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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.

Among mammals, bovids provide some of the most striking examples of sexual dimorphism in colouration and pelage appendages, such as beards and manes. This dimorphism is usually assumed to have evolved through sexual selection on males in the context of intra- or intersexual communication. However, the sexes coloration and pelage appendages look similar between the two sexes in several bovid species thought to be characterized by large opportunities for sexual selection, hinting at fitness costs of dimorphic traits due to other selection pressures. This study applies the comparative method with phylogenetic control to identify the factors promoting and constraining the evolution of dimorphism in coloration and pelage appendages across bovids. We found that trait dimorphism correlated positively with large breeding group size, an indicator of the intensity of sexual selection, and negatively with male territoriality, which is also likely to affect the operation of sexual selection. The relative rarity of color and pelage dimorphism in species with territorial mating systems may be explained by weaker sexual selection due to difficulty in monopolizing females and/or sexual selection targeting other traits, such as territorial quality as an extended phenotype. We also found that dimorphism in color and pelage was reduced in species spending more time in mixed-sex groups outside the breeding season, possibly due to increased predation costs from non-uniformity. This suggests that benefits from integration into mixed-sex groups select against the extravagant male morphologies otherwise promoted by sexual selection.

The ultimate payoff of behaviours depends not only on their direct impact on an individual, but also on the impact on their relatives. Local relatedness-the average relatedness of an individual to their social environment-therefore has profound effects on social and life history evolution. Recent work has begun to show that local relatedness has the potential to change systematically over an individual's lifetime, a process called kinship dynamics. However, it is unclear how general these kinship dynamics are, whether they are predictable in real systems and their effects on behaviour and life history evolution. In this study, we combine modelling with data from real systems to explore the extent and impact of kinship dynamics. We use data from seven group-living mammals with diverse social and mating systems to demonstrate not only that kinship dynamics occur in animal systems, but also that the direction and magnitude of kinship dynamics can be accurately predicted using a simple model. We use a theoretical model to demonstrate that kinship dynamics can profoundly affect lifetime patterns of behaviour and can drive sex differences in helping and harming behaviour across the lifespan in social species. Taken together, this work demonstrates that kinship dynamics are likely to be a fundamental dimension of social evolution, especially when considering age-linked changes and sex differences in behaviour and life history.

Social networks are often constructed from point estimates of edge weights. In many contexts, edge weights are inferred from observational data, and the uncertainty around estimates can be affected by various factors. Though this has been acknowledged in previous work, methods that explicitly quantify uncertainty in edge weights have not yet been widely adopted, and remain undeveloped for many common types of data. Furthermore, existing methods are unable to cope with some of the complexities often found in observational data, and do not propagate uncertainty in edge weights to subsequent statistical analyses.We introduce a unified Bayesian framework for modelling social networks based on observational data. This framework, which we call BISoN, can accommodate many common types of observational social data, can capture confounds and model effects at the level of observations, and is fully compatible with popular methods used in social network analysis.We show how the framework can be applied to common types of data and how various types of downstream statistical analyses can be performed, including non-random association tests and regressions on network properties.Our framework opens up the opportunity to test new types of hypotheses, make full use of observational datasets, and increase the reliability of scientific inferences. We have made example R scripts available to enable adoption of the framework.

Mounting evidence suggests that patterns of local relatedness can change over time in predictable ways, a process termed kinship dynamics. Kinship dynamics may occur at the level of the population or social group, where the mean relatedness across all members of the population or group changes over time, or at the level of the individual, where an individual's relatedness to its local group changes with age. Kinship dynamics are likely to have fundamental consequences for the evolution of social behaviour and life history because they alter the inclusive fitness payoffs to actions taken at different points in time. For instance, growing evidence suggests that individual kinship dynamics have shaped the evolution of menopause and age-specific patterns of helping and harming. To date, however, the consequences of kinship dynamics for social evolution have not been widely explored. Here we review the patterns of kinship dynamics that can occur in natural populations and highlight how taking a kinship dynamics approach has yielded new insights into behaviour and life-history evolution. We discuss areas where analysing kinship dynamics could provide new insight into social evolution, and we outline some of the challenges in predicting and quantifying kinship dynamics in natural populations.

