Animals need information to make decisions, and a quick way to get this information is to watch what others are doing. Animals, like humans, have different social networks that they could acquire this kind of ‘social information’ from, yet we know little about which networks they actually use. In addition, once an animal has obtained social information, some aspect of their lives, such as their sex or social rank, could prevent them from using it. Once again, however, we know very little about the impact of these personal constraints.

Carter et al. found that information about the location of a highly preferred food flowed through a social network of wild baboons that was based on who was regularly in close proximity to whom. However, while individuals with more neighbours were better at obtaining social information about food location, they were not better at using it. Rather, individuals were more likely to successfully exploit such information if they were dominant, bold, male, and had good social bonds with others.

Carter et al.’s results show that the use of social information is a process with several stages – from information acquisition, to its application, and finally its exploitation. Furthermore, the characteristics of an individual can limit their success at each of these stages. The next step is to figure out whether different types of social information – whether short- or long-lived, easy to acquire or more complex – flow through the same networks and have the same personal constraints on who can use them.

Individuals require information to reduce uncertainty about their environment. Information can be acquired in two ways (Dall et al., 2005): by interacting with the environment directly (personal information) or by attending to the behaviour of others (social information). Individuals benefit from both personal and social information in myriad contexts, including foraging, predator avoidance, and mate choice (Giraldeau et al., 2002). Their use is moderated by their expense and reliability: personal information is usually reliable but costly and time consuming to collect, social information is less costly but more likely to become outdated and unreliable (Giraldeau et al., 2002; Laland, 2004). The low costs of social information also allow it to disseminate more rapidly across groups, such that it can play an important role in the formation of traditions and cultures (Whiten, 2000; Castro and Toro, 2004).

Despite the fitness benefits of information use, we have very little understanding of how individuals vary in their ability to capture these benefits. Indeed, theory developed to explain the costs and benefits of information use usually assumes homogeneity within a population (e.g. Pradhan et al., 2012). To understand individual variation in information use, either personal or social, we suggest it is helpful to decompose the process into a sequence of three steps: the acquisition of information, its application, and the exploitation of its benefits (Table 1). Up until now, many studies have implicitly assumed that these three steps are synonymous, but recent evidence indicates that information use is substantially more complex. In particular, Carter et al. (2014) found that the time spent acquiring social information about a task did not correlate with subsequent performance (information application) in wild baboons (Papio ursinus), while Atton et al. (2012) found differences in individual performance between task discovery (information acquisition) and task solving (information exploitation) in three-spine sticklebacks (Gasterosteus aculeatus). The recognition of three sequential steps allows us to begin unpacking the complexity of information use, and to explore variation in the performance of different individuals at different points along the sequence. Distinct sensory and motor capabilities are likely to be involved at each stage, leading to different phenotypic constraints. As a result, individuals who are effective at one step may be less so at another, with significant implications for who captures the most benefits.

The range of phenotypic constraints operating at each step might include cognitive, social, behavioural, ecological and demographic characteristics. The importance of these constraints is likely to differ not only between individuals but also between populations and species. Here, we focus on social, behavioural and demographic constraints on the social information use sequence. To begin with, we consider the social phenotype, i.e., phenotypic traits that emerge from social interactions with others and are likely to be under selection, in this case individual dominance rank (Moore, 1993) and position in the social network (Aplin et al., 2015). Dominant animals can aggressively monopolise resources such as mates (Cowlishaw and Dunbar, 1991) and food (Koenig, 2002), limiting the opportunities for others to apply and exploit information that they have acquired either personally or socially about these resources. This can further lead to voluntary inhibition in the use of information by subordinate animals, e.g., low-ranked rhesus monkeys (Macaca mulatta) only performed a socially-learnt task when high-ranked monkeys were not present (Drea and Wallen, 1999). The social network will likely manifest constraints on different stages of the information use sequence depending on the type of association indexed by the network, i.e., associations according to spatiotemporal proximity or direct interactions. For instance, positions in proximity networks may affect an individual’s opportunities for information acquisition, assuming individuals are more likely to acquire information from others with whom they are more frequently in visual contact (Coussi-Korbel and Fragaszy, 1995; Voelkl and Noë, 2008, 2010), e.g., stickleback proximity networks predict the flow of information about the location of a novel task (Atton et al., 2012). Similarly, positions in interaction networks may limit the application and exploitation of information about resources if social bonds are required to gain access to those resources (Henzi and Barrett, 2002; Clarke et al., 2010), e.g., vervet monkeys (Chlorocebus aethiops) allocate their social effort to access food provided by others (Fruteau et al., 2009).

The behavioural phenotype, specifically personality, may also be important in mediating individuals’ acquisition, application and exploitation of social information. We have previously shown that personality can affect both the first and second steps of the social information use sequence: calmer baboons were more likely to acquire social information but bolder individuals were more likely to apply it (Carter et al., 2014). In geese (Branta leucopsis), personality affected the final step in the sequence: shyer geese were more likely to exploit social information to forage where other geese were successfully foraging (Kurvers et al., 2010). Similarly, fast exploring great tits (Parus major) were more likely to apply social information and change their foraging behaviour to mirror a demonstrator’s (Marchetti and Drent, 2000).

Finally, individual demographic characteristics, particularly age and sex, may affect each step of the social information use sequence. Juveniles may be more reliant on social information because adults have already acquired the necessary information to survive to adulthood (Galef and Laland, 2005). This prediction is supported in baboons, where juveniles spend more time than adults acquiring social information about a novel food (Carter et al., 2014). Similarly, in Japanese macaques (Macaca fuscata), novel socially-transmitted behaviours were more likely to be adopted by juveniles (Huffman et al., 1996). Sex differences in the social information use sequence are also likely due to, e.g., sex-specific costs of competition at resources (e.g. Aragón, 2009).

