Innovative problem solving in wild meerkats Alex Thornton a, * , Jamie Samson b a Department of Experimental Psychology, University of Cambridge, Cambridge, U.K. b Kalahari Meerkat Project, Kuruman River Reserve, Van Zylsrus, South Africa article info Article history: Received 28 October 2011 Initial acceptance 20 December 2011 Final acceptance 22 February 2012 Available online 13 April 2012 MS. number: 11-00870 Keywords: cognition inhibitory control innovation learning meerkat Suricata suricatta Behavioural innovations may have far-reaching evolutionary and ecological consequences, allowing individuals to obtain new resources and cope with environmental change. However, as innovations are rarely observed in nature, their emergence is poorly understood. What drives individuals to innovate, and what psychological mechanisms allow them to do so? We used three novel food extraction tasks to address these questions in groups of wild meerkats, Suricata suricatta. Innovatory tendencies were unrelated to body condition and foraging success, but were affected by age, rank and sex. Juvenile individuals were most likely to interact with tasks, but seldom solved them, perhaps owing to their small size or lack of dexterity. Instead, adult subordinates made up the bulk of the innovators. In cooperatively breeding societies, the inability of subordinate helpers to compete physically with dominant breeders may drive them to seek out solutions to novel problems. Most innovators were males, which, as the dispersing sex, may be particularly prone to solve novel problems, and innovators virtually always persisted longer than other group members when interacting with tasks. Most successful individuals solved tasks more than once, and learned to inhibit ineffective prepotent responses across successive presentations of the same task. They did not learn to manipulate functional parts of the apparatus more efficiently, however, nor did they extract general rules allowing them to solve novel tasks faster. Contrary to recent suggestions that innovation may be cognitively demanding, these results suggest that simple, conserved learning processes and dogged perseverance may suffice to generate solutions to novel problems. Ó 2012 The Association for the Study of Animal Behaviour. Published by Elsevier Ltd. All rights reserved. Innovation is a process that generates a novel learned behaviour in an individual (Ramsey et al. 2007; see also Reader & Laland 2003). It may allow animals to exploit new resources, cope with environmental change and invade new niches, and may lead to the spread of adaptive information through groups, forming local traditions (Reader & Laland 2003; Sol et al. 2005; Ramsey et al. 2007). However, despite these important implications, the factors that lead certain individuals to innovate remain unclear. Following the adage ‘necessity is the mother of invention’ innovative tendencies may often be inversely related to competitive ability. Innovation necessarily involves costs, including potentially wasted time and energy and exposure to danger. Consequently, individuals that cannot physically outcompete others may be particularly likely to take the risks of developing solutions to novel problems (Reader & Laland 2003). This suggestion has considerable empirical support from work on fish (Laland & Reader 1999a, b), birds (Katzir 1982; Biondi et al. 2010; Morand-Ferron et al. 2011; Cole & Quinn 2012) and primates (Reader & Laland 2001; Kendal et al. 2005), in which young or low-ranking individuals tend to show high innovatory propensities. However, other studies have produced conflicting results (Boogert et al. 2006; Gajdon et al. 2006; Bouchard et al. 2007). Indeed, in some species it may be that some individuals may achieve high rank and competitive ability precisely by virtue of being innovative. For example, Goodall (1986) reported that a young male chimpanzee, Pan troglodytes, attained alpha status through the innovative use of empty cans to augment his threat displays. Further work is clearly necessary to understand the relationship between competitive abilities and innovatory tenden- cies across different taxa and social systems. Cooperative breeders may prove particularly useful study systems as they often show extreme variation in competitive abilities, with a single dominant pair monopolizing reproduction while subordinates help raise the dominants’ offspring (Hauber & Lacey 2005; Clutton-Brock et al. 2006). As well as reflecting differences in competitive abilities across factors such as age, rank and sex, innovative tendencies may differ between individuals within these broad categories. For instance, innovation may be associated with short-term hunger (Laland & Reader 1999a) or, over the longer term, with stable differences in * Correspondence: A. Thornton, Department of Experimental Psychology, University of Cambridge, Downing Street, Cambridge CB2 3EB, U.K. E-mail address: [email protected](A. Thornton). Contents lists available at SciVerse ScienceDirect Animal Behaviour journal homepage: www.elsevier.com/locate/anbehav 0003-3472/$38.00 Ó 2012 The Association for the Study of Animal Behaviour. Published by Elsevier Ltd. All rights reserved. doi:10.1016/j.anbehav.2012.03.018 Animal Behaviour 83 (2012) 1459e1468
aDepartment of Experimental Psychology, University of Cambridge, Cambridge, U.K.bKalahari Meerkat Project, Kuruman River Reserve, Van Zylsrus, South Africa
a r t i c l e i n f o
Article history:Received 28 October 2011Initial acceptance 20 December 2011Final acceptance 22 February 2012Available online 13 April 2012MS. number: 11-00870
0003-3472/$38.00 � 2012 The Association for the Studoi:10.1016/j.anbehav.2012.03.018
Behavioural innovations may have far-reaching evolutionary and ecological consequences, allowingindividuals to obtain new resources and cope with environmental change. However, as innovations arerarely observed in nature, their emergence is poorly understood. What drives individuals to innovate,and what psychological mechanisms allow them to do so? We used three novel food extraction tasks toaddress these questions in groups of wild meerkats, Suricata suricatta. Innovatory tendencies wereunrelated to body condition and foraging success, but were affected by age, rank and sex. Juvenileindividuals were most likely to interact with tasks, but seldom solved them, perhaps owing to their smallsize or lack of dexterity. Instead, adult subordinates made up the bulk of the innovators. In cooperativelybreeding societies, the inability of subordinate helpers to compete physically with dominant breedersmay drive them to seek out solutions to novel problems. Most innovators were males, which, as thedispersing sex, may be particularly prone to solve novel problems, and innovators virtually alwayspersisted longer than other group members when interacting with tasks. Most successful individualssolved tasks more than once, and learned to inhibit ineffective prepotent responses across successivepresentations of the same task. They did not learn to manipulate functional parts of the apparatus moreefficiently, however, nor did they extract general rules allowing them to solve novel tasks faster. Contraryto recent suggestions that innovation may be cognitively demanding, these results suggest that simple,conserved learning processes and dogged perseverance may suffice to generate solutions to novelproblems.� 2012 The Association for the Study of Animal Behaviour. Published by Elsevier Ltd. All rights reserved.