The extended female postreproductive life span found in humans and some toothed whales remains an evolutionary puzzle. Theory predicts demographic patterns resulting in increased female relatedness with age (kinship dynamics) can select for a prolonged postreproductive life span due to the combined costs of intergenerational reproductive conflict and benefits of late‐life helping. Here, we test this prediction using >40 years of longitudinal demographic data from the sympatric yet genetically distinct killer whale ecotypes: resident and Bigg's killer whales. The female relatedness with age is predicted to increase in both ecotypes, but with a less steep increase in Bigg's due to their different social structure. Here, we show that there is a significant postreproductive life span in both ecotypes with >30% of adult female years being lived as postreproductive, supporting the general prediction that an increase in local relatedness with age predisposes the evolution of a postreproductive life span. Differences in the magnitude of kinship dynamics however did not influence the timing or duration of the postreproductive life span with females in both ecotypes terminating reproduction before their mid‐40s followed by an expected postreproductive period of about 20 years. Our results highlight the important role of kinship dynamics in the evolution of a long postreproductive life span in long‐lived mammals, while further implying that the timing of menopause may be a robust trait that is persistent despite substantial variation in demographic patterns among populations., The rare life history trait of a long postreproductive period in females have been linked to demographic patterns leading to an increased relatedness to the local group with age (kinship dynamics). Here, we show that two killer whale ecotypes that are genetically distinct and exhibit different demographic patterns as well as different strengths of expected kinship dynamics have evolved a long postreproductive period in females, supporting the prediction that an increased female local relatedness with age predisposes for the evolution of this life history trait. Moreover, this results indicates that the onset of the postreproductive period may be robust to substantial variation in demographic patterns among populations.

The non-independence of social network data is a cause for concern among behavioural ecologists conducting social network analysis. This has led to the adoption of several permutation-based methods for testing common hypotheses. One of the most common types of analysis is nodal regression, where the relationships between node-level network metrics and nodal covariates are analysed using a permutation technique known as node-label permutations. We show that, contrary to accepted wisdom, node-label permutations do not automatically account for the non-independences assumed to exist in network data, because regression-based permutation tests still assume exchangeability of residuals. The same assumption also applies to the quadratic assignment procedure (QAP), a permutation-based method often used for conducting dyadic regression. We highlight that node-label permutations produce the same p-values as equivalent parametric regression models, but that in the presence of non-independence, parametric regression models can also produce accurate effect size estimates. We also note that QAP only controls for a specific type of non-independence between edges that are connected to the same nodes, and that appropriate parametric regression models are also able to account for this type of non-independence. Based on this, we suggest that standard parametric models could be used in the place of permutation-based methods. Moving away from permutation-based methods could have several benefits, including reducing over-reliance on p-values, generating more reliable effect size estimates, and facilitating the adoption of causal inference methods and alternative types of statistical analysis.

Power analysis is used to estimate the probability of correctly rejecting a null hypothesis for a given statistical model and dataset. Conventional power analyses assume complete information, but the stochastic nature of behavioural sampling can mean that true and estimated networks are poorly correlated. Power analyses do not currently take the effect of sampling into account. This could lead to inaccurate estimates of statistical power, potentially yielding misleading results.Here we develop a method for computing network correlation: the correlation between an estimated social network and its true network, using a Gamma-Poisson model of social event rates for networks constructed from count data. We use simulations to assess how the level of network correlation affects the power of nodal regression analyses. We also develop a generic method of power analysis applicable to any statistical test, based on the concept of diminishing returns.We demonstrate that our network correlation estimator is both accurate and moderately robust to its assumptions being broken. We show that social differentiation, mean social event rate, and the harmonic mean of sampling times positively impacts the strength of network correlation. We also show that the required level of network correlation to achieve a given power level depends on many factors, but that 0.80 network correlation usually corresponds to around 0.80 power for nodal regression in ideal circumstances.We provide guidelines for using our network correlation estimator to verify the accuracy of networks built from count data, and to conduct power analysis. This can be used prior to data collection, in post hoc analyses, or even for subsetting networks in dynamic network analysis. The network correlation estimator and custom power analysis methods have been made available as an R package.

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.

One of the most important lessons from the success of deep learning is that learned representations tend to perform much better at any task compared to representations we design by hand. Yet evolution of evolvability algorithms, which aim to automatically learn good genetic representations, have received relatively little attention, perhaps because of the large amount of computational power they require. The recent method Evolvability ES allows direct selection for evolvability with little computation. However, it can only be used to solve problems where evolvability and task performance are aligned. We propose Quality Evolvability ES, a method that simultaneously optimizes for task performance and evolvability and without this restriction. Our proposed approach Quality Evolvability has similar motivation to Quality Diversity algorithms, but with some important differences. While Quality Diversity aims to find an archive of diverse and well-performing, but potentially genetically distant individuals, Quality Evolvability aims to find a single individual with a diverse and well-performing distribution of offspring. By doing so Quality Evolvability is forced to discover more evolvable representations. We demonstrate on robotic locomotion control tasks that Quality Evolvability ES, similarly to Quality Diversity methods, can learn faster than objective-based methods and can handle deceptive problems., 2021 Conference on Artificial Life

