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The genetic structure of animal populations has considerable behavioural, ecological and evolutionary implications and may arise from various demographic traits. Here, we use observational field data and molecular genetics to determine the genetic structure of an invasive population of monk parakeets, Myiopsitta monachus, at a range of spatial scales, and investigate the demographic processes that generate the observed structure. Monk parakeets construct large nests that can house several pairs occupying separate chambers; these nests are often aggregated within nesting trees. We determined patterns of relatedness within compound nests, within nesting trees and between trees. Spatial autocorrelation analyses of pairwise genetic relatedness revealed fine-scale genetic structure with relatives of both sexes spatially clustered within, but not beyond, nesting trees. In addition, males were more related to males sharing their compound nests than to other males occupying the same nesting tree. By contrast, males and females within compound nests were not significantly more closely related than elsewhere in the same tree, and we found no evidence for inbreeding. Adults showed high breeding site fidelity between years despite considerable disturbance of nest sites. Natal dispersal was female-biased, but dispersal distances were relatively short with some natal philopatry observed in both sexes. Sibling coalitions, typically of males, were observed amongst both philopatric and dispersing birds. Our results show significant clustering of kin within compound nests and nesting trees resulting from limited and coordinated natal dispersal, with subsequent breeding site fidelity. The resulting genetic structure has implications for social behaviour in this unusual parrot species.

Because of the nature of social interaction or association data, when testing hypotheses using social network data it is common for network studies to rely on permutations to control for confounding variables, and to not also control for them in the fitted statistical model. This can be a problem because it does not adjust for any bias in effect sizes generated by these confounding effects, and thus the effect sizes are not informative in the presence of counfouding variables.We implemented two network simulation examples and analysed an empirical data set to demonstrate how relying solely on permutations to control for confounding variables can result in highly biased effect size estimates of animal social preferences that are uninformative when quantifying differences in behaviour.Using these simulations, we show that this can sometimes even lead to effect sizes that have the wrong sign and are thus the effect size is not biologically interpretable. We demonstrate how this problem can be addressed by controlling for confounding variables in the statistical dyadic or nodal model.We recommend this approach should be adopted as standard practice in the statistical analysis of animal social network data.

Abstract In studies of social behaviour, social bonds are usually inferred from rates of interaction or association. This approach has revealed many important insights into the proximate formation and ultimate function of animal social structures. However, it remains challenging to compare social structure between systems or time-points because extrinsic factors, such as sampling methodology, can also influence the observed rate of association. As a consequence of these methodological challenges, it is difficult to analyse how patterns of social association change with demographic processes, such as the death of key social partners. Here we develop and illustrate the use of binomial mixture models to quantitatively compare patterns of social association between networks. We then use this method to investigate how patterns of social preferences in killer whales respond to demographic change. Resident killer whales are bisexually philopatric, and both sexes stay in close association with their mother in adulthood. We show that mothers and daughters show reduced social association after the birth of the daughter’s first offspring, but not after the birth of an offspring to the mother. We also show that whales whose mother is dead associate more with their opposite sex siblings and with their grandmother than whales whose mother is alive. Our work demonstrates the utility of using mixture models to compare social preferences between networks and between species. We also highlight other potential uses of this method such as to identify strong social bonds in animal populations. Significance statement Comparing patters of social associations between systems, or between the same systems at different times, is challenging due to the confounding effects of sampling and methodological differences. Here we present a method to allow social associations to be robustly classified and then compared between networks using binomial mixture models. We illustrate this method by showing how killer whales change their patterns of social association in response to the birth of calves and the death of their mother. We show that after the birth of her calf, females associate less with their mother. We also show that whales’ whose mother is dead associate more with their opposite sex siblings and grandmothers than whales’ whose mother is alive. This clearly demonstrates how this method can be used to examine fine scale temporal processes in animal social systems.