Innovation is a process that generates a novel learned behaviourin an individual (Ramsey et al. 2007; see also Reader & Laland2003). It may allow animals to exploit new resources, cope withenvironmental change and invade new niches, and may lead to thespread of adaptive information through groups, forming localtraditions (Reader & Laland 2003; Sol et al. 2005; Ramsey et al.2007). However, despite these important implications, the factorsthat lead certain individuals to innovate remain unclear.
Following the adage ‘necessity is the mother of invention’innovative tendenciesmay often be inversely related to competitiveability. Innovation necessarily involves costs, including potentiallywasted time and energy and exposure to danger. Consequently,individuals that cannot physically outcompete others may beparticularly likely to take the risks of developing solutions to novelproblems (Reader & Laland 2003). This suggestion has considerableempirical support from work on fish (Laland & Reader 1999a, b),birds (Katzir 1982; Biondi et al. 2010; Morand-Ferron et al. 2011;
f Experimental Psychology,CB2 3EB, U.K.
dy of Animal Behaviour. Published
Cole & Quinn 2012) and primates (Reader & Laland 2001; Kendalet al. 2005), in which young or low-ranking individuals tend toshow high innovatory propensities. However, other studies haveproduced conflicting results (Boogert et al. 2006; Gajdon et al. 2006;Bouchard et al. 2007). Indeed, in some species it may be that someindividuals may achieve high rank and competitive ability preciselyby virtue of being innovative. For example, Goodall (1986) reportedthat a young male chimpanzee, Pan troglodytes, attained alphastatus through the innovative use of empty cans to augment histhreat displays. Further work is clearly necessary to understand therelationship between competitive abilities and innovatory tenden-cies across different taxa and social systems. Cooperative breedersmay prove particularly useful study systems as they often showextreme variation in competitive abilities, with a single dominantpair monopolizing reproduction while subordinates help raisethe dominants’ offspring (Hauber & Lacey 2005; Clutton-Brocket al. 2006).
As well as reflecting differences in competitive abilities acrossfactors such as age, rank and sex, innovative tendencies may differbetween individuals within these broad categories. For instance,innovation may be associated with short-term hunger (Laland &Reader 1999a) or, over the longer term, with stable differences in
A. Thornton, J. Samson / Animal Behaviour 83 (2012) 1459e14681460
boldness or neophobia (Webster & Lefebvre 2001; Greenberg2003). Alternatively, individuals in particularly good conditionmay be able to afford the time and energy needed to innovate (the‘spare time’ hypothesis; Kummer & Goodall 1985). Captive animals,for instance, may show heightened levels of innovation becausethey are well fed and free of the need to forage and avoid predators.Similarly, young animals may be able to invest in exploration andinnovation if their energetic needs are met through provisioning bytheir elders (see Kummer & Goodall 1985; Reader & Laland 2003).
Finally, the capacity of animals to generate innovative solutionsto novel problems may be constrained by their cognitive abilities(Hauser 2003), For instance, the capacity to recognize the causalstructure of problems or to generalize rapidly across differentproblems may greatly facilitate innovation. This may explain therelatively large number of field reports of innovative behaviour inlarge-brained birds and primate species, compared to their smaller-brained counterparts (Lefebvre et al. 2004). However, the rela-tionship between cognitive factors and variation in innovativetendencies within and between species remains unclear. Indeed,many cases of animal innovation may simply involve conservedassociative learning processes, which are present across vertebrateand invertebrate taxa (cf. Macphail 1987).
