The effect of temporal resolution and contact duration on Real-Time location system-based contact networks for confined feedlot cattle

Empirical data on livestock contact networks are scarce but digital technologies are increasingly used to characterize animal behavior and describe the dynamics of contact networks. The objective of this study was to use contact network analysis to quantify contacts within three pens of feedlot cattle across three consecutive years at varying temporal resolutions to better inform the construction of network-based disease transmission models for cattle within confined-housing systems. We also aimed to describe the influence of the variation in Real-Time Location System (RTLS) average tag read rates and the effect of increasing minimum contact duration (MCD) on the contact networks of feedlot cattle. Three pens of feedlot steers were tagged with RTLS, one pen in each of three consecutive summers from 2017 to 2019. Contacts were defined with a spatial threshold of 0.71 m and an MCD of either 10, 30, or 60 seconds. Static, undirected, weighted contact networks were created for the full study duration and then split into daily (24-h), 6-h period, and hourly networks to better assess network heterogeneity. For the full study duration time scale, all three networks were found to be densely connected. The networks showed more heterogeneity in network density and clustering coefficient when smaller time scales (6-h period and hourly) were applied. When contacts were defined with a MCD of 30 or 60 seconds, the total number of contacts seen in each network decreased, indicating that most of the contacts observed in our networks may have been transient passing contacts. For example, the total defined contacts for the 2017 native read rate network were 930,843 at 10 s MCD, 95,570 at 30 s MCD, and 19,135 at 60 s MCD. Though the same system was used for all three years, variation in average tag read rate was observed (range: 2.2 readings/min (2018) to 7.4 readings/min (2017)). When the networks were down-scaled from higher average tag read rates to match lower tag read rates, the full study networks maintained similar network density and clustering, though the average edge weight between pairs decreased. Overall, the networks created here from high-resolution spatial and temporal contact observation data provide estimates for a contact network within a commercial US feedlot pen.

Centrality of Hygienic Honey Bee Workers in Colony Social Networks

Many social and environmental variables can affect the interactions among individuals in an insect colony that fundamentally structure its social organization. Along with important attributes such as age and caste, immunity-related factors such as the performance of sanitary tasks or exposure to a pathogen can also influence an individual's social interactions and their place in the resulting social network. Most work on this subject has supported the hypothesis that health-compromised individuals will exhibit altered social or spatial behavior that presumably limits the spread of infection. Here, we test this hypothesis using honey bee workers recently involved in hygienic behavior, an important set of sanitary tasks in which unhealthy brood are uncapped and then removed from the colony. Using static social networks, we quantify the interaction patterns of workers recently involved in hygienic tasks and compare their network centrality to non-hygienic workers. Using dynamic networks, we analyze the capability of hygienic workers to spread a potential infection throughout the colony. We find no substantial differences in how connected hygienic workers are in the network, and we show that hygienic workers would spread a novel infection throughout the colony to the same extent as non-hygienic workers. Our results suggest that experience with certain sanitary tasks may not necessarily produce rapid changes in social behavior. This work highlights the importance of considering the benefits of remaining socially integrated in important information networks and the temporal limitations for how quickly organized immune responses can occur in response to potential infections.

The contribution of movement to social network structure and spreading dynamics under simple and complex transmission

The structure of social networks fundamentally influences spreading dynamics. In general, the more contact between individuals, the more opportunity there is for the transmission of information or disease to take place. Yet, contact between individuals, and any resulting transmission events, are determined by a combination of spatial (where individuals choose to move) and social rules (who they choose to interact with or learn from). Here, we examine the effect of the social-spatial interface on spreading dynamics using a simulation model. We quantify the relative effects of different movement rules (localized, semi-localized, nomadic and resource-based movement) and social transmission rules (simple transmission, anti-conformity, proportional, conformity and threshold rules) to both the structure of social networks and spread of a novel behaviour. Localized movement created weakly connected sparse networks, nomadic movement created weakly connected dense networks, and resource-based movement generated strongly connected modular networks. The resulting rate of spreading varied with different combinations of movement and transmission rules, but-importantly-the relative rankings of transmission rules changed when running simulations on static versus dynamic representations of networks. Our results emphasize that individual-level social and spatial behaviours influence emergent network structure, and are of particular consequence for the spread of information under complex transmission rules.This article is part of the theme issue 'The spatial-social interface: a theoretical and empirical integration'.

