a submission display (submissive begging) in which the recipient avoided or retreated from the antagonist’s aggressive behavior. Aggressive behaviors included pecking, kicking, and displacement, while submissive behaviors included avoiding and retreating (Miyazawa et al., 2020; Ode et al., 2015). In each agonistic interaction, the bird that demonstrated submission, avoidance, or retreat was deemed the loser, whereas the other bird was the winner. We noted the winners and losers, as well as the identities of the combatants.

2.3.2 Determination of group dominance rank

Following confirmation of the linearity of the dominance hierarchy (de Vries, 1995), the I & SI approach, which aims to determine the optimal ordering of dominance rank (de Vries, 1998), was used to identify the dominance rankings of individuals in the experimental group. This method iteratively adjusts rank sequences to meet two criteria: (1) minimizing the number of inconsistent dyads (Inconsistency, ‘I’), where the observed dominance relationship conflicts with that expected from the overall hierarchy; and (2) minimizing rank distance between individuals within inconsistent dyads (strength of inconsistency, ‘SI’) (de Vries, 1995; de Vries and Appleby, 2000). In each dyad, the individual winning ≥ 66% of their agonistic interactions was treated as dominant, whereas the other was considered the subordinate. In dyads with just four or three agonistic interactions in daily observation data, the individual who won ≥ 75% or 100% of the interactions was considered dominant, respectively. For dyads that did not match the winning (losing) rate criteria, we considered the birds’ relationship to be tied with no dominance. If two or fewer agonistic interactions were seen in a dyad, the relationship between the two birds was deemed unknown. The thresholds used to determine the dominance relationships for each dyad followed criteria that have been consistently applied in our previous studies of large-billed crow (Izawa and Watanabe, 2008; Ode et al., 2015; Miyazawa et al., 2020). A sensitivity analysis using a more conservative criterion (≥ 75% wins for all dyads) produced the same overall rank order.

A dominance relationship matrix was generated by allocating 1 and 0 to the dominant and subordinate individuals in a dyad, respectively, and 0.5 to both individuals in tied dyads. We quantified the linearity of the dominance hierarchy using h’, an index of rank linearity (de Vries, 1995). The chi-square test was used to determine the statistical significance of linearity at a 1% level. Following confirmation of linearity, the dominance ranks of individuals were computed using the I & SI procedures (de Vries, 1998). A total of 458 agonistic interactions were recorded, with all 45 dyads showing unambiguous dominance patterns. We found significant linearity in dominance ranks across the 10 individuals (h’ = 0.95, χ² = 58.0, df = 20, p < 0.01). Using the I & SI method, we determined the ranks of 10 birds with only one inconsistency between the 4th- and 7th-ranked males, whose dominance relationship was contrary to their rank order (the 7th-ranked male was dominant over the 4th-ranked male).

2.4 Experimental procedure

2.4.1 Feeder box and experimental design

To examine the effects of fluctuating environmental conditions, specifically repeated changes in the spatial food distribution, on foraging performance, we conducted a group foraging experiment in which the association between food quantity (none, middle, or rich) and locations was changed regularly over 3 months from October to December 2023. The experiment took place in one half of a home aviary, which was separated by a mesh door (5 × 5 × 3 m; Figures 1A, B).

Experimental settings. **(A)** Feeding boxes (I, II, III) were placed at three locations on the floor of the aviary. **(B)** Bird behavior was video recorded from above with a spherical camera and from the sides with three video cameras outside the aviary. **(C)** The number of cups containing food varied depending on the box condition: 12 cups in RICH, 4 cups in MID, and 0 cups in ZERO. The positions of food-containing cups in each box were pseudo-randomly varied between trials. **(D)** Box conditions at the three locations were maintained within blocks and moved to the next location in a clockwise direction between the blocks.
Figure 1. Experimental settings. (A) Feeding boxes (I, II, III) were placed at three locations on the floor of the aviary. (B) Bird behavior was video recorded from above with a spherical camera and from the sides with three video cameras outside the aviary. (C) The number of cups containing food varied depending on the box condition: 12 cups in RICH, 4 cups in MID, and 0 cups in ZERO. The positions of food-containing cups in each box were pseudo-randomly varied between trials. (D) Box conditions at the three locations were maintained within blocks and moved to the next location in a clockwise direction between the blocks.