The majority of studies to date have elicited innovations bypresenting novel problems to isolated captive animals, whoseresponses may not reflect those seen in natural group contexts.Presentations to captive social groups (e.g. Kendal et al. 2005) aresomewhat more realistic, but still may not fully capture the factorsthat drive innovation in nature (Ramsey et al. 2007; Reader & Biro2010). Conversely, studies of innovation in the wild have largelyrelied on compilations of anecdotal reports (e.g. Lefebvre et al.2004; Sol et al. 2005; Overington et al. 2009), making it difficultto ascertain with certainty whether a particular behaviour is trulynovel. A more potentially powerful approach is to examine patternsof innovation by presenting novel problems to animals in theirnatural environment. A handful of such field experiments havebeen conducted to date, although the taxonomic focus remainslargely restricted to birds (Webster & Lefebvre 2001; Boogert et al.2010; Morand-Ferron et al. 2011; but see Biro et al. 2003 for a studyon chimpanzees). However, such studies rarely consider themechanisms by which animals learn to solve novel problems, andfirm conclusions about the influence of individual characteristicsare often restricted by small sample sizes (a recent large-scale studyby Morand-Ferron et al. 2011 on wild great tits, Parus major, andblue tits, Cyanistes caeruleus, is a notable exception). Furthermore,the prediction that innovation may be driven by hunger or poorbody condition remains to be adequately tested in the wild. Esti-mates of body condition have generally been found to be unrelatedto innovatory propensities (Boogert et al. 2010; Cole et al. 2011;Overington et al. 2011; Morand-Ferron et al. 2011), but as theseestimates are derived from morphometric measurements takenbefore testing, they may not adequately reflect current internalstate or condition.
Here, we report the first field experiments on innovation inwildcooperative breeders. We presented groups of wild meerkats, Sur-icata suricatta, with three novel food extraction tasks and examinedwhether innovative tendencies varied with age, dominance, sex,body condition and foraging ability. Meerkats are cooperativelybreeding mongooses that live in groups comprising a dominantpair, which monopolize reproduction, and a variable number ofhelpers of both sexes, which assist in rearing the young (Brothertonet al. 2001). They are generalists with complex foraging skills(Thornton & McAuliffe 2006) and have been the focus of numerousstudies on social learning (reviewed in Thornton & Clutton-Brock2011). However, little is known about the characteristics of indi-viduals that introduce novel behaviours into groups. Young
meerkats are highly playful and exploratory (Sharpe et al. 2002;Thornton 2008a), but have relatively poor foraging abilities and relyextensively on social learning from adults (Thornton & Clutton-Brock 2011). Consequently, we predicted that while they mayshow interest in the apparatus and in the actions of other indi-viduals, they would be relatively unlikely to solve tasks. Amongadults, we expected that dominant individuals would be relativelyunlikely to interact with or solve tasks, relying instead on aggres-sion to scrounge from others. Rather, following suggestions fromprimate studies (Reader & Laland 2001), we predicted that subor-dinate adults, which have fully developed foraging skills but, unlikedominants, can rarely steal from others, would be most likely toinnovate. We also predicted that males would be more likely tosolve novel tasks because, as they conduct frequent extraterritorialforays and may ultimately disperse from their natal group(Stephens et al. 2005; Young et al. 2007), theymay be selected to beexplorative and risk prone. Finally, our ability to weigh individualsregularly provided unique opportunities to test whether innovationis related to body condition and foraging ability. Body weight(corrected for age) is a good indicator of condition in meerkats andis positively related to investment in play (Sharpe et al. 2002),exploration (Thornton 2008a) and cooperative activities (Clutton-Brock et al. 2001) as well as survival and reproductive success(Clutton-Brock et al. 2006).
At a proximal level, innovative tendencies will be affected byemotional responses (e.g. neophobia towards novel objects;Greenberg 2003) and motivation, as well as learning capacities. Todisentangle these effects, we performed separate analyses onindividuals’ tendency to interact with experimental apparatusesand to solve tasks once engaged. We presented each of three novelfood extraction tasks to each group on three consecutive occasions.The tasks each required different motor actions, but shared keyfeatures: all had an opaque, functional element, which could bemanipulated to obtain a food reward from a transparent container.This allowed us to examine three potential cognitive components ofinnovation (Hauser 2003): (1) inhibitory control: resisting thetendency to access the reward through the transparent container;(2) instrumental learning of motor actions required to obtain thereward; and (3) the ability to extract a general rule (attend toopaque parts of the apparatus), and hence solve new tasks faster,rather than learning the contingencies of each task anew.
METHODS
Study Population
We tested seven groups of 14e24 habituated meerkats at theKuruman River Reserve in South Africa between January and May2009. Individuals were classified as pups (<3 months), juveniles(3e6 months), subadults (6e12 months) or adults (more than12 months; Brotherton et al. 2001). Adults were divided intodominants and subordinates. The dominant male and female ineach meerkat group can be unambiguously identified because theyshow rates of aggression an order of magnitude higher than otherindividuals, are the targets of regular submissive behaviours fromothers and, in the case of dominant females, are typically the onlyfemales in the group to breed, or breed more often than otherfemales (Clutton-Brock et al. 1998; Kutsukake & Clutton-Brock2006). Groups were located by radiotracking collared individuals,and all meerkats could be identified through unique dye marks ontheir fur (see Golabek et al. 2008; Thornton 2008b for details ofcollaring and marking procedures). Work was carried out withethics approval from the Universities of Cambridge and Pretoria,under a permit issued by the Northern Cape ConservationAuthority.
A. Thornton, J. Samson / Animal Behaviour 83 (2012) 1459e1468 1461
Weights
Individuals were weighed regularly by enticing them onto anelectronic balance with crumbs of hardboiled egg before theybegan foraging in the morning, and again after the cessation offoraging in the middle of the day (mean time between weighingsessions: 3.40 � 0.03 h). We used average rates of morning weightgain (g/h) over the course of the experiment as a measure offoraging efficiency. The ratio of observed average weights toexpected weights from an asymptotic regression of age againstweight served as a measure of how heavy an individual was for itsage (age-related weight). There was considerable variation in age-related weight between individuals, following a normal distribu-tion ranging from 0.61 to 1.62.
