Many social animals live in stable groups that stay together for years, or even lifetimes. Being in a group offers a range of benefits, such as safety from predators, information on where to find food or water, and more accurate navigation. But these benefits come at a cost. The larger the group, the harder it is to make decisions that balance the needs of each individual. So, while members of a large group should be better at locating resources and finding their way, they may take longer to decide where to go next.

In nature, groups of the same species can vary greatly in size and can have large or small numbers of offspring. This raises the question of whether there is an optimal group size where the benefits of living together are maximized relative to the costs? To help answer this question, Papageorgiou and Farine studied the group behaviour of vulturine guineafowl, a social, ground-dwelling bird found in the savannahs of East Africa.

A lightweight GPS tracker was fitted to the members of 21 different groups of vulturine guineafowl to see how group size affects the movement of these birds. The tags collected data every five minutes from dawn until dusk each day, and remained active over five two-month spans of similar weather conditions. This revealed that groups of intermediate size, which contain 33 to 37 birds, ranged over larger areas allowing them to access more diverse resources, and used less energy by travelling shorter distances. Birds in these groups also explored more new areas, decreasing their chances of encountering a predator, and produced more chicks, suggesting that their collective behaviour gave them a reproductive advantage.

These findings suggest that intermediate sized groups display an optimal level of movement compared to larger or smaller groups. Understanding how social groups of different sizes interact with their environment can aid conservation planning. Future work should focus on how this relationship changes with the seasons. This could reveal more about the effects of group size during challenging conditions, like drought.

Movement behaviour fundamentally influences how individuals encounter resources that are critical for their fitness. In highly social animals, movements are determined by how efficiently a group can coordinate their behaviour (Chapman and Chapman, 2000; Couzin et al., 2002; Strandburg-Peshkin et al., 2017; Strandburg-Peshkin et al., 2015). Coordination requires effectively deciding where and when to move (Conradt and Roper, 2005) as well as maintaining cohesion during movement (Gall and Manser, 2017). Both these factors could be shaped by intragroup characteristics. For example, it has become increasingly evident that group composition (Farine et al., 2015), such as the presence of uniformed individuals (Couzin et al., 2011) or the mixture of individual personality types (Aplin et al., 2014; Jolles et al., 2017), can shape the behavioural properties of groups. In societies where individuals form stable groups, group size is another potentially important group characteristic that may also significantly impact fine-scale collective movement patterns. However, while the balance of benefits (such as decreased predation risk according to the ‘many eyes hypothesis’; Pulliam, 1973) and costs (increased intra-group competition over resources) associated with group size have been extensively explored (Krause and Ruxton, 2002), we still have very little information on whether group size translates to differences in movement behaviour and ranging patterns of naturally occurring groups.

Theory predicts that larger group size can benefit animal groups that move collectively through complex and heterogeneous landscapes. If group members pool their information, then larger groups should be able to reach more accurate estimates of their environment (Cantor et al., 2020; Couzin et al., 2011), as highlighted by the ‘wisdom of the crowd’ theory (Galton, 1907). This process can translate to navigational accuracy, via the ‘many wrongs hypothesis’, as pooling the imperfect individual estimations of directions can generate accurate group-level estimates that allow groups to move more directly towards resources (Simons, 2004). Larger groups are also more likely to contain individuals with knowledge relevant to the current position or situation of a group (the ‘pool of competence hypothesis’; Giraldeau, 1984). For example, studies on killer whales (Brent et al., 2015) and elephants (McComb et al., 2001) have shown that older individuals become leaders under conditions of extreme resource shortages. Such mechanisms should therefore allow larger groups to more effectively navigate their environment, and therefore exhibit a larger home-range size.

Effective navigation, and subsequently larger home ranges, should be beneficial for group members. Larger areas encompass a greater diversity and abundance of resources needed to meet group members’ nutritional needs (Corriale et al., 2013; Hubbs and Boonstra, 1998; Maher and Burger, 2011), and moving to more new areas increases the probability of discovering new food resources. Using larger areas can also allow groups to be more variable in their movements, which could make them more unpredictable to predators (Roth and Vetter, 2008) and thus decreases predation risk (Richardson et al., 2018). Thus, all else being equal, such as environmental characteristics, the members of a group with a relatively larger home range should have higher survival in periods of resource limitations and/or greater reproductive output than an identical group with a smaller home range. In reality, home-range size is often limited by many factors, including competition for space with other groups (Christensen and Radford, 2018). However, many group-living species live in non-territorial societies, where groups can benefit from exploiting a home range without paying the costs of intergroup fights and competition. In such species, groups can interact affiliatively and preferentially with specific other groups, resulting in a multilevel society. In such societies, having a larger home-range size might generate more chances to associate with conspecifics from other groups (Grueter et al., 2020), which, in turn, can increase mating opportunities (by increasing contact with members of the opposite sex; Grueter et al., 2015), dispersal opportunities (as sub-adults often disperse to neighbouring groups; Städele et al., 2015) and information sharing about food resources or predation risk (Whitehead et al., 2012). Thus, there are many ways that groups can benefit from maximizing their space use, up to the point where the costs of travel and navigation do not override the benefits gained.