Artificial foraging patches were created using three identical plastic feeder boxes in the shape of a regular octagonal prism (side length, 16.6 cm; height, 10.0 cm) mounted on an octagonal plate (side length, 24.9 cm). At the top of each box, 16 holes (4.5 cm in diameter) were placed at 6-cm intervals to accommodate a plastic food cup. Each cup contained either a piece of cheese (0.3 g) or nothing (empty), depending on the box condition, and the top of every cup was tightly covered with opaque aluminum foil. This procedure prevented visual inspection of cup contents and was intended to minimize potential olfactory cues (Wascher et al., 2015; Kumar et al., 2025). The subject crows had to break the foil with their beaks to obtain food.

Each experimental trial included three feeder boxes placed 2.5 m apart on the floor of the aviary (box locations I, II, and III in Figures 1A, B). The boxes at these locations were identical in shape and color, but the amount of food contained in the cups (the different conditions) varied by condition: ‘RICH’, where 12 out of 16 cups contained food (Figure 1C); ‘MID’, where 4 out of the 16 cups contained food; and ‘ZERO’, where no cup contained food. In the RICH and MID conditions, the cups containing food in each box were arranged in a pseudo-random manner between trials.

The experiment included 240 trials divided into 6 blocks of 10 sessions (1 session consisted of 4 trials). We conducted two sessions from 9:00−12:30 and 13:30–16:30, and the intervals between experimental days ranged from 0 to 3 days. The relationship between food condition and box locations rotated clockwise over the blocks (Figure 1D). Within each block, this assignment was held constant; across the six blocks, each of the three locations served as the RICH, MID, and ZERO condition twice. Crows in each block had 10 sessions under stable conditions. However, after the end of each block, the food-location assignment was changed at the start of the next session without advance cues. Each trial began when the researcher (Y. N.) removed the covers from the three feeder boxes, allowing the crows to forage, and exited the experimental room. The trial was terminated 3 min after it began when the researcher removed the boxes (Videos S1–S3). The inter-trial interval was approximately 20 min.

2.5 Coded behavior

The behavior of the crows during the trials was video-recorded using an omnidirectional camera (SP360-4K, Kodak) on the ceiling of the aviary and three video cameras (HD CX535, Sony, New York, NY, USA) placed outside the aviary (Figure 1B). The coded behaviors were ‘staying on a box’, ‘opening a cup’, and ‘obtaining a food item’ (Table 1). ‘Opening a cup’ was coded up to a maximum of 48 times per trial (3 boxes × 16 cups). Crows that investigated an already-opened cup were not considered. ‘Obtaining a food item’ was recorded up to 16 times (RICH: 12 cups, MID: 4 cups). We recorded the individual identity and timing of these actions. The time resolution for behavioral coding was approximately 0.3 s based on the video frame. The software BORIS v. 7 (Friard and Gamba, 2016) was used for behavior coding. Aggressive interactions during feeding trials were not analyzed because they occurred only rarely (< 1.5 instance per trial) in the group and were unlikely to substantially affect foraging performance.

Table 1. Coded behaviors.

BehaviorDefinition
Staying on a boxTouching a feeder box including the bottom plastic board by any part of a bird’s body (i.e., bill or foot).
Opening a cupBreaking the lid of a cup by pecking.
Obtaining a food itemPicking up a food item from the cup containing food.