Apparatus and Experimental Design
Each group was presented with three different tasks (Fig. 1).Each task was presented to each group on three consecutiveoccasions, with the order of the different tasks randomized acrossgroups. We baited tasks with a single live scorpion, a highly fav-oured prey item whose smell and movement would be likely toattract interest, andwhich can bemonopolized by a single meerkat.To ensure that there was no risk of injury to the meerkats, weremoved the scorpions’ stings with scissors immediately beforepresentations. All task apparatuses consisted of a transparentcontainer with perforations, allowingmeerkats to see and smell thescorpion inside, and an opaque, functional component that couldbe manipulated to gain access to the scorpion. Tasks were attachedto a round tray of 30 cm diameter that could be pushed into thesand for stability (Fig. 1). The ‘Rotator’was a transparent plastic tubwith an opaque green lid that could be rotated to obtain a scorpion(Fig. 1a). The ‘Pull’ apparatus consisted of a transparent plasticbottle, with a gap 5 cm high and 10 cm wide cut into the bottom.Above this gap, a scorpion was placed on a green tab with a handleof nylon line. By pulling out this tab (either by gripping the tab itselfor pulling on the handle), a meerkat could make the scorpion dropto the bottom of the bottle and scoop it out through the gap(Fig. 1b). Finally, the ‘Jar’ (Fig. 1c) consisted of a transparent plasticjar with perforations, placed on its side. The opening of the jar wascovered with a tin foil lid, which could be ripped open to gainaccess to the scorpion.
Presentations were carried out in the morning when all groupmembers had emerged from the sleeping burrow but before theyset out to forage. For each presentation, we baited the apparatus outof sight of the meerkats and placed it on the ground adjacent to thesleeping burrow, visible to and approximately equidistant from allgroup members. Presentations ended when a meerkat successfullyobtained the scorpion. If no individual was successful after 30 min,the apparatus was removed. To allow us to examine whetherindividuals would learn to solve tasks efficiently, each task was
Figure 1. Task apparatuses, containing a scorpion as a reward (represented here by a sphere(a) rotating the lid, (b) pulling the tab or the attached nylon wire handle and (c) ripping op
presented to groups three times in a row, with the order of thedifferent tasks randomized. The nine consecutive presentations ateach group (three presentations per task) were spaced at least3 days apart (mean days between presentations ¼ 11.1 � 0.5) andwere recorded with a Panasonic NV-GS80 camcorder (PanasonicCorporation, Kadoma, Japan). From the videos, we noted allinstances of aggression (shoving, chasing and biting) and recordedthe latency for every individual to approach to within 1 m of, andmake contact with, the apparatus, andwhether it approached aloneor joined another individual already at the apparatus. Whenmeerkats interacted with the apparatus, we recorded the timespent manipulating the nonfunctional parts (i.e. the transparentsides of the containers) and the functional, opaque parts of theapparatus (i.e. the tin foil lid on the Jar, the rotating lid of theRotator and the tab and/or handle of the Pull task).
Statistical Analyses
Statistical analyses were performed in Genstat 10.1 (RothamstedExperimental Station, Harpenden, U.K.). Multifactorial analyseswere conducted using generalized linear mixed models (GLMM,when data were not normally distributed) or linear mixed models(LMM, for normally distributed data), with random effectscontrolling for repeated measures of groups, individuals or tasks.We began by entering all probable explanatory terms into eachstatistical model. Possible two-way interactions between themwere investigated and terms were sequentially dropped until theminimal model contained only terms whose elimination wouldsignificantly reduce the explanatory power of the model. Waldstatistics and probability values for significant terms were derivedfrom including all significant terms in the model, and values fornonsignificant terms were obtained by adding each term individ-ually to the minimal model (Crawley 2002). Post hoc multiplecomparisons of differences between levels of categorical variableswere conducted using Tukey’s tests based on means and standarderrors of parameter estimates from the minimal model (Zar 1999).Full tables of results for statistical models are in the Appendix.Means are quoted �SE throughout.
Manipulating and solving tasksWe used a GLMM to examine the factors affecting the likelihood
that meerkats would interact with the tasks. Data were fitted toa binomial distribution with a logit link function and binaryresponse terms (1 or 0) indicating whether or not an individualever touched any of the tasks. We fitted individual characteristics(sex, age category, and average age-related weight and foragingweight gain over the period of the study) as explanatory variables.As the total number of meerkats present could affect an individual’stendency to interact with tasks, we fitted group size (mean numberof individuals in the group over the course of the experiment) as anadditional variable. Finally, because trials could be cut short if an
). Meerkats could access rewards using the opaque, functional parts of the apparatus byen the tin foil lid.
A. Thornton, J. Samson / Animal Behaviour 83 (2012) 1459e14681462
individual solved a task, we included the total time over whichtasks were presented. The analysis used data from all 135meerkats,with group identity fitted as a random term.
