Animal social networks: an introduction for complex systems scientists
AAnimal social networks - an introduction for complex systems scientists
Josefine Bohr Brask ∗ , Samuel Ellis , and Darren P. Croft Centre for Research in Animal Behaviour, University of Exeter, Exeter EX4 4QG, UK
Abstract
Many animals live in societies where individuals frequently interact socially with each other.The social structures of these systems can be studied in depth by means of network analysis.A large number of studies on animal social networks in many species have in recent years beencarried out within the biological research field of animal behaviour and have provided new insightsinto behaviour, ecology, and social evolution. This line of research is currently not well connectedto the field of complex systems. Here we provide an introduction to animal social networks forcomplex systems scientists. We believe that an increased integration of animal social networkswith the interdisciplinary field of complex systems & networks would be beneficial for variousreasons. Increased collaboration between researchers in this field and biologists studying animalsocial systems could be valuable in solving challenges of particular importance to animal socialnetwork research. Furthermore, high-resolution datasets of social networks from different animalspecies can be useful for investigating general hypotheses about complex systems. In this paper,we describe what animal social networks are and how they are scientifically important; we give anoverview of the methods commonly used to study animal social networks; and finally we highlightchallenges in the study of animal social networks where input from researchers with expertise incomplex systems could be particularly valuable. We hope that this will help to facilitate furtherinterdisciplinary collaborations involving animal social networks, and lead to better integration ofthese networks into the field of complex systems.
Keywords:
Animal social networks, social structure, network analysis, complex systems, com-plex networks
Animals of many species live in groups, where individuals spend time in close proximity to eachother and frequently interact [1]. The patterns of social interactions and spatial proximity acrossindividuals constitute the social structures of the populations. During the last two decades, scientistsin the biological research field of animal behaviour (and particularly in the subdiscipline of behaviouralecology) have investigated the social structures of a wide range of species by means of network analysis(reviewed in [2]). Quantitative network analysis tools were introduced into the animal behaviour field inthe beginning of this millennium [3–7], and social network analysis has since become a well-integratedpart of animal behaviour research that continues to provide new insights into diverse questions aboutsociality, ecology and evolution [2, 8, 9]. A large amount of research on animal social networks has bynow been conducted within the animal behaviour field, including both empirical studies of animal socialnetworks, and development of analytical methods specifically designed for these networks. Animalsocial networks have also been studied by complex systems researchers, and studies of the networkshave been published outside of biological journals (e.g. [10–14]), but the extensive research on themgoing on in the animal behaviour field remains poorly connected to the broader field of complexsystems.The purpose of this paper is to introduce animal social networks to the wider complex systems& networks research community, in the hope that this can facilitate a better overall integration ofthis area of research into the interdisciplinary field of complex systems & networks, and strengthenthe connection between biologists studying animal social networks and researchers with expertise incomplex systems. We believe that this would benefit both our understanding of animal social systems ∗ [email protected] a r X i v : . [ q - b i o . P E ] J un nd the general research in complex systems for various reasons. Firstly, animal social network researchis facing specific challenges that computational and theoretical scientists with knowledge about complexsystems could potentially help addressing by providing new perspectives and methods. Overcomingthese challenges is a relevant scientific endeavour because animal social networks constitute a classof networks that play a central role in evolutionary and ecological processes [2, 8, 9], and they aretherefore important to study in their own right to understand the workings of nature, as well as toimprove species conservation efforts [15, 16]. Secondly, animal social network quantification has by nowresulted in a large set of time-series of social interactions (or spatial associations) - some in very highresolution - which may be useful for studies that address general questions connected to this type ofnetwork data. Furthermore, non-human animals represent a wide range of study systems that can beused to test network theory empirically under both natural and experimental conditions and therebycontribute to our general understanding of complex systems.In the following we first briefly explain what animal social networks are (Section 2). We thenprovide an overview of some topics where studies of animal social networks are playing an importantrole for gaining new insights (Section 3). We thereafter give an introduction to methods commonlyused in studies of animal social networks (Section 4), followed by an outline of current challenges whereinterdisciplinary collaboration may be particularly valuable (Section 5). We finish with a note on theavailability of animal social network data (Section 6), and a brief conclusion (Section 7). Here we provide a brief explanation of what animal social networks are - what kind of data theyrepresent and what types of patterns are typically observed in them. For further information aboutmethods commonly used in the data collection and in the construction and analysis of the networks,we refer the reader to Section 4 and references therein.Animal social networks quantify the social structure within animal populations (see Fig. 1 forexamples of animal social network graphs). Each node in the network corresponds to a specific indi-vidual, and the (typically weighted) network edges correspond to the social relationships between theindividuals, which are quantified as rates of social interaction or social association between each dyad.Social interactions commonly used for quantifying animal social structure include grooming and fight-ing, whereas social associations are based on spatial proximity of individuals. The network may thusquantify very different dimensions of the social system, depending on what type of social interaction(affiliative, aggressive) or social association it is based on. The network data (the adjacency matrix)will often be accompanied by attribute data , which usually contains information on the individuals(their sex, age, body size, etc.) or the dyads (e.g. their genetic relatedness).The data are often collected by direct observation of the animals, but new technology has allowedfor automatic data collection, which gives highly detailed datasets. The accumulated raw data aretransformed into an adjacency matrix by the application of indices that account for sampling issuessuch as different observation times for each individual, and the network is then typically subjected tostatistical tests to investigate hypotheses about its structure.Animal social network data may be obtained both from wild and captive populations. While fielddata enables the study of social structure under natural conditions, laboratory-based studies allow forexperiments where causality can be tested under controlled conditions. In both cases, the quantifiednetworks are most often relatively small (N < The establishment of network analysis as a common tool in the study of animal behaviour has openedup for a much more comprehensive understanding of the complex social systems found across species.Analyses of animal social networks are now used in investigations of a wide range of questions aboutsocial evolution, behaviour and dynamical processes [2, 8, 9]. As we cannot cover all of these questionshere, we instead describe some research themes where animal social networks seem to be playing aparticularly important role for gaining new insights. It may be noted that the research themes overlapconsiderably with common themes in general complex systems science, thus providing a natural basefor further integration of animal social network research into this field.3 ocial centrality, evolution and fitness.