2.6 Analysis

Based on the observed behaviors, we derived indices of group foraging for each trial, such as ‘number of successful foragers’ and ‘foraging duration’, to evaluate foraging efficiency at the group level (Table 2). The ‘number of successful foragers’ was defined as the number of crows that obtained at least one food item (one piece of cheese) in a trial. ‘Foraging duration’ was defined as the time between the first ‘opening a cup’ and the final (16th) event of ‘obtaining a food item’, regardless of box condition or individual. In trials in which not all 48 cups were opened within 3 min, we calculated the time from the initial ‘staying on a box’ event to the last ‘opening a cup’ event. We also calculated a Gini coefficient separately for each trial to evaluate inequality in distribution of food acquisition among 10 individuals, including those that acquired no food. The Gini coefficient was calculated from the Lorenz curve (Lorenz, 1905) derived from the cumulative ratio of subjects to food gains. Each trial therefore yielded one Gini coefficient, ranging from 0 (perfectly equal) to 1 (maximum unequal). We calculated and adjusted the Gini coefficient to account for the small sample size using the ‘ineq’ R package (version 0.2.13; Zeileis, 2014). Out of 240 trials, 18 were excluded from the analysis: 1 due to video recording issues, 1 where not all the food was consumed, and 16 from the 7th to 10th sessions of the sixth block following the removal of a male individual due to an accidental injury. These exclusions were determined before analysis. The latter trials were excluded because the removal of an individual prevented reliable estimation of a stable dominance hierarchy comparable to that used in the analyses. Thus, the analyses were conducted using 222 trials from 56 sessions.

Table 2. Indices of group foraging.

IndexDefinition
Gini coefficientA measure of inequality in food-gain distribution, calculated from the Lorenz curve obtained from the cumulative ratio of the number of subjects and food gains. This index ranged between 0 and 1.
Number of successful foragersThe number of individuals that gained one or more food item(s) in a trial.
Foraging durationThe duration from the first ‘opening a cup’ to the last ‘obtaining a food item’ event in a trial.

2.6.1 Group-level analysis

We analyzed group-level foraging performance and food-gain inequality using generalized linear models (GLMs). Each foraging index was modeled separately as a response variable: the Gini coefficient (with Gaussian error distribution and identity link function), the number of successful foragers (with Poisson error distribution and log link function), and foraging duration (with Gamma error distribution and log link function).

For each response variable, we performed two sets of analysis at distinct timescales: short-term responses to changes in the spatial distribution of food and long-term cumulative effects. Short-term responses were assessed at five change events corresponding to transitions between blocks. To examine the immediate responses to food-distribution change, sessions were classified into three phases: the pre-change phase (the 10th session of block N), representing baseline; the post-change phase (the 1st session of block N + 1), representing the immediate response; and the post-change +1 phase (the 2nd session of block N + 1), representing early adjustment following the change. To test whether immediate responses varied across repeated food-distribution change, change-event number (1–5) was included as a numeric predictor and mean-centered. The full model therefore included phase, centered change-event number, and their interaction.

Long-term effects of repeated environmental changes were analyzed using GLMs including block number, session number, and their interaction.

2.6.2 Individual-level analysis

We analyzed the effects of environmental change and individual social characteristics, such as dominance rank and sex, on foraging performance using generalized linear mixed models (GLMMs). The number of food gains per individual was used as the response variable, with individual identity as a random effect. Fixed effects included session number, block number, dominance rank, sex, and their interactions. Continuous predictors (dominance rank, block number, and session number) were standardized using design-based means and standard deviations to facilitate model convergence and interpretation. Models were fitted with a Poisson error distribution and log link function. Because dominance rank and sex were strongly associated in large-billed crows (e.g., Miyazawa et al., 2020), models including both predictors showed high multicollinearity and unstable estimates. We therefore analyzed rank-based and sex-based models separately and selected the best model from each set. To aid interpretation of the sex-specific temporal patterns indicated by the block × sex interaction, we fitted additional stratified GLMMs separately for males and females. These stratified models used the same response variable and model structure as the main analysis, and models with and without block were compared.