However, living in large groups can also introduce challenges, particularly in species that maintain stable group membership. Collective movement requires maintaining cohesion, a challenge that increases with group size and in more complex environments (Codling et al., 2007). From a movement perspective, coordination among many individuals could result in slower decision-making, due to both coordination challenges as well as larger groups being likely to harbour more conflicts of interest. For example, one study of collective movement in baboons found that the probability that an individual baboon followed one initiator versus the other was much lower when both disagreement among initiators on where to go and the number of initiators increased (Strandburg-Peshkin et al., 2015). Larger groups are also likely to have slower movement speeds while ‘on-the-go’. We all have personal experiences of how much slower it can be to move or make decisions (such as where to have lunch) when we are in a large group, as the group generally may have to conform to the slowest members (Herbert-Read et al., 2013) or satisfy a diversity of preferences (Strandburg-peshkin et al., 2017). Finally, it may be more challenging for individuals to influence larger groups to move to new areas, and thus to extend the group’s home-range. These factors suggest that the consensus costs borne by larger groups will ultimately limit the size of the home-range that such groups can exploit.

Given that both positive and negative relationships between group size and home-range size may exist, we a priori predict a non-linear relationship between the size of stable groups and collective movement characteristics. This non-linear relationship is likely to represent an optimal group size for movement that reflects the point where the difference between navigational accuracy and coordination problems is maximized. To our knowledge, this relationship remains largely unexplored in wild, free-living groups. A first challenge in quantifying the relationship between home-range size and group size requires long-term tracking across multiple replicated groups and, ideally, replication in time. A second challenge is that many species that live in stable groups (at least most of the well-studied species) either experience increased intra-group competition as group sizes increase (e.g. baboons; Markham et al., 2015) or they are territorial (e.g. meerkats; Bateman et al., 2015). Territoriality introduces several confounding effects. For example, the greater resource-holding potential of larger groups (Bateman et al., 2015; Markham et al., 2015) can translate to larger home-ranges. Further, different territorial groups can vary in the density and distribution of resources within their territory, which could make it appear that smaller groups have larger home-ranges if they live in marginal areas with low resource densities. As a result, the relationship between group movement characteristics and group size has remained relatively unclear.

In this study, we examine the effect of group size on the movement and space use patterns of wild groups of vulturine guineafowl (Acryllium vulturinum), and whether resulting patterns correspond to differences in reproductive output among groups. Our study species lives in a multilevel society consisting of large, stable groups with multiple breeding units and groups form stable associations with specific other groups (Papageorgiou et al., 2019). The stable social structure and the lack of territoriality—the entire population inhabits a small area of approximately 500 ha and all groups can easily access all parts of our study area—makes vulturine guineafowl an ideal species to investigate how group size influences movement characteristics because groups can range without major constraints on ranging imposed by other groups. Further, their large body size (~1.5 kg) allow guineafowl to be fitted with high-resolution solar-powered GPS tags to collect both fine-scale data on their movement as well as long-term data on their ranging behaviour.

We use data from GPS tags fitted to one individual from a total of 21 distinct groups, collected over five 2-month-long non-breeding seasons (Figure 1). We fit a continuous-time movement model (ctmm) to each individual GPS dataset (one bird per group per season) using GPS locations collected every 5 min. From each model, we calculated (1) home-range size, (2) the tendency for groups to use the same areas on consecutive days, and (3) daily travel distance. We then used high-resolution (1 Hz) GPS location data from each individual to quantify (4) the movement speed of its group while ‘on the go’. Because groups move through heterogeneous environments, larger groups should be able to navigate more effectively. Their large size should also result in larger daily travel distances as they search for the resources needed to survive. By contrast, moving as a large group also introduces consensus costs, and these could limit the home-range size of the largest groups by affecting their speed or their tendency to move to new areas. As a result of the interaction between these two effects, we expect that there will be a non-linear relationship between group size and movement, which, if it results in more effective use of available resources, could correspond to an optimal group size for group movement characteristics.

Figure 1

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We predict that intermediate groups will have the largest home-range sizes. This will arise because while larger groups should travel further, intermediate groups will have lower day-to-day home-range fidelity. We further predict that intermediate size groups will present the fastest speed while ‘on the go’. However, these factors may be tempered by group composition. Groups in our population reproduced several times over our study period, and the presence of young group members could restrict the movements of groups. Thus, we run our analysis while controlling for the number of chicks present in the groups, and predict that groups with more chicks should travel slower and shorter distances, thereby restricting their home-range size. Finally, we predict that a non-linear relationship between group size and movement should translate to corresponding peak in fitness, measured as the number of chicks in a group. Such a pattern would suggest that maximising the difference between navigation and coordination represents an optimal group size at which groups are most efficient in exploiting their environment.

Our movement analysis, based on 1,274,434 five-min GPS positions, spanned 45 seasonal observations of individuals (groups) over five 2-month-long seasons (Figure 1, see Supplementary file 1A for a summary of the data). Group sizes, recorded during daily census observations, were widely distributed, with intermediate groups being observed less often (Figure 2).