A major reason why animal social networks are ofscientific interest is that the social environment can impose selection pressures on the individualsand thereby act as an important driver of the evolution of traits (including both physical andbehavioural characteristics of individuals). This means that in order to understand evolution, thesocial environment must be taken into account. Network analysis provides the tools to quantifysocial structure in detail and across different scales, and therefore facilitates comprehensive stud-ies of the role the social environment plays in evolution, across species. One way to investigatethe evolutionary importance of the social environment is to statistically test for relationshipsbetween the social network positions of individuals and their Darwinian fitness (i.e. the extentto which they contribute to the future gene pool, which is commonly estimated by measuresof longevity, reproduction rate, and offspring survival). In recent years, such studies have beencarried out in a range of species, and evidence for correlations between fitness and network cen-trality has been found widely ([18–25]; see also [26]). The study of animal social networks is thusproviding extensive new empirical evidence that social network position is linked to survival andreproduction across species.
Frequency-dependent selection and social structure.
Animal social network studies arealso particularly relevant for understanding the evolution of traits for which fitness is frequencydependent (such that the benefit of the trait to the individual depends on the frequency of it inthe social environment; [27]). One prominent example of such a trait is cooperative behaviour.The evolution of cooperation in structured populations has been studied extensively across sci-entific fields via simulations of strategy dynamics in artificial networks [28, 29], and this researchsuggests that social network structure plays a key role for the persistence of cooperation. Animportant next step is then to unravel to which extent and under which conditions the variousmechanisms predicted from the simulations underlie cooperation in real-world systems. Animalsocial networks seem very useful for this task, and while only few studies have yet investigatedcooperation in connection with real-world animal social structures [30–32], we expect that thesenetworks will have an important role to play for understanding how cooperation, and otherfrequency-dependent traits, evolve in the real world.
Spread of disease and information in networks.
Another area where animal social networksare particularly useful is the investigation of spreading processes in populations, including thepropagation of disease and information. Studies in a range of species have investigated empiricallyto which degree various types of information spread via social links, including knowledge aboutthe location of food [33–36], and innovations such as tool use [37, 38] and other new foragingtechniques [39–43]. Regarding disease spread, empirical studies have uncovered relationshipsbetween individual network position and infection status or parasite load in multiple species [44–48], and simulation studies involving real-world animal social network data have given insightsinto the effect of social structure on disease transmission and the vulnerability of populationsto epidemics [11, 49–52]. Animal social network data in combination with simulations have alsobeen used to investigate more general aspects of spreading processes (e.g.[12]).
Stability, flexibility and robustness of social systems.
Animal social network research isalso providing new empirical knowledge about the general stability of social structures acrossspecies, and how robust and flexible they are under changing conditions. This is studied byinvestigations of how animal social network structures correlate with environmental factors suchas food availability [53, 54] and general seasonal changes [55–59], to which degree the networkstructures are stable across years [59–63], and how they react to perturbations such as node loss(see
Network robustness , Section 5).
Wildlife conservation and animal welfare.
The fact that social network structure hasimportant implications for health, survival and behaviour across species means that animal socialnetwork studies have an important role to play in the conservation of wildlife [15, 16] and inimproving the welfare of farm and zoo animals [64, 65], thus providing important drivers forapplied animal social network studies. Such studies are for example concerned with estimationof the efficiency of disease control strategies in endangered wildlife [66–68], assessment of socialbehaviour in connection with relocation or reintroduction of animals into the wild [69, 70], andinforming the management of captive populations [71].4 ew network methodology.
Finally, the study of animal social networks requires specialtechniques for network construction and analysis (described in Section 4), and this means thatresearch in these networks is accompanied by new methodological developments. The topicsinclude constrained permutation models for statistical testing [72–76], network generation models[77, 78], social complexity measures [79], and implications of missing data for the reliability ofempirical network structures [77, 80–83].
The study of animal social networks is complicated by the fact that the data collection often involvesinevitable sampling biases and missing observations (especially for wild populations). This must betaken into account in the treatment of the data. Specialised methods for construction and analysis ofthe networks have therefore been developed and have somewhat coalesced into a set of standard ap-proaches (although the methodology is continuously evolving). In this section we give an introductoryoverview of methods that are currently commonly used for data collection, network construction, andnetwork analysis in animal social network research.
The type of data collected for quantification of animal social networks and the method of collectiondepends both on the research question and on what behaviour is possible to observe. The latter willdepend on the species as well as the setting (e.g. whether the study population is wild or captive).The data fall into two categories: interaction data and association data . The former concernsdirect behavioural interactions between individuals, whereas the latter concerns the spatial proximityof individuals. Association data can furthermore generally be either group-based or individual-based :Many species live in so-called fission-fusion societies where groups are unstable. In this case, socialassociation is inferred from shared group membership (an approach known as the gambit of the group [84]), and the network data are collected by recording repeatedly over time which individuals aregrouping together in space [85–87]. When groups are either largely stable across the observationperiod or group boundaries cannot easily be defined, then single individuals may instead be observedone after another in focal follows where their nearest neighbour in space, or individuals within a certaindistance, is recorded at regular time intervals. Interaction data are also frequently collected via suchfocal follows, where all interactions with the individual are recorded.Many studies of animal social networks are based on data that are collected by the researchersdirectly observing the animals and recording their social interactions or associations. In this case, theresearchers must be able to recognise each individual. This can sometimes be done by natural markingssuch as fur patterns and scars, whereas in other cases the animals are equipped with artificial tagsbefore the data collection. Animal social network data (especially assciation data) can also be collectedautomatically in various ways (for detailed overviews see [88, 89]), and such methods are becomingincreasingly common due to the continuous optimisation of the involved technology. Highly detaileddata can be obtained via proximity loggers attached to each animal (Fig. 2), which record when eachpair of individuals are close to each other (for example [66, 90–93]); this can give datasets of socialassociations with a sub-second time resolution. The loggers may also contain other sensors, such asaccelerometers, which can provide additional information on the behaviour of the animals. Anotherpossibility is to use RFID tags to record when each animal is present at a specific location (for example[33]). Furthermore, high-resolution social association data can in some circumstances be obtained bysimultaneous automatic tracking of multiple individuals from videos with methods based on machinelearning (for example [94, 95]), either without tagging the animals or with computer-readable tags suchas barcodes. The increase in the development and use of automatic data collection methods meansthat the future is likely to see high-resolution datasets of animal social networks across many species. Most studies of animal social networks do not use the raw counts of social interactions or associationsas edge weights. Instead, the edge weights are estimated with calculations that take into accountpotential sampling biases and pseudo-replication of observations.5igure 2: Examples of animals wearing electronic devices for collection of social network data viaproximity sensing. A) Ewe and lamb wearing a collar and harness with devices attached (photo byEmily Price). B) Great tit wearing a miniature device with antenna on its back (photo by LysanneSnijders).The sampling biases arise from the fact that individuals (in most studies) can be out of sight, orvisible but unidentifiable, for part of the observation period. Which particular types of sampling biasare relevant, and thus how the edge weights are calculated, depends on whether the data are associationdata or interaction data (see the preceding section for descriptions of data