To examine the factors influencing whether an individual eversuccessfully solved a task, we conducted a GLMM including onlythose meerkats that interacted with the apparatus. The binaryresponse term indicated whether or not an individual was eversuccessful in obtaining rewards from any task. Explanatory termswere as in the analysis above. As only one pup ever interacted withthe apparatus, we grouped juveniles and pups together. The anal-ysis used data from the 63 meerkats that interacted with theapparatus, with group identity fitted as a random term.
Joining and initiating aggression towards other individuals at tasksWe used a GLMM to examine the likelihood that when indi-
viduals approached (to within 1 m) or interacted with the appa-ratus, they joined another meerkat already at the apparatus. Datawere fitted to a binomial distributionwith a logit link function, withthe total number of bouts in which an individual joined another atthe apparatus as the numerator, and the total number of bouts inwhich that individual approached or interacted with the apparatusas the denominator. A ‘bout’ refers to a discrete period from themoment a meerkat arrived to within 1 m of the apparatus to themoment it left. The analysis used data from the 102 individuals thatapproached to within 1 m of the apparatus, with group identityfitted as a random term.
We analysed patterns of aggression around the apparatus usinga GLMM. Datawere fitted to a binomial distributionwith a logit linkfunction, with the number of bouts in which an individual initiatedan aggressive interaction within 1 m of the apparatus as thenumerator, and the total number of bouts in which that individualapproached towithin 1 m of the apparatus as the denominator. Thesample size and random term were as in the analysis above.
Learning within tasksFor meerkats that solved a given task more than once (N ¼ 9),
we used LMMs to compare performance between the first andsecond time they solved each task. We conducted separate analysesto determine whether meerkats began manipulating the tasksooner (i.e. latency to first contact with the apparatus) and werefaster at obtaining the reward after first contact (i.e. latency from
Table 1Successful individuals across all task presentations in all groups
Each group received three successive presentations of each task. Letters in parenthesesindicate individual sex, age and status [F ¼ female, M ¼male; A ¼ adult, S ¼ subadult, J ¼on more than one trial. ‘Group size’ refers to the total number of individuals in each gro
first contact to obtaining reward). Reductions in the time taken tomanipulate the apparatus to obtain a reward could be caused byboth enhanced inhibitory control (i.e. spending less time ineffec-tually attacking the transparent sides of the apparatus) andinstrumental learning leading to more efficient manipulation offunctional parts. To discriminate between these possibilities, weconducted separate analyses of the time spent manipulating thenonfunctional (transparent) and functional (opaque) parts of theapparatus. For those individuals that solved a given task threetimes, we also conducted a paired t test to determine whetherperformance changed between their first and third trial. Responseterms were all in seconds, transformed where necessary.
The time elapsed between an individual’s first and secondsuccessful trial may influence the potential for learning effects. Wetherefore conducted additional LMMs to examine whether thenumber of days that had elapsed was related to differences inperformance (latency to first contact and time spent manipulatingnonfunctional and functional parts) between the first and secondtime meerkats solved tasks. We fitted task type as an additionalfixed term, with individual identity nested in task type as a randomfactor.
Learning between tasksTo investigate possible learning effects across tasks, we used
paired t tests to compare latency to touch the apparatus, the totaltime taken to obtain the reward after first contact, and time spentmanipulating functional and nonfunctional parts of the apparatusbetween the first time individuals solved the first task and the firsttime they solved the second task. The analyses were based on eightindividuals that solved more than one different task (see Table 1).
RESULTS
Interacting with and Solving Tasks
Individuals of different ages varied in their likelihood of inter-acting with and solving tasks. Juveniles were particularly likely toapproach and touch the apparatus (Table A1 in the Appendix), butwere seldom successful in obtaining rewards (only one of 26juveniles that made contact with the apparatus ever obtaineda scorpion; Fig. 2a). Among the 63 meerkats (of a total of 135) that
indicate the order of tasks (R ¼ rotator, P ¼ pull, J ¼ jar). Letters in square bracketsjuvenile; D ¼ dominant; S ¼ subordinate]. Individuals shown in bold were successfulup.
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Figure 2. Patterns of interaction, problem solving and learning. (a) Number of individuals that solved tasks (black bars), interacted with tasks but were unsuccessful (grey bars) andnever interacted with tasks (white bars). (D) ¼ dominant, (S) ¼ subordinate. The total height of each bar indicates the total number of individuals in each category. (b) The totalheight of each bar indicates the proportion of bouts in which meerkats that approached to within 1 m of the apparatus joined another meerkat already at the apparatus. Greyshading indicates the proportion of bouts in which meerkats that approached to within 1 m of the apparatus initiated aggression towards an individual already at the apparatus.(D) ¼ dominant, (S) ¼ subordinate. (c) Learning within tasks: time spent manipulating functional and nonfunctional parts the first (white) and second (grey) time individuals solveda given task. (d) Learning between tasks: time spent manipulating the apparatus during first successes with the first and second task. Bars in (b), (c) and (d) show means � SE.*P ¼ 0.012; NS: P > 0.12.
A. Thornton, J. Samson / Animal Behaviour 83 (2012) 1459e1468 1463
interacted with the apparatus, subordinate adults were mostsuccessful (Fig. 2a, Table A2). Most (10/13) successful individualsweremales, although sex did not reach significance (Table A2). Age-related weight, weight gain, group size and the total duration oftask presentations did not significantly affect the probability ofinteraction or success (Tables A1, A2).