Figure 2

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Fitting generalised estimating equations (GEEs) with the number of adults (group size) and the number of chicks as predictors (while accounting for repeated measures of the same groups across seasons) revealed that home-range size was significantly predicted by group size and its quadratic term, but that this relationship was modulated by the presence of chicks. Intermediate groups had the largest home-range, but home-range size decreased with increasing number of chicks (Table 1A, see Supplementary file 1B for all model fits). Average temporal overlap in space use was also significantly predicted by group size and its quadratic term, but was not significantly modulated by the presence of chicks (Table 1B, see Supplementary file 1C for all model fits). Average daily distance travelled increased linearly with group size (Table 1C, see Supplementary file 1D for all model fits). Speed ‘on-the-go’ was not explained by group size or by number of chicks in the group (Table 1D, see Supplementary file 1E for all model fits). Overall, smaller and larger groups expressed smaller home-range sizes and increased average temporal overlap in space use, while larger groups expressed longer daily travel distances than groups of smaller size (see Figure 3 and Figure 4).

Figure 3

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Figure 4

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The number of chicks in the group was also significantly predicted by group size and its quadratic term (Table 1E, Figure 3D, see also Supplementary file 1F for all model fits). We found that intermediate-sized groups had more chicks, with the peak in fitness (group size = 33) closely matching the peak in home-range size (group size = 36) and average daily overlap (group size = 37).

Number of adults (group size) and its quadratic term significantly predicted (A) home-range size and (B) average temporal overlap in space use. The presence of chicks modulated the relationship between group size and home-range size, reducing the overall contribution of the number of adults to home-range size (the negative interaction with number of adults) and reducing the quadratic effect (the positive interaction with number of adults squared, which offsets the negative quadratic coefficient). (C) The average daily distance travelled increased with number of adults (see Figure 3C). (D) Speed while travelling was not significantly impacted by group size or its quadratic term. (E) The number of chicks per group was significantly predicted by group size and its quadratic term (see Figure 3D). Parameter estimates, significance, and R² were generated using the most parsimonious fitted GEEs based on QIC (see Materials and methods). All models tested are summarized in the Supplementary file 1B to 1F.

Our study shows that group size and composition can impact collective movement characteristics. Groups of vulturine guineafowl that have an intermediate size exhibit larger home-ranges and explore more new areas day-by-day. However, this relationship exists despite intermediate groups travelling shorter distances each day compared to larger groups. Even though the presence of chicks does not impact how far or how fast groups travel, having many chicks restricts the home range of their group. Further, our study also shows that groups of intermediate size have higher fitness as they produce more offspring than larger or smaller groups. Our results are therefore consistent with our a priori prediction that a non-linear relationship should exist between group size and collective movement, reflecting the increasing navigation ability with increasing group size and the increasing challenges of coordinating movement in larger groups. The relationship between group size and reproductive output further suggests that intermediate-sized groups are more effective at exploiting space, providing evidence for an optimal group size for collective movement.

The relationships between group size and group movement outcomes provide new insights into how differently sized animal groups interact with their environment. Larger groups cover larger distances during the day, which is likely to be important for encountering sufficient resources to meet their energetic demands, as proposed by the ecological constraints model (Chapman and Chapman, 2000; Ganas and Robbins, 2005; Gillespie and Chapman, 2001; Teichroeb and Sicotte, 2009). However, by exploiting larger areas over a whole season, groups of intermediate size are likely to benefit from having access to a greater abundance and/or diversity of food resources (Maher and Burger, 2011), they potentially also benefit both from being less predictable to predators (Richardson et al., 2018; Roth and Vetter, 2008), and from having access to a more diverse social landscape. In addition, intermediate-sized groups achieve larger home ranges while travelling shorter distances than larger groups, implying that they have lower energy expenditure. It is worth noting that we focused our analysis on 2-month periods each capturing similar ecological conditions. Future studies could investigate whether these non-linear relationships between group-size and movement characteristics change across different environmental conditions and especially in seasons where energy might be under tighter constraints, such as during droughts. Evidence for this could bring important insights into the drivers of group size variation.

While we predicted that group size would translate to differences in movement while ‘on-the-go’, our measure of moment-by-moment movement (speed while travelling) did not reveal any relationship between group size and movement speed. One reason for this could be that larger groups could have better knowledge of the environment, meaning that they can offset consensus costs (i.e. periods of slow movement due to conflict within the group regarding movement directions) with more effective movement once departed (Couzin et al., 2005). For example, the speed and polarization, and subsequently the directionality of group motion, increases with the size of fish schools (Tunstrøm et al., 2013). As group size increases in locusts, they transition from disordered to ordered motion (Buhl et al., 2006). Thus, it is possible that while the realised movement speed does not differ with group size, the characteristics of how the movement is achieved could vary between smaller and larger groups. Alternatively, the challenge of reaching a consensus could mean that larger groups end up repeating the same routes more often, which they can achieve by moving faster as individuals all know where they are going. This explanation is consistent with our findings that larger groups were less variable in where they travelled each day. Such mechanisms linking collective movement with ranging behaviour warrant further investigation.