types). For associationdata, the edge weights are estimated by association indices , the purpose of which is to account for thefollowing two types of sampling bias. Firstly, some individuals can be disproportionately representedin the data when all individuals have, by chance, not been observed for the same amounts of time.Secondly, observations of individuals occurring together - rather than apart - can be overrepresented inthe data (e.g. when groups are more likely to be spotted than single individuals) or underrepresentedin the data (e.g. if it frequently occurs that some individuals in observed groups are out of sight orunidentifiable). A few different association indices are commonly used, and the choice of which ofthem to use in a specific study is based on the assumed likelihood and direction of the second of thetwo types of sampling bias (all the indices account for the first bias. For details see [5, 96, 97]). Forinteraction data, the second of the above-mentioned types of sampling bias is rarely relevant and edgeweights are typically calculated simply as the number of interactions per joint observation time, thusaccounting for the first type of sampling bias.Pseudo-replication in interaction data and association data can arise when repeated observations ofindividuals are correlated due to temporal closeness. When edge weights are estimated with associationindices, pseudo-replication is taken into account by grouping the data into samples (samples here beingsubdivisions of the observation period of equal length, e.g. days), counting the number of samples wherethe individuals of the dyad were observed together or apart (or not seen), and using these sample-basedcounts as input to the index (rather than the raw counts of associations and observations). Pseudo-replication in interaction data can be taken into account by applying definitions of when an interactionbetween a specific dyad is counted as continuing versus starting anew, which can be done either whenpreparing the data for edge weight calculation or during data collection.Before the calculation of edge weights, the data is often restricted by applying a threshold for theminimum number of times an individual should be observed in order to be included in the network, todecrease the amount of uncertainty on the edge weight estimates.With the current increase in the use of automatic data collection methods in animal social networkstudies, some network construction issues become less relevant (e.g. high uncertainty on null associa-tions [89]), while the new data formats require other considerations and development of suitable dataextraction techniques (e.g. inferring spatiotemporal co-occurrences of individuals from data streams[98, 99]). 6 .3 Analysing animal social networks
The properties of animal social network data and the research questions that these networks are usedfor investigating means that standard analytic approaches are often not relevant or applicable. Forexample, compared to many other real-world networks studied, animal social networks are relativelysmall, with the majority of them containing fewer than 200 nodes [100]. This puts certain limits tothe characterisation of the network structure, in particular with regard to the degree distributions,which cannot with high certainty be fitted to theoretical distributions [80, 101], thereby hindering theapplication of hypotheses about for example dynamics and robustness, based on degree distribution.Furthermore, potential sampling biases and data dependencies need to be taken into account. Analysesof animal social networks therefore commonly consist of application of specialised statistical methodsdeveloped for the purpose. These methods are continuously evolving and expanding to fit the diverseresearch questions and data types, but some general approaches are well established. Here we describekey methodological approaches used until now.A common aim of many animal social network analyses is to investigate statistically whetheraspects of the observed network structure are reflecting underlying non-random behaviour, ratherthan resulting from random interaction (or association) and observation biases. Structural aspectstypically considered include: 1) global network structure, 2) correlations between network positions andindividual attributes, and 3) correlations between edge weights and other dyadic data. The fact that thedata points (e.g. node metrics) in network data are inherently non-independent means that they violatethe assumptions of most standard statistical tests. The testing of animal social network structure istherefore instead frequently done by comparing the observed network to an ensemble of null networks where the hypothesised non-random structural feature or relationship is not present. This approach isused in various forms for investigations of all the three above-mentioned structural aspects. Featuresof global structure are tested by comparing global network metrics (e.g. assortativity coefficients, edgeweight variation) to a distribution of the same metric measured on null networks. Relationships betweennetwork position (usually measured by standard centrality metrics) and individual attributes (sex,age, fitness, etc.) are typically tested with linear-model frameworks such as generalized linear mixedmodels, where significance is determined by re-fitting the model to corresponding metrics measured onnull networks. And relationships between edge weights and other dyadic data (e.g. genetic relatedness,space use overlap, and social networks for the same set of individuals measured under other ecological orexperimental conditions) are often tested with null-model based matrix correlation tests (e.g. quadraticassignment procedure tests [102]).Using appropriate null networks is essential for avoiding spurious results when analysing animalsocial networks [76, 103]. The null networks should ideally resemble the observed network in all aspectsexcept the one that is being tested. Therefore, standard network models such as Erd¨os-Renyi randomgraphs are usually not appropriate as null networks. Instead, the null networks are commonly createdby data permutation procedures, which randomise specific structural features while keeping othersconstant (for overviews see [5, 76, 87, 103, 104]). These procedures have two objectives: 1) randomisethe correct feature for testing the hypothesis of interest; 2) account for sampling biases by keepingany structure resulting from them constant. The first objective is essential for valid testing while thesecond one may not need to be fulfilled if relevant sampling biases are controlled for elsewhere in theanalysis.Data permutation to generate the null networks may be applied either before the network (ad-jacency matrix) is constructed ( data stream permutation or pre-network permutation , [72] and seebelow), or afterwards by permuting either features of the observed network such as node labels oredge weights ( network permutation [103]) or residuals from regression models ( residual permutation [102]). Furthermore, various rules for restrictions on which data points can be exchanged may be usedwithin the permutation types. Specialised data stream permutation procedures have been developedin the animal behaviour field that use permutation restrictions to simultaneously account for com-mon sampling biases (esp. the number of sightings of each individual, and biases due to demographicchanges) and data features usually not of interest for the test (esp. group size distribution), whileotherwise randomising the social structure [72–75]. Network permutation and residual permutationmay be restricted (e.g. only permute within sexes) or unrestricted, but the restrictions here usually donot control for sampling biases, and these permutation types therefore often need to be combined withsampling bias control elsewhere in the analysis (e.g. in a regression model [76]). Which permutationtype and restrictions are used depends on the data and the hypothesis being tested [76].7nother common aim of animal social network analyses - which often requires different method-ological approaches than the above described - is to investigate the effect of social structure on theflow of information or disease through animal populations. A frequent methodological approach forstudying the spread of information in animal social networks is to use network-based diffusion analysis ,where observed information acquisition times are compared to models of information flow with socialor non-social learning [105]. Methodological approaches used for investigating disease transmission inanimal social networks include simulation of disease spread in observed networks with standard epi-demic models, and statistical testing for relationships between observed individual network positions,individual attributes, and measured infection states by linear model frameworks [16, 106, 107].Going forward animal social network research is starting to explore and use additional method-ological approaches introduced from other areas of network science, including relational event models[108], exponential random graph models [109], stochastic actor-oriented models [110], time-orderednetworks [111], and multilayer networks [112]. Together this points towards increasingly dynamic andmultidimensional analyses of animal social networks. While animal social network studies have already made valuable scientific contributions (reviewed in[2]), some potentially fruitful directions of research involving these networks are hindered by the factthat appropriate theory and methods for these directions have not yet been developed or have not beenadjusted to this area of network research. In the following, we describe challenges for animal socialnetwork studies where we imagine that input from scientists with expertise in other types of empiricalnetworks or in theoretical aspects of complex systems could be particularly valuable for finding goodsolutions.