Social network methods have become a key tool for describing, modelling, and testing hypotheses about the social structures of animals. However, due to the non-independence of network data and the presence of confounds, specialized statistical techniques are often needed to test hypotheses in these networks. Datastream permutations, originally developed to test the null hypothesis of random social structure, have become a popular tool for testing a wide array of null hypotheses. In particular, they have been used to test whether exogenous factors are related to network structure by interfacing these permutations with regression models.Here, we show that these datastream permutations typically do not represent the null hypothesis of interest to researchers interfacing animal social network analysis with regression modelling, and use simulations to demonstrate the potential pitfalls of using this methodology.Our simulations show that utilizing common datastream permutations to test the coefficients of regression models can lead to extremely high type I (false-positive) error rates (> 30%) in the presence of non-random social structure. The magnitude of this problem is primarily dependent on the degree of non-randomness within the social structure and the intensity of samplingWe strongly recommend against utilizing datastream permutations to test regression models in animal social networks. We suggest that a potential solution may be found in regarding the problems of non-independence of network data and unreliability of observations as separate problems with distinct solutions.

Most ant colonies live in a single nest (monodomy) or a group of nests (polydomy). However, the length of time for which nests are inhabited varies significantly between different species. Although colonies of some species frequently move nest sites, in others, colonies inhabit the same nest or group of nests for many years. Similarly, in some species foraging and resource-sharing trails are highly dynamic, while in other species trails are used for years. Wood ants are a group of keystone species that inhabit many northern hemisphere woodlands, where they are important predators of invertebrates and indirectly act as herbivores through the farming of aphids. Wood ant colonies exhibit both monodomy and polydomy, and can inhabit nests for many years. Trails in wood ant colonies are also thought to be relatively stable. However, information about colony dynamics is mostly anecdotal as, until now, no longitudinal datasets have been collected. In this study, we collected data from ten polydomous wood ant colonies annually for 8 years and a subset of four colonies 16 times over 2 years. We found that most polydomous wood ant nests are abandoned in the first 2 years after being constructed and are more likely to be abandoned in the latter part of the active season. However, the rate of nest abandonment decreases after 2 years and is lower in larger nests. We also found that wood ant trails are relatively static within an active season and become more static later in the season as trails become established.

Social information networks have the potential to shape the spatial structure of ecological communities by promoting the formation of mixed‐species groups. However, what actually drives social affinity between species in the wild will depend on the characteristics of the species available to group. Here we first present an agent‐based model that predicts trait‐related survival benefits from mixed‐species group formation in a multi‐species community and we then test the model predictions in a community‐wide field study of African savannah herbivores using multi‐layered network analysis. We reveal benefits from information transfer about predators as a key determinant of mixed‐species group formation, and that dilution benefits alone are not enough to explain patterns in interspecific sociality. The findings highlight the limitations of classical ecological approaches focusing only on direct trophic interactions when analysing community structure and suggest that declines in species occupying central social network positions, such as key informants, can have significant repercussions throughout communities., Ecological theory has traditionally viewed community structure as the outcome of direct trophic interactions. We here combine agent‐based modelling of mixed‐species group formation with a field study of African savannah herbivores to show that also social interactions between species, such as social information use, can drive the spatial structure of communities. The study highlights that declines in species occupying central social network positions can have community‐wide repercussions.