All analyses were conducted using the statistical software R (version 4.5.2; R Core Team, 2025). Before model fitting, we assessed overdispersion and multicollinearity among fixed effects using the ‘performance’ package (version 0.15.3; Lüdecke et al., 2021). GLMMs were fitted in the ‘lme4’ package (version 1.1.38; Bates et al., 2015). Model selection was based on Akaike’s Information Criterion (AIC), with the model showing the lowest AIC selected as the best-fitting model, using the ‘MuMIn’ package (version 1.48.11; Bartoń, 2025). Wald 95% confidence intervals were calculated using the ‘parameters’ package (version 0.29.0; Lüdecke et al., 2020), and model predictions for figures were generated using the ‘ggeffects’ package (version 2.3.2; Lüdecke, 2018). Model selection was based on AIC. Model comparison results, including ΔAIC values and Akaike weights, are provided in Supplementary Table 1.

3 Results

3.1 Group level

3.1.1 Short-term effects

The GLM analysis for the Gini coefficient selected a model including change-event, phase, and their interaction as the best-fitting model (AICbest = -107.018, AICnull = -85.250; Table 3 and Figure 2A). The Gini coefficient was significantly higher in the post-change phase than in the pre-change phase (β = 0.157, SE = 0.029, p <.001), indicating that food-gain inequality increased immediately after changes in the spatial distribution of food. In contrast, the post-change +1 phase did not differ clearly from the pre-change phase (β = 0.051, SE = 0.029, p = .086), suggesting that the immediate increase was not maintained in the following session. Change-event number had a negative effect (β = -0.040, SE = 0.015, p = .009), indicating that baseline inequality declined across repeated environmental changes. The interaction between change-event number and the post-change phase was marginally positive (β = 0.042, SE = 0.021, p = .050), suggesting that the immediate increase in inequality did not diminish over repeated changes (Figure 2A; Supplementary Videos S1–S3).

Table 3. Outputs of the best model from GLMs in short-term effect analysis.

IndexVariablesEstimateS.E.95% CI([lower, upper])Test statisticp
Gini coefficient
Intercept0.4900.021[0.449, 0.532]**t = 23.566**<.001
Change event-0.0400.015[-0.069, -0.010]**t = -2.691**0.009
Phase
Post0.1570.029[0.098, 0.216]**t = 5.334**<.001
Post+10.0510.029[-0.008, 0.110]t = 1.7470.086
Change event × Phase
Post0.0420.021[0.000, 0.083]t = 2.0030.050
Post+10.0260.021[-0.016, 0.068]t = 1.2520.216
Number of successful foragers
Intercept1.9120.05[1.815, 2.009]**z = 38.526**<.001
Foraging duration
Intercept2.3240.117[2.089, 2.559]**t = 19.807**<.001
Change event-0.060.048[-0.156, 0.036]t = -1.2450.218
Phase
Post0.7660.166[0.433, 1.098]**t = 4.614**<.001
Post+10.3060.166[-0.026, 0.639]t = 1.8450.070

Bold indicates statistically significant results.

Short-term effects of food-distribution change. **(A)** Food-gain inequality (Gini coefficient) increased immediately after changes in the spatial distribution of food. **(B)** No clear short-term effect was observed in the number of successful foragers. **(C)** Foraging duration (time required to consume food items) increased immediately after changes. Pre: the 10th session of each block (immediately before change); Post: 1st session (immediately after change); Post +1: 2nd session (early adjustment phase).
Figure 2. Short-term effects of food-distribution change. (A) Food-gain inequality (Gini coefficient) increased immediately after changes in the spatial distribution of food. (B) No clear short-term effect was observed in the number of successful foragers. (C) Foraging duration (time required to consume food items) increased immediately after changes. Pre: the 10th session of each block (immediately before change); Post: 1st session (immediately after change); Post +1: 2nd session (early adjustment phase).