Residency and space use estimation methods based on passive acoustic telemetry data

Acoustic telemetry has helped overcome many of the challenges faced when studying the movement ecology of aquatic species, allowing to obtain unprecedented amounts of data. This has made it into one of the most widely used methods nowadays. Many ways to analyse acoustic telemetry data have been made available and deciding on how to analyse the data requires considering the type of research objectives, relevant properties of the data (e.g., resolution, study design, equipment), habits of the study species, researcher experience, among others. To ease this decision process, here we showcase (1) some of the methods used to estimate pseudo-positions and positions from raw acoustic telemetry data, (2) methods to estimate residency and (3) methods to estimate two-dimensional home and occurrence range using geometric or hull-based methods and density-distribution methods, a network-based approach, and three-dimensional methods. We provide examples of some of these were tested using a sample of real data. With this we intend to provide the necessary background for the selection of the method(s) that better fit specific research objectives when using acoustic telemetry.

Social responses to the natural loss of individuals in Barbary macaques

In recent years, there has been considerable interest in investigating how animal social structure is affected by the loss of individuals. This is often achieved using simulations that generate predictions regarding how the removal of ‘key’ individuals from a group affects network structure. However, little is known about the effects of such removals in wild and free-ranging populations, particularly the extent to which naturally occurring mortality events and the loss of a large proportion of individuals from a social group affects the overall structure of a social network. Here, we used data from a population of wild Barbary macaques (Macaca sylvanus) that was exposed to an exceptionally harsh winter, culminating in the death of 64% of the adults from two groups. We analysed how social interaction patterns among surviving individuals were affected by the natural loss of group members using social networks based on affiliative (i.e., grooming) and aggressive social interactions. We show that only the structure of the pre-decline grooming networks was conserved in the post-decline networks, suggesting that grooming, but not aggression networks are resilient against the loss of group members. Surviving group members were not significantly different from the non-survivors in terms of their affiliative and agonistic relationships, and did not form assorted communities in the pre-decline networks. Overall, our results suggest that in primates, patterns of affiliative interactions are more resilient to changes in group composition than aggressive interaction patterns, which tend to be used more flexibly in new conditions.

From dyads to collectives: a review of honeybee signalling

The societies of honeybees (Apis spp.) are microcosms of divided labour where the fitness interests of individuals are so closely aligned that, in some contexts, the colony behaves as an entity in itself. Self-organization at this extraordinary level requires sophisticated communication networks, so it is not surprising that the celebrated waggle dance, by which bees share information about locations outside the hive, evolved here. Yet bees within the colony respond to several other lesser-known signalling systems, including the tremble dance, the stop signal and the shaking signal, whose roles in coordinating worker behaviour are not yet fully understood. Here, we firstly bring together the large but disparate historical body of work that has investigated the “meaning” of such signals for individual bees, before going on to discuss how network-based approaches can show how such signals function as a complex system to control the collective foraging effort of these remarkable social insect societies.

A new method for characterising shared space use networks using animal trapping data

Abstract

Studying the social behaviour of small or cryptic species often relies on constructing networks from sparse point-based observations of individuals (e.g. live trapping data). A common approach assumes that individuals that have been detected sequentially in the same trapping location will also be more likely to have come into indirect and/or direct contact. However, there is very little guidance on how much data are required for making robust networks from such data. In this study, we highlight that sequential trap sharing networks broadly capture shared space use (and, hence, the potential for contact) and that it may be more parsimonious to directly model shared space use. We first use empirical data to show that characteristics of how animals use space can help us to establish new ways to model the potential for individuals to come into contact. We then show that a method that explicitly models individuals’ home ranges and subsequent overlap in space among individuals (spatial overlap networks) requires fewer data for inferring observed networks that are more strongly correlated with the true shared space use network (relative to sequential trap sharing networks). Furthermore, we show that shared space use networks based on estimating spatial overlap are also more powerful for detecting biological effects. Finally, we discuss when it is appropriate to make inferences about social interactions from shared space use. Our study confirms the potential for using sparse trapping data from cryptic species to address a range of important questions in ecology and evolution.

Significance statement

Characterising animal social networks requires repeated (co-)observations of individuals. Collecting sufficient data to characterise the connections among individuals represents a major challenge when studying cryptic organisms—such as small rodents. This study draws from existing spatial mark-recapture data to inspire an approach that constructs networks by estimating space use overlap (representing the potential for contact). We then use simulations to demonstrate that the method provides consistently higher correlations between inferred (or observed) networks and the true underlying network compared to current approaches and requires fewer observations to reach higher correlations. We further demonstrate that these improvements translate to greater network accuracy and to more power for statistical hypothesis testing.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00265-022-03222-5.

The social transmission of stress in animal collectives

The stress systems are powerful mediators between the organism's systemic dynamic equilibrium and changes in its environment beyond the level of anticipated fluctuations. Over- or under-activation of the stress systems' responses can impact an animal's health, survival and reproductive success. While physiological stress responses and their influence on behaviour and performance are well understood at the individual level, it remains largely unknown whether-and how-stressed individuals can affect the stress systems of other group members, and consequently their collective behaviour. Stressed individuals could directly signal the presence of a stressor (e.g. via an alarm call or pheromones), or an acute or chronic activation of the stress systems could be perceived by others (as an indirect cue) and spread via social contagion. Such social transmission of stress responses could then amplify the effects of stressors by impacting social interactions, social dynamics and the collective performance of groups. As the neuroendocrine pathways of the stress response are highly conserved among vertebrates, transmission of physiological stress states could be more widespread among non-human animals than previously thought. We therefore suggest that identifying the extent to which stress transmission modulates animal collectives represents an important research avenue.