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

A challenge faced by individuals and groups of many species is determining how resources and activities should be spatially distributed: centralized or decentralized. This distribution problem is hard to understand due to the many costs and benefits of each strategy in different settings. Ant colonies are faced by this problem and demonstrate two solutions: 1) centralizing resources in a single nest (monodomy) and 2) decentralizing by spreading resources across many nests (polydomy). Despite the possibilities for using this system to study the centralization/decentralization problem, the trade-offs associated with using either polydomy or monodomy are poorly understood due to a lack of empirical data and cohesive theory. Here, we present a dynamic network model of a population of ant nests which is based on observations of a facultatively polydomous ant species (Formica lugubris). We use the model to test several key hypotheses for costs and benefits of polydomy and monodomy and show that decentralization is advantageous when resource acquisition costs are high, nest size is limited, resources are clustered, and there is a risk of nest destruction, but centralization prevails when resource availability fluctuates and nest size is limited. Our model explains the phylogenetic and ecological diversity of polydomous ants, demonstrates several trade-offs of decentralization and centralization, and provides testable predictions for empirical work on ants and in other systems., Centralization and decentralization of resources in ant colonies have several costs and benefits. We use a dynamic network model of a population of ant nests to demonstrate that there are several important costs and benefits of centralization and decentralization, which explains the phylogenetic and ecological diversity of methods for distribution of resources across ant species.

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.

Warning signals within species, such as the bright colors of chemically defended animals, are usually considered mutualistic, monomorphic traits. Such a view is however increasingly at odds with the growing empirical literature, showing nontrivial levels of signal variation within prey populations. Key to understanding this variation, we argue, could be a recognition that toxicity levels frequently vary within populations because of environmental heterogeneity. Inequalities in defense may undermine mutualistic monomorphic signaling, causing evolutionary antagonism between loci that determine appearance of less well-defended and better defended prey forms within species. In this article, we apply a stochastic model of evolved phenotypic plasticity to the evolution of prey signals. We show that when toxicity levels vary, then antagonistic interactions can lead to evolutionary conflict between alleles at different signaling loci, causing signal evolution, "red queen-like" evolutionary chase, and one or more forms of signaling equilibria. A key prediction is that variation in the way that predators use information about toxicity levels in their attack behaviors profoundly affects the evolutionary characteristics of the prey signaling systems. Environmental variation is known to cause variation in many qualities that organisms signal; our approach may therefore have application to other signaling systems.

Lay Summary Wood ants nests share resources with neighboring nests, not the whole colony. A single ant colony can either live all in one nest, or split into several separate, but communicating, nests. How and why ant colonies do this is unknown. By treating these separated colonies as networks we show that wood ants exchange food locally, with neighboring nests, without a colony-level plan., An important problem facing organisms in a heterogeneous environment is how to redistribute resources to where they are required. This is particularly complex in social insect societies as resources have to be moved both from the environment into the nest and between individuals within the nest. Polydomous ant colonies are split between multiple spatially separated, but socially connected, nests. Whether, and how, resources are redistributed between nests in polydomous colonies is unknown. We analyzed the nest networks of the facultatively polydomous wood ant Formica lugubris. Our results indicate that resource redistribution in polydomous F. lugubris colonies is organized at the local level between neighboring nests and not at the colony level. We found that internest trails connecting nests that differed more in their amount of foraging were stronger than trails between nests with more equal foraging activity. This indicates that resources are being exchanged directly from nests with a foraging excess to nests that require resources. In contrast, we found no significant relationships between nest properties, such as size and amount of foraging, and network measures such as centrality and connectedness. This indicates an absence of a colony-level resource exchange. This is a clear example of a complex behavior emerging as a result of local interactions between parts of a system.

Efficient and robust transportation networks are key to the effectiveness of many natural systems. In polydomous ant colonies, which consist of two or more spatially separated but socially connected nests, resources must be transported between nests. In this study, we analyse the network structure of the inter-nest trails formed by natural polydomous ant colonies. In contrast to previous laboratory studies, the natural colonies in our study do not form minimum spanning tree networks. Instead the networks contain extra connections, suggesting that in natural colonies, robustness may be an important factor in network construction. Spatial analysis shows that nests are randomly distributed within the colony boundary and we find nests are most likely to connect to their nearest neighbours. However, the network structure is not entirely determined by spatial associations. By showing that the networks do not minimise total trail length and are not determined only by spatial associations, the results suggest that the inter-nest networks produced by ant colonies are influenced by previously unconsidered factors. We show that the transportation networks of polydomous ant colonies balance trail costs with the construction of networks that enable efficient transportation of resources. These networks therefore provide excellent examples of effective biological transport networks which may provide insight into the design and management of transportation systems.

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.