A further challenge involved in elucidating the social information use sequence is the identification of the relevant network through which information diffuses during the acquisition phase. Researchers have usually assumed information transfers primarily between individuals who are in close spatial proximity (for examples, see Kendal et al., 2010; Aplin et al., 2012; Claidiére et al., 2013). However, individuals may preferentially acquire information from others besides those to whom they are associated as neighbours. For instance, individuals may be more attentive to those with whom they have strong affiliative bonds (Coussi-Korbel and Fragaszy, 1995) or to lower ranking animals from whom they can scrounge resources (King et al., 2009). The need to consider alternative networks in the identification of information diffusion paths is well illustrated by Boogert et al. (2014), who showed that the spread of solutions to a novel foraging task in captive starlings (Sturnus vulgaris) was better predicted by a network based on perching associations than foraging associations.

In this study, we explore phenotypic limitations on social information use. We examined information transmission among wild chacma baboons (Papio ursinus) by experimentally introducing ephemeral patches of a highly preferred food while the troops foraged naturally. We first compared which of five networks best predicted the diffusion of information through the troops about the location of a highly preferred food. Next, we investigated how individuals’ social, behavioural and demographic phenotypes affected their abilities to successfully acquire, apply and exploit this social information.

All individuals in both troops acquired social information in at least one experiment (barring one individual who acquired personal information of one patch, but died in the last week of experiments). On average (median), 10 individuals obtained information about the location of the patches (range = 2–27 individuals) in each diffusion experiment (see Video 4 for an example of information diffusion through a network).

Video 4

Download asset

Identifying phenotypic constraints on social information use

We found that individuals’ phenotypes limited the acquisition, application and exploitation of social information about the location of food patches (Table 6, Figure 3). Proximity strength was the only predictor of information acquisition: more central baboons acquired social information more frequently. Proximity strength also showed a similar pattern with information application, but not exploitation. Information application and exploitation were further limited by sex and grooming strength, with males and more central individuals in the grooming network more likely both to apply and exploit acquired information. In combination with sex, dominance rank played a further limiting role on individuals’ exploitation of information, such that females of low rank were most limited in their exploitation of patches. Finally, individuals’ behavioural phenotypes, i.e., boldness, also influenced information exploitation. Overall, individuals that were better connected in the 10 m network were more likely to acquire social information, but it was higher ranking males who were more likely to exploit this information.

Table 6

Response	Predictor	Effect size	S.E.	t	P
Social information acquisition	Intercept	0.23	0.21	1.11	0.27
	Proximity strength	0.66	0.07	8.87	<0.001
Social information application	Intercept	-1.30	0.38	-3.40	<0.001
	Proximity strength	0.65	0.10	6.45	<0.001
	Grooming strength	0.01	<0.001	4.74	<0.001
	Sexa	0.84	0.15	5.48	<0.001
Social information exploitation	Intercept	-2.43	0.39	-6.17	<0.001
	Grooming strength	0.02	<0.001	4.67	<0.001
	Sexa	0.73	0.33	2.21	0.03
	Boldness	0.01	<0.001	3.74	<0.001
	Rank	1.39	0.51	2.71	0.01

1. Presented are the predictor variables, their effect sizes, standard errors (S.E.), t values and p-values.

2. ^aReference category: female

Figure 3

Download asset Open asset

As a result of these constraints on successive steps in the social information use sequence, individuals only acquired social information about the patches on average (median) 6 times, applied social information 2.5 times, and exploited social information once (across a total of 25 trials per group). However, because of phenotypic variation, there was a substantial range around these medians. Thus, while the average individual acquired and exploited social information on <25% and <5% of occasions, respectively, others were able to acquire and exploit information on >50% and >35% of occasions, or not at all, depending on their phenotype.

Our study suggests that the route of information flow about ephemeral food patches is most closely matched by the 10 m proximity network, although the 5 m chain and nearest neighbour networks (both directed and undirected) also provided a good approximation. These results are consistent with several previous studies indicating the importance of spatiotemporal associations for information flow (e.g., great tits: Aplin et al., 2012; three-spine sticklebacks: Atton et al., 2012). While the most appropriate proximity network to capture information flow will vary depending on the type of information, the social forager, and the environment, our study emphasises the need to recognise that not all networks will be equally applicable. These findings build on the suggestions of previous authors (Lehmann and Ross, 2011; Madden et al., 2011; Castles et al., 2014), and provide the first quantitative demonstration that multiple networks should be considered when studying information transmission. In the present analysis, the grooming networks also provided a good match to the pattern of information flow, suggesting that individuals may be more likely to monitor those with whom they share strong social bonds, irrespective of the symmetry of those bonds (the model results for the undirected and directed networks were very similar). However, the absence of an effect of grooming strength on information acquisition at the individual level (Table 6) suggests that such effects are relatively weak in comparison to those of spatial proximity. In contrast, the dominance networks were entirely unrelated to information flow. Since information flow required visual observation, this suggests that the monitoring of conspecifics is independent of dominance rank. A recent study of social attention in wild vervet monkeys reported a similar pattern (Renevey et al., 2013). This may reflect the fact that dominants and subordinates monitor each other equally, albeit for different reasons: dominant animals seek to scrounge the food discoveries of others, while subordinates seek to avoid aggression.