How feedback and feed-forward mechanisms link determinants of social dominance

In many animal societies, individuals differ consistently in their ability to win agonistic interactions, resulting in dominance hierarchies. These differences arise due to a range of factors that can influence individuals' abilities to win agonistic interactions, spanning from genetically driven traits through to individuals' recent interaction history. Yet, despite a century of study since Schjelderup-Ebbe's seminal paper on social dominance, we still lack a general understanding of how these different factors work together to determine individuals' positions in hierarchies. Here, we first outline five widely studied factors that can influence interaction outcomes: intrinsic attributes, resource value asymmetry, winner-loser effects, dyadic interaction-outcome history and third-party support. A review of the evidence shows that a variety of factors are likely important to interaction outcomes, and thereby individuals' positions in dominance hierarchies, in diverse species. We propose that such factors are unlikely to determine dominance outcomes independently, but rather form part of feedback loops whereby the outcomes of previous agonistic interactions (e.g. access to food) impact factors that might be important in subsequent interactions (e.g. body condition). We provide a conceptual framework that illustrates the multitude potential routes through which such feedbacks can occur, and how the factors that determine the outcomes of dominance interactions are highly intertwined and thus rarely act independently of one another. Further, we generalise our framework to include multi-generational feed-forward mechanisms: how interaction outcomes in one generation can influence the factors determining interaction outcomes in the next generation via a range of parental effects. This general framework describes how interaction outcomes and the factors determining them are linked within generations via feedback loops, and between generations via feed-forward mechanisms. We then highlight methodological approaches that will facilitate the study of feedback loops and dominance dynamics. Lastly, we discuss how our framework could shape future research, including: how feedbacks generate variation in the factors discussed, and how this might be studied experimentally; how the relative importance of different feedback mechanisms varies across timescales; the role of social structure in modulating the effect of feedbacks on hierarchy structure and stability; and the routes of parental influence on the dominance status of offspring. Ultimately, by considering dominance interactions as part of a dynamic feedback system that also feeds forward into subsequent generations, we will understand better the factors that structure dominance hierarchies in animal groups.

Temporal Graphs and Temporal Network Characteristics for Bio-Inspired Networks during Optimization

Temporal network analysis and time evolution of network characteristics are powerful tools in describing the changing topology of dynamic networks. This paper uses such approaches to better visualize and provide analytical measures for the changes in performance that we observed in Voronoi-type spatial coverage, particularly for the example of time-evolving networks with a changing number of wireless sensors being deployed. Specifically, our analysis focuses on the role different combinations of impenetrable obstacles and environmental noise play in connectivity and overall network structure. It is shown how the use of (i) temporal network graphs, and (ii) network centrality and regularity measures illustrate the differences between various options developed for the balancing act of energy and time efficiency in network coverage. Last, we compare the outcome of these measures with the less abstract classification variables, such as percent area covered and cumulative distance traveled.

Permutation tests for hypothesis testing with animal social network data: Problems and potential solutions

Permutation tests are widely used to test null hypotheses with animal social network data, but suffer from high rates of type I and II error when the permutations do not properly simulate the intended null hypothesis.Two common types of permutations each have limitations. Pre-network (or datastream) permutations can be used to control 'nuisance effects' like spatial, temporal or sampling biases, but only when the null hypothesis assumes random social structure. Node (or node-label) permutation tests can test null hypotheses that include nonrandom social structure, but only when nuisance effects do not shape the observed network.We demonstrate one possible solution addressing these limitations: using pre-network permutations to adjust the values for each node or edge before conducting a node permutation test. We conduct a range of simulations to estimate error rates caused by confounding effects of social or non-social structure in the raw data.Regressions on simulated datasets suggest that this 'double permutation' approach is less likely to produce elevated error rates relative to using only node permutations, pre-network permutations or node permutations with simple covariates, which all exhibit elevated type I errors under at least one set of simulated conditions. For example, in scenarios where type I error rates from pre-network permutation tests exceed 30%, the error rates from double permutation remain at 5%.The double permutation procedure provides one potential solution to issues arising from elevated type I and type II error rates when testing null hypotheses with social network data. We also discuss alternative approaches that can provide robust inference, including fitting mixed effects models, restricted node permutations, testing multiple null hypotheses and splitting large datasets to generate replicated networks. Finally, we highlight ways that uncertainty can be explicitly considered and carried through the analysis.