For the number of successful foragers, model selection identified the null model as the best-fitting model (AICbest(null) = 239.211; Table 3). This suggests that the number of individuals obtaining food did not show a detectable short-term response to change in food distribution (Figure 2B).

The best-fitting model for foraging duration included change-event number and phase (AICbest = 392.170, AICnull = 414.226; Table 3). Foraging duration was significantly longer in the post-change phase than in the pre-change phase (β = 0.766, SE = 0.166, p <.001), indicating a temporary increase in the time required to consume food following changes in the spatial distribution of food (Figure 2C). The post-change +1 phase showed only a non-significant tendency to be longer than the pre-change phase (β = 0.306, SE = 0.166, p = .070), suggesting that this effect was not maintained into the subsequent session. Change-event number had no significant effect (β = -0.060, SE = 0.048, p = .218), indicating no systematic change in baseline foraging duration across changes in food distribution.

3.1.2 Long-term effects

The GLM for the Gini coefficient selected a model including session, block, and their interaction as the best-fitting model (AICbest = -450.861, AICnull = -386.852; Table 4, Figure 3A). The session × block interaction was significantly negative (β = -0.003, SE = 0.001, p = .009), whereas the main effects of session and block were not significant. This indicates that the within-block decline in the Gini coefficient became steeper in later blocks, suggesting that food-gain inequality progressively decreased as repeated food-distribution changes accumulated.

Table 4. Outputs of the best model from GLMs in long-term effect analysis.

IndexVariablesEstimateS.E.95% CI([lower, upper])Test statisticp
Gini coefficient
Intercept0.6470.028[0.591, 0.703]**t = 22.839**<.001
Block-0.010.007[-0.025, 0.004]t = -1.4010.163
Session0.0010.005[-0.008, 0.010]t = 0.2230.824
Block × Session-0.0030.001[-0.006, -0.001]**t = -2.635**0.009
Number of successful foragers
Intercept1.6320.085[1.466, 1.798]**z = 19.304**<.001
Block0.0550.016[0.023, 0.086]**z = 3.370**<.001
Session0.0140.009[-0.004, 0.032]z = 1.5020.133
Foraging duration
Intercept3.610.148[3.319, 3.901]**t = 24.433**<.001
Block-0.1780.029[-0.236, -0.121]**t = -6.110**<.001
Session-0.0710.017[-0.104, -0.038]**t = -4.244**<.001

Bold indicates statistically significant results.

Long-term effects of repeated changes in the spatial distribution of food. **(A)** The food-gain inequality (Gini coefficient) decreased across blocks, with a steeper within-block decline. **(B)** The number of successful foragers increased across blocks. **(C, D)** Foraging duration decreased both across sessions and blocks. Plots represent trials. Solid lines show model predictions from the best-fitting models, and shaded areas represent 95% confidence intervals.
Figure 3. Long-term effects of repeated changes in the spatial distribution of food. (A) The food-gain inequality (Gini coefficient) decreased across blocks, with a steeper within-block decline. (B) The number of successful foragers increased across blocks. (C, D) Foraging duration decreased both across sessions and blocks. Plots represent trials. Solid lines show model predictions from the best-fitting models, and shaded areas represent 95% confidence intervals.

For the number of successful foragers, model selection identified a model including session and block as the best-fitting model (AICbest = 865.462; AICnull = 873.937; Table 4). The effect of session was not significant (β = 0.014, SE = 0.009, p = .133), whereas block had a significant positive effect (β = 0.055, SE = 0.016, p <.001; Figure 3B). This indicates that the number of individuals obtaining food increased across experimental blocks.

The best-fitting model for foraging duration included session and block (AICbest = 1474.716, AICnull = 1548.145; Table 4). Both session (β = -0.071, SE = 0.017, p <.001; Figure 3C) and block (β = -0.178, SE = 0.029, p <.001; Figure 3D) had significant negative effects, indicating that the time required to consume all food items decreased both within and across experimental blocks.

