Key players and hierarchical organization of prairie dog social networks
Introduction
In the study of social animals, there is growing interest in complex emergent properties of group structure. Social network analysis (SNA) has been increasingly used to study the social dynamics of animal systems (Bergmuller et al., 2010, Brent et al., 2011, Lusseau, 2003, Lusseau and Newman, 2004, Newman, 2003). It is a unifying conceptual framework that can be applied comparatively across all social taxa—from microbes to humans. Social networks can help to identify features of species that are indiscernible (or even invisible) based on studies of individuals or behaviors alone (Croft et al., 2004, Lusseau and Newman, 2004). In other cases, there exists substantial intra-specific variation among networks based, in part, on group attributes, individual differences, and ecological factors (Faust and Skvoretz, 2002, Guimarães et al., 2007, Bhadra et al., 2009, Madden et al., 2009). Furthermore, differences in social networks, whether among taxa or social groups, almost necessarily lead to differences in the spread of diseases, decision making strategies, information or, in some cases, food, through networks (Croft et al., 2004, Drewe et al., 2009, Hamede et al., 2009, Jacobs et al., 2011, Kasper and Voelkl, 2009, Madden et al., 2009).
A key challenge with SNA is how to relate their results to the much larger literature on social interactions that relies on other approaches to distinguish social groups. Prior to the widespread use of SNA, behavioral studies explored social interactions and social groups dynamics using informal clustering techniques (e.g., Hinde, 1976). Our understanding of the social systems of most organisms rests on such traditional approaches. Can the results from these earlier studies be related to those of social network analysis? This question seems to have not been well considered, particularly in the social mammals where research has tended to divide social groups into hierarchical categories. Such groups are constructed out of the existence of interactions among individuals but also the nature of those interactions and whether they are negative, positive, reproductive, relate to food sharing, or have some other defining features. The advantages of SNA are frequently highlighted (e.g., Proulx et al., 2005; Sueur et al., 2011; Wey et al., 2008), but whether SNA builds on, replaces, or conflicts with other approaches is unclear.
Gunnison's prairie dogs, Cynomys gunnisoni, are large, diurnal, highly social ground squirrels whose range is limited to the grasslands of the Colorado Plateau (Hall and Kelson, 1959). Gunnison's prairie dogs colonies contain a variable number of territories occupied by distinct social groups, ranging from 3 to 15 individuals (Travis et al., 1995, Verdolin and Slobodchikoff, 2010) akin to small groups of social insects (e.g., Temnothorax albipennis: Dornhaus and Franks, 2006), primate groups (Chapman and Chapman, 2000), or hunter gatherer societies (Hamilton et al., 2007). Traditionally, ecologists have distinguished prairie dog social groups using behavioral and spatial observations of known individuals over time (King, 1955, Slobodchikoff, 1984, Travis and Slobodchikoff, 1993, Verdolin, 2007), with a strong emphasis on negative interactions, where negative interactions among individuals imply those individuals are from different social groups (Slobodchikoff, 1984, Travis and Slobodchikoff, 1993, Verdolin, 2007). The designation of the size of groups and the identity of individuals within groups also often relies on data on mating behavior and behavioral time allocation (e.g., time spent being vigilant versus feeding; Slobodchikoff, 1984, Travis and Slobodchikoff, 1993, Travis et al., 1995, Verdolin, 2007). The resulting identification of distinct social groups within a site can be robust with regard to individual interactions, but tends to result in a categorical classification of groups, in which individuals either are or are not members of groups and any patterning in social structure above or below the standard social group is either not described or, if described, is in terms of the behavior of individual species and their histories.
Although SNA has been used recently for a variety of social species, its application has focused primarily on individual measurements or full network measurements. When SNA methods are used to find intermediate (within network) structure in the full networks, these methods are referred to as community detection (Leu et al., 2010, Lusseau, 2003, Lusseau and Newman, 2004, Maryanski, 1987). The use of community detection techniques in the analysis of social networks has recently gained traction (Porter et al., 2009). Often network structure is not obvious by simply looking at a list of interactions, or a resulting graph of interactions. Community detection permits a researcher to identify social groups by discerning which individuals in the network have more connections to the other individuals within the group than to individuals outside the group.
If a network-based approach to exploring the social dynamics of Gunnison's prairie dog—or any other species—produces social groupings similar to traditional methods, social network analysis can add to the insights of traditional approaches in several ways. First, comparing social network properties among groups may highlight subtle variation in social structure not readily observable or quantifiable by conventional behavioral studies (Faust and Skvoretz, 2002, Traud et al., 2011, Wolf et al., 2007). Second, network analyses can also reveal emergent properties of social groups, including identifying individuals with central roles—such as the dolphin social brokers—and characterizing variability in group cohesion or hubs, individuals who are connected to an unusually high number of other organisms (Bezanson et al., 2008, Croft et al., 2005, Gero et al., 2013, Lusseau, 2003, Lusseau and Newman, 2004, Madden et al., 2009, Naug, 2008). Third, SNA may provide a method for testing the hypothesis that individuals may group together based on similarities, differences, or random associations (Galef and Laland, 2005, Pedersen et al., 2006; Pepper, 2000; Reader and Biro, 2010, Rendella and Whiteheada, 2001, Ross, 2001). On the other hand, if SNA produces fundamentally different social group clusters than traditional behavioral approaches, it might imply that the two methods describe potentially distinct information and social processes.