Network analysis of intra- and interspecific freshwater fish interactions using year-around tracking

A long-term, yet detailed view into the social patterns of aquatic animals has been elusive. With advances in reality mining tracking technologies, a proximity-based social network (PBSN) can capture detailed spatio-temporal underwater interactions. We collected and analysed a large dataset of 108 freshwater fish from four species, tracked every few seconds over 1 year in their natural environment. We calculated the clustering coefficient of minute-by-minute PBSNs to measure social interactions, which can happen among fish sharing resources or habitat preferences (positive/neutral interactions) or in predator and prey during foraging interactions (agonistic interactions). A statistically significant coefficient compared to an equivalent random network suggests interactions, while a significant aggregated clustering across PBSNs indicates prolonged, purposeful social behaviour. Carp (Cyprinus carpio) displayed within- and among-species interactions, especially during the day and in the winter, while tench (Tinca tinca) and catfish (Silurus glanis) were solitary. Perch (Perca fluviatilis) did not exhibit significant social behaviour (except in autumn) despite being usually described as a predator using social facilitation to increase prey intake. Our work illustrates how methods for building a PBSN can affect the network's structure and highlights challenges (e.g. missing signals, different burst frequencies) in deriving a PBSN from reality mining technologies.

Effect of Infant Presence on Social Networks of Sterilized and Intact Wild Female Balinese Macaques (Macaca fascicularis)

Contraception is increasingly used to control wild animal populations. However, as reproductive condition influences social interactions in primates, the absence of new offspring could influence the females' social integration. We studied two groups of wild macaques (Macaca fascicularis) including females recently sterilized in the Ubud Monkey Forest, Indonesia. We used social network analysis to examine female grooming and proximity networks and investigated the role of infant presence on social centrality and group connectivity, while controlling for the fertility status (sterilized N = 14, intact N = 34). We compared the ego networks of females experiencing different nursing conditions (young infant (YI) vs. old infant (OI) vs. non-nursing (NN) females). YI females were less central in the grooming network than other females while being more central in proximity networks, suggesting they could keep proximity within the group to protect their infant from hazards, while decreasing direct grooming interactions, involving potential risks such as kidnapping. The centrality of sterilized and intact females was similar, except for the proximity network where sterilized females had more partners and a better group connectivity. These results confirm the influence of nursing condition in female macaque social networks and did not show any negative short-term effects of sterilization on social integration.

New dimensions for animal communication networks: space and time

Communication is a social process and usually occurs in a network of signalers and receivers. While social network analysis has received enormous recent attention from animal behaviorists, there have been relatively few attempts to apply these techniques to communication networks. Communication networks have the potential to offer novel insights into social network studies, and yet are especially challenging subjects, largely because of their unique spatiotemporal characteristics. Namely, signals propagate through the environment, often dissociating from the body of the signaler, to influence receiver behavior. The speed of signal propagation and the signal's active space will affect the congruence of communication networks and other types of social network; in extreme cases the signal may persist and only first be detected long after the signaler has left the area. Other signals move more rapidly and over greater distances than the signaler could possibly move to reach receivers. We discuss the spatial and temporal consequences of signaling in networks and highlight the distinction between the physical location of the signaler and the spread of influence of its signals, the effects of signal modality and receiver sensitivity on communication network properties, the potential for feedbacks between network layers, and approaches to analyzing spatial and temporal change in communication networks in conjunction with other network layers.

Fission-fusion dynamics of a pelagic delphinid in the arctic: the white-beaked dolphin (Lagenorhynchus albirostris)

Delphinids exhibit great variability in their social structures. It is therefore important to document lesser known species, in extreme changing habitats, to compare and contrast mechanisms driving sociality. Here, we describe the first long-term assessment of social structure of white-beaked dolphins (Lagenorhynchus albirostris) using a compiled version of SOCPROG 2.8 and an 11-year photo-identification dataset (2002-2013) collected from whale-watching vessels at 2 sites (Faxaflói and Skjálfandi bays) off Iceland. We identified a total of 487 dolphins which are suggested to be part of an open population as shown by the discovery curve obtained analyzing photo-id data. The social analyses were restricted to 35 adults which were sighted on ≥5 different days. The mean residency time of white-beaked dolphins in our 2 study areas was of 95 days (SE = 35.63; 95% CI: 23-171), with the "migration-full interchange" model best describing movements of dolphins in and out of our 2 study sites. Social differentiation in this population was high (Likelihood: S = 0.87, SE = 0.04; r = 0.31, SE = 0.03) indicating diverse, non-random social relationships. Temporal associations best fit the model of "casual acquaintances" against the standardized lagged association rates with the majority of white-beaked dolphin associations being short-term, but with a few long-term across years. Testing for preferred companionship, long-term associations are favored over short-term. In this study, associations among white-beaked dolphins are short-term but with desirably long-term associations fitting into a society with fission-fusion dynamics. This information expands the latitudinal range for which social structure has been described for oceanic dolphin species.