Network similarity.
An important challenge for animal social network research is how to mea-sure the similarity between real-world networks from different sets of individuals in a meaningfulway [101, 113]. Comparison of the social structures of different species, or of populations of thesame species living in different environments or containing different compositions of individuals(e.g. with regard to sex or age) could potentially bring new key insights into the evolution ofsocial systems and how they are shaped by internal and external factors. In animal social net-work research, network similarity is often investigated by quadratic assignment procedure matrixcorrelation methods [102], but these can only be used for networks that contain the same setof individuals (e.g. the same group under different environmental conditions). While networkcomparison methods that control for different sampling biases (see Section 4 for description ofcommon biases) and different network sizes would be very useful, such methods have not yetbeen well integrated into animal social network research (although specific approaches have beensuggested, e.g. motif analysis [113] and exponential random graph models [109]). Given the factthat graph similarity is a fundamental topic of interest in network science, there should be muchscope for interdisciplinary development of network comparison methods specifically designed foranimal social networks.
Social complexity.
Another question of high relevance for research in animal social networksis how social complexity can and should best be defined and measured [114–116]. Animal socialcomplexity, and its variation between and within species, has long held interest from animalbehaviour researchers, both because it provides a framework for understanding the evolution ofsocial systems, and because of its potential links to the evolution of cognitive abilities and com-munication systems [115, 116]. There is currently no consensus about how to define and measureanimal social complexity, and different measures may be relevant for different questions, giventhat they would catch different aspects of social complexity. Factors that have been consideredas indicators of animal social complexity include group size and composition, mating system,social roles, and differentiated social relationships (for details see [114, 115]). The now extensiveresearch in animal social networks raises the questions of how these networks can be used inthe general task of quantifying social complexity in meaningful ways, and how the complexity ofsocial network structures may best be measured and compared across different species and pop-ulations. These questions have not yet been much explored (for exceptions, see [79] for a recent8uggestion for a complexity measure based on animal social networks, and [5] for a discussionof various potential measures). Collaboration between theoretical researchers with expertise incomplexity measures and empirical animal behaviour researchers could potentially advance thisarea, and a foundation has recently been laid for the integration of complex systems thinkinginto general animal social complexity research (see [116]).
Network robustness.
A topic which has got somewhat more attention and may also particu-larly benefit from interdisciplinary collaboration is the robustness of animal social networks (i.e.their ability to withstand perturbations, such as the death or removal of individuals). Knowledgeabout this is important for the conservation of animal populations (e.g. in the face of poachingor habitat destruction, which can lead to network fragmentation and/or reduction in networksize), as well as for understanding social evolution. A number of studies have investigated ro-bustness of animal social networks with various approaches, including actual experimental ornatural removal of individuals from the population [10, 117–119], simulated removal of nodesfrom empirical [10, 120] and artificial networks [121], or application of other experimental per-turbations [122]. These studies have given indications of the level of resilience of animal socialstructures in different species and under various perturbation scenarios. Better integration of per-colation theory and related topics with animal social network research could potentially furtherour understanding of the robustness of social systems across species.
Extraction of information from large datasets.
Finally, the automated data collectionmethods that are now in use (see Section 4) means that animal social network datasets areincreasingly large and multidimensional, and the extraction of information from the raw data isless direct. Optimisation of the treatment of these data is likely to benefit from interaction withareas of complex systems science where large and complex datasets are routinely dealt with.
Data on animal social networks are to an increasing extent being made publicly available in onlinerepositories, including Dryad Digital Repository (datadryad.org), Network Repository (networkrepos-itory.com/asn), and Animal Social Network Repository (bansallab.github.io/asnr ; [123]), allowing foreasy access for complex systems researchers who would like to explore and use such data. Althoughthese data are freely available, we would suggest that the researcher who has provided the data isalways contacted before the data are used in scientific projects. This is not only as a courtesy to theresearcher, but also to make sure that the data are useful for the intended purpose. Factors that maybe relevant to consider in this regard include for example the methods used for data collection, thetime frame over which the data were collected, the type of behaviour used to quantify the networkedges, and potential sampling biases that need to be controlled for in the network construction andanalysis (see Section 4).
It is frequently mentioned in complex systems science that networks can be found on all levels ofnature, including the sub-individual level (e.g. gene and protein networks) and the super-individuallevel (e.g. ecological networks based on species interactions). On the level of the individual (or wholeorganism), often only human social networks are mentioned, reflecting that animal social networks arenot yet generally so well known outside the field of animal behaviour. Nevertheless, to comprehensivelyunderstand nature and the complex systems found in it, we must take the many non-human animalspecies into account.We believe that the best understanding of animal social networks, and the best use of them forunderstanding complex systems, is gained by combining intricate knowledge about the specific studysystems with innovative and rigorous theory, modelling and analysis. We hope with this introduction tohave provided a springboard for future cross-disciplinary collaborations around animal social networksand further integration of animal social network research with complex systems science.9
Acknowledgements
This work was funded by the Carlsberg Foundation (a Postdoctoral Internationalisation Fellowshipto J.B.B.), the Natural Environment Research Council (NE/S010327/1 to D.P.C. and S.E.), and theLeverhulme Trust (an Early Career Research Fellowship to S.E.). We thank Dan Mønster for helpfulcomments.