Significance Why humans and some species of whales go through menopause remains an evolutionary puzzle. In humans, postreproductive females gain genetic benefits by helping family members—particularly increasing their number of surviving grandoffspring. The extent to which these grandmother benefits are important in the evolution of menopause in whales remains unclear. Here, we test the grandmother effect in resident killer whales, where females can live for decades after their last reproductive event. We show that grandmothers increase the survival of their grandoffspring, and these effects are greatest when grandmothers are no longer reproducing. These findings can help explain why killer whales have evolved the longest postreproductive life span of all nonhuman animals., Understanding why females of some mammalian species cease ovulation prior to the end of life is a long-standing interdisciplinary and evolutionary challenge. In humans and some species of toothed whales, females can live for decades after stopping reproduction. This unusual life history trait is thought to have evolved, in part, due to the inclusive fitness benefits that postreproductive females gain by helping kin. In humans, grandmothers gain inclusive fitness benefits by increasing their number of surviving grandoffspring, referred to as the grandmother effect. Among toothed whales, the grandmother effect has not been rigorously tested. Here, we test for the grandmother effect in killer whales, by quantifying grandoffspring survival with living or recently deceased reproductive and postreproductive grandmothers, and show that postreproductive grandmothers provide significant survival benefits to their grandoffspring above that provided by reproductive grandmothers. This provides evidence of the grandmother effect in a nonhuman menopausal species. By stopping reproduction, grandmothers avoid reproductive conflict with their daughters, and offer increased benefits to their grandoffspring. The benefits postreproductive grandmothers provide to their grandoffspring are shown to be most important in difficult times where the salmon abundance is low to moderate. The postreproductive grandmother effect we report, together with the known costs of late-life reproduction in killer whales, can help explain the long postreproductive life spans of resident killer whales.

Understanding how individual behavior shapes the structure and ecology of populations is key to species conservation and management. Like many elasmobranchs, manta rays are highly mobile and wide-ranging species threatened by anthropogenic impacts. In shallow water environments, these pelagic rays often form groups and perform several apparently socially mediated behaviors. Group structures may result from active choices of individual rays to interact or passive processes. Social behavior is known to affect spatial ecology in other elasmobranchs, but this is the first study providing quantitative evidence for structured social relationships in manta rays. To construct social networks, we collected data from more than 500 groups of reef manta rays (Mobula alfredi) over 5 years in the Raja Ampat Regency of West Papua. We used generalized affiliation indices to isolate social preferences from non-social associations, the first study on elasmobranchs to use this method. Longer lasting social preferences were detected mostly between female rays. We detected assortment of social relations by phenotype and variation in social strategies, with the overall social network divided into two main communities. Overall network structure was characteristic of a dynamic fission-fusion society, with differentiated relationships linked to strong fidelity to cleaning station sites. Our results suggest that fine-scale conservation measures will be useful in protecting social groups of M. alfredi in their natural habitats and that a more complete understanding of the social nature of manta rays will help predict population responses to anthropogenic pressures, such as increasing disturbance from dive tourism. In social animals, relationships between individuals have important implications for species conservation. Like many other sharks and rays, manta rays are threatened species, and little is known about their natural behavior or how their populations are structured. This study provides evidence of social structure in a wild, free-ranging population of reef manta rays. We show for the first time that individual manta rays have preferred relationships with others that are maintained over time, and structured societies. This study extends our knowledge of elasmobranch ecology and population structuring. Results suggest that understanding social relationships in manta rays will be important in protecting populations from human impacts and developing sustainable, localized conservation and management initiatives.

The impact of environmental change on the reproduction and survival of wildlife is often behaviourally mediated, placing behavioural ecology in a central position to quantify population- and community-level consequences of anthropogenic threats to biodiversity. This theme issue demonstrates how recent conceptual and methodological advances in the discipline are applied to inform conservation. The issue highlights how the focus in behavioural ecology on understanding variation in behaviour between individuals, rather than just measuring the population mean, is critical to explaining demographic stochasticity and thereby reducing fuzziness of population models. The contributions also show the importance of knowing the mechanisms by which behaviour is achieved, i.e. the role of learning, reasoning and instincts, in order to understand how behaviours change in human-modified environments, where their function is less likely to be adaptive. More recent work has thus abandoned the ‘adaptationist’ paradigm of early behavioural ecology and increasingly measures evolutionary processes directly by quantifying selection gradients and phenotypic plasticity. To support quantitative predictions at the population and community levels, a rich arsenal of modelling techniques has developed, and interdisciplinary approaches show promising prospects for predicting the effectiveness of alternative management options, with the social sciences, movement ecology and epidemiology particularly pertinent. The theme issue furthermore explores the relevance of behaviour for global threat assessment, and practical advice is given as to how behavioural ecologists can augment their conservation impact by carefully selecting and promoting their study systems, and increasing their engagement with local communities, natural resource managers and policy-makers. Its aim to uncover the nuts and bolts of how natural systems work positions behavioural ecology squarely in the heart of conservation biology, where its perspective offers an all-important complement to more descriptive ‘big-picture’ approaches to priority setting. 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’.