Using multilayer network analysis to explore the temporal dynamics of collective behavior

Social organisms often show collective behaviors such as group foraging or movement. Collective behaviors can emerge from interactions between group members and may depend on the behavior of key individuals. When social interactions change over time, collective behaviors may change because these behaviors emerge from interactions among individuals. Despite the importance of, and growing interest in, the temporal dynamics of social interactions, it is not clear how to quantify changes in interactions over time or measure their stability. Furthermore, the temporal scale at which we should observe changes in social networks to detect biologically meaningful changes is not always apparent. Here we use multilayer network analysis to quantify temporal dynamics of social networks of the social spider Stegodyphus dumicola and determine how these dynamics relate to individual and group behaviors. We found that social interactions changed over time at a constant rate. Variation in both network structure and the identity of a keystone individual was not related to the mean or variance of the collective prey attack speed. Individuals that maintained a large and stable number of connections, despite changes in network structure, were the boldest individuals in the group. Therefore, social interactions and boldness are linked across time, but group collective behavior is not influenced by the stability of the social network. Our work demonstrates that dynamic social networks can be modeled in a multilayer framework. This approach may reveal biologically important temporal changes to social structure in other systems.

The problem and promise of scale in multilayer animal social networks

Scale remains a foundational concept in ecology. Spatial scale, for instance, has become a central consideration in the way we understand landscape ecology and animal space use. Meanwhile, scale-dependent social processes can range from fine-scale interactions to co-occurrence and overlapping home ranges. Furthermore, sociality can vary within and across seasons. Multilayer networks promise the explicit integration of the social, spatial, and temporal contexts. Given the complex interplay of sociality and animal space use in heterogeneous landscapes, there remains an important gap in our understanding of the influence of scale on animal social networks. Using an empirical case study, we discuss ways of considering social, spatial, and temporal scale in the context of multilayer caribou social networks. Effective integration of social and spatial processes, including biologically meaningful scales, within the context of animal social networks is an emerging area of research. We incorporate perspectives that link the social environment to spatial processes across scales in a multilayer context.

Social Network Predicts Exposure to Respiratory Infection in a Wild Chimpanzee Group

Respiratory pathogens are expected to spread through social contacts, but outbreaks often occur quickly and unpredictably, making it challenging to simultaneously record social contact and disease incidence data, especially in wildlife. Thus, the role of social contacts in the spread of infectious disease is often treated as an assumption in disease simulation studies, and few studies have empirically demonstrated how pathogens spread through social networks. In July-August 2015, an outbreak of respiratory disease was observed in a wild chimpanzee community in Kibale National Park, Uganda, during an ongoing behavioral study of male chimpanzees, offering a rare opportunity to evaluate how social behavior affects individual exposure to socially transmissible diseases. From May to August 2015, we identified adult and adolescent male chimpanzees displaying coughs and rhinorrhea and recorded 5-m proximity data on males (N = 40). Using the network k-test, we found significant relationships between male network connectivity and the distribution of cases within the network, supporting the importance of short-distance contacts for the spread of the respiratory outbreak. Additionally, chimpanzees central to the network were more likely to display clinical signs than those with fewer connections. Although our analyses were limited to male chimpanzees, these findings underscore the value of social connectivity data in predicting disease outcomes and elucidate a potential evolutionary cost of being social.

Detecting and quantifying social transmission using network-based diffusion analysis

Although social learning capabilities are taxonomically widespread, demonstrating that freely interacting animals (whether wild or captive) rely on social learning has proved remarkably challenging. Network-based diffusion analysis (NBDA) offers a means for detecting social learning using observational data on freely interacting groups. Its core assumption is that if a target behaviour is socially transmitted, then its spread should follow the connections in a social network that reflects social learning opportunities. Here, we provide a comprehensive guide for using NBDA. We first introduce its underlying mathematical framework and present the types of questions that NBDA can address. We then guide researchers through the process of selecting an appropriate social network for their research question; determining which NBDA variant should be used; and incorporating other variables that may impact asocial and social learning. Finally, we discuss how to interpret an NBDA model's output and provide practical recommendations for model selection. Throughout, we highlight extensions to the basic NBDA framework, including incorporation of dynamic networks to capture changes in social relationships during a diffusion and using a multi-network NBDA to estimate information flow across multiple types of social relationship. Alongside this information, we provide worked examples and tutorials demonstrating how to perform analyses using the newly developed nbda package written in the R programming language.