In our analysis of phenotypic constraints on social information use, we identified three steps in the information use sequence: acquisition, application, and exploitation. In the first step, we found that information acquisition was independent of almost all phenotypic traits tested, i.e., age, sex, rank, personality, and social bonds (grooming strength). The only important trait was individual centrality in the 10 m proximity network. This suggests that visual information about the patch was inexpensive to collect, and limited only by an individual’s spatial associations with other group members. Where the acquisition of social information is more costly, we might expect other phenotypic traits to become important. For instance, among juvenile chimpanzees (Pan troglodytes), sex differences in attentiveness are believed to explain why females spend more time observing, and are faster to learn, the challenging skill of ‘termite fishing’ (Lonsdorf, 2005).

In the second and third steps of the information use sequence, the application and exploitation of information were closely linked. Both steps involved patch entry, but the latter also involved the successful capture of foraging benefits from the patch. The similarities and dissimilarities in phenotypic predictors between the two models are highly informative, not only with respect to understanding the different constraints that can operate along the information use sequence, but also in elucidating how information use mediates the acquisition of monopolisable resources. The latter is possible because our experimental design makes information exploitation synonymous with resource acquisition. Clearly, in many other cases, information exploitation will be unrelated to monopolisable resources but rather involve other types of knowledge, such as foraging skills, predation risk, and mate compatibility. In these instances, the phenotypic constraints on information application and exploitation may be quite different to those observed here.

We begin our assessment of phenotypic constraints on information application and exploitation with dominance rank. Dominant animals were far more likely to successfully exploit social information, because they were able to monopolise food patches. Indeed, as information about the patch spread, increasingly dominant animals would become informed and enter the patch, supplanting lower ranked occupants and preventing others from entering subsequently until the patch was exhausted. This pattern is consistent with how dominant animals scrounge from others in this population (King et al., 2009; Marshall et al., 2012) and across social foragers generally (Barta and Giraldeau, 1998). Surprisingly, however, dominance did not predict information application. The reason for this is that many subordinates also entered the patch, but only after the dominant animal had left (see Video 2). Some of these animals were late arrivals, but a large number would be waiting (‘queuing’) nearby for the dominant animal to leave. Since the patch was largely depleted when the dominant animal left, the subsequent patch entries by subordinates imply the application of outdated information, at a surprisingly large scale given over half of those entering the patch failed to exploit it. One possible explanation for such apparently maladaptive behaviour is that, while baboons are able to collect social information about patch location, they are unable to collect ‘public’ information about patch quality. A similar pattern has been observed in three-spined sticklebacks (Coolen et al., 2003). However, other experimental work in this population indicates that baboons are able to collect such information (Lee, 2015). A more likely explanation is that lower ranking individuals are aware that the patch is largely empty, but there is minimal cost in entering it and sifting through the sand in case any food items remain. Indeed, in a small number of cases, late arrivals did find a small number of kernels that had been overlooked.

Proximity strength remained a strong predictor for information application, as might be expected: individuals had to find the patch before they could enter it. However, because only a very small fraction of those who acquired and applied the information were able to benefit from it, the effect of proximity strength was lost in information exploitation. Grooming strength, however, was important for both information application and exploitation, possibly reflecting the role that social bonds play in tolerance at feeding sites, both in this baboon population (Sick et al., 2014) and in primates more generally (Ventura et al., 2006; Tiddi et al., 2011). Nevertheless, tolerance of direct co-feeding at the patch (patch sharing) was rare in this experiment: of the 293 patch entries observed in the 44 experiments for which these data could be extracted, only 14 (4.8%) resulted in co-feeding (where two or more individuals fed simultaneously from the patch). Moreover, in 4 of these cases (including one group of 3 co-feeders), there were vocal protests of intolerance from one or both parties (see Video 3 for an example of a vocal protest during co-feeding). Thus toleration of co-feeding could be said to occur on only 9 occasions (3.1%). Instead, the tolerance observed in this experiment was primarily of close proximity of individuals to the patch, which allowed individuals to queue for and quickly enter the patch on a dominant’s exit (see Video 3). There are thus two strategies for information application to occur, both of which may rely on social bonds: tolerated co-feeding and tolerated (close-proximity) queuing. A change in the dimensions of the patch to allow more foragers concurrently to occupy it, and thus a greater possibility of tolerated co-feeding, may show a greater effect of grooming strength on both social information exploitation and application.

Sex also played a role: females were less likely to apply and exploit social information. As we control for rank in the analyses, this finding may reflect female reproductive constraints. Previous work on nine-spine sticklebacks (Pungitius pungitius) has indicated that female gravidity can influence the use of social information (Webster and Laland, 2010). In our case, many of the observed females were either pregnant or lactating, and females in these states experience higher foraging demands (Silk, 1987; Barrett et al., 2006), potentially making them less willing either to forego valuable foraging time to queue for patch entry (information application) or to spend excessive time searching for food once in the patch (information exploitation). Shy animals also showed lower rates of information exploitation, presumably because they were more nervous and therefore spent more time in the social monitoring of conspecifics (Edwards et al., 2013). The observation that bolder animals were more likely to successfully exploit information is consistent with the finding that bolder animals are also more likely to demonstrate social learning in this population (Carter et al., 2014). In comparison, Harcourt et al. (2010) found no effect of boldness on social information use in three-spined sticklebacks. However, that study only went as far as the information application step of the information use sequence. We only found an effect of personality in the final information exploitation step.