Several studies in the past have investigated the relationship between group size and ranging behaviour. Most notably, Markham et al., 2015 found that intermediate-sized groups of wild baboons (Papio cynocephalus) utilized smaller areas, covered shorter distances per day, and used space less evenly than smaller and larger groups—patterns which are largely opposite to our findings. Stevenson and Castellanos, 2000 found that woolly monkeys (Lagothrix lagothricha) in groups of intermediate size travelled less during the day than larger and smaller groups. Finally, research on meerkats (Suricata suricatta) Bateman et al., 2015 found no relationship between group size and group movement characteristics. However, in most previously studied systems, group movement characteristics are likely to be impacted not only by the ability for groups to move, but also by intra-group contest competition and inter-group competition (Markham et al., 2015). For example, smaller groups have the weakest resource-holding potential, meaning that they should have the smallest territories. At the same time, they might be excluded from the best, most resource-rich, habitats by larger groups (who need more resources due to intra-group competition), meaning that they could also end up having to use equally sized or even larger areas (Markham et al., 2015; Stevenson and Castellanos, 2000). In contrast to previous studies, our evidence for a non-linear relationship between group size and movement characteristics comes from a species that not only presents minimum intergroup competition but also groups use largely overlapping areas. These features reduce the confounds on the link between group size and group movement behaviours. Our study is therefore more likely to reveal effects arising from movement-based mechanisms without confounding effects that are exogenous to the group.

Vulturine guineafowl live in a multilevel society, where groups preferentially associate with other groups (Papageorgiou et al., 2019). In such societies, intergroup associations can bring many important benefits, such as information sharing on predators and resources, as well as mating and dispersal opportunities (Grueter et al., 2020). A large home range size in a multilevel society could provide groups with access to more communal roosts (we have observed up to five groups in a single roost, Papageorgiou et al., 2019) and more glades, and thus more opportunities to associate with more groups. However, the benefits of associating with other groups might not increase linearly with home range size, as associating with a large number of conspecifics can also imply costs, such as disease transmission or increased competitions for mates (Krause and Ruxton, 2002). More work is needed on the interactions between ranging behaviour of groups and the broader social environment in multilevel societies.

We built on existing insights from studies of collective behaviour to develop an a priori prediction that the relationship between group size and movement characteristic should be non-linear. Our prediction proposed that maximising the use of space (i.e. a larger home range) through effective movement (faster movement and less repetitive ranging) should result in more efficient exploitation of the resources available to a group. A key question is whether groups actually benefit from this maximisation? By analysing data on the reproductive success across the groups, we found striking evidence for a peak in fitness, with intermediate-sized groups having more chicks. This peak, which closely matches the group size peaks in movement characteristics (home range size and average daily overlap), suggests that intermediate group sizes might be linked to higher reproductive success. Our findings therefore provide evidence for an optimal group size in the context of collective movement.

An interesting additional finding from our study is that optimally sized groups (33–37 individuals) were relatively rare. Instead, the distribution of group size appears to be bimodal with most groups being either smaller or larger than the intermediate group size. This observation aligns with the hypothesis that while an optimal group size results in maximum individual fitness (Pride, 2005) groups at the optimal size should be unstable and therefore rarely observed (Sibly, 1983). Groups in our study grow either through reproduction or through immigration. We found that intermediate-sized groups have high reproductive success, which naturally pushes them beyond the optimal. Further, because dispersing individuals should prefer optimal size groups, they should also turn intermediate-sized groups into groups that are larger than the optimal size. Future studies should examine whether groups near the optimal size for collective movement also highly attract more dispersers.

The observed variation in results between our study and previous studies investigating the effect of group size on ranging behaviour in mammals could also potentially arise due to methodological differences. For example, we used high-resolution tracks and continuous-time movement modelling to get much more precise estimates of movement characteristics. By contrast, other studies have used much lower sampling resolutions, often relying on hand-held GPS devices (Bateman et al., 2015; Markham et al., 2015) or used simplistic minimum convex polygons to model home-ranges (Markham et al., 2015). Minimum convex polygons are unlikely to be very good estimates of home-ranges as they are sensitive to outliers (e.g. from transcription errors or poor GPS fixes) and capture only one element of space use (the extreme boundaries, which could be influenced by a range of factors such as habitat geometry; He et al., 2019). However, despite these potential methodological differences, we believe that conflicting findings are more likely to suggest that the effect of group size on ranging behaviour and collective movement varies according to the properties of the different animal social systems. For example, we predict that the outcome of the many factors that contribute to the ranging characteristics of social groups will be modulated by the distribution of food resources each species exploits. Vulturine guineafowl have very little need to defend areas from other groups or compete for resources within groups, as their food source (insects and small seeds) are widespread and they roost on the top of dense stands of trees, a habitat feature which is common in the study area. When competition is decreased, we predict that group size could become a more important driver of collective movement outcomes. We hope that by collecting more data on this question across species there will soon be sufficient studies to fully test this prediction.

In addition to the effects of the number of adults in the group, we also revealed the impact of having young present in the group. Vulturine guineafowl are precocial, meaning that they are very small when they join the group, and mature over long periods of time—at 1 year of age they are only approximately 65% of the full adult size. For a number of months after hatching, chicks are unlikely to contribute to making decisions, but are highly vulnerable to predators—causing them to remain more in cover. At the same time, chicks are likely to move more slowly, or cover less ground, due to their smaller body size. We found that having chicks in the group has a profound effect on home-range size. Our results indicate that groups with more chicks use smaller areas, thereby limiting groups of intermediate sizes from exhibiting larger home-ranges. The limitations that chicks have on group movements raise interesting questions about the costs of group living. The fact that groups of vulturine guineafowl remain cohesive across seasons and years suggests that any costs borne from collective movement will be shared by all group members, including by individuals which have failed to reproduce (vulturine guineafowl are plural breeders; Papageorgiou et al., 2019) or by subadults who have yet to disperse. Thus, our data reveals a potentially important source of conflict among group members. Such conflicts may be common in plural-breeding groups, which are commonly found in multilevel societies.