Here, we generated social network matrices using data on positive social interactions of Gunnison's prairie dogs. We then used community detection analysis to discern distinct social groups simply from the network data and compared them to social groups identified by traditional behavioral approaches (Traud et al., 2011). Next, we used SNA to examine whether there were features of Gunnison prairie dog social behavior detectable only through SNA or behavioral studies alone. Based on the differences we found between SNA and traditional methods, we expanded our analysis to further explore aspects of sociality not detectable by traditional methods.
Section snippets
Study area
A detailed description of live-trapping, handling, and marking methods are available in Verdolin (2007). A Scientific Collector's Permit (Arizona Game and Fish Permit no. SP742094) was obtained prior to trapping and all procedures were in compliance with Stony Brook University IACUC (IACUC no. 2009-1745, Stony Brook University). Individuals were trapped with veterinary supervision from mid-February (upon emergence from hibernation) through August at two colonies, Country Club (CC) and Humane
Results
A total of 220 focal samples for 80 prairie dogs were collected. In addition, a total of 5, 5, and 4 social groups were identified using behavioral observations and spatial locations for populations HSI, HSII, and CCI, respectively. Network analysis resulted in three different weighted networks, where each connection between a pair of prairie dogs was weighted by the number of interactions between the prairie dogs in that pair (Fig. 1). Overall, CCI, HSI, and HSII, consisted of 46, 32, and 47
Discussion and conclusions
We found that the majority of the prairie dogs were placed in social network communities that were consistent with their traditional behavioral social group placement (Fig. 1). More importantly, the Social Network Analysis (SNA) approach also recovered additional structure within those groups, as well as previously undetected structure within those social groups. Within network-based social groups, individuals were subdivided into smaller subunits of individuals that mostly interact with each
Acknowledgements
JLV was supported by NESCent (EF-0905606). ALT was supported by the Statistical and Applied Mathematical Science Institute Complex Network Fellowship, NC State Mathematics Department and a NASA Biodiversity Grant (ROSES-NNX09AK22G). RRD was supported by a US DOE PER award (DE-FG02-08ER64510), a NASA Biodiversity Grant (ROSES-NNX09AK22G) and an NSF Career grant (0953390). For assistance in the field many thanks to Dr. David Washabau, Bill and Theresa Emig, Carolyn Parker, Perry Crompton, Kristen
References (75)
- et al.
A comparative social network analysis of wasp colonies and classrooms: linking network structure to functioning
Ecol. Complex.
(2009) - et al.
Environmental uncertainty and the global biogeography of cooperative breeding in birds
Curr. Biol.
(2011) - et al.
Association networks reveal social organization in the sleepy lizard
Anim. Behav.
(2010) Social networking in the Columbian ground squirrel, Spermophilus columbianus
Anim. Behav.
(2008)African ape social structure: is there strength in weak ties?
Soc. Netw.
(1987)- et al.
A note on the calculation of empirical p values from Monte Carlo procedures
Am. J. Hum. Genet.
(2003) - et al.
Node centrality in weighted networks: generalizing degree and shortest paths
Soc. Netw.
(2010) Relatedness in trait group models of social evolution
J Theor Bio.
(2000)- et al.
Network thinking in ecology and evolution
Trends Ecol. Evol.
(2005) - et al.
Social network analysis of animal behaviour: a promising tool for the study of sociality
Anim. Behav.
(2008)
Focused grooming networks and stress alleviation in wild female baboons
Horm. Behav.
Social structure in a colonial mammal: unravelling hidden structural layers and their foundations by network analysis
Anim. Behav.
Observational study of behavior: sampling methods
Behaviour
Evolutionary causes and consequences of consistent individual variation in cooperative behaviour
Philos. Trans. R. Soc. B: Biol.
Patterns of subgrouping and spatial affiliation in a community of mantled howling monkeys (Alouatta palliata)
Am. J. Primatol.
Fast unfolding of communities in large networks
J. Stat. Mech.
Fluctuating environments, sexual selection and the evolution of flexible mate choice in birds
PLoS One
Social network analysis in the study of nonhuman primates: a historical perspective
Am. J. Primatol.
Determinants of group size in primates: the importance of travel costs
Assortative interactions and social networks in fish
Oecologia
Behavioural trait assortment in a social network: patterns and implications
Behav. Ecol. Sociobiol.
Social networks in the guppy (Poecilia reticulata)
Proc. R. Soc. Lond. Ser. B: Biol. Sci.
igraph: Routines for Network Analysis R Package
Colony size affects collective decision-making in the ant Temnothorax albipennis
Insectes Sociaux
The social network structure of a wild meerkat population: 1. Inter-group interactions
Behav. Ecol. Sociobiol.
Randomization Tests
On random graphs
Publ. Math.
Comparing networks across space and time, size and species
Social structure, robustness, and policing cost in a cognitively sophisticated species
Am Nat
The Social Biology of Ropalidia marginata
Social learning in animals: empirical studies and theoretical models
Bioscience
Calves as social hubs: dynamics of the social network within sperm whale units
Proc. R. Soc. B: Biol. Sci.
Vulnerability of a killer whale social network to disease outbreaks
Phys. Rev. E: Stat. Nonlinear Soft Matter Phys.
The Mammals of North America
Contact networks in a wild Tasmanian devil (Sarcophilus harrisii) population: using social network analysis to reveal seasonal variability in social behaviour and its implications for transmission of devil facial tumour disease
Ecol. Lett.
The complex structure of hunter–gatherer social networks
Proc. R. Soc. B: Biol. Sci.
Interactions, relationships and social-structure
Man
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