Phenotypic variation was also observed in asocial learning. Younger, more dominant males were more efficient at finding food patches. The most likely explanation for this pattern is that dominant juveniles were more likely to be at the leading edge of a foraging group, and therefore the first to encounter the patches. Similarly, juvenile ring-tailed coatis (Nasua nasua) occupy positions at the leading edge of their foraging groups (Hirsch, 2011). The multiplicative effect further suggests that the probability of younger dominant males occupying this spatial position increased the better connected they were in their social network. Notably, we found no effect of age on social information use. While this is not surprising at the information acquisition stage, where there were no phenotypic constraints other than network position, it is more surprising for information application and exploitation, especially since juveniles appear to show greater propensity for social learning than adults, both in baboons (Carter et al., 2014) and more generally (e.g., meerkats, Suricata suricatta: Thornton and Malapert, 2009). However, this propensity usually refers to social learning tasks involving novelty or complexity, reflecting the tendency for juveniles to be more curious and exploratory about their environment (Reader and Laland, 2001; Kendal et al., 2005; Benson-Amram and Holekamp, 2012). In our case, the task simply involved entering and exploiting a patch of familiar food, and for such a task it might be expected that individuals of all ages would show equal abilities.

Our study raises a further point about the type of social information that is transmitted. We provided individuals with the opportunity to acquire social information about the location of a highly preferred food source in a novel location. These novel patches were rapidly depleted and the social information quickly became outdated. Such short-lived, ephemeral information may be easy and cheap to acquire and, as we have found, more likely be transmitted through proximity rather than interaction networks. Whilst our results support previous findings that proximity social networks are important for the transmission of ephemeral and/or easy-to-acquire information among individuals in the wild (Aplin et al., 2012), evidence from starlings suggests that more complex information diffuses through alternative networks. Boogert et al. (2014) showed that information about novel foraging skills diffused through the perching rather than foraging social networks. Whilst it is challenging to determine the difficulty with which species acquire different types of information (Griffin et al., 2015), research is needed to elucidate how the type of social information influences how quickly and through which networks it is transmitted. In the baboon system, we might similarly expect that social information that takes longer to acquire and/or process will transmit through different networks to those that transmit easy-to-acquire information.

Our finding that phenotype limits information use builds on previous work indicating that individual state, such as uncertainty or the possession of outdated information, can influence social learning strategies (reviewed in Rendell et al., 2011). Our study extends this work, revealing fundamental individual differences in the ability to use social information. Decomposing social information use into three constituent steps has further illuminated how these individual differences limit information use. The implications of such sequential phenotypic constraints are manifold. We conclude by considering two points. First, only a small number of individuals who acquire or apply social information may successfully exploit it. Similarly, Racine et al. (2012) report that ring-billed gulls (Larus delawarensis) may acquire social information about the location of food resources but be unable to apply it because of parenting constraints. Second, where phenotypic traits are expressed in relation to conspecifics (e.g., dominance rank, network position), social information use will vary markedly between social environments. For instance, in a captive setting where competition is minimised (e.g. by testing individuals when isolated and not at risk of aggression: Call et al., 2005) (c.f. Drea and Wallen, 1999), subordinate animals may show a propensity for copying others (e.g. Kendal et al., 2015), but in the wild, where subordinates are more vulnerable to aggression and where food resources are relatively more valuable, subordinates may rarely copy others. Together, these two points highlight a critical disjunction between an ability to acquire information and to capture its benefits. This disconnection is likely to have a fundamental impact on selection for social information use, such that even in social information-rich environments, only a small number of individuals of a particular phenotype may be selected to use it.

1. 1. Carter AJ
   2. Torrents Ticó M
   3. Cowlishaw G

   (2015) Data from: Sequential phenotypic constraints on social information use

   Available at figshare under a CCBY Attribution licence.

   https://dx.doi.org/10.6084/m9.figshare.2010396

1. 1. Aplin LM
   2. Farine DR
   3. Morand-Ferron J
   4. Sheldon BC

   (2012) Social networks predict patch discovery in a wild population of songbirds

   Proceedings. Biological Sciences / the Royal Society 279:4199–4205.

   https://doi.org/10.1098/rspb.2012.1591

   • Google Scholar
2. 1. Aplin LM
   2. Firth JA
   3. Farine DR
   4. Voelkl B
   5. Crates RA
   6. Culina A
   7. Garroway CJ
   8. Hinde CA
   9. Kidd LR
   10. Psorakis I
   11. Milligan ND
   12. Radersma R
   13. Verhelst BL
   14. Sheldon BC

   (2015) Consistent individual differences in the social phenotypes of wild great tits, Parus major

   Animal Behaviour 108:117–127.

   https://doi.org/10.1016/j.anbehav.2015.07.016

   • Google Scholar
3. 1. Aragón P

   (2009) Sex-dependent use of information on conspecific feeding activities in an amphibian urodelian

   Functional Ecology 23:380–388.

   https://doi.org/10.1111/j.1365-2435.2008.01519.x

   • Google Scholar
4. 1. Atton N
   2. Hoppitt W
   3. Webster MM
   4. Galef BG
   5. Laland KN

   (2012) Information flow through threespine stickleback networks without social transmission

   Proceedings. Biological Sciences / the Royal Society 279:4272–4278.

   https://doi.org/10.1098/rspb.2012.1462

   • Google Scholar
5. 1. Barrett L
   2. Halliday J
   3. Henzi SP

   (2006) The ecology of motherhood: the structuring of lactation costs by chacma baboons