We have shown that using synchronous and high-resolution GPS tracking of multiple social groups that reside in the same area can reveal new insights into the movement and social ecology of species. Specifically, we demonstrated that group size and composition can predict collective movement and space use characteristics. Our results are consistent with the prediction that increased navigation accuracy but decreased coordination ability could produce a non-linear relationship between group size and collective movement. However, the mechanisms that underlie the interaction between coordination maintenance and navigation accuracy in free-living groups of various sizes remain to be discovered. Further, we have identified dimensions of movement—specifically that intermediate-sized groups have larger home-ranges and use a larger diversity of areas—that translate into intermediate-sized groups having greater efficacy at exploiting their environment, and, therefore, higher fitness. These results suggest the presence of an optimal group size for collective movement. A further interesting observation is that the benefits of having an optimal group size are not present when groups have offspring, and that few groups are of an optimal size, highlighting a potentially important feedback between current and future reproductive success. The ability to develop and test our novel hypotheses relating to collective movement of animals in the wild was only possible by conducting a large-scale multi-group tracking study in a species that does exhibit little intra-group and inter-group competition. We hope that cheaper, more reliable, and smaller devices, will continue to reveal similar insights into the lives of wild animals across the diversity of social systems.

The code to run the ctmms and conduct the statistics is available on https://github.com/DanPapageorgiou/Group_size (copy archived at https://archive.softwareheritage.org/swh:1:dir:fcd6639f112b1d96bfe8d122f96a2c0ed2a1673b). All data used in this study are stored on Movebank under the study name Avulturinum_Farine: https://www.movebank.org/cms/webapp?gwt_fragment=page=studies,path=study475851705.

1. 1. Aplin LM
   2. Farine DR
   3. Mann RP
   4. Sheldon BC

   (2014) Individual-level personality influences social foraging and collective behaviour in wild birds

   Proceedings of the Royal Society B: Biological Sciences 281:20141016.

   https://doi.org/10.1098/rspb.2014.1016

   • Google Scholar
2. 1. Bateman AW
   2. Lewis MA
   3. Gall G
   4. Manser MB
   5. Clutton-Brock TH

   (2015) Territoriality and home-range dynamics in meerkats, Suricata suricatta : a mechanistic modelling approach

   Journal of Animal Ecology 84:260–271.

   https://doi.org/10.1111/1365-2656.12267

   • Google Scholar
3. 1. Bender JA
   2. Simpson EM
   3. Tietz BR
   4. Daltorio KA
   5. Quinn RD
   6. Ritzmann RE

   (2011) Kinematic and behavioral evidence for a distinction between trotting and ambling gaits in the cockroach Blaberus discoidalis

   Journal of Experimental Biology 214:2057–2064.

   https://doi.org/10.1242/jeb.056481

   • Google Scholar
4. 1. Brent LJN
   2. Franks DW
   3. Foster EA
   4. Balcomb KC
   5. Cant MA
   6. Croft DP

   (2015) Ecological knowledge, leadership, and the evolution of menopause in killer whales

   Current Biology 25:746–750.

   https://doi.org/10.1016/j.cub.2015.01.037

   • PubMed
   • Google Scholar
5. 1. Buhl J
   2. Sumpter DJ
   3. Couzin ID
   4. Hale JJ
   5. Despland E
   6. Miller ER
   7. Simpson SJ

   (2006) From disorder to order in marching locusts

   Science 312:1402–1406.

   https://doi.org/10.1126/science.1125142

   • PubMed
   • Google Scholar
6. 1. Calabrese JM
   2. Fleming CH
   3. Gurarie E

   (2016) Ctmm: an r package for analyzing animal relocation data as a continuous‐time stochastic process

   Methods in Ecology and Evolution 7:1124–1132.

   https://doi.org/10.1111/2041-210X.12559

   • Google Scholar
7. 1. Cantor M
   2. Aplin LM
   3. Farine DR

   (2020) A primer on the relationship between group size and group performance

   Animal Behaviour 166:139–146.

   https://doi.org/10.1016/j.anbehav.2020.06.017

   • Google Scholar
8. Book

   1. Chapman CA
   2. Chapman LJ

   (2000) On the Move: How and Why Animals Travel in Groups

   Chicago: University of Chicago Press.

   https://doi.org/10.1046/j.1439-0310.2000.0624a.x

   • Google Scholar
9. 1. Christensen C
   2. Radford AN

   (2018) Dear enemies or nasty neighbors? causes and consequences of variation in the responses of group-living species to territorial intrusions

   Behavioral Ecology 29:1004–1013.

   https://doi.org/10.1093/beheco/ary010

   • Google Scholar
10. 1. Codling EA
    2. Pitchford JW
    3. Simpson SD

    (2007) Group navigation and the "many-wrongs principle" in models of animal movement

    Ecology 88:1864–1870.

    https://doi.org/10.1890/06-0854.1

    • PubMed
    • Google Scholar
11. 1. Conradt L
    2. Roper TJ

    (2005) Consensus decision making in animals

    Trends in Ecology & Evolution 20:449–456.