3.2 Individual level

Separate GLMMs examining the effects of dominance rank or sex on food gain identified a model including block, sex, and their interaction as the best-fitting model, whereas models including dominance rank were not supported (AICbest = 6470.365, AICnull = 6475.008; Table 5 and S2; Figure 4). In this model, block had a significant positive effect, whereas the block × sex interaction was significantly negative. With females as the reference level in the model, this indicates that food acquisition increased across blocks in females, while this increase was significantly weaker in males. Stratified GLMMs supported this sex-specific pattern. In males, the model including block was selected (AICbest = 3014.412, AICnull = 3017.666), and block had a significant negative effect (β = -0.035, SE = 0.015, p = .022; Table 6). In females, on the other hand, the best-fitting model including block (AICbest = 3449.452, AICnull = 3451.993) showed a significant positive effect (β = 0.030, SE = 0.014, p = .033; Table 6). Together, these results indicate that food acquisition decreased in males but increased in females across experimental blocks.

Table 5. Outputs of the best GLMM testing the effect of sex on food gain.

VariablesEstimateS.E.95% CI([lower, upper])Test statisticp
Intercept0.4070.767[-1.097, 1.911]z = 0.5310.596
block0.0560.026[0.005, 0.108]**z = 2.133**0.033
Sex (male)-1.0331.099[-3.186, 1.120]z = -0.9400.347
Block× Sex (male)-0.1210.039[-0.197, -0.045]**z = -3.130**0.002

Bold indicates statistically significant results.

Sex-specific effects on food gain. Females showed an increase in food gains across blocks, whereas males showed a weaker or decreasing trend. Plots represent trials (jittered by individuals). Solid lines show model predictions from the best-fitting model.
Figure 4. Sex-specific effects on food gain. Females showed an increase in food gains across blocks, whereas males showed a weaker or decreasing trend. Plots represent trials (jittered by individuals). Solid lines show model predictions from the best-fitting model.

Table 6. Stratified GLMMs fitted separately to the male and the female datasets.

VariablesEstimateS.E.95% CI ([lower, upper])Test statisticp
Males
Intercept-0.7461.303[-3.301, 1.808]z = -0.5730.567
block-0.0350.015[-0.065, -0.005]**z = -2.291**0.022
Females
Intercept0.3060.261[-0.205, 0.817]z = 1.1740.240
block0.0300.014[0.002, 0.058]**z = 2.133**0.033

Bold indicates statistically significant results.

4 Discussion

In this study, we examined how repeated changes in the spatial allocation of food resources across feeding sites affect inequality in food acquisition in a group of large-billed crows. Our results revealed three key patterns. First, inequality in food acquisition increased immediately after changes in the spatial distribution of food but declined progressively over repeated changes. Second, this long-term reduction in inequality was associated with a gradual increase in food acquisition by females. Third, contrary to expectations based on previous studies of dominance-related inequality, we did not detect a significant effect of dominance rank on individual food acquisition. Together, these findings indicate that patterns of resource acquisition are not solely determined by stable social hierarchies but can be dynamically reshaped over time under repeated environmental change. In particular, the results highlight that individual differences in food acquisition may diminish over longer temporal scales, even in species with well-defined dominance structures.

The contrast between short-term and long-term responses is particularly noteworthy. Immediately after changes in the spatial distribution of food, inequality temporarily increased, indicating that some individuals were able to rapidly exploit newly favorable conditions. However, the present analyses do not allow us to determine whether these temporary increases in inequality were consistently driven by the same individuals across change events or by different individuals on different occasions. The former would suggest stable individual differences in the ability to exploit redistributed resources, whereas the latter could arise from variation in which individuals first encountered profitable feeding sites. Nevertheless, this effect was not sustained, and repeated changes caused a gradual reduction in disparities among individuals. This pattern suggests that short-term advantages in resource acquisition do not necessarily translate into long-term inequality when environmental conditions continue to change.