   The Journal of Animal Ecology 75:875–886.

   https://doi.org/10.1111/j.1365-2656.2006.01105.x

   • Google Scholar
6. 1. Barta Z
   2. Giraldeau L-A

   (1998) The effect of dominance hierarchy on the use of alternative foraging tactics: a phenotype-limited producing-scrounging game

   Behavioral Ecology and Sociobiology 42:217–223.

   https://doi.org/10.1007/s002650050433

   • Google Scholar
7. 1. Bates D
   2. Mächler M
   3. Bolker B
   4. Walker S

   (2015) Fitting Linear Mixed-Effects Models Using lme4

   Journal of Statistical Software 67:1–448.

   https://doi.org/10.18637/jss.v067.i01

   • Google Scholar
8. 1. Benson-Amram S
   2. Holekamp KE

   (2012) Innovative problem solving by wild spotted hyenas

   Proceedings of the Royal Society B: Biological Sciences 279:4087–4095.

   https://doi.org/10.1098/rspb.2012.1450

   • Google Scholar
9. 1. Boogert NJ
   2. Nightingale GF
   3. Hoppitt W
   4. Laland KN

   (2014) Perching but not foraging networks predict the spread of novel foraging skills in starlings

   Behavioural Processes 109 Pt B:135–144.

   https://doi.org/10.1016/j.beproc.2014.08.016

   • Google Scholar
10. 1. Cairns SJ
    2. Schwager SJ

    (1987) A comparison of association indices

    Animal Behaviour 35:1454–1469.

    https://doi.org/10.1016/S0003-3472(87)80018-0

    • Google Scholar
11. 1. Call J
    2. Carpenter M
    3. Tomasello M

    (2005) Copying results and copying actions in the process of social learning: chimpanzees (Pan troglodytes) and human children (Homo sapiens)

    Animal Cognition 8:151–163.

    https://doi.org/10.1007/s10071-004-0237-8

    • Google Scholar
12. Data

    1. Carter A
    2. Torrents Ticó M
    3. Cowlishaw G

    (authors) (2015) Data from: "Sequential phenotypic constraints on social information use"

    Figshare.

    https://doi.org/10.6084/m9.figshare.2010396.v1
13. 1. Carter AJ
    2. Macdonald SL
    3. Thomson VA
    4. Goldizen AW

    (2009) Structured association patterns and their energetic benefits in female eastern grey kangaroos, Macropus giganteus

    Animal Behaviour 77:839–846.

    https://doi.org/10.1016/j.anbehav.2008.12.007

    • Google Scholar
14. 1. Carter AJ
    2. Marshall HH
    3. Heinsohn R
    4. Cowlishaw G

    (2012a) Evaluating animal personalities: do observer assessments and experimental tests measure the same thing?

    Behavioral Ecology and Sociobiology 66:153–160.

    https://doi.org/10.1007/s00265-011-1263-6

    • Google Scholar
15. 1. Carter AJ
    2. Marshall HH
    3. Heinsohn R
    4. Cowlishaw G

    (2012b) How not to measure boldness: novel object and antipredator responses are not the same in wild baboons

    Animal Behaviour 84:603–609.

    https://doi.org/10.1016/j.anbehav.2012.06.015

    • Google Scholar
16. 1. Carter AJ
    2. Marshall HH
    3. Heinsohn R
    4. Cowlishaw G

    (2013) Personality predicts decision making only when information is unreliable

    Animal Behaviour 86:633–639.

    https://doi.org/10.1016/j.anbehav.2013.07.009

    • Google Scholar
17. 1. Carter AJ
    2. Marshall HH
    3. Heinsohn R
    4. Cowlishaw G

    (2014) Personality predicts the propensity for social learning in a wild primate

    PeerJ 2:e283.

    https://doi.org/10.7717/peerj.283

    • Google Scholar
18. 1. Castles M
    2. Heinsohn R
    3. Marshall HH
    4. Lee AEG
    5. Cowlishaw G
    6. Carter AJ

    (2014) Social networks created with different techniques are not comparable

    Animal Behaviour 96:59–67.

    https://doi.org/10.1016/j.anbehav.2014.07.023

    • Google Scholar
19. 1. Castro L
    2. Toro MA

    (2004) The evolution of culture: From primate social learning to human culture

    Proceedings of the National Academy of Sciences of the United States of America 101:10235–10240.

    https://doi.org/10.1073/pnas.0400156101

    • Google Scholar
20. 1. Claidiére N
    2. Messer EJE
    3. Hoppitt W
    4. Whiten A

    (2013) Diffusion Dynamics of Socially Learned Foraging Techniques in Squirrel Monkeys

    Current Biology 23:1–5.

    https://doi.org/10.1016/j.cub.2012.10.044

    • Google Scholar
21. 1. Claidiére N
    2. Messer EJE
    3. Hoppitt W
    4. Whiten A

    (2013) Diffusion Dynamics of Socially Learned Foraging Techniques in Squirrel Monkeys

    Current Biology 23:1–5.

    https://doi.org/10.1016/j.cub.2012.10.044

    • Google Scholar
22. 1. Clarke PMR
    2. Halliday JEB
    3. Barrett L
    4. Henzi SP

    (2010) Chacma baboon mating markets: competitor suppression mediates the potential for intersexual exchange

    Behavioral Ecology 21:1211–1220.

    https://doi.org/10.1093/beheco/arq125

    • Google Scholar
23. 1. Coolen I
    2. Bergen YV
    3. Day RL
    4. Laland KN

    (2003) Species difference in adaptive use of public information in sticklebacks

    Proceedings of the Royal Society B: Biological Sciences 270:2413–2419.