    https://doi.org/10.1016/j.tree.2005.05.008

    • PubMed
    • Google Scholar
12. 1. Corriale MJ
    2. Muschetto E
    3. Herrera EA

    (2013) Influence of group sizes and food resources in home-range sizes of capybaras from Argentina

    Journal of Mammalogy 94:19–28.

    https://doi.org/10.1644/12-MAMM-A-030.1

    • Google Scholar
13. 1. Couzin ID
    2. Krause J
    3. James R
    4. Ruxton GD
    5. Franks NR

    (2002) Collective Memory and Spatial Sorting in Animal Groups

    Journal of Theoretical Biology 218:1–11.

    https://doi.org/10.1006/jtbi.2002.3065

    • Google Scholar
14. 1. Couzin ID
    2. Krause J
    3. Franks NR
    4. Levin SA

    (2005) Effective leadership and decision-making in animal groups on the move

    Nature 433:513–516.

    https://doi.org/10.1038/nature03236

    • Google Scholar
15. 1. Couzin ID
    2. Ioannou CC
    3. Demirel G
    4. Gross T
    5. Torney CJ
    6. Hartnett A
    7. Conradt L
    8. Levin SA
    9. Leonard NE

    (2011) Uninformed individuals promote democratic consensus in animal groups

    Science 334:1578–1580.

    https://doi.org/10.1126/science.1210280

    • PubMed
    • Google Scholar
16. 1. Dammhahn M
    2. Kappeler PM

    (2009) Scramble or contest competition over food in solitary foraging mouse Lemurs (Microcebus spp.): New insights from stable isotopes

    American Journal of Physical Anthropology 141:181–189.

    https://doi.org/10.1002/ajpa.21129

    • Google Scholar
17. 1. Farine DR
    2. Montiglio PO
    3. Spiegel O

    (2015) From individuals to groups and back: the evolutionary implications of group phenotypic composition

    Trends in Ecology & Evolution 30:609–621.

    https://doi.org/10.1016/j.tree.2015.07.005

    • PubMed
    • Google Scholar
18. 1. Fleming CH
    2. Fagan WF
    3. Mueller T
    4. Olson KA
    5. Leimgruber P
    6. Calabrese JM

    (2015) Rigorous home range estimation with movement data: a new autocorrelated kernel density estimator

    Ecology 96:1182–1188.

    https://doi.org/10.1890/14-2010.1

    • PubMed
    • Google Scholar
19. Software

    1. Fleming CH
    2. Calabrese JM

    (2018) ctmm: Continuous-Time Movement Modeling, version 0.5.10

    R Package.

    http://finzi.psych.upenn.edu/library/ctmm/html/ctmm-package.html
20. 1. Gall GEC
    2. Manser MB

    (2017) Group cohesion in foraging meerkats: follow the moving 'vocal hot spot'

    Royal Society Open Science 4:170004.

    https://doi.org/10.1098/rsos.170004

    • PubMed
    • Google Scholar
21. 1. Galton F

    (1907) Vox populi (The wisdom of crowds)

    Nature 75:450–451.

    https://doi.org/10.1038/075450a0

    • Google Scholar
22. 1. Ganas J
    2. Robbins MM

    (2005) Ranging behavior of the mountain gorillas (Gorilla beringei beringei) in Bwindi Impenetrable National Park, Uganda: a test of the ecological constraints model

    Behavioral Ecology and Sociobiology 58:277–288.

    https://doi.org/10.1007/s00265-005-0920-z

    • Google Scholar
23. 1. Gillespie T
    2. Chapman C

    (2001) Determinants of group size in the red Colobus monkey ( Procolobus badius ): an evaluation of the generality of the ecological-constraints model

    Behavioral Ecology and Sociobiology 50:329–338.

    https://doi.org/10.1007/s002650100371

    • Google Scholar
24. 1. Giraldeau L-A

    (1984) Group foraging: the skill pool effect and Frequency-Dependent learning

    The American Naturalist 124:72–79.

    https://doi.org/10.1086/284252

    • Google Scholar
25. 1. Grueter CC
    2. Isler K
    3. Dixson BJ

    (2015) Are badges of status adaptive in large complex primate groups?

    Evolution and Human Behavior 36:398–406.

    https://doi.org/10.1016/j.evolhumbehav.2015.03.003

    • Google Scholar
26. 1. Grueter CC
    2. Qi X
    3. Zinner D
    4. Bergman T
    5. Li M
    6. Xiang Z
    7. Zhu P
    8. Migliano AB
    9. Miller A
    10. Krützen M
    11. Fischer J
    12. Rubenstein DI
    13. Vidya TNC
    14. Li B
    15. Cantor M
    16. Swedell L

    (2020) Multilevel organisation of animal sociality

    Trends in Ecology & Evolution 35:834–847.

    https://doi.org/10.1016/j.tree.2020.05.003

    • PubMed
    • Google Scholar
27. 1. He P
    2. Maldonado-Chaparro AA
    3. Farine DR

    (2019) The role of habitat configuration in shaping social structure: a gap in studies of animal social complexity

    Behavioral Ecology and Sociobiology 73:9.

    https://doi.org/10.1007/s00265-018-2602-7

    • Google Scholar
28. 1. Herbert-Read JE
    2. Krause S
    3. Morrell LJ
    4. Schaerf TM
    5. Krause J
    6. Ward AJW