A similar general pattern of innovativeness by subordinate adultmales can be seen if we adopt Reader & Laland’s (2003) strictdefinition, whereby only the first performance of a novel behaviourin a group is classified as innovation. Based on only on the firstsolution of each task in each group, five groups (CD, D, F, KU,W) hadonly a single innovator. These individuals were all subordinatemales, and four of the five were adults (the remaining individualwas a subadult; see Table 1). Across all groups, subordinate adultmales were the only of category of individual for which theproportion of innovators in the Reader & Laland (2003) sense (fiveof 23 individuals ¼ 21%) differed significantly from the proportion
of innovators in the rest of the population (five of 112 individu-als ¼ 4.5%; chi-square test: c2 ¼ 8.30, P ¼ 0.004).
Joining and Initiating Aggression towards Other Individuals at Tasks
Adult subordinates were particularly likely to approach theapparatus alone (Fig. 2b, Table A3), whereas younger individualsfrequently joined others. Dominant adults also commonly joinedothers, and showed an elevated tendency to use aggression whenthey did so (Table A4). We recorded one instance of kleptoparasi-tism, in which the dominant female in group F snatched the scor-pion after FM127 had removed it from the apparatus.
Learning within Tasks
Persistence was critical for solving tasks. The first time each taskwas solved in each group, the successful individual was the group
A. Thornton, J. Samson / Animal Behaviour 83 (2012) 1459e14681464
member that spent the most timemanipulating the apparatus in 18of the 19 sessions (mean time for first solvers of each task:79.8 � 14.9 s, N ¼ 9 individuals; nonsolvers: 34.2 � 11.3 s, N ¼ 21),but was the first to arrive only twice. There was also some evidencefor learning within tasks. Among individuals that solved tasks, most(9/13) were successful more than once (Table 1; mean number ofsuccesses ¼ 3.8 � 0.8; range 1e8; number of cases where an indi-vidual solved a given task more than once ¼ 16). These individuals’latency to make contact with the apparatus did not declinesignificantly between the first and second time they solved a giventask (Table A5). However, the total time from first contact with theapparatus to obtaining the reward fell significantly (Table A6). Thisimprovement came about from a reduction in the time spentmanipulating nonfunctional parts (Fig. 2c, Table A7). Inspection ofindividual performance across all trials supports this pattern ofwithin-task reductions in time spent manipulating nonfunctionalparts: out of 16 cases in which one individual had more than onesuccess with a given task, 11 showed a decline from first to lastsuccess (Fig. 3).
Successful individuals, however, did not reduce the time theyspent manipulating functional parts from their first to their secondsuccess with a given task (Fig. 2c, Table A8). Among individuals thatsolved a given task three times, there was also no reduction in timespent handling functional parts between first and third success(N ¼ 8 tasks by five individuals: paired t test: t7 ¼ �0.28, P ¼ 0.787).Similarly, inspection of individual performance across all trialsreveals no clear pattern in time spent manipulating functionalparts: in nine cases there was a decline while the other sevenshowed an increase from first to last success with a given task(Fig. 3).
The number of days that had elapsed between an individual’sfirst and second success on a task had no significant effect on thechange in the latency to make contact with the apparatus (LMM:c2 ¼ 2.86, P ¼ 0.115) or the time spent manipulating nonfunctional(c2 ¼ 0.00, P ¼ 0.974) or functional parts (c2 ¼ 1.71, P ¼ 0.191).
Learning between Tasks
Eight individuals solved more than one different task (Table 1).We found no evidence that solving one task improved efficiency inother tasks. Comparisons between the first time individuals solvedtheir first and second task showed no significant differences in thetotal time taken to obtain the reward after first contact with thetask (paired t test: N ¼ 8 individuals: t7 ¼ 1.39, P ¼ 0.209) or in thetime spent handling nonfunctional (paired t test: t7 ¼ 1.67,P ¼ 0.138; Fig. 2d) or functional parts (t7 ¼ 0.90, P ¼ 0.399; Fig. 2d).Nor was there any reduction in latency to first contact with theapparatus (t7 ¼ 1.06, P ¼ 0.322).
The sample size of individuals that solved three tasks (N ¼ 4)was too small for formal statistical analyses of learning effectsacross all tasks. However, descriptive statistics comparing individ-uals’ performance between the first time they solved the first taskand the first time they solved the third task reveal no consistentdifferences (Table A9). Similarly, Fig. 3 shows no clear pattern ofindividual improvement across successive tasks.
DISCUSSION
Our results suggest that certain categories of individuals andindeed certain specific individuals may often be responsible forintroducing new foraging behaviours into meerkat groups. Whilejuveniles often interacted with novel tasks, they were seldomsuccessful, perhaps owing to their relatively small size or lack ofdexterity. This parallels findings fromwork on callitrichid monkeys(Kendal et al. 2005), and suggests that explorative tendencies or
neophilia alone do not suffice for innovations to emerge. Amongindividuals that interacted with tasks, subordinate adults showedthe highest probability of success. Adult behaviour may followproducerescrounger dynamics, with subordinates producinginnovative solutions and unearthing new resources while domi-nants scrounge from these discoveries (Reader & Laland 2001).Consistent with this view, dominants relatively rarely interactedwith tasks themselves, but often joined others at the apparatus andcommonly used aggression to displace them, presumably in anattempt to steal the food reward (we saw one instance of klepto-parasitism, by the dominant female in group F). Young meerkatsalso frequently joined others but were rarely aggressive. Thissupports our previous findings that young meerkats gainsubstantial benefits by observing and learning from others(reviewed in Thornton & Clutton-Brock 2011). There was someindication that males were more likely to solve tasks than females(77% of solvers were male), consistent with the hypothesis that thedispersing sex may be more innovative, but further work isnecessary to confirm this effect.