Understanding Allogrooming Through a Dynamic Social Network Approach: An Example in a Group of Dairy Cows

For gregarious species such as domestic cattle, the social environment is a very important determinant of their welfare and fitness. Understanding the complexity of cows' relationships can assist the development of management practices that are more integrated with the cows' social behavioral processes. The two aims of this study were: (1) to determine the dynamics of affiliative relationships, as indicated by allogrooming, by means of stochastic actor-oriented modeling, in dairy cows during early lactation; (2) to explore the underlying processes and the individual attributes, such as age, social rank and reproductive state, that could shape network pattern changes in grooming contacts between individual. We observed the allogrooming behavior of a dynamic group of 38 dairy cows for 4 h per day for 30 days. Using stochastic actor-oriented models, we modeled the dynamics of weekly contacts and studied how structural processes (e.g., reciprocity, transitivity, or popularity) and individual attributes (i.e., age, social rank, and reproductive state) influence network changes. We found that cows tended to groom individuals that had previously groomed them, implying a possible cooperation. Cows that groomed more actively did not appear to have a preference for specific individuals in the herd, and in return, tended to be groomed by fewer cows over time. Older individuals groomed more cows than younger ones, indicating that allogrooming could be related to seniority. Cows groomed mainly individuals of similar age, suggesting that familiarity and growing up together enhanced social grooming. Over time, cows with higher social rank were groomed by fewer cows and individuals recently reintroduced to the group groomed more herdmates. The study of social network dynamics can be used to better understand the complexity and non-linearity of cow relationships. Our findings, along with further research, can complement and strengthen the design of improved management practices that are more in line with the natural social behavior of cows.

A citizen science approach reveals long-term social network structure in an urban parrot, Cacatua galerita

Parrots are often referenced in discussions of social and cognitive complexity, yet relatively little is known of their social organization in the wild. In particular, the presence of long-lasting social ties has been highlighted as a hallmark of social complexity; however, the presence of such ties can be masked in fission-fusion systems like that exhibited by most parrot species. Social network analysis has the potential to elucidate such multi-level dynamics. While most parrot species are tropical canopy dwellers, a subset has successfully colonized urban habitats, where they are often the focus of much public interest. Our study takes advantage of this to use citizen science to collect observations of wing-tagged sulphur-crested cockatoos in central Sydney and record their social associations over multiple years. Using a specifically designed mobile phone application 'Wingtags', we collected over >27,000 citizen science reports of wing-tagged cockatoos, and built social networks from spatial-temporal co-occurrences in observations for 130 tagged birds. To validate this novel methodology, we GPS-tagged a subset of wing-tagged birds and compared networks built from both data collection methods. We then examined correlates of social network structure before exploring the temporal dynamics of network structure and social associations. Social networks constructed from GPS data and citizen science data were highly correlated, suggesting that this novel methodology is robust. Network structure exhibited little seasonal variability and was largely driven by roost site choice; however, individuals also showed a surprising degree of mixing between roosts in their foraging associations. Finally, within this larger fission-fusion system, individuals tended to maintain specific social ties for long periods of time. There was an effect of age on these temporal dynamics, with aging individuals increasing both social stability and longevity of associations. Our findings highlight the utility of citizen science to measure social networks in urban species, and add to the evidence that long-lasting social associations can persist in fission-fusion social systems such as those observed in wild sulphur-crested cockatoos.

Applications of weighted association networks applied to compositional data in biology

Next-generation sequencing technologies have generated, and continue to produce, an increasingly large corpus of biological data. The data generated are inherently compositional as they convey only relative information dependent upon the capacity of the instrument, experimental design and technical bias. There is considerable information to be gained through network analysis by studying the interactions between components within a system. Network theory methods using compositional data are powerful approaches for quantifying relationships between biological components and their relevance to phenotype, environmental conditions or other external variables. However, many of the statistical assumptions used for network analysis are not designed for compositional data and can bias downstream results. In this mini-review, we illustrate the utility of network theory in biological systems and investigate modern techniques while introducing researchers to frameworks for implementation. We overview (1) compositional data analysis, (2) data transformations and (3) network theory along with insight on a battery of network types including static-, temporal-, sample-specific- and differential-networks. The intention of this mini-review is not to provide a comprehensive overview of network methods, rather to introduce microbiology researchers to (semi)-unsupervised data-driven approaches for inferring latent structures that may give insight into biological phenomena or abstract mechanics of complex systems.

Measuring social networks in primates: wearable sensors versus direct observations

Network analysis represents a valuable and flexible framework to understand the structure of individual interactions at the population level in animal societies. The versatility of network representations is moreover suited to different types of datasets describing these interactions. However, depending on the data collection method, different pictures of the social bonds between individuals could a priori emerge. Understanding how the data collection method influences the description of the social structure of a group is thus essential to assess the reliability of social studies based on different types of data. This is however rarely feasible, especially for animal groups, where data collection is often challenging. Here, we address this issue by comparing datasets of interactions between primates collected through two different methods: behavioural observations and wearable proximity sensors. We show that, although many directly observed interactions are not detected by the sensors, the global pictures obtained when aggregating the data to build interaction networks turn out to be remarkably similar. Moreover, sensor data yield a reliable social network over short time scales and can be used for long-term studies, showing their important potential for detailed studies of the evolution of animal social groups.