References [1] J. Krause and G. D. Ruxton,
Living in groups (Oxford University Press, 2002).[2] J. Krause, R. James, D. W. Franks, and D. P. Croft,
Animal social networks (Oxford UniversityPress, USA, 2015).[3] J. Krause, D. P. Croft, and R. James, “Social network theory in the behavioural sciences:potential applications,” Behavioral Ecology and Sociobiology , 15–27 (2007).[4] D. P. Croft, R. James, and J. Krause, Exploring animal social networks (Princeton UniversityPress, 2008).[5] H. Whitehead,
Analyzing animal societies: quantitative methods for vertebrate social analysis (University of Chicago Press, 2008).[6] T. Wey, D. T. Blumstein, W. Shen, and F. Jord´an, “Social network analysis of animal behaviour:a promising tool for the study of sociality,” Animal behaviour , 333–344 (2008).[7] A. Sih, S. F. Hanser, and K. A. McHugh, “Social network theory: new insights and issues forbehavioral ecologists,” Behavioral Ecology and Sociobiology , 975–988 (2009).[8] R. H. Kurvers, J. Krause, D. P. Croft, A. D. Wilson, and M. Wolf, “The evolutionary and eco-logical consequences of animal social networks: emerging issues,” Trends in ecology & evolution , 326–335 (2014).[9] D. P. Croft, S. K. Darden, and T. W. Wey, “Current directions in animal social networks,”Current opinion in behavioral sciences , 52–58 (2016).[10] J. C. Flack, M. Girvan, F. B. De Waal, and D. C. Krakauer, “Policing stabilizes constructionof social niches in primates,” Nature , 426–429 (2006).[11] P. R. Guimar˜aes Jr, M. A. de Menezes, R. W. Baird, D. Lusseau, P. Guimaraes, and S. F.Dos Reis, “Vulnerability of a killer whale social network to disease outbreaks,” Physical ReviewE , 042901 (2007).[12] T. Gernat, V. D. Rao, M. Middendorf, H. Dankowicz, N. Goldenfeld, and G. E. Robinson,“Automated monitoring of behavior reveals bursty interaction patterns and rapid spreadingdynamics in honeybee social networks,” Proceedings of the National Academy of Sciences ,1433–1438 (2018).[13] B. Beisner, N. Braun, M. P´osfai, J. Vandeleest, R. DSouza, and B. McCowan, “A multiplexcentrality metric for complex social networks: sex, social status, and family structure predictmultiplex centrality in rhesus macaques,” PeerJ , e8712 (2020).[14] V. Gelardi, J. Godard, D. Paleressompoulle, N. Claidi`ere, and A. Barrat, “Measuring socialnetworks in primates: wearable sensors versus direct observations,” Proceedings of the RoyalSociety A , 20190737 (2020).[15] L. Snijders, D. T. Blumstein, C. R. Stanley, and D. W. Franks, “Animal social network theorycan help wildlife conservation,” Trends in ecology & evolution , 567–577 (2017).[16] M. J. Silk, D. J. Hodgson, C. Rozins, D. P. Croft, R. J. Delahay, M. Boots, and R. A. McDonald,“Integrating social behaviour, demography and disease dynamics in network models: applicationsto disease management in declining wildlife populations,” Philosophical Transactions of the RoyalSociety B , 20180211 (2019). 1017] L. J. Brent, A. Ruiz-Lambides, and M. Platt, “Persistent social isolation reflects identity andsocial context but not maternal effects or early environment,” Scientific reports , 1–11 (2017).[18] S. Ellis, D. W. Franks, S. Nattrass, M. A. Cant, M. N. Weiss, D. Giles, K. Balcomb, and D. P.Croft, “Mortality risk and social network position in resident killer whales: Sex differences andthe importance of resource abundance,” Proceedings of the Royal Society B: Biological Sciences , 20171313 (2017).[19] E. Z. Cameron, T. H. Setsaas, and W. L. Linklater, “Social bonds between unrelated femalesincrease reproductive success in feral horses,” Proceedings of the National Academy of Sciences , 13850–13853 (2009).[20] C. H. Fr`ere, M. Kr¨utzen, J. Mann, R. C. Connor, L. Bejder, and W. B. Sherwin, “Social andgenetic interactions drive fitness variation in a free-living dolphin population,” Proceedings ofthe National Academy of Sciences , 19949–19954 (2010).[21] J. B. Silk, J. C. Beehner, T. J. Bergman, C. Crockford, A. L. Engh, L. R. Moscovice, R. M.Wittig, R. M. Seyfarth, and D. L. Cheney, “Strong and consistent social bonds enhance thelongevity of female baboons,” Current biology , 1359–1361 (2010).[22] A. Barocas, A. Ilany, L. Koren, M. Kam, and E. Geffen, “Variance in centrality within rockhyrax social networks predicts adult longevity,” PloS one (2011).[23] E. Vander Wal, M. Festa-Bianchet, D. R´eale, D. Coltman, and F. Pelletier, “Sex-based differ-ences in the adaptive value of social behavior contrasted against morphology and environment,”Ecology , 631–641 (2015).[24] J. Lehmann, B. Majolo, and R. McFarland, “The effects of social network position on thesurvival of wild barbary macaques, macaca sylvanus,” Behavioral Ecology , 20–28 (2016).[25] D. T. Blumstein, D. M. Williams, A. N. Lim, S. Kroeger, and J. G. Martin, “Strong social rela-tionships are associated with decreased longevity in a facultatively social mammal,” Proceedingsof the Royal Society B: Biological Sciences , 20171934 (2018).[26] J. Ostner and O. Sch¨ulke, “Linking sociality to fitness in primates: a call for mechanisms,” in Advances in the Study of Behavior , Vol. 50 (Elsevier, 2018) pp. 127–175.[27] J. M. Smith,