    https://doi.org/10.1098/rspb.2003.2525

    • Google Scholar
24. 1. Coussi-Korbel S
    2. Fragaszy DM

    (1995) On the relation between social dynamics and social learning

    Animal Behaviour 50:1441–1453.

    https://doi.org/10.1016/0003-3472(95)80001-8

    • Google Scholar
25. 1. Cowlishaw G
    2. Davies JG

    (1997) Flora of the Pro-Namib Desert Swakop River Catchment, Namibia: community classification and implications for desert vegetation sampling

    Journal of Arid Environments 36:271–290.

    https://doi.org/10.1006/jare.1996.0203

    • Google Scholar
26. 1. Cowlishaw G
    2. Dunbar RIM

    (1991) Dominance rank and mating success in male primates

    Animal Behaviour 41:1045–1056.

    https://doi.org/10.1016/S0003-3472(05)80642-6

    • Google Scholar
27. 1. Cowlishaw GUY

    (1997) Trade-offs between foraging and predation risk determine habitat use in a desert baboon population

    Animal Behaviour 53:667–686.

    https://doi.org/10.1006/anbe.1996.0298

    • Google Scholar
28. 1. Croft DP
    2. James R
    3. Krause J

    (2008)

    Exploring Animal Social Networks, Princeton, Princeton University Press

    Exploring Animal Social Networks, Princeton, Princeton University Press.

    • Google Scholar
29. 1. Csardi G
    2. Nepusz T

    (2006)

    The igraph software package for complex network research

    InterJournal Complex Systems 1695:.

    • Google Scholar
30. 1. Dall SR
    2. Giraldeau LA
    3. Olsson O
    4. McNamara JM
    5. Stephens DW

    (2005) Information and its use by animals in evolutionary ecology

    Trends in Ecology & Evolution 20:187–193.

    https://doi.org/10.1016/j.tree.2005.01.010

    • Google Scholar
31. 1. Dormann CF
    2. Elith J
    3. Bacher S
    4. Buchmann C
    5. Carl G
    6. Carré G
    7. Marquéz JRG
    8. Gruber B
    9. Lafourcade B
    10. Leitão PJ
    11. Münkemüller T
    12. McClean C
    13. Osborne PE
    14. Reineking B
    15. Schröder B
    16. Skidmore AK
    17. Zurell D
    18. Lautenbach S

    (2013) Collinearity: a review of methods to deal with it and a simulation study evaluating their performance

    Ecography 36:27–46.

    https://doi.org/10.1111/j.1600-0587.2012.07348.x

    • Google Scholar
32. 1. Drea CM
    2. Wallen K

    (1999) Low-status monkeys "play dumb" when learning in mixed social groups

    Proceedings of the National Academy of Sciences of the United States of America 96:12965–12969.

    https://doi.org/10.1073/pnas.96.22.12965

    • Google Scholar
33. 1. Edwards AM
    2. Best EC
    3. Blomberg SP
    4. Goldizen AW

    (2013) Individual traits influence vigilance in wild female eastern grey kangaroos

    Australian Journal of Zoology 61:332–341.

    https://doi.org/10.1071/ZO13025

    • Google Scholar
34. 1. Franz M
    2. Nunn CL

    (2009) Network-based diffusion analysis: a new method for detecting social learning

    Proceedings. Biological Sciences / the Royal Society 276:1829–1836.

    https://doi.org/10.1098/rspb.2008.1824

    • Google Scholar
35. 1. Fruteau C
    2. Voelkl B
    3. van Damme E
    4. Noe R

    (2009) Supply and demand determine the market value of food providers in wild vervet monkeys

    Proceedings of the National Academy of Sciences of the United States of America 106:12007–12012.

    https://doi.org/10.1073/pnas.0812280106

    • Google Scholar
36. 1. Galef BG
    2. Laland KN

    (2005) Social Learning in Animals: Empirical Studies and Theoretical Models

    BioScience 55:489–499.

    https://doi.org/10.1641/0006-3568(2005)055[0489:SLIAES]2.0.CO;2

    • Google Scholar
37. 1. Giraldeau LA
    2. Valone TJ
    3. Templeton JJ

    (2002) Potential disadvantages of using socially acquired information

    Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences 357:.

    https://doi.org/10.1098/rstb.2002.1065

    • Google Scholar
38. 1. Griffin AS
    2. Guillette LM
    3. Healy SD

    (2015) Cognition and personality: an analysis of an emerging field

    Trends in Ecology & Evolution 30:207–214.

    https://doi.org/10.1016/j.tree.2015.01.012

    • Google Scholar
39. 1. Harcourt JL
    2. Biau S
    3. Johnstone R
    4. Manica A

    (2010) Boldness and Information Use in Three-Spined Sticklebacks

    Ethology 116:440–447.

    https://doi.org/10.1111/j.1439-0310.2010.01757.x

    • Google Scholar
40. 1. Henzi SP
    2. Barrett L

    (2002) Infants as a commodity in a baboon market

    Animal Behaviour 63:915–921.

    https://doi.org/10.1006/anbe.2001.1986

    • Google Scholar
41. 1. Hirsch BT

    (2011) Spatial position and feeding success in ring-tailed coatis

    Behavioral Ecology and Sociobiology 65:581–591.