Access to resources depends on an individual's position within the environment. This is particularly important to animals that invest heavily in nest construction, such as social insects. Many ant species have a polydomous nesting strategy: a single colony inhabits several spatially separated nests, often exchanging resources between the nests. Different nests in a polydomous colony potentially have differential access to resources, but the ecological consequences of this are unclear. In this study, we investigate how nest survival and budding in polydomous wood ant (Formica lugubris) colonies are affected by being part of a multi‐nest system. Using field data and novel analytical approaches combining survival models with dynamic network analysis, we show that the survival and budding of nests within a polydomous colony are affected by their position in the nest network structure. Specifically, we find that the flow of resources through a nest, which is based on its position within the wider nest network, determines a nest's likelihood of surviving and of founding new nests. Our results highlight how apparently disparate entities in a biological system can be integrated into a functional ecological unit. We also demonstrate how position within a dynamic network structure can have important ecological consequences.

Social relationships can have important consequences for fitness in animals. Whilst numerous studies have shown that individuals often join larger groups in response to perceived predation risk (i.e. fear of predation), the importance of predation risk in driving the formation and stability of social relationships within groups has been relatively ignored. We experimentally tested how predation threat influenced fine-scale social network structure using Trinidadian guppies (Poecilia reticulata). When perceived predation risk was high, individuals developed stable and more differentiated social ties compared to when perceived risk was low. Intriguingly, social differentiation coincided with shoals being somewhat smaller under high-perceived risk, suggesting a possible conflict between forming stable social relationships and larger social groups. Individuals most at risk of predation (large and bold individuals) showed the most exaggerated responses in several social measures. Taken together, we provide the first experimental evidence that proximate risk of predation can increase the intensity of social relationships and fine-scale social structure in animal populations.

There are many situations in which the ability of animals to distinguish between two similar looking objects can have significant selective consequences. For example, the objects that require discrimination may be edible versus defended prey, predators versus non-predators, or mates of varying quality. Working from the premise that there are situations in which discrimination may be more or less successful, we hypothesized that individuals find it more difficult to distinguish between stimuli when they encounter them sequentially rather than simultaneously. Our study has wide biological and psychological implications from the perspective of signal perception, signal evolution, and discrimination, and could apply to any system where individuals are making relative judgments or choices between two or more stimuli or signals. While this is a general principle that might seem intuitive, it has not been experimentally tested in this context, and is often not considered in the design of models or experiments, or in the interpretation of a wide range of studies. Our study is different from previous studies in psychology in that a) the level of similarity of stimuli are gradually varied to obtain selection gradients, and b) we discuss the implications of our study for specific areas in ecology, such as the level of perfection of mimicry in predator-prey systems. Our experiments provide evidence that it is indeed more difficult to distinguish between stimuli – and to learn to distinguish between stimuli – when they are encountered sequentially rather than simultaneously, even if the intervening time interval is short.

Group navigation is of great importance for many animals, such as migrating flocks of birds or shoals of fish. One theory states that group membership can improve navigational accuracy compared to limited or less accurate individual navigational ability in groups without leaders (“Many-wrongs principle”). Here, we simulate leaderless group navigation that includes social connections as preferential interactions between individuals. Our results suggest that underlying social networks can reduce navigational errors of groups and increase group cohesion. We use network summary statistics, in particular network motifs, to study which characteristics of networks lead to these improvements. It is networks in which preferences between individuals are not clustered, but spread evenly across the group that are advantageous in group navigation by effectively enhancing long-distance information exchange within groups. We suggest that our work predicts a base-line for the type of social structure we might expect to find in group-living animals that navigate without leaders [Current Zoology 58 (2): 329–341, 2012].

For the majority of social species, group composition is dynamic, and individuals are interconnected in a heterogeneous social network. Social network structure has far-reaching implications for the ecology of individuals and populations. However, we have little understanding of how ecological variables shape this structure. We used a long-term data set (1984e2007) to examine the relationship between food availability and social network structure in the endangered southern resident killer whales. During the summer months individuals in this population feed primarily on chinook salmon, Oncorhynchus tshawytscha, which show annual variation in abundance. We tested the hypothesis that temporal variation in chinook salmon will correlate with variation in social network structure. Using a null model that controlled for population demography, group size and sampling effort, we found a significant relationship between the connectivity of the social network and salmon abundance, with a more interconnected social network in years of high salmon abundance. Our results demonstrate that resource availability may be an important determinant of social network structure. Given the central importance of the social network for population processes such as the maintenance of cooperation and the transmission of information and disease, a change in social network structure caused by a change in food availability may have significant ecological and evolutionary consequences. 2012 The Association for the Study of Animal Behaviour. Published by Elsevier Ltd. All rights reserved.