Evolution and the Theory of Games (Cambridge university press, 1982).[28] G. Szab´o and G. Fath, “Evolutionary games on graphs,” Physics reports , 97–216 (2007).[29] C. P. Roca, J. A. Cuesta, and A. S´anchez, “Evolutionary game theory: Temporal and spatialeffects beyond replicator dynamics,” Physics of life reviews , 208–249 (2009).[30] B. Voelkl and C. Kasper, “Social structure of primate interaction networks facilitates the emer-gence of cooperation,” Biology letters , 462–464 (2009).[31] G. S. van Doorn and M. Taborsky, “The evolution of generalized reciprocity on social interactionnetworks,” Evolution: International Journal of Organic Evolution , 651–664 (2012).[32] J. B. Brask, D. P. Croft, M. Edenbrow, R. James, B. H. Bleakley, I. W. Ramnarine, R. J.Heathcote, C. R. Tyler, P. B. Hamilton, T. Dabelsteen, et al. , “Evolution of non-kin cooperation:social assortment by cooperative phenotype in guppies,” Royal Society open science , 181493(2019).[33] L. M. Aplin, D. R. Farine, J. Morand-Ferron, and B. C. Sheldon, “Social networks predict patchdiscovery in a wild population of songbirds,” Proceedings of the Royal Society B: BiologicalSciences , 4199–4205 (2012).[34] M. M. Webster, N. Atton, W. J. Hoppitt, and K. N. Laland, “Environmental complexity influ-ences association network structure and network-based diffusion of foraging information in fishshoals,” The American Naturalist , 235–244 (2013).1135] J. A. Firth, B. C. Sheldon, and D. R. Farine, “Pathways of information transmission among wildsongbirds follow experimentally imposed changes in social foraging structure,” Biology letters ,20160144 (2016).[36] T. B. Jones, L. M. Aplin, I. Devost, and J. Morand-Ferron, “Individual and ecological determi-nants of social information transmission in the wild,” Animal Behaviour , 93–101 (2017).[37] C. Hobaiter, T. Poisot, K. Zuberb¨uhler, W. Hoppitt, and T. Gruber, “Social network analysisshows direct evidence for social transmission of tool use in wild chimpanzees,” PLoS biology ,e1001960 (2014).[38] S. Wild, S. J. Allen, M. Kr¨utzen, S. L. King, L. Gerber, and W. J. Hoppitt, “Multi-network-based diffusion analysis reveals vertical cultural transmission of sponge tool use within dolphinmatrilines,” Biology letters , 20190227 (2019).[39] J. Allen, M. Weinrich, W. Hoppitt, and L. Rendell, “Network-based diffusion analysis revealscultural transmission of lobtail feeding in humpback whales,” Science , 485–488 (2013).[40] N. Claidiere, E. J. Messer, W. Hoppitt, and A. Whiten, “Diffusion dynamics of socially learnedforaging techniques in squirrel monkeys,” Current Biology , 1251–1255 (2013).[41] N. J. Boogert, G. F. Nightingale, W. Hoppitt, and K. N. Laland, “Perching but not foragingnetworks predict the spread of novel foraging skills in starlings,” Behavioural processes ,135–144 (2014).[42] L. M. Aplin, D. R. Farine, J. Morand-Ferron, A. Cockburn, A. Thornton, and B. C. Sheldon,“Experimentally induced innovations lead to persistent culture via conformity in wild birds,”Nature , 538–541 (2015).[43] I. G. Kulahci, D. I. Rubenstein, T. Bugnyar, W. Hoppitt, N. Mikus, and C. Schwab, “Socialnetworks predict selective observation and information spread in ravens,” Royal Society openscience , 160256 (2016).[44] J. A. Drewe, “Who infects whom? social networks and tuberculosis transmission in wildmeerkats,” Proceedings of the Royal Society B: Biological Sciences , 633–642 (2010).[45] K. L. VanderWaal, E. R. Atwill, S. Hooper, K. Buckle, and B. McCowan, “Network structure andprevalence of cryptosporidium in beldings ground squirrels,” Behavioral Ecology and Sociobiology , 1951–1959 (2013).[46] N. Weber, S. P. Carter, S. R. Dall, R. J. Delahay, J. L. McDonald, S. Bearhop, and R. A.McDonald, “Badger social networks correlate with tuberculosis infection,” Current Biology ,R915–R916 (2013).[47] R. Rimbach, D. Bisanzio, N. Galvis, A. Link, A. Di Fiore, and T. R. Gillespie, “Brown spidermonkeys (ateles hybridus): a model for differentiating the role of social networks and physicalcontact on parasite transmission dynamics,” Philosophical Transactions of the Royal Society B:Biological Sciences , 20140110 (2015).[48] A. E. Williams, K. E. Worsley-Tonks, and V. O. Ezenwa, “Drivers and consequences of variationin individual social connectivity,” Animal Behaviour , 1–9 (2017).[49] P. C. Cross, J. O. Lloyd-Smith, J. A. Bowers, C. T. Hay, M. Hofmeyr, and W. M. Getz,“Integrating association data and disease dynamics in a social ungulate: bovine tuberculosis inafrican buffalo in the kruger national park,” in Annales Zoologici Fennici (JSTOR, 2004) pp.879–892.[50] C. Carne, S. Semple, H. Morrogh-Bernard, K. Zuberbuehler, and J. Lehmann, “The risk ofdisease to great apes: simulating disease spread in orang-utan (pongo pygmaeus wurmbii) andchimpanzee (pan troglodytes schweinfurthii) association networks,” PloS one , e95039 (2014).[51] P. Sah, S. T. Leu, P. C. Cross, P. J. Hudson, and S. Bansal, “Unraveling the disease consequencesand mechanisms of modular structure in animal social networks,” Proceedings of the NationalAcademy of Sciences , 4165–4170 (2017).1252] C. Rozins, M. J. Silk, D. P. Croft, R. J. Delahay, D. J. Hodgson, R. A. McDonald, N. Weber,and M. Boots, “Social structure contains epidemics and regulates individual roles in diseasetransmission in a group-living mammal,” Ecology and Evolution , 12044–12055 (2018).[53] S. Henzi, D. Lusseau, T. Weingrill, C. Van Schaik, and L. Barrett, “Cyclicity in the structureof female baboon social networks,” Behavioral Ecology and Sociobiology , 1015–1021 (2009).[54] E. A. Foster, D. W. Franks, L. J. Morrell, K. C. Balcomb, K. M. Parsons, A. van Ginneken, andD. P. Croft, “Social network correlates of food availability in an endangered population of killerwhales, orcinus orca,” Animal Behaviour , 731–736 (2012).[55] G. Wittemyer, I. Douglas-Hamilton, and W. M. Getz, “The socioecology of elephants: analysisof the processes creating multitiered social structures,” Animal behaviour , 1357–1371 (2005).[56] L. J. Brent, A. MacLarnon, M. L. Platt, and S. Semple, “Seasonal changes in the structure ofrhesus macaque social networks,” Behavioral Ecology and Sociobiology , 349–359 (2013).[57] S. Nandini, P. Keerthipriya, and T. Vidya, “Seasonal variation in female asian elephant socialstructure in nagarahole-bandipur, southern india,” Animal Behaviour , 135–145 (2017).[58] M. J. Silk, N. Weber, L. C. Steward, R. J. Delahay, D. P. Croft, D. J. Hodgson, M. Boots, andR. A. McDonald, “Seasonal variation in daily patterns of social contacts in the european badgermeles meles,” Ecology and evolution , 9006–9015 (2017).[59] S. G. Prehn, B. E. Laesser, C. G. Clausen, K. Jønck, T. Dabelsteen, and J. B. Brask, “Seasonalvariation and stability across years in a social network of wild giraffe,” Animal Behaviour ,95–104 (2019).