The finding that particular meerkats in our experiment solvedtasks repeatedly raises the possibility that there may be consistentdifferences between the innovative tendencies of individualswithin broad categories of age, rank or sex. It seems unlikely thatsuch interindividual differences arise solely as a result of differ-ences in current energetic state or condition, as our analysesrevealed no significant effects of body weight or foraging success,despite the use of detailed weight records. Instead, we suggest twopossible underlying causes of individual differences. One possibilityis that certain intrinsic individual characteristics may make someindividuals particularly likely to innovate irrespective of currentbody state, as suggested for great tits, for which field and laboratoryexperiments revealed long-lasting consistency in individualtendencies to solve novel food extraction tasks (Cole et al. 2011;Morand-Ferron et al. 2011). Such stable innovatory tendenciesmight covary with other behavioural traits, forming part ofa behavioual syndrome (Sih et al. 2004) or personality (Dall et al.2004). For example, some studies have found that bold, fastexploring or very tame animals are particularly likely to innovate(Webster & Lefebvre 2001; Boogert et al. 2006, 2010; Overingtonet al. 2011), although others have found no relationship betweeninnovation and other traits (Cole et al. 2011). Our results do notpermit clear conclusions about innovation in relation to otherbehaviours, but the finding that individuals that solved tasks werenot usually the first to arrive suggests that exploratory tendenciesand problem solving may not be tightly linked (cf. Cole et al. 2011).A second possibility is that apparent individual consistency in ourstudy might arise as a result of learning effects. That is, havinglearned to obtain food from one task, individuals are likely to revisitand attempt to solve that and similar tasks in future. Examining theextent to which stable individual characteristics come aboutthrough experience may be a fruitful avenue for future research.
Some authors have suggested that innovation is closely linked tocognitive complexity or intelligence, pointing to correlationsbetween measures of brain size and species-level rates of innova-tion, as tallied from anecdotal reports (Lefebvre et al. 2004; Ramseyet al. 2007). However, in the absence of knowledge about thecognitive mechanisms underpinning animal innovations, we mustbe cautious in assuming that innovation is cognitively demanding.First, there is no direct evidence from any species to suggest thatparticularly innovative individuals possess elevated cognitiveabilities. Instead, innovators may often simply be particularlymotivated or persistent, as in our experiments, in which thesuccessful meerkats were almost invariably those that spent mosttimemanipulating the apparatus. Second, there is no clear evidencethat innovative problem solving calls upon apparently complex
Figure 3. Performance of successful individuals across all trials. Time spent manipulating nonfunctional (filled symbols and solid lines) and functional (open symbols and dashedlines) parts of the apparatus. Panels show the performance of successful individuals in each group (group names are indicated by the letters in the top left corner). Only thoseindividuals that were successful on more than one occasion are shown.
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cognitive mechanisms such as causal reasoning or the ability toextract generalizable rules. Indeed, our results suggest that simpletrial-and-error based on evolutionarily conserved associativelearning mechanisms may suffice to generate solutions to novelproblems. Innovators in our study typically spent a considerableamount of time ineffectually manipulating the transparent parts ofthe apparatus and so might have inadvertently moved a functionalpart. This might have drawn their attention to that area andsubsequently allowed them to solve the task relatively quickly.Individuals that solved a given task more than once did appear tolearn to inhibit their tendency to attack rewards through the sidesof the transparent apparatus, but they did not become more effi-cient at manipulating functional parts of the apparatus. Moreover,learned behavioural inhibition did not appear to carry over to noveltasks, nor did individuals seem to generalize across the differenttasks and learn to attend immediately to opaque, functional parts. Itis possible that meerkats would have shown greater evidence forimprovements in manipulating functional parts and generalizingacross tasks if task presentations had always been spaced closelytogether, if the colour of functional parts had been the same acrosstasks, or if they had been given a greater number of trials. However,we note that the number of days between task presentations didnot significantly affect performance. Moreover, our experiment islikely to have exhibited considerably more regularity than is typicalin nature, where novel problems may not appear consistently onmultiple successive occasions, and where different problems mayseldom share precise similarities in visible features. We suggestthat the innovative problem solving in meerkats may be moreparsimoniously explained by simple, conserved associativeprocesses than by more cognitively demanding abilities of ruleextraction or causal reasoning. Indeed, our results are reminiscentof the performance of pigeons, Columba livia, on learning sets,which learn each new stimulus configuration by rote, in sharpcontrast to the rapid rule-extracting abilities of large-brained cor-vids (Corvus spp.; Mackintosh 1988).