    (2013) The role of individuality in collective group movement

    Proceedings of the Royal Society B: Biological Sciences 280:20122564.

    https://doi.org/10.1098/rspb.2012.2564

    • Google Scholar
29. Software

    1. Hocking DJ

    (2014) QICpack: Model selection for generalized estimating equations using QIC, version 0.9.1

    R Package.

    https://danieljhocking.wordpress.com/2012/11/15/gee-qic-update/
30. 1. Hubbs AH
    2. Boonstra R

    (1998) Effects of food and predators on the home-range sizes of arctic ground squirrel ( Spermophilus parryii )

    Canadian Journal of Zoology 76:592–596.

    https://doi.org/10.1139/z97-215

    • Google Scholar
31. 1. Jolles JW
    2. Boogert NJ
    3. Sridhar VH
    4. Couzin ID
    5. Manica A

    (2017) Consistent individual differences drive collective behavior and group functioning of schooling fish

    Current Biology 27:2862–2868.

    https://doi.org/10.1016/j.cub.2017.08.004

    • PubMed
    • Google Scholar
32. Book

    1. Kenward R

    (2001) A Manual for Wildlife Radio Tagging

    London: Academic Press.

    https://doi.org/10.2307/4164350

    • Google Scholar
33. Book

    1. Krause J
    2. Ruxton G

    (2002)

    Living in Groups

    Oxford University Press.

    • Google Scholar
34. 1. Maher CR
    2. Burger JR

    (2011) Intraspecific variation in space use, group size, and mating systems of caviomorph rodents

    Journal of Mammalogy 92:54–64.

    https://doi.org/10.1644/09-MAMM-S-317.1

    • PubMed
    • Google Scholar
35. 1. Markham AC
    2. Gesquiere LR
    3. Alberts SC
    4. Altmann J

    (2015) Optimal group size in a highly social mammal

    PNAS 112:14882–14887.

    https://doi.org/10.1073/pnas.1517794112

    • PubMed
    • Google Scholar
36. 1. McComb K
    2. Moss C
    3. Durant SM
    4. Baker L
    5. Sayialel S

    (2001) Matriarchs as repositories of social knowledge in african elephants

    Science 292:491–494.

    https://doi.org/10.1126/science.1057895

    • PubMed
    • Google Scholar
37. 1. Naude VN
    2. Smyth LK
    3. Weideman EA
    4. Krochuk BA
    5. Amar A

    (2019) Using web-sourced photography to explore the diet of a declining african raptor, the martial eagle (Polemaetus bellicosus)

    The Condor 121:1–9.

    https://doi.org/10.1093/condor/duy015

    • Google Scholar
38. 1. Papageorgiou D
    2. Christensen C
    3. Gall GEC
    4. Klarevas-Irby JA
    5. Nyaguthii B
    6. Couzin ID
    7. Farine DR

    (2019) The multilevel society of a small-brained bird

    Current Biology 29:R1120–R1121.

    https://doi.org/10.1016/j.cub.2019.09.072

    • PubMed
    • Google Scholar
39. Software

    1. Papageorgiou D

    (2020) GitHub, version 15ea08e

    GitHub.

    https://github.com/DanPapageorgiou/Group_size
40. 1. Pride RE

    (2005) Optimal group size and seasonal stress in ring-tailed Lemurs (Lemur catta)

    Behavioral Ecology 16:550–560.

    https://doi.org/10.1093/beheco/ari025

    • Google Scholar
41. 1. Pulliam HR

    (1973) On the advantages of flocking

    Journal of Theoretical Biology 38:419–422.

    https://doi.org/10.1016/0022-5193(73)90184-7

    • PubMed
    • Google Scholar
42. Software

    1. R Development Core Team

    (2018) R: A Language and Environment for Statistical Computing

    R Foundation for Statistical Computing, Vienna, Austria.

    http://www.r-project.org
43. 1. Richardson G
    2. Dickinson P
    3. Burman OHP
    4. Pike TW

    (2018) Unpredictable movement as an anti-predator strategy

    Proceedings of the Royal Society B: Biological Sciences 285:20181112.

    https://doi.org/10.1098/rspb.2018.1112

    • Google Scholar
44. 1. Roth TC
    2. Vetter WE

    (2008) The effect of feeder hotspots on the predictability and home range use of a small bird in winter

    Ethology 114:398–404.

    https://doi.org/10.1111/j.1439-0310.2008.01477.x

    • Google Scholar
45. 1. Sibly RM

    (1983) Optimal group size is unstable

    Animal Behaviour 31:947–948.

    https://doi.org/10.1016/S0003-3472(83)80250-4

    • Google Scholar
46. 1. Simons AM

    (2004) Many wrongs: the advantage of group navigation

    Trends in Ecology & Evolution 19:453–455.

    https://doi.org/10.1016/j.tree.2004.07.001

    • PubMed
    • Google Scholar
47. 1. Städele V
    2. Van Doren V
    3. Pines M
    4. Swedell L
    5. Vigilant L

    (2015) Fine-scale genetic assessment of sex-specific dispersal patterns in a multilevel primate society

    Journal of Human Evolution 78:103–113.