[60] G. Kerth, N. Perony, and F. Schweitzer, “Bats are able to maintain long-term social relationshipsdespite the high fission–fusion dynamics of their groups,” Proceedings of the Royal Society B:Biological Sciences , 2761–2767 (2011).[61] S. S. Godfrey, A. Sih, and C. M. Bull, “The response of a sleepy lizard social network to alteredecological conditions,” Animal Behaviour , 763–772 (2013).[62] C. Borgeaud, S. Sosa, C. Sueur, and R. Bshary, “The influence of demographic variation onsocial network stability in wild vervet monkeys,” Animal Behaviour , 155–165 (2017).[63] C. R. Stanley, C. Mettke-Hofmann, R. Hager, and S. Shultz, “Social stability in semiferal ponies:networks show interannual stability alongside seasonal flexibility,” Animal Behaviour , 175–184 (2018).[64] B. Beisner and B. McCowan, “Social networks and animal welfare,” (Oxford University Press,Oxford, 2015) Chap. 11, pp. 111–121.[65] P. Rose and D. Croft, “The potential of social network analysis as a tool for the management ofzoo animals,” Animal Welfare , 123–138 (2015).[66] R. K. Hamede, J. Bashford, H. McCallum, and M. Jones, “Contact networks in a wild tasmaniandevil (sarcophilus harrisii) population: using social network analysis to reveal seasonal variabilityin social behaviour and its implications for transmission of devil facial tumour disease,” Ecologyletters , 1147–1157 (2009).[67] J. Rushmore, D. Caillaud, R. J. Hall, R. M. Stumpf, L. A. Meyers, and S. Altizer, “Network-based vaccination improves prospects for disease control in wild chimpanzees,” Journal of theRoyal Society Interface , 20140349 (2014).[68] M. N. Weiss, D. W. Franks, K. C. Balcomb, D. K. Ellifrit, M. J. Silk, M. A. Cant, and D. P.Croft, “Modelling cetacean morbillivirus outbreaks in an endangered killer whale population,”Biological Conservation , 108398 (2020).[69] E. J. Dunston, J. Abell, R. E. Doyle, J. Kirk, V. B. Hilley, A. Forsyth, E. Jenkins, and R. Freire,“An assessment of african lion panthera leo sociality via social network analysis: prereleasemonitoring for an ex situ reintroduction program,” Current zoology , 301–311 (2017).1370] V. R. Franks, C. E. Andrews, J. G. Ewen, M. McCready, K. A. Parker, and R. Thorogood,“Changes in social groups across reintroductions and effects on post-release survival,” AnimalConservation (2018).[71] P. E. Rose and D. P. Croft, “Quantifying the social structure of a large captive flock of greaterflamingos (phoenicopterus roseus): Potential implications for management in captivity,” Be-havioural processes , 66–74 (2018).[72] L. Bejder, D. Fletcher, and S. Br¨ager, “A method for testing association patterns of socialanimals,” Animal behaviour , 719–725 (1998).[73] H. Whitehead, “Testing association patterns of social animals,” Animal Behaviour , F26–F29(1999).[74] H. Whitehead, L. Bejder, and C. A. Ottensmeyer, “Testing association patterns: issues arisingand extensions,” Animal Behaviour , e1 (2005).[75] S. Krause, L. Mattner, R. James, T. Guttridge, M. J. Corcoran, S. H. Gruber, and J. Krause,“Social network analysis and valid markov chain monte carlo tests of null models,” BehavioralEcology and Sociobiology , 1089–1096 (2009).[76] M. N. Weiss, D. W. Franks, L. J. N. Brent, S. Ellis, M. J. Silk, and D. P. Croft, “Commondatastream permutations of animal social network data are not appropriate for hypothesis testingusing regression models,” bioRxiv (2020), 10.1101/2020.04.29.068056.[77] D. W. Franks, R. James, J. Noble, and G. D. Ruxton, “A foundation for developing a method-ology for social network sampling,” Behavioral Ecology and Sociobiology , 1079–1088 (2009).[78] J. A. Firth, B. C. Sheldon, and L. J. Brent, “Indirectly connected: simple social differences canexplain the causes and apparent consequences of complex social network positions,” Proceedingsof the Royal Society B: Biological Sciences , 20171939 (2017).[79] M. N. Weiss, D. W. Franks, D. P. Croft, and H. Whitehead, “Measuring the complexity of socialassociations using mixture models,” Behavioral ecology and sociobiology , 8 (2019).[80] C. Perreault, “A note on reconstructing animal social networks from independent small-groupobservations,” Animal Behaviour , 551–562 (2010).[81] B. Voelkl, C. Kasper, and C. Schwab, “Network measures for dyadic interactions: stability andreliability,” American Journal of Primatology , 731–740 (2011).[82] M. J. Silk, A. L. Jackson, D. P. Croft, K. Colhoun, and S. Bearhop, “The consequences ofunidentifiable individuals for the analysis of an animal social network,” Animal Behaviour ,1–11 (2015).[83] G. H. Davis, M. C. Crofoot, and D. R. Farine, “Estimating the robustness and uncertainty ofanimal social networks using different observational methods,” Animal Behaviour , 29–44(2018).[84] D. W. Franks, G. D. Ruxton, and R. James, “Sampling animal association networks with thegambit of the group,” Behavioral ecology and sociobiology , 493–503 (2010).[85] H. Whitehead, “Analysing animal social structure,” Animal behaviour , 1053–1067 (1997).[86] H. Whitehead and S. Dufault, “Techniques for analyzing vertebrate social structure using iden-tified individuals,” Adv Stud Behav , 33–74 (1999).[87] D. R. Farine and H. Whitehead, “Constructing, conducting and interpreting animal social net-work analysis,” Journal of Animal Ecology , 1144–1163 (2015).[88] J. Krause, A. D. Wilson, and D. P. Croft, “New technology facilitates the study of socialnetworks,” Trends in Ecology & Evolution , 5–6 (2011).[89] J. Krause, S. Krause, R. Arlinghaus, I. Psorakis, S. Roberts, and C. Rutz, “Reality mining ofanimal social systems,” Trends in ecology & evolution , 541–551 (2013).1490] L. Snijders, E. P. van Rooij, J. M. Burt, C. A. Hinde, K. Van Oers, and M. Naguib, “Socialnetworking in territorial great tits: slow explorers have the least central social network positions,”Animal Behaviour , 95–102 (2014).