Sulawesi Crested Macaque (Macaca nigra) Grooming Networks Are Robust to Perturbation While Individual Associations Are More Labile

Social and environmental disturbance occurs naturally, and species in bonded social groups should be resilient to it. Empirical evidence of social responses to disturbance in primates, however, remains limited. We constructed social networks using group-level scan samples (N = 299) to test the robustness of grooming networks in a captive group of 20 Sulawesi crested macaques (Macaca nigra) to two management interventions involving environmental and social disturbance. During the first, the institution removed six castrated males and one female, contracepted six of the nine remaining females, and moved the group to a new enclosure. The second involved the introduction of a novel, reproductive male five weeks later. Networks remained stable following the first intervention. However, after introduction of the male, the number of grooming partners and the frequency of grooming with non-maternal kin increased in female-only networks. We observed less marked increases in the grooming frequency and number of grooming partners in whole group networks. Ten weeks later, network structure was more similar to that of pre-intervention networks than post-intervention networks. Our results suggest that reproductive males play a more important role in structuring Sulawesi crested macaque social networks than castrated males, as networks expanded and relationships between non-maternal kin occurred more frequently after introduction of the reproductive male. However, network responses to interventions appeared to be temporary as networks following a period of acclimation more closely resembled pre-intervention networks than post-intervention networks. Our study demonstrates the utility of social network analysis for understanding the impact of disturbance on stable social groups.

Early-life social environment predicts social network position in wild zebra finches

Early-life experience can fundamentally shape individual life-history trajectories. Previous research has suggested that exposure to stress during development causes differences in social behaviour later in life. In captivity, juvenile zebra finches exposed to elevated corticosterone levels were less socially choosy and more central in their social networks when compared to untreated siblings. These differences extended to other aspects of social life, with 'stress-exposed' juveniles switching social learning strategies and juvenile males less faithfully learning their father's song. However, while this body of research suggests that the impacts of early-life stress could be profound, it remains unknown whether such effects are strong enough to be expressed under natural conditions. Here, we collected data on social associations of zebra finches in the Australian desert after experimentally manipulating brood sizes. Juveniles from enlarged broods experienced heightened sibling competition, and we predicted that they would express similar patterns of social associations to stress-treated birds in the captive study by having more, but less differentiated, relationships. We show striking support for the suggested consequences of developmental stress on social network positions, with our data from the wild replicating the same results in 9 out of 10 predictions previously tested in captivity. Chicks raised in enlarged broods foraged with greater numbers of conspecifics but were less 'choosy' and more central in the social network. Our results confirm that the natural range of variation in early-life experience can be sufficient to predict individuals' social trajectories and support theory highlighting the potential importance of developmental conditions on behaviour.

Wild primates copy higher-ranked individuals in a social transmission experiment

Little is known about how multiple social learning strategies interact and how organisms integrate both individual and social information. Here we combine, in a wild primate, an open diffusion experiment with a modeling approach: Network-Based Diffusion Analysis using a dynamic observation network. The vervet monkeys we study were not provided with a trained model; instead they had access to eight foraging boxes that could be opened in either of two ways. We report that individuals socially learn the techniques they observe in others. After having learnt one option, individuals are 31x more likely to subsequently asocially learn the other option than individuals naïve to both options. We discover evidence of a rank transmission bias favoring learning from higher-ranked individuals, with no evidence for age, sex or kin bias. This fine-grained analysis highlights a rank transmission bias in a field experiment mimicking the diffusion of a behavioral innovation.

Age differences in the temporal stability of a male African elephant (Loxodonta africana) social network

Social animals live in complex and variable socio-ecological environments where individuals adapt their behavior to local conditions. Recently, there have been calls for studies of animal social networks to take account of temporal dynamics in social relationships as these have implications for the spread of information and disease, group cohesion, and the drivers of sociality, and there is evidence that maintaining stable social relationships has fitness benefits. It has recently been recognized that male elephants form strong social bonds with other males. The nature of these relationships, and thus network structure, may vary over time in response to varying environmental conditions and as individuals age. Using social network analysis, we examine the stability of relationships and network centrality in a population of male African elephants. Our results suggest that males may maintain stable social relationships with others over time. Older males show greater stability in network centrality than younger males, suggesting younger males face uncertainty in transitioning to adult society. For elephants, where older individuals function as social repositories of knowledge, maintaining a social network underpinned by older males could be of particular importance.