The long-term reduction in inequality appears to have been driven, at least in part, by sex-specific changes in food acquisition. Females gradually increased their food gains across blocks, whereas males did not. One possible explanation is that the determinants of foraging success in the present experiment differed from those typically assumed in dominance-based competition. The total amount of food in each trial was constant and limited to fewer than two items per individual, and as foraging efficiency increased, most food items were consumed within the first several seconds of each trial. Under such conditions, the benefits of attempting to monopolize food through aggression may have been reduced relative to the benefits of rapid access, because temporally clumped or rapidly depleted resources are generally more difficult to monopolize (Grant, 1993; Grant and Kramer, 1992).

Several alternative explanations may account for the progressive increase in food acquisition by females. Females may have responded differently to repeated changes in resource distribution, become more efficient at locating profitable feeding sites, or adopted exploratory and foraging strategies that differed from those of males. Previous studies have reported sex differences in learning, responses to novelty, and problem-solving behavior in birds (Brodin and Urhan, 2015; Danel et al., 2024). However, the present data do not allow us to distinguish among these possibilities.

Aggressive interactions may nevertheless have influenced access to resources. In large-billed crows, males are more aggressive than females, whereas aggression directed toward females is less frequent than that among males (Izawa and Watanabe, 2008; Miyazawa et al., 2020; Ode et al., 2015). Females may therefore have been less likely to be displaced from feeding sites. The combination of rapid exploitation being advantageous and females being less likely to be excluded may help explain why sex emerged as a significant predictor of food acquisition in this study.

Importantly, however, the absence of a detectable effect of dominance rank does not necessarily indicate that dominance is unimportant in shaping resource acquisition. In large-billed crows, males are typically dominant over females, and the strong association between sex and dominance rank in this group limited the ability of our analysis to disentangle their effects statistically. In addition, this study was based on a single social group, and the sample size at the group level was therefore limited. Future studies including multiple groups with varying social structures and larger sample sizes will be necessary to more rigorously assess the relative contributions of dominance rank and sex. Such designs would enable simultaneous modeling of these factors and provide a clearer understanding of their respective roles. The present results therefore complement, rather than reject, previous views that dominance contributes to inequality in resource acquisition.

Our results also differ from previous experimental work showing that dominant individuals can monopolize food under temporally structured resource conditions. In convict cichlids, dominant individuals gained disproportionate access to food when reward distribution across feeding sites remained stable, approximating an ideal despotic distribution (Grand and Grant, 1994). In the present study, by contrast, repeated changes in the allocation of food across feeding sites did not lead to sustained dominance-based monopolization, even though the pattern of change was regular. One possible reason is that the broader socio-ecological factors differed substantially between the two study species. Whereas convict cichlids exploit defendable feeding patches under conditions more closely linked to territorial competition, the juvenile large-billed crows used in this study, similarly to ravens and other crow species, are non-territorial foragers in a mobile social group, in which access to food may depend less on persistent site defense and more on rapid movement, encounter dynamics, and short-term competitive interactions (Boucherie et al., 2022; Heinrich and Marzluff, 1991; Izawa, 2011). Another possibility is that the observed reduction in inequality reflected behavioral adaptation resulting from repeated experience with the experimental conditions. The regular rotation of food-rich sites may have gradually altered how individuals responded to the foraging environment over time. Individuals may have become increasingly familiar with the feeder boxes, improved their efficiency in searching for food, or become better at locating profitable feeding sites (Sutherland and Gass, 1995). Because all individuals repeatedly experienced the same changing resource conditions throughout the experiment, the present design does not allow us to distinguish between the effects of repeated changes in resource distribution and those of behavioral adaptation. In addition, because the present study did not directly assess learning, prediction, behavioral flexibility, or other cognitive processes, these possibilities remain to be tested.