[91] J. J. St Clair, Z. T. Burns, E. M. Bettaney, M. B. Morrissey, B. Otis, T. B. Ryder, R. C. Fleischer,R. James, and C. Rutz, “Experimental resource pulses influence social-network dynamics andthe potential for information flow in tool-using crows,” Nature Communications , 7197 (2015).[92] S. Ripperger, L. G¨unther, H. Wieser, N. Duda, M. Hierold, B. Cassens, R. Kapitza, A. Koelpin,and F. Mayer, “Proximity sensors on common noctule bats reveal evidence that mothers guidejuveniles to roosts but not food,” Biology letters , 20180884 (2019).[93] L. Ozella, J. Langford, L. Gauvin, E. Price, C. Cattuto, and D. P. Croft, “The effect of age,environment and management on social contact patterns in sheep,” Applied Animal BehaviourScience , 104964 (2020).[94] D. P. Mersch, A. Crespi, and L. Keller, “Tracking individuals shows spatial fidelity is a keyregulator of ant social organization,” Science , 1090–1093 (2013).[95] J. E. Herbert-Read, L. Kremer, R. Bruintjes, A. N. Radford, and C. C. Ioannou, “Anthropogenicnoise pollution from pile-driving disrupts the structure and dynamics of fish shoals,” Proceedingsof the Royal Society B: Biological Sciences , 20171627 (2017).[96] S. J. Cairns and S. J. Schwager, “A comparison of association indices,” Animal Behaviour ,1454–1469 (1987).[97] W. J. Hoppitt and D. R. Farine, “Association indices for quantifying social relationships: how todeal with missing observations of individuals or groups,” Animal Behaviour , 227–238 (2018).[98] I. Psorakis, S. J. Roberts, I. Rezek, and B. C. Sheldon, “Inferring social network structure inecological systems from spatio-temporal data streams,” Journal of the Royal Society Interface ,3055–3066 (2012).[99] J. J. Valletta, C. Torney, M. Kings, A. Thornton, and J. Madden, “Applications of machinelearning in animal behaviour studies,” Animal Behaviour , 203–220 (2017).[100] Q. M. Webber and E. Vander Wal, “Trends and perspectives on the use of animal social networkanalysis in behavioural ecology: a bibliometric approach,” Animal behaviour , 77–87 (2019).[101] R. James, D. P. Croft, and J. Krause, “Potential banana skins in animal social network analysis,”Behavioral Ecology and Sociobiology , 989–997 (2009).[102] D. Dekker, D. Krackhardt, and T. A. Snijders, “Sensitivity of mrqap tests to collinearity andautocorrelation conditions,” Psychometrika , 563–581 (2007).[103] D. P. Croft, J. R. Madden, D. W. Franks, and R. James, “Hypothesis testing in animal socialnetworks,” Trends in ecology & evolution , 502–507 (2011).[104] D. R. Farine, “A guide to null models for animal social network analysis,” Methods in Ecologyand Evolution , 1309–1320 (2017).[105] M. Franz and C. L. Nunn, “Network-based diffusion analysis: a new method for detecting sociallearning,” Proceedings of the Royal Society B: Biological Sciences , 1829–1836 (2009).[106] L. A. White, J. D. Forester, and M. E. Craft, “Using contact networks to explore mechanismsof parasite transmission in wildlife,” Biological Reviews , 389–409 (2017).[107] M. J. Silk, D. P. Croft, R. J. Delahay, D. J. Hodgson, N. Weber, M. Boots, and R. A. McDonald,“The application of statistical network models in disease research,” Methods in Ecology andEvolution , 1026–1041 (2017).[108] M. Tranmer, C. S. Marcum, F. B. Morton, D. P. Croft, and S. R. de Kort, “Using the relationalevent model (rem) to investigate the temporal dynamics of animal social networks,” Animalbehaviour , 99–105 (2015). 15109] M. J. Silk and D. N. Fisher, “Understanding animal social structure: exponential random graphmodels in animal behaviour research,” Animal Behaviour , 137–146 (2017).[110] D. N. Fisher, A. Ilany, M. J. Silk, and T. Tregenza, “Analysing animal social network dynamics:the potential of stochastic actor-oriented models,” Journal of Animal Ecology , 202–212 (2017).[111] B. Blonder, T. W. Wey, A. Dornhaus, R. James, and A. Sih, “Temporal dynamics and networkanalysis,” Methods in Ecology and Evolution , 958–972 (2012).[112] K. R. Finn, M. J. Silk, M. A. Porter, and N. Pinter-Wollman, “The use of multilayer networkanalysis in animal behaviour,” Animal behaviour , 7–22 (2019).[113] N. Pinter-Wollman, E. A. Hobson, J. E. Smith, A. J. Edelman, D. Shizuka, S. De Silva, J. S.Waters, S. D. Prager, T. Sasaki, G. Wittemyer, et al. , “The dynamics of animal social networks:analytical, conceptual, and theoretical advances,” Behavioral Ecology , 242–255 (2014).[114] P. M. Kappeler, “A framework for studying social complexity,” Behavioral Ecology and Sociobi-ology , 13 (2019).[115] P. Kappeler, T. Clutton-Brock, S. Shultz, and D. Lukas, “Social complexity: patterns, processes,and evolution,” Behavioural Ecology and Sociobiology (2019).[116] E. A. Hobson, V. Ferdinand, A. Kolchinsky, and J. Garland, “Rethinking animal social com-plexity measures with the help of complex systems concepts,” Animal Behaviour , 287–296(2019).[117] D. Naug, “Structure and resilience of the social network in an insect colony as a function ofcolony size,” Behavioral Ecology and Sociobiology , 1023–1028 (2009).[118] M. Franz, J. Altmann, and S. C. Alberts, “Knockouts of high-ranking males have limited impacton baboon social networks,” Current zoology , 107–113 (2015).[119] J. A. Firth, B. Voelkl, R. A. Crates, L. M. Aplin, D. Biro, D. P. Croft, and B. C. Sheldon,“Wild birds respond to flockmate loss by increasing their social network associations to others,”Proceedings of the Royal Society B: Biological Sciences , 20170299 (2017).[120] R. Williams and D. Lusseau, “A killer whale social network is vulnerable to targeted removals,”Biology letters , 497–500 (2006).[121] I. Puga-Gonzalez, S. Sosa, and C. Sueur, “Social style and resilience of macaques networks, atheoretical investigation,” Primates , 233–246 (2019).[122] V. Formica, C. Wood, P. Cook, and E. Brodie III, “Consistency of animal social networks afterdisturbance,” Behavioral Ecology , arw128 (2016).[123] P. Sah, J. D. M´endez, and S. Bansal, “A multi-species repository of social networks,” ScientificData6