Behavioural ecology and infectious disease: implications for conservation of biodiversity

Behaviour underpins interactions among conspecifics and between species, with consequences for the transmission of disease-causing parasites. Because many parasites lead to declines in population size and increased risk of extinction for threatened species, understanding the link between host behaviour and disease transmission is particularly important for conservation management. Here, we consider the intersection of behaviour, ecology and parasite transmission, broadly encompassing micro- and macroparasites. We focus on behaviours that have direct impacts on transmission, as well as the behaviours that result from infection. Given the important role of parasites in host survival and reproduction, the effects of behaviour on parasitism can scale up to population-level processes, thus affecting species conservation. Understanding how conservation and infectious disease control strategies actually affect transmission potential can therefore often only be understood through a behavioural lens. We highlight how behavioural perspectives of disease ecology apply to conservation by reviewing the different ways that behavioural ecology influences parasite transmission and conservation goals. This article is part of the theme issue 'Linking behaviour to dynamics of populations and communities: application of novel approaches in behavioural ecology to conservation'.

Social network plasticity decreases disease transmission in a eusocial insect

Animal social networks are shaped by multiple selection pressures, including the need to ensure efficient communication and functioning while simultaneously limiting disease transmission. Social animals could potentially further reduce epidemic risk by altering their social networks in the presence of pathogens, yet there is currently no evidence for such pathogen-triggered responses. We tested this hypothesis experimentally in the ant Lasius niger using a combination of automated tracking, controlled pathogen exposure, transmission quantification, and temporally explicit simulations. Pathogen exposure induced behavioral changes in both exposed ants and their nestmates, which helped contain the disease by reinforcing key transmission-inhibitory properties of the colony's contact network. This suggests that social network plasticity in response to pathogens is an effective strategy for mitigating the effects of disease in social groups.

The use of multilayer network analysis in animal behaviour

Network analysis has driven key developments in research on animal behaviour by providing quantitative methods to study the social structures of animal groups and populations. A recent formalism, known as multilayer network analysis, has advanced the study of multifaceted networked systems in many disciplines. It offers novel ways to study and quantify animal behaviour through connected 'layers' of interactions. In this article, we review common questions in animal behaviour that can be studied using a multilayer approach, and we link these questions to specific analyses. We outline the types of behavioural data and questions that may be suitable to study using multilayer network analysis. We detail several multilayer methods, which can provide new insights into questions about animal sociality at individual, group, population and evolutionary levels of organization. We give examples for how to implement multilayer methods to demonstrate how taking a multilayer approach can alter inferences about social structure and the positions of individuals within such a structure. Finally, we discuss caveats to undertaking multilayer network analysis in the study of animal social networks, and we call attention to methodological challenges for the application of these approaches. Our aim is to instigate the study of new questions about animal sociality using the new toolbox of multilayer network analysis.

Inferring longitudinal hierarchies: Framework and methods for studying the dynamics of dominance

Social inequality is a consistent feature of animal societies, often manifesting as dominance hierarchies, in which each individual is characterized by a dominance rank denoting its place in the network of competitive relationships among group members. Most studies treat dominance hierarchies as static entities despite their true longitudinal, and sometimes highly dynamic, nature. To guide study of the dynamics of dominance, we propose the concept of a longitudinal hierarchy: the characterization of a single, latent hierarchy and its dynamics over time. Longitudinal hierarchies describe the hierarchy position (r) and dynamics (∆) associated with each individual as a property of its interaction data, the periods into which these data are divided based on a period delineation rule (p) and the method chosen to infer the hierarchy. Hierarchy dynamics result from both active (∆a) and passive (∆p) processes. Methods that infer longitudinal hierarchies should optimize accuracy of rank dynamics as well as of the rank orders themselves, but no studies have yet evaluated the accuracy with which different methods infer hierarchy dynamics. We modify three popular ranking approaches to make them better suited for inferring longitudinal hierarchies. Our three "informed" methods assign ranks that are informed by data from the prior period rather than calculating ranks de novo in each observation period and use prior knowledge of dominance correlates to inform placement of new individuals in the hierarchy. These methods are provided in an R package. Using both a simulated dataset and a long-term empirical dataset from a species with two distinct sex-based dominance structures, we compare the performance of these methods and their unmodified counterparts. We show that choice of method has dramatic impacts on inference of hierarchy dynamics via differences in estimates of ∆a. Methods that calculate ranks de novo in each period overestimate hierarchy dynamics, but incorporation of prior information leads to more accurately inferred ∆a. Of the modified methods, Informed MatReorder infers the most conservative estimates of hierarchy dynamics and Informed Elo infers the most dynamic hierarchies. This work provides crucially needed conceptual framing and methodological validation for studying social dominance and its dynamics.