    https://doi.org/10.1016/j.jhevol.2014.10.019

    • PubMed
    • Google Scholar
48. 1. Stevenson PR
    2. Castellanos MC

    (2000) Feeding Rates and Daily Path Range of the Colombian Woolly Monkeys as Evidence for Between- and Within-Group Competition

    Folia Primatologica 71:399–408.

    https://doi.org/10.1159/000052737

    • Google Scholar
49. 1. Strandburg-Peshkin A
    2. Farine DR
    3. Couzin ID
    4. Crofoot MC

    (2015) Shared decision-making drives collective movement in wild baboons

    Science 348:1358–1361.

    https://doi.org/10.1126/science.aaa5099

    • PubMed
    • Google Scholar
50. 1. Strandburg-Peshkin A
    2. Farine DR
    3. Crofoot MC
    4. Couzin ID

    (2017) Habitat and social factors shape individual decisions and emergent group structure during baboon collective movement

    eLife 6:e19505.

    https://doi.org/10.7554/eLife.19505

    • PubMed
    • Google Scholar
51. 1. Strandburg-peshkin A
    2. Papageorgiou D
    3. Crofoot MC
    4. Farine DR

    (2017) Inferring influence and leadership in moving animal groups

    Philos Trans R Soc B 373:6.

    https://doi.org/10.1098/rstb.2017.0006

    • Google Scholar
52. 1. Teichroeb JA
    2. Sicotte P

    (2009) Test of the ecological-constraints model on ursine Colobus monkeys (Colobus vellerosus) in Ghana

    American Journal of Primatology 71:49–59.

    https://doi.org/10.1002/ajp.20617

    • PubMed
    • Google Scholar
53. 1. Tunstrøm K
    2. Katz Y
    3. Ioannou CC
    4. Huepe C
    5. Lutz MJ
    6. Couzin ID

    (2013) Collective states, multistability and transitional behavior in schooling fish

    PLOS Computational Biology 9:e1002915.

    https://doi.org/10.1371/journal.pcbi.1002915

    • PubMed
    • Google Scholar
54. 1. Whitehead H
    2. Antunes R
    3. Gero S
    4. Wong SNP
    5. Engelhaupt D
    6. Rendell L

    (2012) Multilevel societies of female sperm whales (Physeter macrocephalus) in the Atlantic and Pacific: why are they so different?

    International Journal of Primatology 33:1142–1164.

    https://doi.org/10.1007/s10764-012-9598-z

    • Google Scholar
55. Website

    1. Wikelski M
    2. Kays R

    (2018) Movebank: archive, analysis and sharing of animal movement data. . www.movebank.org

    Hosted by the Max Planck Institute for Ornithology. Accessed March 17, 2020.

    http://www.movebank.org/
56. 1. Wilson RS
    2. Husak JF
    3. Halsey LG
    4. Clemente CJ

    (2015) Predicting the movement speeds of animals in natural environments

    Integrative and Comparative Biology 55:1125–1141.

    https://doi.org/10.1093/icb/icv106

    • PubMed
    • Google Scholar
57. 1. Winner K
    2. Noonan MJ
    3. Fleming CH
    4. Olson KA
    5. Mueller T
    6. Sheldon D
    7. Calabrese JM

    (2018) Statistical inference for home range overlap

    Methods in Ecology and Evolution 9:1679–1691.

    https://doi.org/10.1111/2041-210X.13027

    • Google Scholar
58. 1. Young TP
    2. Patridge N
    3. Macrae A

    (1995) Long-Term glades in Acacia bushland and their edge effects in Laikipia, Kenya

    Ecological Applications 5:97–108.

    https://doi.org/10.2307/1942055

    • Google Scholar
59. 1. Zheng B

    (2000) Summarizing the goodness of fit of generalized linear models for longitudinal data

    Statistics in Medicine 19:1265–1275.

    https://doi.org/10.1002/(SICI)1097-0258(20000530)19:10<1265::AID-SIM486>3.0.CO;2-U

    • PubMed
    • Google Scholar

Author details

1. Danai Papageorgiou

   1. Max Planck Institute of Animal Behavior, Department of Collective Behavior, Universitätsstraße, Konstanz, Germany
   2. University of Konstanz, Department of Biology, Universitätsstraße, Konstanz, Germany
   3. University of Konstanz, Center for the Advanced Study of Collective Behaviour, Universitätsstraße, Konstanz, Germany
   4. Kenya Wildlife Service, Nairobi, Kenya

   Contribution

   Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Visualization, Methodology, Writing - original draft, Project administration, Writing - review and editing

   For correspondence

   dpapageorgiou@ab.mpg.de

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-5535-6133

2. Damien Roger Farine

   1. Max Planck Institute of Animal Behavior, Department of Collective Behavior, Universitätsstraße, Konstanz, Germany
   2. University of Konstanz, Department of Biology, Universitätsstraße, Konstanz, Germany
   3. University of Konstanz, Center for the Advanced Study of Collective Behaviour, Universitätsstraße, Konstanz, Germany
   4. Department of Ornithology, National Museums of Kenya, Nairobi, Kenya

   Present address

   Department of Evolutionary Biology and Environmental Studies, University of Zurich, Zurich, Switzerland

   Contribution

   Conceptualization, Formal analysis, Supervision, Funding acquisition, Methodology, Writing - review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-2208-7613

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