    https://doi.org/10.1007/s00265-010-1058-1

    • Google Scholar
42. 1. Hoppitt W
    2. Boogert NJ
    3. Laland KN

    (2010) Detecting social transmission in networks

    Journal of Theoretical Biology 263:544–555.

    https://doi.org/10.1016/j.jtbi.2010.01.004

    • Google Scholar
43. Book

    1. Hoppitt W
    2. Laland KN

    (2013) Social Learning: An Introduction to Mechanisms, Methods, and Models

    Princeton: Princeton University Press.

    https://doi.org/10.1515/9781400846504

    • Google Scholar
44. Book

    1. Huffman MA

    (1996) Acquisition of Innovative Cultural Behaviors in Nonhuman Primates

    In: Heyes C. M, Galef Jr B. G, editors. Social Learning in Animals. London: Elsevier. pp. 267–289.

    https://doi.org/10.1016/B978-012273965-1/50014-5

    • Google Scholar
45. 1. Kahumbu P
    2. Eley RM

    (1991) Teeth emergence in wild olive baboons in Kenya and formulation of a dental schedule for aging wild baboon populations

    American Journal of Primatology 23:1–9.

    https://doi.org/10.1002/ajp.1350230102

    • Google Scholar
46. 1. Kendal R
    2. Hopper LM
    3. Whiten A
    4. Brosnan SF
    5. Lambeth SP
    6. Schapiro SJ
    7. Hoppitt W

    (2015) Chimpanzees copy dominant and knowledgeable individuals: implications for cultural diversity

    Evolution and Human Behavior 36:65–72.

    https://doi.org/10.1016/j.evolhumbehav.2014.09.002

    • Google Scholar
47. 1. Kendal RL
    2. Coe RL
    3. Laland KN

    (2005) Age differences in neophilia, exploration, and innovation in family groups of callitrichid monkeys

    American Journal of Primatology 66:167–188.

    https://doi.org/10.1002/ajp.20136

    • Google Scholar
48. 1. Kendal RL
    2. Custance DM
    3. Kendal JR
    4. Vale G
    5. Stoinski TS
    6. Rakotomalala NL
    7. Rasamimanana H

    (2010) Evidence for social learning in wild lemurs (Lemur catta)

    Learning & Behavior 38:220–234.

    https://doi.org/10.3758/LB.38.3.220

    • Google Scholar
49. 1. King AJ
    2. Douglas CMS
    3. Huchard E
    4. Isaac NJB
    5. Cowlishaw G

    (2008) Dominance and Affiliation Mediate Despotism in a Social Primate

    Current Biology 18:1833–1838.

    https://doi.org/10.1016/j.cub.2008.10.048

    • Google Scholar
50. 1. King AJ
    2. Isaac NJB
    3. Cowlishaw G

    (2009) Ecological, social, and reproductive factors shape producer-scrounger dynamics in baboons

    Behavioral Ecology 20:1039–1049.

    https://doi.org/10.1093/beheco/arp095

    • Google Scholar
51. 1. Koenig A

    (2002) Competition for resources and its behavioural consequences among female primates

    International Journal of Primatology 23:759–783.

    https://doi.org/10.1023/A:1015524931226

    • Google Scholar
52. 1. Kurvers RH
    2. Prins HH
    3. van Wieren SE
    4. van Oers K
    5. Nolet BA
    6. Ydenberg RC

    (2010) The effect of personality on social foraging: shy barnacle geese scrounge more

    Proceedings. Biological Sciences / the Royal Society 277:601–608.

    https://doi.org/10.1098/rspb.2009.1474

    • Google Scholar
53. 1. Laland KN

    (2004) Social learning strategies

    Learning & Behavior 32:4–14.

    https://doi.org/10.3758/bf03196002

    • Google Scholar
54. 1. Lee AEG

    (2015)

    The evolutionary ecology of animal information use and social dominance

    The evolutionary ecology of animal information use and social dominance, University of Oxford.

    • Google Scholar
55. 1. Lehmann J
    2. Ross C

    (2011) Baboon (Papio anubis) social complexity--a network approach

    American Journal of Primatology 73:775–789.

    https://doi.org/10.1002/ajp.20967

    • Google Scholar
56. 1. Lonsdorf EV

    (2005) Sex differences in the development of termite-fishing skills in the wild chimpanzees, Pan troglodytes schweinfurthii, of Gombe National Park, Tanzania

    Animal Behaviour 70:673–683.

    https://doi.org/10.1016/j.anbehav.2004.12.014

    • Google Scholar
57. 1. Madden JR
    2. Drewe JA
    3. Pearce GP
    4. Clutton-Brock TH

    (2011) The social network structure of a wild meerkat population: 3. Position of individuals within networks

    Behavioral Ecology and Sociobiology 65:1857–1871.

    https://doi.org/10.1007/s00265-011-1194-2

    • Google Scholar
58. 1. Marchetti C
    2. Drent PJ

    (2000) Individual differences in the use of social information in foraging by captive great tits

    Animal Behaviour 60:131–140.

    https://doi.org/10.1006/anbe.2000.1443

    • Google Scholar
59. 1. Marshall HH
    2. Carter AJ
    3. Coulson T
    4. Rowcliffe JM
    5. Cowlishaw G

    (2012) Exploring foraging decisions in a social primate using discrete-choice models

    The American Naturalist 180:481–495.

    https://doi.org/10.1086/667587

    • Google Scholar
60. 1. Moore AJ

    (1993) Towards an evolutionary view of social dominance

    Animal Behaviour 46:594–596.