More generally, our findings are consistent with previous work showing that the effects of dominance on foraging success depend strongly on environmental conditions and resource defensibility. For example, rank-related differences in foraging outcomes become more pronounced when resources are highly clumped and monopolizable, whereas more equal access can emerge when resources are less defensible or when social interactions facilitate information use and collective exploitation (Lee and Cowlishaw, 2017; Marshall et al., 2015). Equality and efficiency are therefore not necessarily opposing outcomes in social foraging systems.

Overall, our findings demonstrate that repeated environmental changes can gradually reduce inequality in food acquisition over the long term, as indicated by the decline in the Gini coefficient across experimental blocks. This pattern was associated with a progressive increase in food acquisition by females. Although the underlying mechanisms remain to be tested directly, this sex-specific pattern may reflect differences in social interactions under the foraging conditions used here. More broadly, these results highlight the importance of incorporating temporally dynamic resource conditions into studies of group foraging. The observed reduction in inequality suggests that patterns of resource acquisition may change over time under repeated changes in resource distribution. However, the present study was conducted using a single captive social group, and caution is therefore required when generalizing the observed pattern to other groups or populations. Replication across multiple groups that differ in social composition and dominance structure will be necessary to determine the generality of the reduction in inequality observed here.

The experimental framework used here provides a useful basis for future studies of inequality in social foraging. Extending this approach to multiple groups and manipulations of resource abundance, predictability, and spatial allocation should help clarify the mechanisms through which environmental dynamics influence inequality. More generally, integrating internal social factors with external environmental variation may lead to a deeper understanding of how inequality emerges, persists, and is reduced in animal societies, including humans.

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Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fevo.2026.1906000/full#supplementary-material

Supplementary material Supplementary material at the source

Supplementary material Supplementary material at the source

Supplementary material Supplementary material at the source

Supplementary material Supplementary material at the source

Supplementary material Supplementary material at the source

Footnotes

Edited by: Anindita Bhadra, Indian Institute of Science Education and Research Kolkata, India

Reviewed by: Masaki Shirai, Central Research Institute of Electric Power Industry (CRIEPI), Japan

Ashwani Kumar, Gautam Buddha University, India

Statements

Conflict of interest

The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Funding

The author(s) declared that financial support was received for this work and/or its publication. The present study was financially supported by JSPS KAKENHI 21H04421, 21H00962, 23K25755, 23H00074, and Keio University Grant-in-Aid for Innovative Collaborative Research Projects #MKJ1905 to E-II.

Data availability

The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: https://doi.org/10.6084/m9.figshare.27124326.v4.

Ethics approval

The animal study was approved by Animal Care and Use Committee of the Keio University. The study was conducted in accordance with the local legislation and institutional requirements.

Author contributions

YN: Conceptualization, Data curation, Formal Analysis, Investigation, Methodology, Validation, Visualization, Writing – original draft. MT: Data curation, Formal Analysis, Investigation, Validation, Writing – review & editing. E-II: Conceptualization, Data curation, Funding acquisition, Investigation, Methodology, Project administration, Resources, Supervision, Validation, Writing – review & editing.

Acknowledgements

We are grateful to Ayumi Motai and Kosuke Miyazaki for conducting prototype experiments of the present study. We also thank the members of the Hiyoshi field station of Animal Psychology Lab, Keio University for their support in the daily care of the animals. A previous version of this manuscript was uploaded as a preprint to the bioRxiv repository (Niki et al., 2025).

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Citation

Niki, Y., et al. (2026). Repeated changes in resource distribution mitigate inter-individual inequality of food acquisition in a captive group of large-billed crows. Frontiers in Ecology and Evolution, Volume 14. https://doi.org/
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Copyright © 2026 Niki, Takano and Izawa.

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*Correspondence: Yasuyuki Niki; Ei-Ichi Izawa

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