The dynamics of wildlife populations often depend heavily on interspecific interactions and understanding the underlying principles can be an important step in designing conservation strategies. Behavioural ecological studies can here provide useful insights into the structure and function of communities and their likely response to environmental changes. In this study of the Masai Mara herbivore community, we use a social network approach to investigate social affinities between species and how these change over the year in response to seasonal changes in ecological conditions. We find that even though social networks were correlated across different ecological conditions, for half the species dyads in the community, the strength of social affinities responded to changes in rainfall and/or the presence of migratory wildebeest. Several species consequentially adopted more or less central positions in the network depending on the ecological conditions. The findings point out interspecific social links that are likely to be attenuated or strengthened as a consequence of human-induced environmental changes and therefore call for particular attention from conservation managers. The eco-evolutionary ramifications of the perturbations of social affinities still require further study. 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’.

Animal social structure is shaped by environmental conditions, such as food availability. This is important as conditions are likely to change in the future and changes to social structure can have cascading ecological effects. Wood ants are a useful taxon for the study of the relationship between social structure and environmental conditions, as some populations form large nest networks and they are ecologically dominant in many northern hemisphere woodlands. Nest networks are formed when a colony inhabits more than one nest, known as polydomy. Polydomous colonies are composed of distinct sub-colonies that inhabit spatially distinct nests and that share resources with each other. In this study, we performed a controlled experiment on 10 polydomous wood ant (Formica lugubris) colonies to test how changing the resource environment affects the social structure of a polydomous colony. We took network maps of all colonies for 5 years before the experiment to assess how the networks changes under natural conditions. After this period, we prevented ants from accessing an important food source for a year in five colonies and left the other five colonies undisturbed. We found that preventing access to an important food source causes polydomous wood ant colony networks to fragment into smaller components and begin foraging on previously unused food sources. These changes were not associated with a reduction in the growth of populations inhabiting individual nests (sub-colonies), foundation of new nests or survival, when compared with control colonies. Colony splitting likely occurred as the availability of food in each nest changed causing sub-colonies to change their inter-nest connections. Consequently, our results demonstrate that polydomous colonies can adjust to environmental changes by altering their social network.

[Robeck et al. (2015)][1] claim that reproductive and actuarial senescence is common in mammalian species and therefore not an unexpected finding in killer whales. However, in most mammals, reproductive and somatic senescence are aligned, and reproduction gradually declines with age. In contrast, there is a substantial evidence that reproductive senescence is unusually accelerated relative to somatic senescence in resident killer whales, resulting in a prolonged postreproductive lifespan. We demonstrate that a postreproductive lifespan is a key component of resident killer whale life history, and is robust to reasonable error in age estimates. [1]: #ref-32

We propose a method for examining and measuring the complexity of animal social networks that are characterized using association indices. The method focusses on the diversity of types of dyadic relationship within the social network. Binomial mixture models cluster dyadic relationships into relationship types, and variation in the preponderance and strength of these relationship types can be used to estimate association complexity using Shannon’s information index. We use simulated data to test the method and find that models chosen using integrated complete likelihood give estimates of complexity that closely reflect the true complexity of social systems, but these estimates can be downwardly biased by low-intensity sampling and upwardly biased by extreme overdispersion within components. We also illustrate the use of the method on two real datasets. The method could be extended for use on interaction rate data using Poisson mixture models or on multidimensional relationship data using multivariate mixture models. Animals from many species interact socially with multiple individuals, and these interactions form a social network. Pairs of individuals have social relationships that differ in their strength and type. This social complexity has long interested behavioural biologists, particularly in the context of social cognition. Measuring social complexity, however, presents challenges. We propose a new method for measuring the complexity of animal social networks. Our approach is based on quantifying variation in the strengths of social connections (measured using association indices) which we use to classify different types of pairwise relationships. We, then, use the number, strength and prevalence of these different types of relationships to measure association complexity. Our approach can be used to compare association complexity between populations and/or species. We provide code that researchers can use with their own datasets.