    https://doi.org/10.1006/anbe.1993.1226

    • Google Scholar
61. 1. Pradhan GR
    2. Tennie C
    3. van Schaik CP

    (2012) Social organization and the evolution of cumulative technology in apes and hominins

    Journal of Human Evolution 63:180–190.

    https://doi.org/10.1016/j.jhevol.2012.04.008

    • Google Scholar
62. 1. Racine F
    2. Giraldeau L-A
    3. Patenaude-Monette M
    4. Giroux J-F

    (2012) Evidence of social information on food location in a ring-billed gull colony, but the birds do not use it

    Animal Behaviour 84:175–182.

    https://doi.org/10.1016/j.anbehav.2012.04.028

    • Google Scholar
63. 1. Reader SM
    2. Laland KN

    (2001) Primate innovation: Sex, age and social rank differences

    International Journal of Primatology 22:787–805.

    https://doi.org/10.1023/A:1012069500899

    • Google Scholar
64. 1. Rendell L
    2. Fogarty L
    3. Hoppitt WJ
    4. Morgan TJ
    5. Webster MM
    6. Laland KN

    (2011) Cognitive culture: theoretical and empirical insights into social learning strategies

    Trends in Cognitive Sciences 15:68–76.

    https://doi.org/10.1016/j.tics.2010.12.002

    • Google Scholar
65. 1. Renevey N
    2. Bshary R
    3. van de Waal E

    (2013) Philopatric vervet monkey females are the focus of social attention rather independently of rank

    Behaviour 150:599–615.

    https://doi.org/10.1163/1568539X-00003072

    • Google Scholar
66. 1. Sick C
    2. Carter AJ
    3. Marshall HH
    4. Knapp LA
    5. Dabelsteen T
    6. Cowlishaw G

    (2014) Evidence for varying social strategies across the day in chacma baboons

    Biology Letters 10:.

    https://doi.org/10.1098/rsbl.2014.0249

    • Google Scholar
67. 1. Silk JB

    (1987) Activities and feeding behavior of free-ranging pregnant baboons

    International Journal of Primatology 8:593–613.

    https://doi.org/10.1007/BF02735779

    • Google Scholar
68. Website

    1. Team RDC

    (2011) R: A Language and Environment for Statistical Computing. URL

    http://www.R-project.org/
69. 1. Thornton A
    2. Malapert A

    (2009) The rise and fall of an arbitrary tradition: an experiment with wild meerkats

    Proceedings of the Royal Society B: Biological Sciences 276:1269–1276.

    https://doi.org/10.1098/rspb.2008.1794

    • Google Scholar
70. 1. Tiddi B
    2. Aureli F
    3. Polizzi di Sorrentino E
    4. Janson CH
    5. Schino G

    (2011) Grooming for tolerance? Two mechanisms of exchange in wild tufted capuchin monkeys

    Behavioral Ecology 22:663–669.

    https://doi.org/10.1093/beheco/arr028

    • Google Scholar
71. 1. Ventura R
    2. Majolo B
    3. Koyama NF
    4. Hardie S
    5. Schino G

    (2006) Reciprocation and interchange in wild Japanese macaques: grooming, cofeeding, and agonistic support

    American Journal of Primatology 68:1138–1149.

    https://doi.org/10.1002/ajp.20314

    • Google Scholar
72. 1. Voelkl B
    2. Noë R

    (2008) The influence of social structure on the propagation of social information in artificial primate groups: a graph-based simulation approach

    Journal of Theoretical Biology 252:77–86.

    https://doi.org/10.1016/j.jtbi.2008.02.002

    • Google Scholar
73. 1. Voelkl B
    2. Noë R

    (2010) Simulation of information propagation in real-life primate networks: longevity, fecundity, fidelity

    Behavioral Ecology and Sociobiology 64:1449–1459.

    https://doi.org/10.1007/s00265-010-0960-x

    • Google Scholar
74. 1. Webster MM
    2. Atton N
    3. Hoppitt WJ
    4. Laland KN

    (2013) Environmental complexity influences association network structure and network-based diffusion of foraging information in fish shoals

    The American Naturalist 181:235–244.

    https://doi.org/10.1086/668825

    • Google Scholar
75. 1. Webster MM
    2. Laland KN

    (2010) Reproductive State Affects Reliance on Public Information in Sticklebacks

    Proceedings of the Royal Society of London B: Biological Sciences.

    https://doi.org/10.1098/rspb.2010.1562

    • Google Scholar
76. 1. Whiten A

    (2000) Primate Culture and Social Learning

    Cognitive Science 24:477–508.

    https://doi.org/10.1207/s15516709cog2403_6

    • Google Scholar

Author details

1. Alecia J Carter

   Department of Zoology, University of Cambridge, Cambridge, United Kingdom

   Contribution

   AJC, Conception and design, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article

   For correspondence

   ac854@cam.ac.uk

   Competing interests

   AJC: Early Career Advisor for eLife.

   "This ORCID iD identifies the author of this article:" 0000-0001-5550-9312

2. Miquel Torrents Ticó

   Zoological Society of London, Tsaobis Baboon Project, Institute of Zoology, London, United Kingdom

   Contribution

   MTT, Acquisition of data, Drafting or revising the article

   Competing interests

   No competing interests declared.

3. Guy Cowlishaw

   Zoological Society of London, Institute of Zoology, London, United Kingdom

   Contribution

   GC, Conception and design, Analysis and interpretation of data, Drafting or revising the article

   Competing interests

   No competing interests declared.

• 1,809

  views

• 273

  downloads

• 35

  citations

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