Innovative behaviour is an important form of phenotypic plas-ticity, allowing animals to exploit novel resources and cope withenvironmental change. Our findings support the hypothesis that aninability to outcompete others physically over access to resourcesmay drive individuals to innovate. Such effects may be particularlyprevalent in cooperative breeders in which extreme discrepanciesin the competitive abilities of dominant breeders and subordinatehelpersmay force the latter to seek out solutions to novel problems.Innovative tendencies may also be enhanced in the dispersing sex,which is more likely to encounter novel challenges. Our results,combined with our previous work on social learning (reviewed inThornton & Clutton-Brock 2011), also suggest that different cate-gories of individuals may be responsible for the generation of novelbehaviours and their subsequent dissemination through animalsocieties. In meerkat groups, subordinate adults with fully devel-oped foraging skills but little potential for kleptoparasitism appearto be the prime innovators, but the subsequent spread of innova-tions occurs primarily through social learning by young individuals.Finally, we suggest that innovations need not require particularlysophisticated cognitive faculties, but may often arise froma combination of exploration, persistence and simple learningprocesses. Further work elucidating the cognitive mechanismsthrough which individuals solve novel problems is essential if weare to understand the distribution of innovatory propensitieswithin and between species.
Acknowledgments
We thank the Kotze family and Northern Cape Conservation forpermission to work in the Kalahari, Tim Clutton-Brock and Marta
Manser for logistical support and access to the meerkats andeveryone at the Kalahari Meerkat Project for their help. NeeltjeBoogert and Sinead English provided valuable comments on themanuscript. A.T. was funded by Pembroke College, Cambridge anda BBSRC David Phillips Fellowship.
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Table A2GLMM on factors affecting whether individuals ever solved tasks
Wald statistic (c2) df P
Full modelAge category 8.57 3 0.045Age-related weight (g) 1.80 1 0.180Sex 1.31 1 0.258Weight gain (g/h) 0.99 1 0.319Total duration (s) 0.46 1 0.500Group size 0.03 1 0.874
Minimal model Effect size SEConstant �0.12 0.49Age categoryAdult subordinate 0 0Adult dominant �1.61 0.80Subadult �1.09 �0.81Juvenile and pup �3.14 1.50
The analysis used data from 63 meerkats that interacted with the apparatus, withgroup identity fitted as a random term (estimated variance component � SE:0.00 � 0.00). Tukey’s tests: juveniles and pups versus other age categories: P < 0.05;adult subordinates versus dominants: P < 0.05.
Table A3GLMM on factors affecting the likelihood of joining another individual at theapparatus
Wald statistic (c2) df P
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Appendix
Coefficient estimates in all tables represent the change in thedependent variable relative to the baseline category and can thusbe interpreted as measures of effect size.
Table A1GLMM on factors affecting whether individuals interacted with tasks
Wald statistic (c2) df P
Full modelAge category 13.26 4 0.010Age-related weight (g) 0.51 1 0.474Sex 0.48 1 0.487Weight gain (g/h) 0.27 1 0.606Total duration (s) 0.32 1 0.574Group size 0.05 1 0.829
The analysis used data from all 135 meerkats, with group identity fitted as a randomterm (estimated variance component � SE: 0.29 � 0.32). Post hoc Tukey’s tests:juveniles versus other age categories: P < 0.001.
initiated aggression when within 1 m of the apparatus
Wald statistic (c2) df P
Full modelAge category 11.03 4 0.034Sex 4.44 1 0.026Weight gain (g/h) 2.73 1 0.099Total duration (s) 1.37 1 0.242Group size 2.14 1 0.904Age-related weight (g) 0.43 1 0.513
The analysis used data from the 102 individuals that approached to within 1 m ofthe apparatus, with group identity fitted as a random term (estimated variancecomponent � SE for random term: 0.79 � 0.58). Tukey’s tests: adult dominantsversus other age categories: P < 0.05.
Table A5LMM on latency to make contact with the apparatus during first and second trialswithin tasks
Wald statistic (c2) df P
Full modelTrial (first, second) 2.89 1 0.110Task type (jar, rotator, pull) 3.27 1 0.233
The analysis used data from successful task solutions by nine individuals, withindividual identity nested in task type as a random term (estimated variancecomponent � SE for random terms: 70 042 � 25576).
Table A8LMM on time spent manipulating functional parts of the apparatus
Wald statistic (c2) df P
Full modelTrial (first, second) 1.98 1 0.180Task type (jar, rotator, pull) 0.30 1 0.863
The sample size was as in Table A5 and the response term was log transformed.Estimated variance component � SE for random terms: individual identity nested intask type ¼ 0.012 � 0.063.
Table A7LMM on time spent manipulating nonfunctional parts of the apparatus within tasks
Wald statistic (c2) df P
Full modelTrial (first, second) 12.04 1 0.002Task type (jar, rotator, pull) 2.69 1 0.295
The sample size was as in Table A5 and the response term was log transformed.Estimated variance component � SE for random term: individual identity nested intask type ¼ 0.12 � 0.15.
Table A9Time spent manipulating the apparatus during first successes on first and third tasks
Individual Task Nonfunctional Functional
DM108 First (R) 27 7Third (P) 104 18
FM127 First (J) 52 4Third (P) 91 33
KUM006 First (P) 137 28Third (R) 11 8
WM123 First (R) 41 18Third (P) 10 14
Means First 64.3�24.8 14.3�5.5Third 54.3�25.1 18.3�5.3
Letters in parentheses indicate tasks (R ¼ rotator, P ¼ pull, J ¼ jar).
A. Thornton, J. Samson / Animal Behaviour 83 (2012) 1459e14681468