The emergence of novel diseases represents a major hurdle for the recovery of endangered populations, and in some cases may even present the threat of extinction. In recent years, epizootics of infectious diseases have emerged as a major threat to marine mammal populations, particularly group-living odontocetes. However, little research has explored the potential consequences of novel pathogens in endangered cetacean populations. Here, we present the first study predicting the spread of infectious disease over the social network of an entire free-ranging cetacean population, the southern resident killer whale community (SRKW). Utilizing 5 years of detailed data on close contacts between individuals, we build a fine-scale social network describing potential transmission pathways in this population. We then simulate the spread of cetacean morbillivirus (CeMV) over this network. Our analysis suggests that the SRKW population is highly vulnerable to CeMV. The majority of simulations resulted in unusual mortality events (UMEs), with mortality rates predicted to be at least twice the recorded maximum annual mortality. We find only limited evidence that this population's social structure inhibits disease spread. Vaccination is not likely to be an efficient strategy for reducing the likelihood of UMEs, with over 40 vaccinated individuals (>50% of the population) required to reduce the likelihood of UMEs below 5%. This analysis highlights the importance of modelling efforts in designing strategies to mitigate disease, and suggests that populations with strong social preferences and distinct social units may still be highly vulnerable to disease outbreaks.

Summary Why females of some species cease ovulation prior to the end of their natural lifespan is a long-standing evolutionary puzzle [1–4]. The fitness benefits of post-reproductive helping could in principle select for menopause [1, 2, 5], but the magnitude of these benefits appears insufficient to explain the timing of menopause [6–8]. Recent theory suggests that the cost of inter-generational reproductive conflict between younger and older females of the same social unit is a critical missing term in classical inclusive fitness calculations (the "reproductive conflict hypothesis" [6, 9]). Using a unique long-term dataset on wild resident killer whales, where females can live decades after their final parturition, we provide the first test of this hypothesis in a non-human animal. First, we confirm previous theoretical predictions that local relatedness increases with female age up to the end of reproduction. Second, we construct a new evolutionary model and show that given these kinship dynamics, selection will favor younger females that invest more in competition, and thus have greater reproductive success, than older females (their mothers) when breeding at the same time. Third, we test this prediction using 43 years of individual-based demographic data in resident killer whales and show that when mothers and daughters co-breed, the mortality hazard of calves from older-generation females is 1.7 times that of calves from younger-generation females. Intergenerational conflict combined with the known benefits conveyed to kin by post-reproductive females can explain why killer whales have evolved the longest post-reproductive lifespan of all non-human animals.

Networks describe a range of social, biological and technical phenomena. An important property of a network is its degree correlation or assortativity, describing how nodes in the network associate based on their number of connections. Social networks are typically thought to be distinct from other networks in being assortative (possessing positive degree correlations); well-connected individuals associate with other well-connected individuals, and poorly connected individuals associate with each other. We review the evidence for this in the literature and find that, while social networks are more assortative than non-social networks, only when they are built using group-based methods do they tend to be positively assortative. Non-social networks tend to be disassortative. We go on to show that connecting individuals due to shared membership of a group, a commonly used method, biases towards assortativity unless a large enough number of censuses of the network are taken. We present a number of solutions to overcoming this bias by drawing on advances in sociological and biological fields. Adoption of these methods across all fields can greatly enhance our understanding of social networks and networks in general.